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Expressions

Standard Expressions are basic level operations that can be added across the platform such as finding the max value in a column, extracting the year from a date field, or removing the leading zeroes in a text field.

Scalar Functions

Aggregate Functions

AI Functions

Specialized Functions

System and Table Functions

Other Functions

Full Index

1 - Aggregate Functions

Aggregate functions are essential tools in SQL that allow you to perform calculations on a set of values and return a single result.

These functions help you extract and summarize data from databases to gain valuable insights.

Function NameWhat It Does
ANYChecks if any row meets the specified condition
APPROX_COUNT_DISTINCTEstimates the number of distinct values with HyperLogLog
ARG_MAXFinds the arg value for the maximum val value
ARG_MINFinds the arg value for the minimum val value
AVG_IFCalculates the average for rows meeting a condition
ARRAY_AGGConverts all the values of a column to an Array
AVGCalculates the average value of a specific column
COUNT_DISTINCTCounts the number of distinct values in a column
COUNT_IFCounts rows meeting a specified condition
COUNTCounts the number of rows that meet certain criteria
COVAR_POPReturns the population covariance of a set of number pairs
COVAR_SAMPReturns the sample covariance of a set of number pairs
GROUP_ARRAY_MOVING_AVGReturns an array with elements calculates the moving average of input values
GROUP_ARRAY_MOVING_SUMReturns an array with elements calculates the moving sum of input values
KURTOSISCalculates the excess kurtosis of a set of values
MAX_IFFinds the maximum value for rows meeting a condition
MAXFinds the largest value in a specific column
MEDIANCalculates the median value of a specific column
MEDIAN_TDIGESTCalculates the median value of a specific column using t-digest algorithm
MIN_IFFinds the minimum value for rows meeting a condition
MINFinds the smallest value in a specific column
QUANTILE_CONTCalculates the interpolated quantile for a specific column
QUANTILE_DISCCalculates the quantile for a specific column
QUANTILE_TDIGESTCalculates the quantile using t-digest algorithm
QUANTILE_TDIGEST_WEIGHTEDCalculates the quantile with weighted using t-digest algorithm
RETENTIONCalculates retention for a set of events
SKEWNESSCalculates the skewness of a set of values
STDDEV_POPCalculates the population standard deviation of a column
STDDEV_SAMPCalculates the sample standard deviation of a column
STRING_AGGConverts all the non-NULL values to String, separated by the delimiter
SUM_IFAdds up the values meeting a condition of a specific column
SUMAdds up the values of a specific column
WINDOW_FUNNELAnalyzes user behavior in a time-ordered sequence of events

1.1 - ANY

Aggregate function.

The ANY() function selects the first encountered (non-NULL) value, unless all rows have NULL values in that column. The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. To get a determinate result, you can use the ‘min’ or ‘max’ function instead of ‘any’.

Analyze Syntax

func.any(<expr>)

Analyze Examples

func.any(table.product_name).alias('any_product_name')

| any_product_name |
|------------------|
| Laptop           |

SQL Syntax

ANY(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression

Return Type

The first encountered (non-NULL) value, in the type of the value. If all values are NULL, the return value is NULL.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE product_data (
  id INT,
  product_name VARCHAR NULL,
  price FLOAT NULL
);

INSERT INTO product_data (id, product_name, price)
VALUES (1, 'Laptop', 1000),
       (2, NULL, 800),
       (3, 'Keyboard', NULL),
       (4, 'Mouse', 25),
       (5, 'Monitor', 150);

Query Demo: Retrieve the First Encountered Non-NULL Product Name

SELECT ANY(product_name) AS any_product_name
FROM product_data;

Result

| any_product_name |
|------------------|
| Laptop           |

1.2 - APPROX_COUNT_DISTINCT

Estimates the number of distinct values in a data set with the HyperLogLog algorithm.

The HyperLogLog algorithm provides an approximation of the number of unique elements using little memory and time. Consider using this function when dealing with large data sets where an estimated result can be accepted. In exchange for some accuracy, this is a fast and efficient method of returning distinct counts.

To get an accurate result, use COUNT_DISTINCT. See Examples for more explanations.

Analyze Syntax

func.approx_count_distinct(<expr>)

Analyze Examples

func.approx_count_distinct(table.user_id).alias('approx_distinct_user_count')

| approx_distinct_user_count |
|----------------------------|
|             4              |

SQL Syntax

APPROX_COUNT_DISTINCT(<expr>)

Return Type

Integer.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE user_events (
  id INT,
  user_id INT,
  event_name VARCHAR
);

INSERT INTO user_events (id, user_id, event_name)
VALUES (1, 1, 'Login'),
       (2, 2, 'Login'),
       (3, 3, 'Login'),
       (4, 1, 'Logout'),
       (5, 2, 'Logout'),
       (6, 4, 'Login'),
       (7, 1, 'Login');

Query Demo: Estimate the Number of Distinct User IDs

SELECT APPROX_COUNT_DISTINCT(user_id) AS approx_distinct_user_count
FROM user_events;

Result

| approx_distinct_user_count |
|----------------------------|
|             4              |

1.3 - ARG_MAX

Calculates the arg value for a maximum val value. If there are several values of arg for maximum values of val, returns the first of these values encountered.

Analyze Syntax

func.arg_max(<expr>)

Analyze Examples

func.arg_max(table.product, table.price).alias('max_price_product')

| max_price_product |
| ----------------- |
| Product C         |

SQL Syntax

ARG_MAX(<arg>, <val>)

Arguments

ArgumentsDescription
<arg>Argument of any data type that PlaidCloud Lakehouse supports
<val>Value of any data type that PlaidCloud Lakehouse supports

Return Type

arg value that corresponds to maximum val value.

matches arg type.

SQL Examples

Creating a Table and Inserting Sample Data

Let's create a table named "sales" and insert some sample data:

CREATE TABLE sales (
  id INTEGER,
  product VARCHAR(50),
  price FLOAT
);

INSERT INTO sales (id, product, price)
VALUES (1, 'Product A', 10.5),
       (2, 'Product B', 20.75),
       (3, 'Product C', 30.0),
       (4, 'Product D', 15.25),
       (5, 'Product E', 25.5);

Query: Using ARG_MAX() Function

Now, let's use the ARG_MAX() function to find the product that has the maximum price:

SELECT ARG_MAX(product, price) AS max_price_product
FROM sales;

The result should look like this:

| max_price_product |
| ----------------- |
| Product C         |

1.4 - ARG_MIN

Calculates the arg value for a minimum val value. If there are several different values of arg for minimum values of val, returns the first of these values encountered.

Analyze Syntax

func.arg_min(<expr>)

Analyze Examples

func.arg_min(table.name, table.score).alias('student_name')

| student_name |
|--------------|
| Charlie      |

SQL Syntax

ARG_MIN(<arg>, <val>)

Arguments

ArgumentsDescription
<arg>Argument of any data type that PlaidCloud Lakehouse supports
<val>Value of any data type that PlaidCloud Lakehouse supports

Return Type

arg value that corresponds to minimum val value.

matches arg type.

SQL Examples

Let's create a table students with columns id, name, and score, and insert some data:

CREATE TABLE students (
  id INT,
  name VARCHAR,
  score INT
);

INSERT INTO students (id, name, score) VALUES
  (1, 'Alice', 80),
  (2, 'Bob', 75),
  (3, 'Charlie', 90),
  (4, 'Dave', 80);

Now, we can use ARG_MIN to find the name of the student with the lowest score:

SELECT ARG_MIN(name, score) AS student_name
FROM students;

Result:

| student_name |
|--------------|
| Charlie      |

1.5 - ARRAY_AGG

The ARRAY_AGG function (also known by its alias LIST) transforms all the values, including NULL, of a specific column in a query result into an array.

Analyze Syntax

func.array_agg(<expr>)

Analyze Examples

table.movie_title, func.array_agg(table.rating).alias('ratings')

| movie_title |  ratings   |
|-------------|------------|
| Inception   | [5, 4, 5]  |

SQL Syntax

ARRAY_AGG(<expr>)

LIST(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression

Return Type

Returns an Array with elements that are of the same type as the original data.

SQL Examples

This example demonstrates how the ARRAY_AGG function can be used to aggregate and present data in a convenient array format:

-- Create a table and insert sample data
CREATE TABLE movie_ratings (
  id INT,
  movie_title VARCHAR,
  user_id INT,
  rating INT
);

INSERT INTO movie_ratings (id, movie_title, user_id, rating)
VALUES (1, 'Inception', 1, 5),
       (2, 'Inception', 2, 4),
       (3, 'Inception', 3, 5),
       (4, 'Interstellar', 1, 4),
       (5, 'Interstellar', 2, 3);

-- List all ratings for Inception in an array
SELECT movie_title, ARRAY_AGG(rating) AS ratings
FROM movie_ratings
WHERE movie_title = 'Inception'
GROUP BY movie_title;

| movie_title |  ratings   |
|-------------|------------|
| Inception   | [5, 4, 5]  |

1.6 - AVG

Aggregate function.

The AVG() function returns the average value of an expression.

Note: NULL values are not counted.

Analyze Syntax

func.avg(<column>)

Analyze Examples

func.avg(table.price).alias('avg_price')

| avg_price |
| --------- |
| 20.4      |

SQL Syntax

AVG(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

double

SQL Examples

Creating a Table and Inserting Sample Data

Let's create a table named "sales" and insert some sample data:

CREATE TABLE sales (
  id INTEGER,
  product VARCHAR(50),
  price FLOAT
);

INSERT INTO sales (id, product, price)
VALUES (1, 'Product A', 10.5),
       (2, 'Product B', 20.75),
       (3, 'Product C', 30.0),
       (4, 'Product D', 15.25),
       (5, 'Product E', 25.5);

Query: Using AVG() Function

Now, let's use the AVG() function to find the average price of all products in the "sales" table:

SELECT AVG(price) AS avg_price
FROM sales;

The result should look like this:

| avg_price |
| --------- |
| 20.4      |

1.7 - AVG_IF

The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition.

Analyze Syntax

func.avg_if(<column>, <cond>)

Analyze Examples

func.avg_if(table.salary, table.department=='IT').alias('avg_salary_it')

| avg_salary_it   |
|-----------------|
|     65000.0     |

SQL Syntax

AVG_IF(<column>, <cond>)

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE employees (
  id INT,
  salary INT,
  department VARCHAR
);

INSERT INTO employees (id, salary, department)
VALUES (1, 50000, 'HR'),
       (2, 60000, 'IT'),
       (3, 55000, 'HR'),
       (4, 70000, 'IT'),
       (5, 65000, 'IT');

Query Demo: Calculate Average Salary for IT Department

SELECT AVG_IF(salary, department = 'IT') AS avg_salary_it
FROM employees;

Result

| avg_salary_it   |
|-----------------|
|     65000.0     |

1.8 - COUNT

The COUNT() function returns the number of records returned by a SELECT query.

Analyze Syntax

func.count(<column>)

Analyze Examples

func.count(table.grade).alias('count_valid_grades')

| count_valid_grades |
|--------------------|
|          4         |

SQL Syntax

COUNT(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression.
This may be a column name, the result of another function, or a math operation.
* is also allowed, to indicate pure row counting.

Return Type

An integer.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE students (
  id INT,
  name VARCHAR,
  age INT,
  grade FLOAT NULL
);

INSERT INTO students (id, name, age, grade)
VALUES (1, 'John', 21, 85),
       (2, 'Emma', 22, NULL),
       (3, 'Alice', 23, 90),
       (4, 'Michael', 21, 88),
       (5, 'Sophie', 22, 92);

Query Demo: Count Students with Valid Grades

SELECT COUNT(grade) AS count_valid_grades
FROM students;

Result

| count_valid_grades |
|--------------------|
|          4         |

1.9 - COUNT_DISTINCT

Aggregate function.

The count(distinct ...) function calculates the unique value of a set of values.

To obtain an estimated result from large data sets with little memory and time, consider using APPROX_COUNT_DISTINCT.

Analyze Syntax

func.count_distinct(<column>)

Analyze Examples

func.count_distinct(table.category).alias('unique_categories')

| unique_categories |
|-------------------|
|         2         |

SQL Syntax

COUNT(distinct <expr> ...)
UNIQ(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression, size of the arguments is [1, 32]

Return Type

UInt64

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE products (
  id INT,
  name VARCHAR,
  category VARCHAR,
  price FLOAT
);

INSERT INTO products (id, name, category, price)
VALUES (1, 'Laptop', 'Electronics', 1000),
       (2, 'Smartphone', 'Electronics', 800),
       (3, 'Tablet', 'Electronics', 600),
       (4, 'Chair', 'Furniture', 150),
       (5, 'Table', 'Furniture', 300);

Query Demo: Count Distinct Categories

SELECT COUNT(DISTINCT category) AS unique_categories
FROM products;

Result

| unique_categories |
|-------------------|
|         2         |

1.10 - COUNT_IF

The suffix _IF can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition.

Analyze Syntax

func.count_if(<column>, <cond>)

Analyze Examples

func.count_if(table.status, table.status=='Completed').alias('completed_orders')

| completed_orders |
|------------------|
|        3         |

SQL Example

COUNT_IF(<column>, <cond>)

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE orders (
  id INT,
  customer_id INT,
  status VARCHAR,
  total FLOAT
);

INSERT INTO orders (id, customer_id, status, total)
VALUES (1, 1, 'completed', 100),
       (2, 2, 'completed', 200),
       (3, 1, 'pending', 150),
       (4, 3, 'completed', 250),
       (5, 2, 'pending', 300);

Query Demo: Count Completed Orders

SELECT COUNT_IF(status, status = 'completed') AS completed_orders
FROM orders;

Result

| completed_orders |
|------------------|
|        3         |

1.11 - COVAR_POP

COVAR_POP returns the population covariance of a set of number pairs.

Analyze Syntax

func.covar_pop(<expr1>, <expr2>)

Analyze Examples

func.covar_pop(table.units_sold, table.revenue).alias('covar_pop_units_revenue')

| covar_pop_units_revenue |
|-------------------------|
|        20000.0          |

SQL Syntax

COVAR_POP(<expr1>, <expr2>)

Arguments

ArgumentsDescription
<expr1>Any numerical expression
<expr2>Any numerical expression

Return Type

float64

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE product_sales (
  id INT,
  product_id INT,
  units_sold INT,
  revenue FLOAT
);

INSERT INTO product_sales (id, product_id, units_sold, revenue)
VALUES (1, 1, 10, 1000),
       (2, 2, 20, 2000),
       (3, 3, 30, 3000),
       (4, 4, 40, 4000),
       (5, 5, 50, 5000);

Query Demo: Calculate Population Covariance between Units Sold and Revenue

SELECT COVAR_POP(units_sold, revenue) AS covar_pop_units_revenue
FROM product_sales;

Result

| covar_pop_units_revenue |
|-------------------------|
|        20000.0          |

1.12 - COVAR_SAMP

Aggregate function.

The covar_samp() function returns the sample covariance (Σ((x - x̅)(y - y̅)) / (n - 1)) of two data columns.

Analyze Syntax

func.covar_samp(<expr1>, <expr2>)

Analyze Examples

func.covar_samp(table.items_sold, table.profit).alias('covar_samp_items_profit')

| covar_samp_items_profit |
|-------------------------|
|        250000.0         |

SQL Syntax

COVAR_SAMP(<expr1>, <expr2>)

Arguments

ArgumentsDescription
<expr1>Any numerical expression
<expr2>Any numerical expression

Return Type

float64, when n <= 1, returns +∞.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE store_sales (
  id INT,
  store_id INT,
  items_sold INT,
  profit FLOAT
);

INSERT INTO store_sales (id, store_id, items_sold, profit)
VALUES (1, 1, 100, 1000),
       (2, 2, 200, 2000),
       (3, 3, 300, 3000),
       (4, 4, 400, 4000),
       (5, 5, 500, 5000);

Query Demo: Calculate Sample Covariance between Items Sold and Profit

SELECT COVAR_SAMP(items_sold, profit) AS covar_samp_items_profit
FROM store_sales;

Result

| covar_samp_items_profit |
|-------------------------|
|        250000.0         |

1.13 - GROUP_ARRAY_MOVING_AVG

The GROUP_ARRAY_MOVING_AVG function calculates the moving average of input values. The function can take the window size as a parameter. If left unspecified, the function takes the window size equal to the number of input values.

Analyze Syntax

func.group_array_moving_avg(<expr1>)

Analyze Examples

table.user_id, func.group_array_moving_avg(table.request_num).alias('avg_request_num')

| user_id | avg_request_num  |
|---------|------------------|
|       1 | [5.0,11.5,21.5]  |
|       3 | [10.0,22.5,35.0] |
|       2 | [7.5,18.0,31.0]  |

SQL Syntax

GROUP_ARRAY_MOVING_AVG(<expr>)

GROUP_ARRAY_MOVING_AVG(<window_size>)(<expr>)

Arguments

ArgumentsDescription
<window_size>Any numerical expression
<expr>Any numerical expression

Return Type

Returns an Array with elements of double or decimal depending on the source data type.

SQL Examples

-- Create a table and insert sample data
CREATE TABLE hits (
  user_id INT,
  request_num INT
);

INSERT INTO hits (user_id, request_num)
VALUES (1, 10),
       (2, 15),
       (3, 20),
       (1, 13),
       (2, 21),
       (3, 25),
       (1, 30),
       (2, 41),
       (3, 45);

SELECT user_id, GROUP_ARRAY_MOVING_AVG(2)(request_num) AS avg_request_num
FROM hits
GROUP BY user_id;

| user_id | avg_request_num  |
|---------|------------------|
|       1 | [5.0,11.5,21.5]  |
|       3 | [10.0,22.5,35.0] |
|       2 | [7.5,18.0,31.0]  |

1.14 - GROUP_ARRAY_MOVING_SUM

The GROUP_ARRAY_MOVING_SUM function calculates the moving sum of input values. The function can take the window size as a parameter. If left unspecified, the function takes the window size equal to the number of input values.

Analyze Syntax

func.group_array_moving_sum(<expr>)

Analyze Examples

table.user_id, func.group_array_moving_sum(table.request_num)

| user_id | request_num |
|---------|-------------|
|       1 | [10,23,43]  |
|       2 | [20,45,70]  |
|       3 | [15,36,62]  |

SQL Syntax

GROUP_ARRAY_MOVING_SUM(<expr>)

GROUP_ARRAY_MOVING_SUM(<window_size>)(<expr>)

Arguments

ArgumentsDescription
<window_size>Any numerical expression
<expr>Any numerical expression

Return Type

Returns an Array with elements that are of the same type as the original data.

SQL Examples

-- Create a table and insert sample data
CREATE TABLE hits (
  user_id INT,
  request_num INT
);

INSERT INTO hits (user_id, request_num)
VALUES (1, 10),
       (2, 15),
       (3, 20),
       (1, 13),
       (2, 21),
       (3, 25),
       (1, 30),
       (2, 41),
       (3, 45);

SELECT user_id, GROUP_ARRAY_MOVING_SUM(2)(request_num) AS request_num
FROM hits
GROUP BY user_id;

| user_id | request_num |
|---------|-------------|
|       1 | [10,23,43]  |
|       2 | [20,45,70]  |
|       3 | [15,36,62]  |

1.15 - HISTOGRAM

Generates a data distribution histogram using an "equal height" bucketing strategy.

Analyze Syntax

func.histogram(<expr>)

Analyze Examples

See SQL Example for details

SQL Syntax

HISTOGRAM(<expr>)

-- The following two forms are equivalent:
HISTOGRAM(<max_num_buckets>)(<expr>)
HISTOGRAM(<expr> [, <max_num_buckets>])
ParameterDescription
exprThe data type of expr should be sortable.
max_num_bucketsOptional positive integer specifying the maximum number of buckets. Default is 128.

Return Type

Returns either an empty string or a JSON object with the following structure:

  • buckets: List of buckets with detailed information:
    • lower: Lower bound of the bucket.
    • upper: Upper bound of the bucket.
    • count: Number of elements in the bucket.
    • pre_sum: Cumulative count of elements up to the current bucket.
    • ndv: Number of distinct values in the bucket.

SQL Examples

This example shows how the HISTOGRAM function analyzes the distribution of c_int values in the histagg table, returning bucket boundaries, distinct value counts, element counts, and cumulative counts:

CREATE TABLE histagg (
  c_id INT,
  c_tinyint TINYINT,
  c_smallint SMALLINT,
  c_int INT
);

INSERT INTO histagg VALUES
  (1, 10, 20, 30),
  (1, 11, 21, 33),
  (1, 11, 12, 13),
  (2, 21, 22, 23),
  (2, 31, 32, 33),
  (2, 10, 20, 30);

SELECT HISTOGRAM(c_int) FROM histagg;

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                                              histogram(c_int)                                                                                                             │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [{"lower":"13","upper":"13","ndv":1,"count":1,"pre_sum":0},{"lower":"23","upper":"23","ndv":1,"count":1,"pre_sum":1},{"lower":"30","upper":"30","ndv":1,"count":2,"pre_sum":2},{"lower":"33","upper":"33","ndv":1,"count":2,"pre_sum":4}] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

The result is returned as a JSON array:

[
  {
    "lower": "13",
    "upper": "13",
    "ndv": 1,
    "count": 1,
    "pre_sum": 0
  },
  {
    "lower": "23",
    "upper": "23",
    "ndv": 1,
    "count": 1,
    "pre_sum": 1
  },
  {
    "lower": "30",
    "upper": "30",
    "ndv": 1,
    "count": 2,
    "pre_sum": 2
  },
  {
    "lower": "33",
    "upper": "33",
    "ndv": 1,
    "count": 2,
    "pre_sum": 4
  }
]

This example shows how HISTOGRAM(2) groups c_int values into two buckets:

SELECT HISTOGRAM(2)(c_int) FROM histagg;
-- Or
SELECT HISTOGRAM(c_int, 2) FROM histagg;

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                  histogram(2)(c_int)                                                  │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [{"lower":"13","upper":"30","ndv":3,"count":4,"pre_sum":0},{"lower":"33","upper":"33","ndv":1,"count":2,"pre_sum":4}] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

The result is returned as a JSON array:

[
  {
    "lower": "13",
    "upper": "30",
    "ndv": 3,
    "count": 4,
    "pre_sum": 0
  },
  {
    "lower": "33",
    "upper": "33",
    "ndv": 1,
    "count": 2,
    "pre_sum": 4
  }
]

1.16 - JSON_ARRAY_AGG

Converts values into a JSON array while skipping NULLs.

See also: JSON_OBJECT_AGG

Analyze Syntax

func.json_array_agg(<expr>)

Analyze Examples

See SQL Example for details

SQL Syntax

JSON_ARRAY_AGG(<expr>)

Return Type

JSON array.

Examples

This example demonstrates how JSON_ARRAY_AGG aggregates values from each column into JSON arrays:

CREATE TABLE d (
    a DECIMAL(10, 2), 
    b STRING, 
    c INT, 
    d VARIANT, 
    e ARRAY(STRING)
);

INSERT INTO d VALUES
    (20, 'abc', NULL, '{"k":"v"}', ['a','b']),
    (10, 'de', 100, 'null', []),
    (4.23, NULL, 200, '"uvw"', ['x','y']),
    (5.99, 'xyz', 300, '[1,2,3]', ['z']);

SELECT 
    json_array_agg(a) AS aggregated_a, 
    json_array_agg(b) AS aggregated_b, 
    json_array_agg(c) AS aggregated_c, 
    json_array_agg(d) AS aggregated_d, 
    json_array_agg(e) AS aggregated_e
FROM d;

-[ RECORD 1 ]-----------------------------------
aggregated_a: [20.0,10.0,4.23,5.99]
aggregated_b: ["abc","de","xyz"]
aggregated_c: [100,200,300]
aggregated_d: [{"k":"v"},null,"uvw",[1,2,3]]
aggregated_e: [["a","b"],[],["x","y"],["z"]]

1.17 - JSON_OBJECT_AGG

Converts key-value pairs into a JSON object. For each row in the input, it generates a key-value pair where the key is derived from the <key_expression> and the value is derived from the <value_expression>. These key-value pairs are then combined into a single JSON object.

See also: JSON_ARRAY_AGG

Analyze Syntax

func.json_object_agg(<key_expression>, <value_expression>)

Analyze Examples

See SQL Example for details

SQL Syntax

JSON_OBJECT_AGG(<key_expression>, <value_expression>)
ParameterDescription
key_expressionSpecifies the key in the JSON object. Only supports string expressions. If the key_expression evaluates to NULL, the key-value pair is skipped.
value_expressionSpecifies the value in the JSON object. It can be any supported data type. If the value_expression evaluates to NULL, the key-value pair is skipped.

Return Type

JSON object.

SQL Examples

This example demonstrates how JSON_OBJECT_AGG can be used to aggregate different types of data—such as decimals, integers, JSON variants, and arrays—into JSON objects, with the column b as the key for each JSON object:

CREATE TABLE d (
    a DECIMAL(10, 2), 
    b STRING, 
    c INT, 
    d VARIANT, 
    e ARRAY(STRING)
);

INSERT INTO d VALUES
    (20, 'abc', NULL, '{"k":"v"}', ['a','b']),
    (10, 'de', 100, 'null', []),
    (4.23, NULL, 200, '"uvw"', ['x','y']),
    (5.99, 'xyz', 300, '[1,2,3]', ['z']);

SELECT
    json_object_agg(b, a) AS json_a,
    json_object_agg(b, c) AS json_c,
    json_object_agg(b, d) AS json_d,
    json_object_agg(b, e) AS json_e
FROM
    d;

-[ RECORD 1 ]-----------------------------------
json_a: {"abc":20.0,"de":10.0,"xyz":5.99}
json_c: {"de":100,"xyz":300}
json_d: {"abc":{"k":"v"},"de":null,"xyz":[1,2,3]}
json_e: {"abc":["a","b"],"de":[],"xyz":["z"]}

1.18 - KURTOSIS

Aggregate function.

The KURTOSIS() function returns the excess kurtosis of all input values.

Analyze Syntax

func.kurtosis(<expr>)

Analyze Examples

func.kurtosis(table.price).alias('excess_kurtosis')

|     excess_kurtosis     |
|-------------------------|
| 0.06818181325581445     |

SQL Syntax

KURTOSIS(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

Nullable Float64.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE stock_prices (
  id INT,
  stock_symbol VARCHAR,
  price FLOAT
);

INSERT INTO stock_prices (id, stock_symbol, price)
VALUES (1, 'AAPL', 150),
       (2, 'AAPL', 152),
       (3, 'AAPL', 148),
       (4, 'AAPL', 160),
       (5, 'AAPL', 155);

Query Demo: Calculate Excess Kurtosis for Apple Stock Prices

SELECT KURTOSIS(price) AS excess_kurtosis
FROM stock_prices
WHERE stock_symbol = 'AAPL';

Result

|     excess_kurtosis     |
|-------------------------|
| 0.06818181325581445     |

1.19 - MAX

Aggregate function.

The MAX() function returns the maximum value in a set of values.

Analyze Syntax

func.max(<column>)

Analyze Examples

table.city, func.max(table.temperature).alias('max_temperature')

|    city    | max_temperature |
|------------|-----------------|
| New York   |       32        |

SQL Syntax

MAX(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression

Return Type

The maximum value, in the type of the value.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE temperatures (
  id INT,
  city VARCHAR,
  temperature FLOAT
);

INSERT INTO temperatures (id, city, temperature)
VALUES (1, 'New York', 30),
       (2, 'New York', 28),
       (3, 'New York', 32),
       (4, 'Los Angeles', 25),
       (5, 'Los Angeles', 27);

Query Demo: Find Maximum Temperature for New York City

SELECT city, MAX(temperature) AS max_temperature
FROM temperatures
WHERE city = 'New York'
GROUP BY city;

Result

|    city    | max_temperature |
|------------|-----------------|
| New York   |       32        |

1.20 - MAX_IF

The suffix _IF can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition.

Analyze Syntax

func.max_if(<column>, <cond>)

Analyze Examples

func.max_if(table.revenue, table.salesperson_id==1).alias('max_revenue_salesperson_1')

| max_revenue_salesperson_1 |
|---------------------------|
|           3000            |

SQL Example

MAX_IF(<column>, <cond>)

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE sales (
  id INT,
  salesperson_id INT,
  product_id INT,
  revenue FLOAT
);

INSERT INTO sales (id, salesperson_id, product_id, revenue)
VALUES (1, 1, 1, 1000),
       (2, 1, 2, 2000),
       (3, 1, 3, 3000),
       (4, 2, 1, 1500),
       (5, 2, 2, 2500);

Query Demo: Find Maximum Revenue for Salesperson with ID 1

SELECT MAX_IF(revenue, salesperson_id = 1) AS max_revenue_salesperson_1
FROM sales;

Result

| max_revenue_salesperson_1 |
|---------------------------|
|           3000            |

1.21 - MEDIAN

Aggregate function.

The MEDIAN() function computes the median of a numeric data sequence.

Analyze Syntax

func.median(<expr>)

Analyze Examples

func.median(table.score).alias('median_score')

|  median_score  |
|----------------|
|      85.0      |

SQL Syntax

MEDIAN(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

the type of the value.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE exam_scores (
  id INT,
  student_id INT,
  score INT
);

INSERT INTO exam_scores (id, student_id, score)
VALUES (1, 1, 80),
       (2, 2, 90),
       (3, 3, 75),
       (4, 4, 95),
       (5, 5, 85);

Query Demo: Calculate Median Exam Score

SELECT MEDIAN(score) AS median_score
FROM exam_scores;

Result

|  median_score  |
|----------------|
|      85.0      |

1.22 - MEDIAN_TDIGEST

Computes the median of a numeric data sequence using the t-digest algorithm.

Analyze Syntax

func.median_tdigest(<expr>)

Analyze Examples

func.median_tdigest(table.score).alias('median_score')

|  median_score  |
|----------------|
|      85.0      |

SQL Syntax

MEDIAN_TDIGEST(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

Returns a value of the same data type as the input values.

SQL Examples

-- Create a table and insert sample data
CREATE TABLE exam_scores (
  id INT,
  student_id INT,
  score INT
);

INSERT INTO exam_scores (id, student_id, score)
VALUES (1, 1, 80),
       (2, 2, 90),
       (3, 3, 75),
       (4, 4, 95),
       (5, 5, 85);

-- Calculate median exam score
SELECT MEDIAN_TDIGEST(score) AS median_score
FROM exam_scores;

|  median_score  |
|----------------|
|      85.0      |

1.23 - MIN

Aggregate function.

The MIN() function returns the minimum value in a set of values.

Analyze Syntax

func.min(<column>)

Analyze Examples

table.station_id, func.min(table.price).alias('min_price')

| station_id | min_price |
|------------|-----------|
|     1      |   3.45    |

SQL Syntax

MIN(<expr>)

Arguments

ArgumentsDescription
<expr>Any expression

Return Type

The minimum value, in the type of the value.

SQL Examples

title: MIN

Aggregate function.

The MIN() function returns the minimum value in a set of values.

SQL Syntax

MIN(expression)

Arguments

ArgumentsDescription
expressionAny expression

Return Type

The minimum value, in the type of the value.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE gas_prices (
  id INT,
  station_id INT,
  price FLOAT
);

INSERT INTO gas_prices (id, station_id, price)
VALUES (1, 1, 3.50),
       (2, 1, 3.45),
       (3, 1, 3.55),
       (4, 2, 3.40),
       (5, 2, 3.35);

Query Demo: Find Minimum Gas Price for Station 1

SELECT station_id, MIN(price) AS min_price
FROM gas_prices
WHERE station_id = 1
GROUP BY station_id;

Result

| station_id | min_price |
|------------|-----------|
|     1      |   3.45    |

1.24 - MIN_IF

The suffix _IF can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition.

Analyze Syntax

func.min_if(<column>, <cond>)

Analyze Examples

func.min_if(table.budget, table.departing=='IT').alias('min_it_budget')

| min_it_budget |
|---------------|
|     2000      |

SQL Syntax

MIN_IF(<column>, <cond>)

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE project_budgets (
  id INT,
  project_id INT,
  department VARCHAR,
  budget FLOAT
);

INSERT INTO project_budgets (id, project_id, department, budget)
VALUES (1, 1, 'HR', 1000),
       (2, 1, 'IT', 2000),
       (3, 1, 'Marketing', 3000),
       (4, 2, 'HR', 1500),
       (5, 2, 'IT', 2500);

Query Demo: Find Minimum Budget for IT Department

SELECT MIN_IF(budget, department = 'IT') AS min_it_budget
FROM project_budgets;

Result

| min_it_budget |
|---------------|
|     2000      |

1.25 - QUANTILE_CONT

Aggregate function.

The QUANTILE_CONT() function computes the interpolated quantile number of a numeric data sequence.

Analyze Syntax

func.quantile_cont(<levels>, <expr>)

Analyze Examples

func.quantile_cont(0.5, table.sales_amount).alias('median_sales_amount')

|  median_sales_amount  |
|-----------------------|
|        6000.0         |

SQL Syntax

QUANTILE_CONT(<levels>)(<expr>)
    
QUANTILE_CONT(level1, level2, ...)(<expr>)

Arguments

ArgumentsDescription
<level(s)level(s) of quantile. Each level is constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]
<expr>Any numerical expression

Return Type

Float64 or float64 array based on level number.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE sales_data (
  id INT,
  sales_person_id INT,
  sales_amount FLOAT
);

INSERT INTO sales_data (id, sales_person_id, sales_amount)
VALUES (1, 1, 5000),
       (2, 2, 5500),
       (3, 3, 6000),
       (4, 4, 6500),
       (5, 5, 7000);

Query Demo: Calculate 50th Percentile (Median) of Sales Amount using Interpolation

SELECT QUANTILE_CONT(0.5)(sales_amount) AS median_sales_amount
FROM sales_data;

Result

|  median_sales_amount  |
|-----------------------|
|        6000.0         |

1.26 - QUANTILE_DISC

Aggregate function.

The QUANTILE_DISC() function computes the exact quantile number of a numeric data sequence. The QUANTILE alias to QUANTILE_DISC

Analyze Syntax

func.quantile_disc(<levels>, <expr>)

Analyze Examples

func.quantile_disc([0.25, 0.75], table.salary).alias('salary_quantiles')

|  salary_quantiles   |
|---------------------|
| [55000.0, 65000.0]  |

SQL Syntax

QUANTILE_DISC(<levels>)(<expr>)
    
QUANTILE_DISC(level1, level2, ...)(<expr>)

Arguments

ArgumentsDescription
level(s)level(s) of quantile. Each level is constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]
<expr>Any numerical expression

Return Type

InputType or array of InputType based on level number.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE salary_data (
  id INT,
  employee_id INT,
  salary FLOAT
);

INSERT INTO salary_data (id, employee_id, salary)
VALUES (1, 1, 50000),
       (2, 2, 55000),
       (3, 3, 60000),
       (4, 4, 65000),
       (5, 5, 70000);

Query Demo: Calculate 25th and 75th Percentile of Salaries

SELECT QUANTILE_DISC(0.25, 0.75)(salary) AS salary_quantiles
FROM salary_data;

Result

|  salary_quantiles   |
|---------------------|
| [55000.0, 65000.0]  |

1.27 - QUANTILE_TDIGEST

Computes an approximate quantile of a numeric data sequence using the t-digest algorithm.

Analyze Syntax

func.quantile_tdigest(<levels>, <expr>)

Analyze Examples

func.quantile_tdigest([0.5, 0.8], table.sales_amount).alias('sales_amounts')

| sales_amounts         |
|-----------------------+
| [6000.0,7000.0]       |

SQL Syntax

QUANTILE_TDIGEST(<level1>[, <level2>, ...])(<expr>)

Arguments

ArgumentsDescription
<level n>A level of quantile represents a constant floating-point number ranging from 0 to 1. It is recommended to use a level value in the range of [0.01, 0.99].
<expr>Any numerical expression

Return Type

Returns either a Float64 value or an array of Float64 values, depending on the number of quantile levels specified.

SQL Examples

-- Create a table and insert sample data
CREATE TABLE sales_data (
  id INT,
  sales_person_id INT,
  sales_amount FLOAT
);

INSERT INTO sales_data (id, sales_person_id, sales_amount)
VALUES (1, 1, 5000),
       (2, 2, 5500),
       (3, 3, 6000),
       (4, 4, 6500),
       (5, 5, 7000);

SELECT QUANTILE_TDIGEST(0.5)(sales_amount) AS median_sales_amount
FROM sales_data;

median_sales_amount|
-------------------+
             6000.0|

SELECT QUANTILE_TDIGEST(0.5, 0.8)(sales_amount)
FROM sales_data;

quantile_tdigest(0.5, 0.8)(sales_amount)|
----------------------------------------+
[6000.0,7000.0]                         |

1.28 - QUANTILE_TDIGEST_WEIGHTED

Computes an approximate quantile of a numeric data sequence using the t-digest algorithm. This function takes into account the weight of each sequence member. Memory consumption is log(n), where n is a number of values.

Analyze Syntax

func.quantile_tdigest_weighted(<levels>, <expr>, <weight_expr>)

Analyze Examples

func.quantile_tdigest_weighted([0.5, 0.8], table.sales_amount, 1).alias('sales_amounts')

| sales_amounts         |
|-----------------------+
| [6000.0,7000.0]       |

SQL Syntax

QUANTILE_TDIGEST_WEIGHTED(<level1>[, <level2>, ...])(<expr>, <weight_expr>)

Arguments

ArgumentsDescription
<level n>A level of quantile represents a constant floating-point number ranging from 0 to 1. It is recommended to use a level value in the range of [0.01, 0.99].
<expr>Any numerical expression
<weight_expr>Any unsigned integer expression. Weight is a number of value occurrences.

Return Type

Returns either a Float64 value or an array of Float64 values, depending on the number of quantile levels specified.

SQL Examples

-- Create a table and insert sample data
CREATE TABLE sales_data (
  id INT,
  sales_person_id INT,
  sales_amount FLOAT
);

INSERT INTO sales_data (id, sales_person_id, sales_amount)
VALUES (1, 1, 5000),
       (2, 2, 5500),
       (3, 3, 6000),
       (4, 4, 6500),
       (5, 5, 7000);

SELECT QUANTILE_TDIGEST_WEIGHTED(0.5)(sales_amount, 1) AS median_sales_amount
FROM sales_data;

median_sales_amount|
-------------------+
             6000.0|

SELECT QUANTILE_TDIGEST_WEIGHTED(0.5, 0.8)(sales_amount, 1)
FROM sales_data;

quantile_tdigest_weighted(0.5, 0.8)(sales_amount)|
-------------------------------------------------+
[6000.0,7000.0]                                  |

1.29 - RETENTION

Aggregate function

The RETENTION() function takes as arguments a set of conditions from 1 to 32 arguments of type UInt8 that indicate whether a certain condition was met for the event.

Any condition can be specified as an argument (as in WHERE).

The conditions, except the first, apply in pairs: the result of the second will be true if the first and second are true, of the third if the first and third are true, etc.

Analyze Syntax

func.retention(<cond1> , <cond2> , ..., <cond32>)

Analyze Examples

table.user_id, func.retention(table.event_type=='signup', table.event_type='login', table.event_type='purchase').alias('sales_amounts')

| user_id | retention |
|---------|-----------|
|   1     | [1, 1, 0] |
|   2     | [1, 0, 1] |
|   3     | [1, 1, 0] |

SQL Syntax

RETENTION( <cond1> , <cond2> , ..., <cond32> );

Arguments

ArgumentsDescription
<cond>An expression that returns a Boolean result

Return Type

The array of 1 or 0.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE user_events (
  id INT,
  user_id INT,
  event_date DATE,
  event_type VARCHAR
);

INSERT INTO user_events (id, user_id, event_date, event_type)
VALUES (1, 1, '2022-01-01', 'signup'),
       (2, 1, '2022-01-02', 'login'),
       (3, 2, '2022-01-01', 'signup'),
       (4, 2, '2022-01-03', 'purchase'),
       (5, 3, '2022-01-01', 'signup'),
       (6, 3, '2022-01-02', 'login');

Query Demo: Calculate User Retention Based on Signup, Login, and Purchase Events

SELECT
  user_id,
  RETENTION(event_type = 'signup', event_type = 'login', event_type = 'purchase') AS retention
FROM user_events
GROUP BY user_id;

Result

| user_id | retention |
|---------|-----------|
|   1     | [1, 1, 0] |
|   2     | [1, 0, 1] |
|   3     | [1, 1, 0] |

1.30 - SKEWNESS

Aggregate function.

The SKEWNESS() function returns the skewness of all input values.

Analyze Syntax

func.skewness(<expr>)

Analyze Examples

func.skewness(table.temperature).alias('temperature_skewness')

| temperature_skewness |
|----------------------|
|      0.68            |

SQL Syntax

SKEWNESS(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

Nullable Float64.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE temperature_data (
                                  id INT,
                                  city_id INT,
                                  temperature FLOAT
);

INSERT INTO temperature_data (id, city_id, temperature)
VALUES (1, 1, 60),
       (2, 1, 65),
       (3, 1, 62),
       (4, 2, 70),
       (5, 2, 75);

Query Demo: Calculate Skewness of Temperature Data

SELECT SKEWNESS(temperature) AS temperature_skewness
FROM temperature_data;

Result

| temperature_skewness |
|----------------------|
|      0.68            |

1.31 - STDDEV_POP

Aggregate function.

The STDDEV_POP() function returns the population standard deviation(the square root of VAR_POP()) of an expression.

Analyze Syntax

func.stddev_pop(<expr>)

Analyze Examples

func.stddev_pop(table.score).alias('test_score_stddev_pop')

| test_score_stddev_pop |
|-----------------------|
|        7.07107        |

SQL Syntax

STDDEV_POP(<expr>)
STDDEV(<expr>)
STD(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

double

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE test_scores (
  id INT,
  student_id INT,
  score FLOAT
);

INSERT INTO test_scores (id, student_id, score)
VALUES (1, 1, 80),
       (2, 2, 85),
       (3, 3, 90),
       (4, 4, 95),
       (5, 5, 100);

Query Demo: Calculate Population Standard Deviation of Test Scores

SELECT STDDEV_POP(score) AS test_score_stddev_pop
FROM test_scores;

Result

| test_score_stddev_pop |
|-----------------------|
|        7.07107        |

1.32 - STDDEV_SAMP

Aggregate function.

The STDDEV_SAMP() function returns the sample standard deviation(the square root of VAR_SAMP()) of an expression.

Analyze Syntax

func.stddev_samp(<expr>)

Analyze Examples

func.stddev_samp(table.height).alias('height_stddev_samp')

| height_stddev_samp |
|--------------------|
|      0.240         |

SQL Syntax

STDDEV_SAMP(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

double

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE height_data (
  id INT,
  person_id INT,
  height FLOAT
);

INSERT INTO height_data (id, person_id, height)
VALUES (1, 1, 5.8),
       (2, 2, 6.1),
       (3, 3, 5.9),
       (4, 4, 5.7),
       (5, 5, 6.3);

Query Demo: Calculate Sample Standard Deviation of Heights

SELECT STDDEV_SAMP(height) AS height_stddev_samp
FROM height_data;

Result

| height_stddev_samp |
|--------------------|
|      0.240         |

1.33 - STRING_AGG

Aggregate function.

The STRING_AGG() function converts all the non-NULL values of a column to String, separated by the delimiter.

Analyze Syntax

func.string_agg(<expr> [, delimiter])

Analyze Examples

func.string_agg(table.language_name).alias('concatenated_languages')

|          concatenated_languages         |
|-----------------------------------------|
| Python, JavaScript, Java, C#, Ruby      |

SQL Syntax

STRING_AGG(<expr>)
STRING_AGG(<expr> [, delimiter])

Arguments

ArgumentsDescription
<expr>Any string expression (if not a string, use ::VARCHAR to convert)
delimiterOptional constant String, if not specified, use empty String

Return Type

the String type

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE programming_languages (
  id INT,
  language_name VARCHAR
);

INSERT INTO programming_languages (id, language_name)
VALUES (1, 'Python'),
       (2, 'JavaScript'),
       (3, 'Java'),
       (4, 'C#'),
       (5, 'Ruby');

Query Demo: Concatenate Programming Language Names with a Delimiter

SELECT STRING_AGG(language_name, ', ') AS concatenated_languages
FROM programming_languages;

Result

|          concatenated_languages         |
|------------------------------------------|
| Python, JavaScript, Java, C#, Ruby      |

1.34 - SUM

Aggregate function.

The SUM() function calculates the sum of a set of values.

Analyze Syntax

func.sum(<column>)

Analyze Examples

func.sum(table.quantity).alias('total_quantity_sold')

| total_quantity_sold |
|---------------------|
|         41          |

SQL Syntax

SUM(<expr>)

Arguments

ArgumentsDescription
<expr>Any numerical expression

Return Type

A double if the input type is double, otherwise integer.

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE sales_data (
  id INT,
  product_id INT,
  quantity INT
);

INSERT INTO sales_data (id, product_id, quantity)
VALUES (1, 1, 10),
       (2, 2, 5),
       (3, 3, 8),
       (4, 4, 3),
       (5, 5, 15);

Query Demo: Calculate the Total Quantity of Products Sold

SELECT SUM(quantity) AS total_quantity_sold
FROM sales_data;

Result

| total_quantity_sold |
|---------------------|
|         41          |

1.35 - SUM_IF

The suffix -If can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument – a condition.

Analyze Syntax

func.sum_if(<column>, <cond>)

Analyze Examples

func.sum_if(table.amount, table.status=='Completed').alias('total_amount_completed')

| total_amount_completed |
|------------------------|
|         270.0          |

SQL Syntax

SUM_IF(<column>, <cond>)

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE order_data (
  id INT,
  customer_id INT,
  amount FLOAT,
  status VARCHAR
);

INSERT INTO order_data (id, customer_id, amount, status)
VALUES (1, 1, 100, 'Completed'),
       (2, 2, 50, 'Completed'),
       (3, 3, 80, 'Cancelled'),
       (4, 4, 120, 'Completed'),
       (5, 5, 75, 'Cancelled');

Query Demo: Calculate the Total Amount of Completed Orders

SELECT SUM_IF(amount, status = 'Completed') AS total_amount_completed
FROM order_data;

Result

| total_amount_completed |
|------------------------|
|         270.0          |

1.36 - WINDOW_FUNNEL

Funnel Analysis

Similar to windowFunnel in ClickHouse (they were created by the same author), it searches for event chains in a sliding time window and calculates the maximum number of events from the chain.

The function works according to the algorithm:

  • The function searches for data that triggers the first condition in the chain and sets the event counter to 1. This is the moment when the sliding window starts.

  • If events from the chain occur sequentially within the window, the counter is incremented. If the sequence of events is disrupted, the counter isn’t incremented.

  • If the data has multiple event chains at varying completion points, the function will only output the size of the longest chain.

SQL Syntax

WINDOW_FUNNEL( <window> )( <timestamp>, <cond1>, <cond2>, ..., <condN> )

Arguments

  • <timestamp> — Name of the column containing the timestamp. Data types supported: integer types and datetime types.
  • <cond> — Conditions or data describing the chain of events. Must be Boolean datatype.

Parameters

  • <window> — Length of the sliding window, it is the time interval between the first and the last condition. The unit of window depends on the timestamp itself and varies. Determined using the expression timestamp of cond1 <= timestamp of cond2 <= ... <= timestamp of condN <= timestamp of cond1 + window.

Returned value

The maximum number of consecutive triggered conditions from the chain within the sliding time window. All the chains in the selection are analyzed.

Type: UInt8.

Example

Determine if a set period of time is enough for the user to SELECT a phone and purchase it twice in the online store.

Set the following chain of events:

  1. The user logged into their account on the store (event_name = 'login').
  2. The user land the page (event_name = 'visit').
  3. The user adds to the shopping cart(event_name = 'cart').
  4. The user complete the purchase (event_name = 'purchase').
CREATE TABLE events(user_id BIGINT, event_name VARCHAR, event_timestamp TIMESTAMP);

INSERT INTO events VALUES(100123, 'login', '2022-05-14 10:01:00');
INSERT INTO events VALUES(100123, 'visit', '2022-05-14 10:02:00');
INSERT INTO events VALUES(100123, 'cart', '2022-05-14 10:04:00');
INSERT INTO events VALUES(100123, 'purchase', '2022-05-14 10:10:00');

INSERT INTO events VALUES(100125, 'login', '2022-05-15 11:00:00');
INSERT INTO events VALUES(100125, 'visit', '2022-05-15 11:01:00');
INSERT INTO events VALUES(100125, 'cart', '2022-05-15 11:02:00');

INSERT INTO events VALUES(100126, 'login', '2022-05-15 12:00:00');
INSERT INTO events VALUES(100126, 'visit', '2022-05-15 12:01:00');

Input table:

+---------+------------+----------------------------+
| user_id | event_name | event_timestamp            |
+---------+------------+----------------------------+
|  100123 | login      | 2022-05-14 10:01:00.000000 |
|  100123 | visit      | 2022-05-14 10:02:00.000000 |
|  100123 | cart       | 2022-05-14 10:04:00.000000 |
|  100123 | purchase   | 2022-05-14 10:10:00.000000 |
|  100125 | login      | 2022-05-15 11:00:00.000000 |
|  100125 | visit      | 2022-05-15 11:01:00.000000 |
|  100125 | cart       | 2022-05-15 11:02:00.000000 |
|  100126 | login      | 2022-05-15 12:00:00.000000 |
|  100126 | visit      | 2022-05-15 12:01:00.000000 |
+---------+------------+----------------------------+

Find out how far the user user_id could get through the chain in an hour window slides.

Query:

SELECT
    level,
    count() AS count
FROM
(
    SELECT
        user_id,
        window_funnel(3600000000)(event_timestamp, event_name = 'login', event_name = 'visit', event_name = 'cart', event_name = 'purchase') AS level
    FROM events
    GROUP BY user_id
)
GROUP BY level ORDER BY level ASC;

Result:

+-------+-------+
| level | count |
+-------+-------+
|     2 |     1 |
|     3 |     1 |
|     4 |     1 |
+-------+-------+
  • User 100126 level is 2 (login -> visit) .
  • user 100125 level is 3 (login -> visit -> cart).
  • User 100123 level is 4 (login -> visit -> cart -> purchase).

2 - AI Functions

Using SQL-based AI Functions for Knowledge Base Search and Text Completion

This document demonstrates how to leverage PlaidCloud Lakehouse's built-in AI functions for creating document embeddings, searching for similar documents, and generating text completions based on context.

2.1 - AI_EMBEDDING_VECTOR

Creating embeddings using the ai_embedding_vector function in PlaidCloud Lakehouse

This document provides an overview of the ai_embedding_vector function in PlaidCloud Lakehouse and demonstrates how to create document embeddings using this function.

The main code implementation can be found here.

By default, PlaidCloud Lakehouse leverages the text-embedding-ada model for generating embeddings.

Overview of ai_embedding_vector

The ai_embedding_vector function in PlaidCloud Lakehouse is a built-in function that generates vector embeddings for text data. It is useful for natural language processing tasks, such as document similarity, clustering, and recommendation systems.

The function takes a text input and returns a high-dimensional vector that represents the input text's semantic meaning and context. The embeddings are created using pre-trained models on large text corpora, capturing the relationships between words and phrases in a continuous space.

Creating embeddings using ai_embedding_vector

To create embeddings for a text document using the ai_embedding_vector function, follow the example below.

  1. Create a table to store the documents:
CREATE TABLE documents (
                           id INT,
                           title VARCHAR,
                           content VARCHAR,
                           embedding ARRAY(FLOAT32)
);
  1. Insert example documents into the table:
INSERT INTO documents(id, title, content)
VALUES
    (1, 'A Brief History of AI', 'Artificial intelligence (AI) has been a fascinating concept of science fiction for decades...'),
    (2, 'Machine Learning vs. Deep Learning', 'Machine learning and deep learning are two subsets of artificial intelligence...'),
    (3, 'Neural Networks Explained', 'A neural network is a series of algorithms that endeavors to recognize underlying relationships...'),
  1. Generate the embeddings:
UPDATE documents SET embedding = ai_embedding_vector(content) WHERE length(embedding) = 0;

After running the query, the embedding column in the table will contain the generated embeddings.

The embeddings are stored as an array of FLOAT32 values in the embedding column, which has the ARRAY(FLOAT32) column type.

You can now use these embeddings for various natural language processing tasks, such as finding similar documents or clustering documents based on their content.

  1. Inspect the embeddings:
SELECT length(embedding) FROM documents;
+-------------------+
| length(embedding) |
+-------------------+
|              1536 |
|              1536 |
|              1536 |
+-------------------+

The query above shows that the generated embeddings have a length of 1536(dimensions) for each document.

2.2 - AI_TEXT_COMPLETION

Generating text completions using the ai_text_completion function in PlaidCloud Lakehouse

This document provides an overview of the ai_text_completion function in PlaidCloud Lakehouse and demonstrates how to generate text completions using this function.

The main code implementation can be found here.

Overview of ai_text_completion

The ai_text_completion function in PlaidCloud Lakehouse is a built-in function that generates text completions based on a given prompt. It is useful for natural language processing tasks, such as question answering, text generation, and autocompletion systems.

The function takes a text prompt as input and returns a generated completion for the prompt. The completions are created using pre-trained models on large text corpora, capturing the relationships between words and phrases in a continuous space.

Generating text completions using ai_text_completion

Here is a simple example using the ai_text_completion function in PlaidCloud Lakehouse to generate a text completion:

SELECT ai_text_completion('What is artificial intelligence?') AS completion;

Result:

+--------------------------------------------------------------------------------------------------------------------+
| completion                                                                                                          |
+--------------------------------------------------------------------------------------------------------------------+
| Artificial intelligence (AI) is the field of study focused on creating machines and software capable of thinking, learning, and solving problems in a way that mimics human intelligence. This includes areas such as machine learning, natural language processing, computer vision, and robotics. |
+--------------------------------------------------------------------------------------------------------------------+

In this example, we provide the prompt "What is artificial intelligence?" to the ai_text_completion function, and it returns a generated completion that briefly describes artificial intelligence.

2.3 - AI_TO_SQL

Converts natural language instructions into SQL queries with the latest model text-davinci-003.

PlaidCloud Lakehouse offers an efficient solution for constructing SQL queries by incorporating OLAP and AI. Through this function, instructions written in a natural language can be converted into SQL query statements that align with the table schema. For example, the function can be provided with a sentence like "Get all items that cost 10 dollars or less" as an input and generate the corresponding SQL query "SELECT * FROM items WHERE price <= 10" as output.

The main code implementation can be found here.

Analyze Syntax

func.ai_to_sql('<natural-language-instruction>')

Analyze Examples

In this example, an SQL query statement is generated from an instruction with the AI_TO_SQL function, and the resulting statement is executed to obtain the query results.

func.ai_to_sql('List the total amount spent by users from the USA who are older than 30 years, grouped by their names, along with the number of orders they made in 2022')

A SQL statement is generated by the function as the output:

*************************** 1. row ***************************
     database: openai
generated_sql: SELECT name, SUM(price) AS total_spent, COUNT(order_id) AS total_orders
               FROM users
                        JOIN orders ON users.id = orders.user_id
               WHERE country = 'USA' AND age > 30 AND order_date BETWEEN '2022-01-01' AND '2022-12-31'
               GROUP BY name;

SQL Syntax

USE <your-database>;
SELECT * FROM ai_to_sql('<natural-language-instruction>');

SQL Examples

In this example, an SQL query statement is generated from an instruction with the AI_TO_SQL function, and the resulting statement is executed to obtain the query results.

  1. Prepare data.
CREATE DATABASE IF NOT EXISTS openai;
USE openai;

CREATE TABLE users(
    id INT,
    name VARCHAR,
    age INT,
    country VARCHAR
);

CREATE TABLE orders(
    order_id INT,
    user_id INT,
    product_name VARCHAR,
    price DECIMAL(10,2),
    order_date DATE
);

-- Insert sample data into the users table
INSERT INTO users VALUES (1, 'Alice', 31, 'USA'),
                         (2, 'Bob', 32, 'USA'),
                         (3, 'Charlie', 45, 'USA'),
                         (4, 'Diana', 29, 'USA'),
                         (5, 'Eva', 35, 'Canada');

-- Insert sample data into the orders table
INSERT INTO orders VALUES (1, 1, 'iPhone', 1000.00, '2022-03-05'),
                          (2, 1, 'OpenAI Plus', 20.00, '2022-03-06'),
                          (3, 2, 'OpenAI Plus', 20.00, '2022-03-07'),
                          (4, 2, 'MacBook Pro', 2000.00, '2022-03-10'),
                          (5, 3, 'iPad', 500.00, '2022-03-12'),
                          (6, 3, 'AirPods', 200.00, '2022-03-14');
  1. Run the AI_TO_SQL function with an instruction written in English as the input.
SELECT * FROM ai_to_sql(
    'List the total amount spent by users from the USA who are older than 30 years, grouped by their names, along with the number of orders they made in 2022');

A SQL statement is generated by the function as the output:

*************************** 1. row ***************************
     database: openai
generated_sql: SELECT name, SUM(price) AS total_spent, COUNT(order_id) AS total_orders
               FROM users
                        JOIN orders ON users.id = orders.user_id
               WHERE country = 'USA' AND age > 30 AND order_date BETWEEN '2022-01-01' AND '2022-12-31'
               GROUP BY name;
  1. Run the generated SQL statement to get the query results.
+---------+-------------+-------------+
| name    | order_count | total_spent |
+---------+-------------+-------------+
| Bob     |           2 |     2020.00 |
| Alice   |           2 |     1020.00 |
| Charlie |           2 |      700.00 |
+---------+-------------+-------------+

2.4 - COSINE_DISTANCE

Measuring similarity using the cosine_distance function in PlaidCloud Lakehouse

This document provides an overview of the cosine_distance function in PlaidCloud Lakehouse and demonstrates how to measure document similarity using this function.

The cosine_distance function in PlaidCloud Lakehouse is a built-in function that calculates the cosine distance between two vectors. It is commonly used in natural language processing tasks, such as document similarity and recommendation systems.

Cosine distance is a measure of similarity between two vectors, based on the cosine of the angle between them. The function takes two input vectors and returns a value between 0 and 1, with 0 indicating identical vectors and 1 indicating orthogonal (completely dissimilar) vectors.

Analyze Syntax

func.cosine_distance(<vector1>, <vector2>)

SQL Examples

Creating a Table and Inserting Sample Data

Let's create a table to store some sample text documents and their corresponding embeddings:

CREATE TABLE articles (
    id INT,
    title VARCHAR,
    content VARCHAR,
    embedding ARRAY(FLOAT32)
);

Now, let's insert some sample documents into the table:

INSERT INTO articles (id, title, content, embedding)
VALUES
    (1, 'Python for Data Science', 'Python is a versatile programming language widely used in data science...', ai_embedding_vector('Python is a versatile programming language widely used in data science...')),
    (2, 'Introduction to R', 'R is a popular programming language for statistical computing and graphics...', ai_embedding_vector('R is a popular programming language for statistical computing and graphics...')),
    (3, 'Getting Started with SQL', 'Structured Query Language (SQL) is a domain-specific language used for managing relational databases...', ai_embedding_vector('Structured Query Language (SQL) is a domain-specific language used for managing relational databases...'));

Querying for Similar Documents

Now, let's find the documents that are most similar to a given query using the cosine_distance function:

SELECT
    id,
    title,
    content,
    cosine_distance(embedding, ai_embedding_vector('How to use Python in data analysis?')) AS similarity
FROM
    articles
ORDER BY
    similarity ASC
    LIMIT 3;

Result:

+------+--------------------------+---------------------------------------------------------------------------------------------------------+------------+
| id   | title                    | content                                                                                                 | similarity |
+------+--------------------------+---------------------------------------------------------------------------------------------------------+------------+
|    1 | Python for Data Science  | Python is a versatile programming language widely used in data science...                               |  0.1142081 |
|    2 | Introduction to R        | R is a popular programming language for statistical computing and graphics...                           | 0.18741018 |
|    3 | Getting Started with SQL | Structured Query Language (SQL) is a domain-specific language used for managing relational databases... | 0.25137568 |
+------+--------------------------+---------------------------------------------------------------------------------------------------------+------------+

3 - Array Functions

This section provides reference information for the array functions in PlaidCloud Lakehouse.

3.1 - ARRAY_AGGREGATE

Aggregates elements in the array with an aggregate function.

Analyze Syntax

func.array_aggregate( <array>, '<agg_func>' )

  • Supported aggregate functions include avg, count, max, min, sum, any, stddev_samp, stddev_pop, stddev, std, median, approx_count_distinct, kurtosis, and skewness.

  • The syntax can be rewritten as func.array_<agg_func>( <array> ). For example, func.array_avg( <array> ).

Analyze Examples

func.array_aggregate([1, 2, 3, 4], 'sum'), func.array_sum([1, 2, 3, 4])

┌──────────────────────────────────────────────────────────────────────────┐
│ func.array_aggregate([1, 2, 3, 4], 'sum')  │ func.array_sum([1, 2, 3, 4])│
├────────────────────────────────────────────┼─────────────────────────────┤
│                                         10 │                          10 │
└──────────────────────────────────────────────────────────────────────────┘

SQL Syntax

ARRAY_AGGREGATE( <array>, '<agg_func>' )

  • Supported aggregate functions include avg, count, max, min, sum, any, stddev_samp, stddev_pop, stddev, std, median, approx_count_distinct, kurtosis, and skewness.

  • The syntax can be rewritten as ARRAY_<agg_func>( <array> ). For example, ARRAY_AVG( <array> ).

SQL Examples

SELECT ARRAY_AGGREGATE([1, 2, 3, 4], 'SUM'), ARRAY_SUM([1, 2, 3, 4]);

┌────────────────────────────────────────────────────────────────┐
│ array_aggregate([1, 2, 3, 4], 'sum') │ array_sum([1, 2, 3, 4]) │
├──────────────────────────────────────┼─────────────────────────┤
│                                   10 │                      10 │
└────────────────────────────────────────────────────────────────┘

3.2 - ARRAY_APPEND

Prepends an element to the array.

Analyze Syntax

func.array_append( <array>, <element>)

Analyze Examples

func.array_append([3, 4], 5)

┌──────────────────────────────┐
│ func.array_append([3, 4], 5) │
├──────────────────────────────┤
│ [3,4,5]                      │
└──────────────────────────────┘

SQL Syntax

ARRAY_APPEND( <array>, <element>)

SQL Examples

SELECT ARRAY_APPEND([3, 4], 5);

┌─────────────────────────┐
│ array_append([3, 4], 5) │
├─────────────────────────┤
│ [3,4,5]                 │
└─────────────────────────┘

3.3 - ARRAY_APPLY

Alias for ARRAY_TRANSFORM.

3.4 - ARRAY_CONCAT

Concats two arrays.

Analyze Syntax

func.array_concat( <array1>, <array2> )

Analyze Examples

func.array_concat([1, 2], [3, 4])

┌────────────────────────────────────┐
│ func.array_concat([1, 2], [3, 4])  │
├────────────────────────────────────┤
│ [1,2,3,4]                          │
└────────────────────────────────────┘

SQL Syntax

ARRAY_CONCAT( <array1>, <array2> )

SQL Examples

SELECT ARRAY_CONCAT([1, 2], [3, 4]);

┌──────────────────────────────┐
│ array_concat([1, 2], [3, 4]) │
├──────────────────────────────┤
│ [1,2,3,4]                    │
└──────────────────────────────┘

3.5 - ARRAY_CONTAINS

Alias for CONTAINS.

3.6 - ARRAY_DISTINCT

Removes all duplicates and NULLs from the array without preserving the original order.

Analyze Syntax

func.array_distinct( <array> )

Analyze Examples

func.array_distinct([1, 2, 2, 4, 3])

┌───────────────────────────────────────┐
│ func.array_distinct([1, 2, 2, 4, 3])  │
├───────────────────────────────────────┤
│ [1,2,4,3]                             │
└───────────────────────────────────────┘

SQL Syntax

ARRAY_DISTINCT( <array> )

SQL Examples

SELECT ARRAY_DISTINCT([1, 2, 2, 4, 3]);

┌─────────────────────────────────┐
│ array_distinct([1, 2, 2, 4, 3]) │
├─────────────────────────────────┤
│ [1,2,4,3]                       │
└─────────────────────────────────┘

3.7 - ARRAY_FILTER

Constructs an array from those elements of the input array for which the lambda function returns true.

Analyze Syntax

func.array_filter( <array>, <lambda> )

Analyze Examples

func.array_filter([1, 2, 3], x -> (x > 1))

┌─────────────────────────────────────────────┐
│ func.array_filter([1, 2, 3], x -> (x > 1))  │
├─────────────────────────────────────────────┤
│ [2,3]                                       │
└─────────────────────────────────────────────┘

SQL Syntax

ARRAY_FILTER( <array>, <lambda> )

SQL Examples

SELECT ARRAY_FILTER([1, 2, 3], x -> x > 1);

┌───────────────────────────────────────┐
│ array_filter([1, 2, 3], x -> (x > 1)) │
├───────────────────────────────────────┤
│ [2,3]                                 │
└───────────────────────────────────────┘

3.8 - ARRAY_FLATTEN

Flattens nested arrays, converting them into a single-level array.

Analyze Syntax

func.array_flatten( <array> )

Analyze Examples

func.array_flatten([[1, 2], [3, 4, 5]]) 

┌──────────────────────────────────────────┐
│ func.array_flatten([[1, 2], [3, 4, 5]])  │
├──────────────────────────────────────────┤
│ [1,2,3,4,5]                              │
└──────────────────────────────────────────┘

SQL Syntax

ARRAY_FLATTEN( <array> )

SQL Examples

SELECT ARRAY_FLATTEN([[1,2], [3,4,5]]);

┌────────────────────────────────────┐
│ array_flatten([[1, 2], [3, 4, 5]]) │
├────────────────────────────────────┤
│ [1,2,3,4,5]                        │
└────────────────────────────────────┘

3.9 - ARRAY_GET

Alias for GET.

3.10 - ARRAY_INDEXOF

Returns the index(1-based) of an element if the array contains the element.

Analyze Syntax

func.array_indexof( <array>, <element> )

Analyze Examples

func.array_indexof([1, 2, 9], 9)

┌───────────────────────────────────┐
│ func.array_indexof([1, 2, 9], 9)  │
├───────────────────────────────────┤
│                                 3 │
└───────────────────────────────────┘

SQL Syntax

ARRAY_INDEXOF( <array>, <element> )

SQL Examples

SELECT ARRAY_INDEXOF([1, 2, 9], 9);

┌─────────────────────────────┐
│ array_indexof([1, 2, 9], 9) │
├─────────────────────────────┤
│                           3 │
└─────────────────────────────┘

3.11 - ARRAY_LENGTH

Returns the length of an array.

Analyze Syntax

func.array_length( <array> )

Analyze Examples

func.array_length([1, 2])

┌────────────────────────────┐
│ func.array_length([1, 2])  │
├────────────────────────────┤
│                          2 │
└────────────────────────────┘

SQL Syntax

ARRAY_LENGTH( <array> )

SQL Examples

SELECT ARRAY_LENGTH([1, 2]);

┌──────────────────────┐
│ array_length([1, 2]) │
├──────────────────────┤
│                    2 │
└──────────────────────┘

3.12 - ARRAY_PREPEND

Prepends an element to the array.

Analyze Syntax

func.array_prepend( <element>, <array> )

Analyze Examples

func.array_prepend(1, [3, 4])

┌────────────────────────────────┐
│ func.array_prepend(1, [3, 4])  │
├────────────────────────────────┤
│ [1,3,4]                        │
└────────────────────────────────┘

SQL Syntax

ARRAY_PREPEND( <element>, <array> )

SQL Examples

SELECT ARRAY_PREPEND(1, [3, 4]);

┌──────────────────────────┐
│ array_prepend(1, [3, 4]) │
├──────────────────────────┤
│ [1,3,4]                  │
└──────────────────────────┘

3.13 - ARRAY_REDUCE

Applies iteratively the lambda function to the elements of the array, so as to reduce the array to a single value.

Analyze Syntax

func.array_reduce( <array>, <lambda> )

Analyze Examples

func.array_reduce([1, 2, 3, 4], (x, y) -> (x + y))

┌─────────────────────────────────────────────────────┐
│ func.array_reduce([1, 2, 3, 4], (x, y) -> (x + y))  │
├─────────────────────────────────────────────────────┤
│                                                  10 │
└─────────────────────────────────────────────────────┘

SQL Syntax

ARRAY_REDUCE( <array>, <lambda> )

SQL Examples

SELECT ARRAY_REDUCE([1, 2, 3, 4], (x,y) -> x + y);

┌───────────────────────────────────────────────┐
│ array_reduce([1, 2, 3, 4], (x, y) -> (x + y)) │
├───────────────────────────────────────────────┤
│                                            10 │
└───────────────────────────────────────────────┘

3.14 - ARRAY_REMOVE_FIRST

Removes the first element from the array.

Analyze Syntax

func.array_remove_first( <array> )

Analyze Examples

func.array_remove_first([1, 2, 3]) 

┌─────────────────────────────────────┐
│ func.array_remove_first([1, 2, 3])  │
├─────────────────────────────────────┤
│ [2,3]                               │
└─────────────────────────────────────┘

SQL Syntax

ARRAY_REMOVE_FIRST( <array> )

SQL Examples

SELECT ARRAY_REMOVE_FIRST([1, 2, 3]);

┌───────────────────────────────┐
│ array_remove_first([1, 2, 3]) │
├───────────────────────────────┤
│ [2,3]                         │
└───────────────────────────────┘

3.15 - ARRAY_REMOVE_LAST

Removes the last element from the array.

Analyze Syntax

func.array_remove_last( <array> )

Analyze Examples

func.array_remove_last([1, 2, 3]) 

┌────────────────────────────────────┐
│ func.array_remove_last([1, 2, 3])  │
├────────────────────────────────────┤
│ [1,2]                              │
└────────────────────────────────────┘

SQL Syntax

ARRAY_REMOVE_LAST( <array> )

SQL Examples

SELECT ARRAY_REMOVE_LAST([1, 2, 3]);

┌──────────────────────────────┐
│ array_remove_last([1, 2, 3]) │
├──────────────────────────────┤
│ [1,2]                        │
└──────────────────────────────┘

3.16 - ARRAY_SIZE

Alias for ARRAY_LENGTH.

3.17 - ARRAY_SLICE

Alias for SLICE.

3.18 - ARRAY_SORT

Sorts elements in the array in ascending order.

Analyze Syntax

func.array_sort( <array>[, <order>, <nullposition>] )
ParameterDefaultDescription
orderASCSpecifies the sorting order as either ascending (ASC) or descending (DESC).
nullpositionNULLS FIRSTDetermines the position of NULL values in the sorting result, at the beginning (NULLS FIRST) or at the end (NULLS LAST) of the sorting output.

Analyze Examples

func.array_sort([1, 4, 3, 2])

┌────────────────────────────────┐
│ func.array_sort([1, 4, 3, 2])  │
├────────────────────────────────┤
│ [1,2,3,4]                      │
└────────────────────────────────┘

SQL Syntax

ARRAY_SORT( <array>[, <order>, <nullposition>] )
ParameterDefaultDescription
orderASCSpecifies the sorting order as either ascending (ASC) or descending (DESC).
nullpositionNULLS FIRSTDetermines the position of NULL values in the sorting result, at the beginning (NULLS FIRST) or at the end (NULLS LAST) of the sorting output.

SQL Examples

SELECT ARRAY_SORT([1, 4, 3, 2]);

┌──────────────────────────┐
│ array_sort([1, 4, 3, 2]) │
├──────────────────────────┤
│ [1,2,3,4]                │
└──────────────────────────┘

3.19 - ARRAY_TO_STRING

Concatenates elements of an array into a single string, using a specified separator.

Analyze Syntax

func.array_to_string( <array>, '<separator>' )

Analyze Examples

func.array_to_string(['apple', 'banana', 'cherry'], ', ') 

┌────────────────────────────────────────────────────────────┐
│ func.array_to_string(['apple', 'banana', 'cherry'], ', ')  │
├────────────────────────────────────────────────────────────┤
│ Apple, Banana, Cherry                                      │
└────────────────────────────────────────────────────────────┘

SQL Syntax

ARRAY_TO_STRING( <array>, '<separator>' )

SQL Examples

SELECT ARRAY_TO_STRING(['Apple', 'Banana', 'Cherry'], ', ');

┌──────────────────────────────────────────────────────┐
│ array_to_string(['apple', 'banana', 'cherry'], ', ') │
├──────────────────────────────────────────────────────┤
│ Apple, Banana, Cherry                                │
└──────────────────────────────────────────────────────┘

3.20 - ARRAY_TRANSFORM

Returns an array that is the result of applying the lambda function to each element of the input array.

Analyze Syntax

func.array_transform( <array>, <lambda> )

Analyze Examples

func.array_transform([1, 2, 3], x -> (x + 1))

┌───────────────────────────────────────────────┐
│ func.array_transform([1, 2, 3], x -> (x + 1)) │
├───────────────────────────────────────────────┤
│ [2,3,4]                                       │
└───────────────────────────────────────────────┘

SQL Syntax

ARRAY_TRANSFORM( <array>, <lambda> )

Aliases

SQL Examples

SELECT ARRAY_TRANSFORM([1, 2, 3], x -> x + 1);

┌──────────────────────────────────────────┐
│ array_transform([1, 2, 3], x -> (x + 1)) │
├──────────────────────────────────────────┤
│ [2,3,4]                                  │
└──────────────────────────────────────────┘

3.21 - ARRAY_UNIQUE

Counts unique elements in the array (except NULL).

Analyze Syntax

func.array_unique( <array> )

Analyze Examples

func.array_unique([1, 2, 3, 3, 4])

┌─────────────────────────────────────┐
│ func.array_unique([1, 2, 3, 3, 4])  │
├─────────────────────────────────────┤
│                                   4 │
└─────────────────────────────────────┘

SQL Syntax

ARRAY_UNIQUE( <array> )

SQL Examples

SELECT ARRAY_UNIQUE([1, 2, 3, 3, 4]);

┌───────────────────────────────┐
│ array_unique([1, 2, 3, 3, 4]) │
├───────────────────────────────┤
│                             4 │
└───────────────────────────────┘

3.22 - ARRAYS_ZIP

Merges multiple arrays into a single array tuple.

Analyze Syntax

func.arrays_zip( <array1> [, ...] )

Analyze Examples

func.arrays_zip([1, 2, 3], ['a', 'b', 'c'])

┌──────────────────────────────────────────────┐
│ func.arrays_zip([1, 2, 3], ['a', 'b', 'c'])  │
├──────────────────────────────────────────────┤
│  [(1,'a'),(2,'b'),(3,'c')]                   │
└──────────────────────────────────────────────┘

SQL Syntax

ARRAYS_ZIP( <array1> [, ...] )

Arguments

ArgumentsDescription
<arrayN>The input ARRAYs.

:::note

  • The length of each array must be the same. :::

Return Type

Array(Tuple).

SQL Examples

SELECT ARRAYS_ZIP([1, 2, 3], ['a', 'b', 'c']);
┌────────────────────────────────────────┐
│ arrays_zip([1, 2, 3], ['a', 'b', 'c']) │
├────────────────────────────────────────┤
│ [(1,'a'),(2,'b'),(3,'c')]              │
└────────────────────────────────────────┘

3.23 - CONTAINS

Checks if the array contains a specific element.

Analyze Syntax

func.contains( <array>, <element> )

Analyze Examples

func.contains([1, 2], 1)

┌───────────────────────────┐
│ func.contains([1, 2], 1)  │
├───────────────────────────┤
│ true                      │
└───────────────────────────┘

SQL Syntax

CONTAINS( <array>, <element> )

Aliases

SQL Examples

SELECT ARRAY_CONTAINS([1, 2], 1), CONTAINS([1, 2], 1);

┌─────────────────────────────────────────────────┐
│ array_contains([1, 2], 1) │ contains([1, 2], 1) │
├───────────────────────────┼─────────────────────┤
│ true                      │ true                │
└─────────────────────────────────────────────────┘

3.24 - GET

Returns an element from an array by index (1-based).

Analyze Syntax

func.get( <array>, <index> )

Analyze Examples

func.get([1, 2], 2)

┌─────────────────────┐
│ func.get([1, 2], 2) │
├─────────────────────┤
│                   2 │
└─────────────────────┘

SQL Syntax

GET( <array>, <index> )

Aliases

SQL Examples

SELECT GET([1, 2], 2), ARRAY_GET([1, 2], 2);

┌───────────────────────────────────────┐
│ get([1, 2], 2) │ array_get([1, 2], 2) │
├────────────────┼──────────────────────┤
│              2 │                    2 │
└───────────────────────────────────────┘

3.25 - RANGE

Returns an array collected by [start, end).

Analyze Syntax

func.range( <start>, <end> )

SQAnalyzeL Examples

func.range(1, 5)

┌────────────────────┐
│  func.range(1, 5)  │
├────────────────────┤
│ [1,2,3,4]          │
└────────────────────┘

SQL Syntax

RANGE( <start>, <end> )

SQL Examples

SELECT RANGE(1, 5);

┌───────────────┐
│  range(1, 5)  │
├───────────────┤
│ [1,2,3,4]     │
└───────────────┘

3.26 - SLICE

Extracts a slice from the array by index (1-based).

Analyze Syntax

func.slice( <array>, <start>[, <end>] )

Analyze Examples

func.slice([1, 21, 32, 4], 2, 3)

┌──────────────────────────────────┐
│ func.slice([1, 21, 32, 4], 2, 3) │
├──────────────────────────────────┤
│ [21,32]                          │ 
└──────────────────────────────────┘

SQL Syntax

SLICE( <array>, <start>[, <end>] )

Aliases

SQL Examples

SELECT ARRAY_SLICE([1, 21, 32, 4], 2, 3), SLICE([1, 21, 32, 4], 2, 3);

┌─────────────────────────────────────────────────────────────────┐
│ array_slice([1, 21, 32, 4], 2, 3) │ slice([1, 21, 32, 4], 2, 3) │
├───────────────────────────────────┼─────────────────────────────┤
│ [21,32]                           │ [21,32]                     │
└─────────────────────────────────────────────────────────────────┘

3.27 - UNNEST

Unnests the array and returns the set of elements.

Analyze Syntax

func.unnest( <array> )

Analyze Examples

func.unnest([1, 2])

┌──────────────────────┐
│  func.unnest([1, 2]) │
├──────────────────────┤
│                    1 │
│                    2 │
└──────────────────────┘

SQL Syntax

UNNEST( <array> )

SQL Examples

SELECT UNNEST([1, 2]);

┌─────────────────┐
│  unnest([1, 2]) │
├─────────────────┤
│               1 │
│               2 │
└─────────────────┘

-- UNNEST(array) can be used as a table function.
SELECT * FROM UNNEST([1, 2]);

┌─────────────────┐
│      value      │
├─────────────────┤
│               1 │
│               2 │
└─────────────────┘

A Practical Example

In the examples below, we will use the following table called contacts with the phones column defined with an array of text.

CREATE TABLE contacts (
  id SERIAL PRIMARY KEY, 
  name VARCHAR (100), 
  phones TEXT []
);

The phones column is a one-dimensional array that holds various phone numbers that a contact may have.

To define multiple dimensional array, you add the square brackets.

For example, you can define a two-dimensional array as follows:

column_name data_type [][]

An example of inserting data into that table

INSERT INTO contacts (name, phones)
VALUES('John Doe',ARRAY [ '(408)-589-5846','(408)-589-5555' ]);

or

INSERT INTO contacts (name, phones)
VALUES('Lily Bush','{"(408)-589-5841"}'),
      ('William Gate','{"(408)-589-5842","(408)-589-5843"}');

The unnest() function expands an array to a list of rows. For example, the following query expands all phone numbers of the phones array.

SELECT 
  name, 
  unnest(phones) 
FROM 
  contacts;

Output:

nameunnest
John Doe(408)-589-5846
John Doe(408)-589-5555
Lily Bush(408)-589-5841
William Gate(408)-589-5843

4 - Bitmap Functions

This section provides reference information for the bitmap functions in PlaidCloud Lakehouse.

4.1 - BITMAP_AND

Performs a bitwise AND operation on the two bitmaps.

Analyze Syntax

func.bitmap_and( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_and(func.build_bitmap([1, 4, 5]), func.cast(build_bitmap([4, 5])), string)

┌────────────────────────────────────────────────────────────────────────────────────────┐
│ func.bitmap_and(func.build_bitmap([1, 4, 5]), func.cast(build_bitmap([4, 5])), string) │
├────────────────────────────────────────────────────────────────────────────────────────┤
│ 4,5                                                                                    │
└────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_AND( <bitmap1>, <bitmap2> )

SQL Examples

SELECT BITMAP_AND(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([4,5]))::String;

┌───────────────────────────────────────────────────────────────────┐
│ bitmap_and(build_bitmap([1, 4, 5]), build_bitmap([4, 5]))::string │
├───────────────────────────────────────────────────────────────────┤
│ 4,5                                                               │
└───────────────────────────────────────────────────────────────────┘

4.2 - BITMAP_AND_COUNT

Counts the number of bits set to 1 in the bitmap by performing a logical AND operation.

Analyze Syntax

func.bitmap_and_count( <bitmap> )

Analyze Examples

func.bitmap_and_count(to_bitmap('1, 3, 5'))

┌─────────────────────────────────────────────┐
│ func.bitmap_and_count(to_bitmap('1, 3, 5')) │
├─────────────────────────────────────────────┤
│                                           3 │
└─────────────────────────────────────────────┘

SQL Syntax

BITMAP_AND_COUNT( <bitmap> )

SQL Examples

SELECT BITMAP_AND_COUNT(TO_BITMAP('1, 3, 5'));

┌────────────────────────────────────────┐
│ bitmap_and_count(to_bitmap('1, 3, 5')) │
├────────────────────────────────────────┤
│                                      3 │
└────────────────────────────────────────┘

4.3 - BITMAP_AND_NOT

Alias for BITMAP_NOT.

4.4 - BITMAP_CARDINALITY

Alias for BITMAP_COUNT.

4.5 - BITMAP_CONTAINS

Checks if the bitmap contains a specific value.

Analyze Syntax

func.bitmap_contains( <bitmap>, <value> )

Analyze Examples

func.bitmap_contains(build_bitmap([1, 4, 5]), 1)

┌───────────────────────────────────────────────────┐
│ func.bitmap_contains(build_bitmap([1, 4, 5]), 1)  │
├───────────────────────────────────────────────────┤
│ true                                              │
└───────────────────────────────────────────────────┘

SQL Syntax

BITMAP_CONTAINS( <bitmap>, <value> )

SQL Examples

SELECT BITMAP_CONTAINS(BUILD_BITMAP([1,4,5]), 1);

┌─────────────────────────────────────────────┐
│ bitmap_contains(build_bitmap([1, 4, 5]), 1) │
├─────────────────────────────────────────────┤
│ true                                        │
└─────────────────────────────────────────────┘

4.6 - BITMAP_COUNT

Counts the number of bits set to 1 in the bitmap.

Analyze Syntax

func.bitmap_count( <bitmap> )

Analyze Examples

func.bitmap_count(build_bitmap([1, 4, 5]))

┌────────────────────────────────────────────┐
│ func.bitmap_count(build_bitmap([1, 4, 5])) │
├────────────────────────────────────────────┤
│                                          3 │
└────────────────────────────────────────────┘

SQL Syntax

BITMAP_COUNT( <bitmap> )

Aliases

SQL Examples

SELECT BITMAP_COUNT(BUILD_BITMAP([1,4,5])), BITMAP_CARDINALITY(BUILD_BITMAP([1,4,5]));

┌─────────────────────────────────────────────────────────────────────────────────────┐
│ bitmap_count(build_bitmap([1, 4, 5])) │ bitmap_cardinality(build_bitmap([1, 4, 5])) │
├───────────────────────────────────────┼─────────────────────────────────────────────┤
│                                     3 │                                           3 │
└─────────────────────────────────────────────────────────────────────────────────────┘

4.7 - BITMAP_HAS_ALL

Checks if the first bitmap contains all the bits in the second bitmap.

Analyze Syntax

func.bitmap_has_all( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_has_all(build_bitmap([1, 4, 5]), build_bitmap([1, 2])) 

┌─────────────────────────────────────────────────────────────────────┐
│ func.bitmap_has_all(build_bitmap([1, 4, 5]), build_bitmap([1, 2]))  │
├─────────────────────────────────────────────────────────────────────┤
│ false                                                               │
└─────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_HAS_ALL( <bitmap1>, <bitmap2> )

SQL Examples

SELECT BITMAP_HAS_ALL(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([1,2]));

┌───────────────────────────────────────────────────────────────┐
│ bitmap_has_all(build_bitmap([1, 4, 5]), build_bitmap([1, 2])) │
├───────────────────────────────────────────────────────────────┤
│ false                                                         │
└───────────────────────────────────────────────────────────────┘

4.8 - BITMAP_HAS_ANY

Checks if the first bitmap has any bit matching the bits in the second bitmap.

Analyze Syntax

func.bitmap_has_any( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_has_any(func.build_bitmap([1, 4, 5]), func.build_bitmap([1, 2]))

┌───────────────────────────────────────────────────────────────────────────────┐
│ func.bitmap_has_any(func.build_bitmap([1, 4, 5]), func.build_bitmap([1, 2]))  │
├───────────────────────────────────────────────────────────────────────────────┤
│ true                                                                          │
└───────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_HAS_ANY( <bitmap1>, <bitmap2> )

SQL Examples

SELECT BITMAP_HAS_ANY(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([1,2]));

┌───────────────────────────────────────────────────────────────┐
│ bitmap_has_any(build_bitmap([1, 4, 5]), build_bitmap([1, 2])) │
├───────────────────────────────────────────────────────────────┤
│ true                                                          │
└───────────────────────────────────────────────────────────────┘

4.9 - BITMAP_INTERSECT

Counts the number of bits set to 1 in the bitmap by performing a logical INTERSECT operation.

Analyze Syntax

func.bitmap_intersect( <bitmap> )

Analyze Examples

func.bitmap_intersect(func.to_bitmap('1, 3, 5'))

┌──────────────────────────────────────────────────┐
│ func.bitmap_intersect(func.to_bitmap('1, 3, 5')) │
├──────────────────────────────────────────────────┤
│ 1,3,5                                            │
└──────────────────────────────────────────────────┘

SQL Syntax

BITMAP_INTERSECT( <bitmap> )

SQL Examples

SELECT BITMAP_INTERSECT(TO_BITMAP('1, 3, 5'))::String;

┌────────────────────────────────────────────────┐
│ bitmap_intersect(to_bitmap('1, 3, 5'))::string │
├────────────────────────────────────────────────┤
│ 1,3,5                                          │
└────────────────────────────────────────────────┘

4.10 - BITMAP_MAX

Gets the maximum value in the bitmap.

Analyze Syntax

func.bitmap_max( <bitmap> )

Analyze Examples

func.bitmap_max(func.build_bitmap([1, 4, 5]))

┌───────────────────────────────────────────────┐
│ func.bitmap_max(func.build_bitmap([1, 4, 5])) │
├───────────────────────────────────────────────┤
│                                             5 │
└───────────────────────────────────────────────┘

SQL Syntax

BITMAP_MAX( <bitmap> )

SQL Examples

SELECT BITMAP_MAX(BUILD_BITMAP([1,4,5]));

┌─────────────────────────────────────┐
│ bitmap_max(build_bitmap([1, 4, 5])) │
├─────────────────────────────────────┤
│                                   5 │
└─────────────────────────────────────┘

4.11 - BITMAP_MIN

Gets the minimum value in the bitmap.

Analyze Syntax

func.bitmap_min( <bitmap> )

Analyze Examples

func.bitmap_min(func.build_bitmap([1, 4, 5]))

┌───────────────────────────────────────────────┐
│ func.bitmap_min(func.build_bitmap([1, 4, 5])) │
├───────────────────────────────────────────────┤
│                                             1 │
└───────────────────────────────────────────────┘

SQL Syntax

BITMAP_MIN( <bitmap> )

SQL Examples

SELECT BITMAP_MIN(BUILD_BITMAP([1,4,5]));

┌─────────────────────────────────────┐
│ bitmap_min(build_bitmap([1, 4, 5])) │
├─────────────────────────────────────┤
│                                   1 │
└─────────────────────────────────────┘

4.12 - BITMAP_NOT

Generates a new bitmap with elements from the first bitmap that are not in the second one.

Analyze Syntax

func.bitmap_not( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_not(func.build_bitmap([1, 4, 5]), func.cast(func.build_bitmap([5, 6, 7])), Text)

┌───────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.bitmap_not(func.build_bitmap([1, 4, 5]), func.cast(func.build_bitmap([5, 6, 7])), Text)  │
├───────────────────────────────────────────────────────────────────────────────────────────────┤
│ 1,4                                                                                           │
└───────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_NOT( <bitmap1>, <bitmap2> )

Aliases

SQL Examples

SELECT BITMAP_NOT(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([5,6,7]))::String;

┌──────────────────────────────────────────────────────────────────────┐
│ bitmap_not(build_bitmap([1, 4, 5]), build_bitmap([5, 6, 7]))::string │
├──────────────────────────────────────────────────────────────────────┤
│ 1,4                                                                  │
└──────────────────────────────────────────────────────────────────────┘

SELECT BITMAP_AND_NOT(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([5,6,7]))::String;

┌──────────────────────────────────────────────────────────────────────────┐
│ bitmap_and_not(build_bitmap([1, 4, 5]), build_bitmap([5, 6, 7]))::string │
├──────────────────────────────────────────────────────────────────────────┤
│ 1,4                                                                      │
└──────────────────────────────────────────────────────────────────────────┘

4.13 - BITMAP_NOT_COUNT

Counts the number of bits set to 0 in the bitmap by performing a logical NOT operation.

Analyze Syntax

func.bitmap_not_count( <bitmap> )

Analyze Examples

func.bitmap_not_count(func.to_bitmap('1, 3, 5'))

┌──────────────────────────────────────────────────┐
│ func.bitmap_not_count(func.to_bitmap('1, 3, 5')) │
├──────────────────────────────────────────────────┤
│                                                3 │
└──────────────────────────────────────────────────┘

SQL Syntax

BITMAP_NOT_COUNT( <bitmap> )

SQL Examples

SELECT BITMAP_NOT_COUNT(TO_BITMAP('1, 3, 5'));

┌────────────────────────────────────────┐
│ bitmap_not_count(to_bitmap('1, 3, 5')) │
├────────────────────────────────────────┤
│                                      3 │
└────────────────────────────────────────┘

4.14 - BITMAP_OR

Performs a bitwise OR operation on the two bitmaps.

Analyze Syntax

func.bitmap_or( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_or(func.build_bitmap([1, 4, 5]), func.build_bitmap([6, 7]))

┌─────────────────────────────────────────────────────────────────────────┐
│ func.bitmap_or(func.build_bitmap([1, 4, 5]), func.build_bitmap([6, 7])) │
├─────────────────────────────────────────────────────────────────────────┤
│ 1,4,5,6,7                                                               │
└─────────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_OR( <bitmap1>, <bitmap2> )

SQL Examples

SELECT BITMAP_OR(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([6,7]))::String;

┌──────────────────────────────────────────────────────────────────┐
│ bitmap_or(build_bitmap([1, 4, 5]), build_bitmap([6, 7]))::string │
├──────────────────────────────────────────────────────────────────┤
│ 1,4,5,6,7                                                        │
└──────────────────────────────────────────────────────────────────┘

4.15 - BITMAP_OR_COUNT

Counts the number of bits set to 1 in the bitmap by performing a logical OR operation.

Analyze Syntax

func.bitmap_or_count( <bitmap> )

Analyze Examples

func.bitmap_or_count(func.to_bitmap('1, 3, 5'))

┌─────────────────────────────────────────────────┐
│ func.bitmap_or_count(func.to_bitmap('1, 3, 5')) │
├─────────────────────────────────────────────────┤
│                                               3 │
└─────────────────────────────────────────────────┘

SQL Syntax

BITMAP_OR_COUNT( <bitmap> )

SQL Examples

SELECT BITMAP_OR_COUNT(TO_BITMAP('1, 3, 5'));

┌───────────────────────────────────────┐
│ bitmap_or_count(to_bitmap('1, 3, 5')) │
├───────────────────────────────────────┤
│                                     3 │
└───────────────────────────────────────┘

4.16 - BITMAP_SUBSET_IN_RANGE

Generates a sub-bitmap of the source bitmap within a specified range.

Analyze Syntax

func.bitmap_subset_in_range( <bitmap>, <start>, <end> )

Analyze Examples

func.bitmap_subset_in_range(func.build_bitmap([5, 7, 9]), 6, 9)

┌─────────────────────────────────────────────────────────────────┐
│ func.bitmap_subset_in_range(func.build_bitmap([5, 7, 9]), 6, 9) │
├─────────────────────────────────────────────────────────────────┤
│ 7                                                               │
└─────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_SUBSET_IN_RANGE( <bitmap>, <start>, <end> )

SQL Examples

SELECT BITMAP_SUBSET_IN_RANGE(BUILD_BITMAP([5,7,9]), 6, 9)::String;

┌───────────────────────────────────────────────────────────────┐
│ bitmap_subset_in_range(build_bitmap([5, 7, 9]), 6, 9)::string │
├───────────────────────────────────────────────────────────────┤
│ 7                                                             │
└───────────────────────────────────────────────────────────────┘

4.17 - BITMAP_SUBSET_LIMIT

Generates a sub-bitmap of the source bitmap, beginning with a range from the start value, with a size limit.

Analyze Syntax

func.bitmap_subset_limit( <bitmap>, <start>, <limit> )

Analyze Examples

func.bitmap_subset_limit(func.build_bitmap([1, 4, 5]), 2, 2)

┌──────────────────────────────────────────────────────────────┐
│ func.bitmap_subset_limit(func.build_bitmap([1, 4, 5]), 2, 2) │
├──────────────────────────────────────────────────────────────┤
│ 4,5                                                          │
└──────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_SUBSET_LIMIT( <bitmap>, <start>, <limit> )

SQL Examples

SELECT BITMAP_SUBSET_LIMIT(BUILD_BITMAP([1,4,5]), 2, 2)::String;

┌────────────────────────────────────────────────────────────┐
│ bitmap_subset_limit(build_bitmap([1, 4, 5]), 2, 2)::string │
├────────────────────────────────────────────────────────────┤
│ 4,5                                                        │
└────────────────────────────────────────────────────────────┘

4.18 - BITMAP_UNION

Counts the number of bits set to 1 in the bitmap by performing a logical UNION operation.

Analyze Syntax

func.bitmap_union( <bitmap> )

Analyze Examples

func.bitmap_union(func.to_bitmap('1, 3, 5'))

┌──────────────────────────────────────────────┐
│ func.bitmap_union(func.to_bitmap('1, 3, 5')) │
├──────────────────────────────────────────────┤
│ 1,3,5                                        │
└──────────────────────────────────────────────┘

SQL Syntax

BITMAP_UNION( <bitmap> )

SQL Examples

SELECT BITMAP_UNION(TO_BITMAP('1, 3, 5'))::String;

┌────────────────────────────────────────────┐
│ bitmap_union(to_bitmap('1, 3, 5'))::string │
├────────────────────────────────────────────┤
│ 1,3,5                                      │
└────────────────────────────────────────────┘

4.19 - BITMAP_XOR

Performs a bitwise XOR (exclusive OR) operation on the two bitmaps.

Analyze Syntax

func.bitmap_xor( <bitmap1>, <bitmap2> )

Analyze Examples

func.bitmap_xor(func.build_bitmap([1, 4, 5]), func.build_bitmap([5, 6, 7]))

┌─────────────────────────────────────────────────────────────────────────────┐
│ func.bitmap_xor(func.build_bitmap([1, 4, 5]), func.build_bitmap([5, 6, 7])) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 1,4,6,7                                                                     │
└─────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

BITMAP_XOR( <bitmap1>, <bitmap2> )

SQL Examples

SELECT BITMAP_XOR(BUILD_BITMAP([1,4,5]), BUILD_BITMAP([5,6,7]))::String;

┌──────────────────────────────────────────────────────────────────────┐
│ bitmap_xor(build_bitmap([1, 4, 5]), build_bitmap([5, 6, 7]))::string │
├──────────────────────────────────────────────────────────────────────┤
│ 1,4,6,7                                                              │
└──────────────────────────────────────────────────────────────────────┘

4.20 - BITMAP_XOR_COUNT

Counts the number of bits set to 1 in the bitmap by performing a logical XOR (exclusive OR) operation.

Analyze Syntax

func.bitmap_xor_count( <bitmap> )

Analyze Examples

func.bitmap_xor_count(func.to_bitmap('1, 3, 5'))

┌──────────────────────────────────────────────────┐
│ func.bitmap_xor_count(func.to_bitmap('1, 3, 5')) │
├──────────────────────────────────────────────────┤
│                                                3 │
└──────────────────────────────────────────────────┘

SQL Syntax

BITMAP_XOR_COUNT( <bitmap> )

SQL Examples

SELECT BITMAP_XOR_COUNT(TO_BITMAP('1, 3, 5'));

┌────────────────────────────────────────┐
│ bitmap_xor_count(to_bitmap('1, 3, 5')) │
├────────────────────────────────────────┤
│                                      3 │
└────────────────────────────────────────┘

4.21 - INTERSECT_COUNT

Counts the number of intersecting bits between two bitmap columns.

Analyze Syntax

func.intersect_count(( '<bitmap1>', '<bitmap2>' ), ( <bitmap_column1>, <bitmap_column2> ))

Analyze Examples

# Given a dataset like this:

┌───────────────────────────────────────┐
│        id       │ tag  │      v       │
├─────────────────┼─────────────────────┤
│               1 │   a  │  0, 1        │
│               3 │   b  │  0, 1, 2     │
│               2 │   c  │  1, 3, 4     │
└───────────────────────────────────────┘

# This is produced
func.intersect_count(('b', 'c'), (v, tag))
┌──────────────────────────────────────────────────────────┐
│        id       │ func.intersect_count('b', 'c')(v, tag) │
├─────────────────┼────────────────────────────────────────┤
│               1 │                                      0 │
│               3 │                                      3 │
│               2 │                                      3 │
└──────────────────────────────────────────────────────────┘

SQL Syntax

INTERSECT_COUNT( '<bitmap1>', '<bitmap2>' )( <bitmap_column1>, <bitmap_column2> )

SQL Examples

CREATE TABLE agg_bitmap_test(id Int, tag String, v Bitmap);

INSERT INTO
  agg_bitmap_test(id, tag, v)
VALUES
  (1, 'a', to_bitmap('0, 1')),
  (2, 'b', to_bitmap('0, 1, 2')),
  (3, 'c', to_bitmap('1, 3, 4'));

SELECT id, INTERSECT_COUNT('b', 'c')(v, tag) 
FROM agg_bitmap_test GROUP BY id;

┌─────────────────────────────────────────────────────┐
│        id       │ intersect_count('b', 'c')(v, tag) │
├─────────────────┼───────────────────────────────────┤
│               1 │                                 0 │
│               3 │                                 3 │
│               2 │                                 3 │
└─────────────────────────────────────────────────────┘

4.22 - SUB_BITMAP

Generates a sub-bitmap of the source bitmap, beginning from the start index, with a specified size.

Analyze Syntax

func.sub_bitmap( <bitmap>, <start>, <size> )

Analyze Examples

func.sub_bitmap(func.build_bitmap([1, 2, 3, 4, 5]), 1, 3)

┌───────────────────────────────────────────────────────────┐
│ func.sub_bitmap(func.build_bitmap([1, 2, 3, 4, 5]), 1, 3) │
├───────────────────────────────────────────────────────────┤
│ 2,3,4                                                     │
└───────────────────────────────────────────────────────────┘

SQL Syntax

SUB_BITMAP( <bitmap>, <start>, <size> )

SQL Examples

SELECT SUB_BITMAP(BUILD_BITMAP([1, 2, 3, 4, 5]), 1, 3)::String;

┌─────────────────────────────────────────────────────────┐
│ sub_bitmap(build_bitmap([1, 2, 3, 4, 5]), 1, 3)::string │
├─────────────────────────────────────────────────────────┤
│ 2,3,4                                                   │
└─────────────────────────────────────────────────────────┘

5 - Conditional Functions

This section provides reference information for the conditional functions in PlaidCloud Lakehouse.

5.1 - [ NOT ] BETWEEN

Returns true if the given numeric or string <expr> falls inside the defined lower and upper limits.

Analyze Syntax

table.column.between(<lower_limit>, <upper_limit>

Analyze Examples

table.column.between(0, 5)

SQL Syntax

<expr> [ NOT ] BETWEEN <lower_limit> AND <upper_limit>

SQL Examples

SELECT 'true' WHERE 5 BETWEEN 0 AND 5;

┌────────┐
│ 'true' │
├────────┤
│ true   │
└────────┘

SELECT 'true' WHERE 'data' BETWEEN 'data' AND 'databendcloud';

┌────────┐
│ 'true' │
├────────┤
│ true   │
└────────┘

5.2 - [ NOT ] IN

Checks whether a value is (or is not) in an explicit list.

Analyze Syntax

table.columns.in_((<value1>, <value2> ...))

Analyze Examples

table.columns.in_((<value1>, <value2> ...))

┌──────────────────────────┐
│ table.column.in_((2, 3)) │
├──────────────────────────┤
│ true                     │
└──────────────────────────┘

SQL Syntax

<value> [ NOT ] IN (<value1>, <value2> ...)

SQL Examples

SELECT 1 NOT IN (2, 3);

┌────────────────┐
│ 1 not in(2, 3) │
├────────────────┤
│ true           │
└────────────────┘

5.3 - AND

Conditional AND operator. Checks whether both conditions are true.

Analyze Syntax

and_(<expr1>[, <expr2> ...])

Analyze Examples

and_(  
    table.color == 'green',  
    table.shape == 'circle',  
    table.price >= 1.25  
)

SQL Syntax

<expr1> AND <expr2>

SQL Examples

SELECT * FROM table WHERE
    table.color = 'green'
    AND table.shape = 'circle'  
    AND table.price >= 1.25;

5.4 - CASE

Handles IF/THEN logic. It is structured with at least one pair of WHEN and THEN statements. Every CASE statement must be concluded with the END keyword. The ELSE statement is optional, providing a way to capture values not explicitly specified in the WHEN and THEN statements.

SQL Syntax

case(
    (<condition_1>, <value_1>),
    (<condition_2>, <value_2>),
    [ ... ]
    [ else_=<value_n>]
)

Analyze Examples

A simple example

This example returns a person's name. It starts off searching to see if the first name column has a value (the "if"). If there is a value, concatenate the first name with the last name and return it (the "then"). If there isn't a first name, then return the last name only (the "else").

case(
    (table.first_name.is_not(None), func.concat(table.first_name, table.last_name)), 
    else_=table.last_name
)

A more complex example with multiple conditions

This example returns a price based on quantity. "If" the quantity in the order is more than 100, then give the customer the special price. If it doesn't satisfy the first condition, go to the second. If the quantity is greater than 10 (11-100), then give the customer the bulk price. Otherwise give the customer the regular price.

case( 
    (order_table.qty > 100, item_table.specialprice), 
    (order_table.qty > 10, item_table.bulkprice), 
    else_=item_table.regularprice
)

This example returns the first initial of the person's first name. If the user's name is wendy, return W. Otherwise if the user's name is jack, return J. Otherwise return E.

case( 
    (users_table.name == "wendy", "W"), 
    (users_table.name == "jack", "J"), 
    else_='E'
)

The above may also be written in shorthand as:

case(
    {"wendy": "W", "jack": "J"}, 
    value=users_table.name, 
    else_='E' 
)

Other Examples

In this example is from a Table:Lookup step where we are updating the "dock_final" column when the table1.dock_final value is Null.

case(
    (table1.dock_final == Null, table2.dock_final),
    else_ = table1.dock_final
)

This example is from a Table:Lookup step where we are updating the "Marketing Channel" column when "Marketing Channel" in table1 is not 'none' or the "Serial Number" contains a '_'.

case(
    (get_column(table1, 'Marketing Channel') != 'none', get_column(table1, 'Marketing Channel')),
    (get_column(table1, 'Serial Number').contains('_'), get_column(table1, 'Marketing Channel')),
    (get_column(table2, 'Marketing Channel').is_not(Null), get_column(table2, 'Marketing Channel')), 
    else_ = 'none'
)

SQL Syntax

CASE
    WHEN <condition_1> THEN <value_1>
  [ WHEN <condition_2> THEN <value_2> ]
  [ ... ]
  [ ELSE <value_n> ]
END AS <column_name>

SQL Examples

This example categorizes employee salaries using a CASE statement, presenting details with a dynamically assigned column named "SalaryCategory":

-- Create a sample table
CREATE TABLE Employee (
    EmployeeID INT,
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    Salary INT
);

-- Insert some sample data
INSERT INTO Employee VALUES (1, 'John', 'Doe', 50000);
INSERT INTO Employee VALUES (2, 'Jane', 'Smith', 60000);
INSERT INTO Employee VALUES (3, 'Bob', 'Johnson', 75000);
INSERT INTO Employee VALUES (4, 'Alice', 'Williams', 90000);

-- Add a new column 'SalaryCategory' using CASE statement
-- Categorize employees based on their salary
SELECT
    EmployeeID,
    FirstName,
    LastName,
    Salary,
    CASE
        WHEN Salary < 60000 THEN 'Low'
        WHEN Salary >= 60000 AND Salary < 80000 THEN 'Medium'
        WHEN Salary >= 80000 THEN 'High'
        ELSE 'Unknown'
    END AS SalaryCategory
FROM
    Employee;

┌──────────────────────────────────────────────────────────────────────────────────────────┐
│    employeeid   │     firstname    │     lastname     │      salary     │ salarycategory │
├─────────────────┼──────────────────┼──────────────────┼─────────────────┼────────────────┤
│               1 │ John             │ Doe              │           50000 │ Low            │
│               2 │ Jane             │ Smith            │           60000 │ Medium         │
│               4 │ Alice            │ Williams         │           90000 │ High           │
│               3 │ Bob              │ Johnson          │           75000 │ Medium         │
└──────────────────────────────────────────────────────────────────────────────────────────┘

5.5 - COALESCE

Returns the first non-NULL expression within its arguments; if all arguments are NULL, it returns NULL.

Analyze Syntax

func.coalesce(<expr1>[, <expr2> ...])

Analyze Examples

func.coalesce(table.UOM,  'none', \n)

func.coalesce(get_column(table2, 'TECHNOLOGY_RATE'), 0.0)

func.coalesce(table_beta.adjusted_price, table_alpha.override_price, table_alpha.price) * table_beta.quantity_sold

SQL Syntax

COALESCE(<expr1>[, <expr2> ...])

SQL Examples

SELECT COALESCE(1), COALESCE(1, NULL), COALESCE(NULL, 1, 2);

┌────────────────────────────────────────────────────────┐
│ coalesce(1) │ coalesce(1, null) │ coalesce(null, 1, 2) │
├─────────────┼───────────────────┼──────────────────────┤
│           1 │                 1 │                    1 │
└────────────────────────────────────────────────────────┘

SELECT COALESCE('a'), COALESCE('a', NULL), COALESCE(NULL, 'a', 'b');

┌────────────────────────────────────────────────────────────────┐
│ coalesce('a') │ coalesce('a', null) │ coalesce(null, 'a', 'b') │
├───────────────┼─────────────────────┼──────────────────────────┤
│ a             │ a                   │ a                        │
└────────────────────────────────────────────────────────────────┘

5.6 - Comparison Methods

These comparison methods are available in Analyze expressions.

Category    ExpressionStructureExampleDescription
General Usage>>table.column > 23Greater Than
General Usage<<table.column < 23Less Than
General Usage>=>=table.column >= 23Greater than or equal to
General Usage<=<=table.column <= 23Less than or equal to
General Usage====table.column == 23Equal to
General Usage!=!=table.column != 23Not Equal to
General Usageand_and_()and_(table.a > 23, table.b == u'blue')
Additional Examples		Creates an AND SQL condition
General Usageany_any_()table.column.any(('red', 'blue', 'yellow'))Applies the SQL ANY() condition to a column
General Usagebetweenbetweentable.column.between(23, 46)

get_column(table, 'LAST_CHANGED_DATE').between({start_date}, {end_date})		Applies the SQL BETWEEN condition
General Usagecontainscontainstable.column.contains('mno')

table.SOURCE_SYSTEM.contains('TEST')		Applies the SQL LIKE '%%'
General Usageendswithendswithtable.column.endswith('xyz')

table.Parent.endswith(':EBITX')

table.PERIOD.endswith("01")		Applies the SQL LIKE '%%'
General UsageFALSEFALSEFALSEFalse, false, FALSE - Alias for Python False
General Usageilikeiliketable.column.ilike('%foobar%')Applies the SQL ILIKE method
General Usagein_in_()table.column.in_((1, 2, 3))

get_column(table, 'Source Country').in_(['CN','SG','BR'])

table.MONTH.in_(['01','02','03','04','05','06','07','08','09'])		Test if values are with a tuple of values
General Usageis_is_table.column.is_(None)

get_column(table, 'Min SafetyStock').is_(None)

get_column(table, 'date_pod').is_(None)		Applies the SQL is the IS for things like IS NULL
General Usageisnotisnottable.column.isnot(None)Applies the SQL is the IS for things like IS NOT NULL
General Usagelikeliketable.column.like('%foobar%')

table.SOURCE_SYSTEM.like('%Adjustments%')		Applies the SQL LIKE method
General Usagenot_not_()not_(and_(table.a > 23, table.b == u'blue'))Inverts the condition
General Usagenotilikenotiliketable.column.notilike('%foobar%')Applies the SQL NOT ILIKE method
General Usagenotinnotintable.column.notin((1, 2, 3))

table.LE.notin_(['12345','67890'])		Inverts the IN condition
General Usagenotlikenotliketable.column.notlike('%foobar%')Applies the SQL NOT LIKE method
General UsageNULLNULLNULLNull, null, NULL - Alias for Python None
General Usageor_or_()or_(table.a > 23, table.b == u'blue')
Additional Examples		Creates an OR SQL condition
General Usagestartswithstartswithtable.column.startswith('abc')

get_column(table, 'Zip Code').startswith('9')

get_column(table1, 'GL Account').startswith('CORP')		Applies the SQL LIKE '%'
General UsageTRUETRUETRUETrue, true, TRUE - Alias for Python True
Math Expressions+++2+3=5
Math Expressions––-2–3=-1
Math Expressions***2*3=6
Math Expressions///4/2=2
Math Expressionscolumn.opcolumn.op(operator)column.op('%')5%4=1
Math Expressionscolumn.opcolumn.op(operator)column.op('^')2.0^3.0=8
Math Expressionscolumn.opcolumn.op(operator)column.op('!')5!=120
Math Expressionscolumn.opcolumn.op(operator)column.op('!!')!!5=120
Math Expressionscolumn.opcolumn.op(operator)column.op('@')@-5.0=5
Math Expressionscolumn.opcolumn.op(operator)column.op('&')91&15=11
Math Expressionscolumn.opcolumn.op(operator)column.op('#')17##5=20
Math Expressionscolumn.opcolumn.op(operator)column.op('~')~1=-2
Math Expressionscolumn.opcolumn.op(operator)column.op('<<')1<<4=16
Math Expressionscolumn.opcolumn.op(operator)column.op('>>')8>>2=2

5.7 - ERROR_OR

Returns the first non-error expression among its inputs. If all expressions result in errors, it returns NULL.

Analyze Syntax

func.error_or(expr1, expr2, ...)

Analyze Examples

# Returns the valid date if no errors occur
# Returns the current date if the conversion results in an error
func.now(), func.error_or(func.to_date('2024-12-25'), func.now())

┌──────────────────────────────────────────────────────────────────────────────────────────┐
│            func.now()           │ func.error_or(func.to_date('2024-12-25'), func.now())  │
├─────────────────────────────────┼────────────────────────────────────────────────────────┤
│ 2024-03-18 01:22:39.460320      │ 2024-12-25                                             │
└──────────────────────────────────────────────────────────────────────────────────────────┘

# Returns NULL because the conversion results in an error
func.error_or(func.to_date('2024-1234'))

┌────────────────────────────────────────────┐
│ func.error_or(func.to_date('2024-1234'))   │
├────────────────────────────────────────────┤
│ NULL                                       │
└────────────────────────────────────────────┘

SQL Syntax

ERROR_OR(expr1, expr2, ...)

SQL Examples

-- Returns the valid date if no errors occur
-- Returns the current date if the conversion results in an error
SELECT NOW(), ERROR_OR('2024-12-25'::DATE, NOW()::DATE);

┌────────────────────────────────────────────────────────────────────────┐
│            now()           │ error_or('2024-12-25'::date, now()::date) │
├────────────────────────────┼───────────────────────────────────────────┤
│ 2024-03-18 01:22:39.460320 │ 2024-12-25                                │
└────────────────────────────────────────────────────────────────────────┘

-- Returns NULL because the conversion results in an error
SELECT ERROR_OR('2024-1234'::DATE);

┌─────────────────────────────┐
│ error_or('2024-1234'::date) │
├─────────────────────────────┤
│ NULL                        │
└─────────────────────────────┘

5.8 - GREATEST

Returns the maximum value from a set of values.

Analyze Syntax

func.greatest(<value1>, <value2> ...)

Analyze Examples

func.greatest((5, 9, 4))

┌──────────────────────────┐
│ func.greatest((5, 9, 4)) │
├──────────────────────────┤
│                        9 │
└──────────────────────────┘

SQL Syntax

GREATEST(<value1>, <value2> ...)

SQL Examples

SELECT GREATEST(5, 9, 4);

┌───────────────────┐
│ greatest(5, 9, 4) │
├───────────────────┤
│                 9 │
└───────────────────┘

5.9 - IF

If <cond1> is TRUE, it returns <expr1>. Otherwise if <cond2> is TRUE, it returns <expr2>, and so on.

Analyze Syntax

func.if(<cond1>, <expr1>, [<cond2>, <expr2> ...], <expr_else>)

Analyze Examples

func.if((1 > 2), 3, (4 < 5), 6, 7)

┌────────────────────────────────────┐
│ func.if((1 > 2), 3, (4 < 5), 6, 7) │
├────────────────────────────────────┤
│                                  6 │
└────────────────────────────────────┘

SQL Syntax

IF(<cond1>, <expr1>, [<cond2>, <expr2> ...], <expr_else>)

SQL Examples

SELECT IF(1 > 2, 3, 4 < 5, 6, 7);

┌───────────────────────────────┐
│ if((1 > 2), 3, (4 < 5), 6, 7) │
├───────────────────────────────┤
│                             6 │
└───────────────────────────────┘

5.10 - IFNULL

If <expr1> is NULL, returns <expr2>, otherwise returns <expr1>.

Analyze Syntax

func.ifnull(<expr1>, <expr2>)

Analyze Examples

func.ifnull(null, 'b'), func.ifnull('a', 'b')

┌────────────────────────────────────────────────┐
│ func.ifnull(null, 'b') │ func.ifnull('a', 'b') │
├────────────────────────┼───────────────────────┤
│ b                      │ a                     │
└────────────────────────────────────────────────┘

func.ifnull(null, 2), func.ifnull(1, 2)

┌──────────────────────────────────────────┐
│ func.ifnull(null, 2) │ func.ifnull(1, 2) │
├──────────────────────┼───────────────────┤
│                    2 │                 1 │
└──────────────────────────────────────────┘

SQL Syntax

IFNULL(<expr1>, <expr2>)

Aliases

SQL Examples

SELECT IFNULL(NULL, 'b'), IFNULL('a', 'b');

┌──────────────────────────────────────┐
│ ifnull(null, 'b') │ ifnull('a', 'b') │
├───────────────────┼──────────────────┤
│ b                 │ a                │
└──────────────────────────────────────┘

SELECT IFNULL(NULL, 2), IFNULL(1, 2);

┌────────────────────────────────┐
│ ifnull(null, 2) │ ifnull(1, 2) │
├─────────────────┼──────────────┤
│               2 │            1 │
└────────────────────────────────┘

5.11 - IS [ NOT ] DISTINCT FROM

Compares whether two expressions are equal (or not equal) with awareness of nullability, meaning it treats NULLs as known values for comparing equality.

SQL Syntax

<expr1> IS [ NOT ] DISTINCT FROM <expr2>

SQL Examples

SELECT NULL IS DISTINCT FROM NULL;

┌────────────────────────────┐
│ null is distinct from null │
├────────────────────────────┤
│ false                      │
└────────────────────────────┘

5.12 - IS_ERROR

Returns a Boolean value indicating whether an expression is an error value.

See also: IS_NOT_ERROR

Analyze Syntax

func.is_error( <expr> )

Analyze Examples

# Indicates division by zero, hence an error
func.is_error((1 / 0)), func.is_not_error((1 / 0))

┌─────────────────────────────────────────────────────┐
│ func.is_error((1 / 0)) │ func.is_not_error((1 / 0)) │
├────────────────────────┼────────────────────────────┤
│ true                   │ false                      │
└─────────────────────────────────────────────────────┘

# The conversion to DATE is successful, hence not an error
func.is_error(func.to_date('2024-03-17')), func.is_not_error(func.to_date('2024-03-17'))

┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_error(func.to_date('2024-03-17')) │ func.is_not_error(func.to_date('2024-03-17')) │
├───────────────────────────────────────────┼───────────────────────────────────────────────┤
│ false                                     │ true                                          │
└───────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_ERROR( <expr> )

Return Type

Returns true if the expression is an error, otherwise false.

SQL Examples

-- Indicates division by zero, hence an error
SELECT IS_ERROR(1/0), IS_NOT_ERROR(1/0);

┌───────────────────────────────────────────┐
│ is_error((1 / 0)) │ is_not_error((1 / 0)) │
├───────────────────┼───────────────────────┤
│ true              │ false                 │
└───────────────────────────────────────────┘

-- The conversion to DATE is successful, hence not an error
SELECT IS_ERROR('2024-03-17'::DATE), IS_NOT_ERROR('2024-03-17'::DATE);

┌─────────────────────────────────────────────────────────────────┐
│ is_error('2024-03-17'::date) │ is_not_error('2024-03-17'::date) │
├──────────────────────────────┼──────────────────────────────────┤
│ false                        │ true                             │
└─────────────────────────────────────────────────────────────────┘

5.13 - IS_NOT_ERROR

Returns a Boolean value indicating whether an expression is an error value.

See also: IS_ERROR

Analyze Syntax

func.is_error( <expr> )

Analyze Examples

# Indicates division by zero, hence an error
func.is_error((1 / 0)), func.is_not_error((1 / 0))

┌─────────────────────────────────────────────────────┐
│ func.is_error((1 / 0)) │ func.is_not_error((1 / 0)) │
├────────────────────────┼────────────────────────────┤
│ true                   │ false                      │
└─────────────────────────────────────────────────────┘

# The conversion to DATE is successful, hence not an error
func.is_error(func.to_date('2024-03-17')), func.is_not_error(func.to_date('2024-03-17'))

┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_error(func.to_date('2024-03-17')) │ func.is_not_error(func.to_date('2024-03-17')) │
├───────────────────────────────────────────┼───────────────────────────────────────────────┤
│ false                                     │ true                                          │
└───────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_NOT_ERROR( <expr> )

Return Type

Returns true if the expression is not an error, otherwise false.

SQL Examples

-- Indicates division by zero, hence an error
SELECT IS_ERROR(1/0), IS_NOT_ERROR(1/0);

┌───────────────────────────────────────────┐
│ is_error((1 / 0)) │ is_not_error((1 / 0)) │
├───────────────────┼───────────────────────┤
│ true              │ false                 │
└───────────────────────────────────────────┘

-- The conversion to DATE is successful, hence not an error
SELECT IS_ERROR('2024-03-17'::DATE), IS_NOT_ERROR('2024-03-17'::DATE);

┌─────────────────────────────────────────────────────────────────┐
│ is_error('2024-03-17'::date) │ is_not_error('2024-03-17'::date) │
├──────────────────────────────┼──────────────────────────────────┤
│ false                        │ true                             │
└─────────────────────────────────────────────────────────────────┘

5.14 - IS_NOT_NULL

Checks whether a value is not NULL.

Analyze Syntax

func.is_not_null(<expr>)

Analyze Examples

func.is_not_null(1)

┌─────────────────────┐
│ func.is_not_null(1) │
├─────────────────────┤
│ true                │
└─────────────────────┘

SQL Syntax

IS_NOT_NULL(<expr>)

SQL Examples

SELECT IS_NOT_NULL(1);

┌────────────────┐
│ is_not_null(1) │
├────────────────┤
│ true           │
└────────────────┘

5.15 - IS_NULL

Checks whether a value is NULL.

Analyze Syntax

func.is_null(<expr>)

Analyze Examples

func.is_null(1)

┌─────────────────┐
│ func.is_null(1) │
├─────────────────┤
│ false           │
└─────────────────┘

SQL Syntax

IS_NULL(<expr>)

SQL Examples

SELECT IS_NULL(1);

┌────────────┐
│ is_null(1) │
├────────────┤
│ false      │
└────────────┘

5.16 - LEAST

Returns the minimum value from a set of values.

Analyze Syntax

func.least((<value1>, <value2> ...))

Analyze Examples

func.least((5, 9, 4))

┌───────────────────────┐
│ func.least((5, 9, 4)) │
├───────────────────────┤
│                     4 │
└───────────────────────┘

SQL Syntax

LEAST(<value1>, <value2> ...)

SQL Examples

SELECT LEAST(5, 9, 4);

┌────────────────┐
│ least(5, 9, 4) │
├────────────────┤
│              4 │
└────────────────┘

5.17 - NULLIF

Returns NULL if two expressions are equal. Otherwise return expr1. They must have the same data type.

Analyze Syntax

func.nullif(<expr1>, <expr2>)

Analyze Examples

func.nullif(0, null) 

┌──────────────────────┐
│ func.nullif(0, null) │
├──────────────────────┤
│                    0 │
└──────────────────────┘

SQL Syntax

NULLIF(<expr1>, <expr2>)

SQL Examples

SELECT NULLIF(0, NULL);

┌─────────────────┐
│ nullif(0, null) │
├─────────────────┤
│               0 │
└─────────────────┘

5.18 - NVL

If <expr1> is NULL, returns <expr2>, otherwise returns <expr1>.

Analyze Syntax

func.nvl(<expr1>, <expr2>)

Analyze Examples

func.nvl(null, 'b'), func.nvl('a', 'b')

┌──────────────────────────────────────────┐
│ func.nvl(null, 'b') │ func.nvl('a', 'b') │
├─────────────────────┼────────────────────┤
│ b                   │ a                  │
└──────────────────────────────────────────┘

func.nvl(null, 2), func.nvl(1, 2)

┌────────────────────────────────────┐
│ func.nvl(null, 2) │ func.nvl(1, 2) │
├───────────────────┼────────────────┤
│                 2 │              1 │
└────────────────────────────────────┘

SQL Syntax

NVL(<expr1>, <expr2>)

Aliases

SQL Examples

SELECT NVL(NULL, 'b'), NVL('a', 'b');

┌────────────────────────────────┐
│ nvl(null, 'b') │ nvl('a', 'b') │
├────────────────┼───────────────┤
│ b              │ a             │
└────────────────────────────────┘

SELECT NVL(NULL, 2), NVL(1, 2);

┌──────────────────────────┐
│ nvl(null, 2) │ nvl(1, 2) │
├──────────────┼───────────┤
│            2 │         1 │
└──────────────────────────┘

5.19 - NVL2

Returns <expr2> if <expr1> is not NULL; otherwise, it returns <expr3>.

Analyze Syntax

func.nvl2(<expr1> , <expr2> , <expr3>)

Analyze Examples

func.nvl2('a', 'b', 'c'), func.nvl2(null, 'b', 'c')

┌──────────────────────────────────────────────────────┐
│ func.nvl2('a', 'b', 'c') │ func.nvl2(null, 'b', 'c') │
├──────────────────────────┼───────────────────────────┤
│ b                        │ c                         │
└──────────────────────────────────────────────────────┘

func.nvl2(1, 2, 3), func.nvl2(null, 2, 3)

┌────────────────────────────────────────────┐
│ func.nvl2(1, 2, 3) │ func.nvl2(null, 2, 3) │
├────────────────────┼───────────────────────┤
│                  2 │                     3 │
└────────────────────────────────────────────┘

SQL Syntax

NVL2(<expr1> , <expr2> , <expr3>)

SQL Examples

SELECT NVL2('a', 'b', 'c'), NVL2(NULL, 'b', 'c');

┌────────────────────────────────────────────┐
│ nvl2('a', 'b', 'c') │ nvl2(null, 'b', 'c') │
├─────────────────────┼──────────────────────┤
│ b                   │ c                    │
└────────────────────────────────────────────┘

SELECT NVL2(1, 2, 3), NVL2(NULL, 2, 3);

┌──────────────────────────────────┐
│ nvl2(1, 2, 3) │ nvl2(null, 2, 3) │
├───────────────┼──────────────────┤
│             2 │                3 │
└──────────────────────────────────┘

5.20 - OR

Conditional OR operator. Checks whether either condition is true.

Analyze Syntax

or_(<expr1>[, <expr2> ...])

Analyze Examples

or_(  
    table.color == 'green',  
    table.shape == 'circle',  
    table.price >= 1.25  
)

SQL Syntax

<expr1> OR <expr2>

SQL Examples

SELECT * FROM table WHERE
    table.color = 'green'
    OR table.shape = 'circle'  
    OR table.price >= 1.25;

6 - Context Functions

This section provides reference information for the context-related functions in PlaidCloud Lakehouse.

6.1 - CONNECTION_ID

Returns the connection ID for the current connection.

Analyze Syntax

func.connection_id()

Analyze Examples

func.connection_id()

┌──────────────────────────────────────┐
│       func.connection_id()           │
├──────────────────────────────────────┤
│ 23cb06ec-583e-4eba-b790-7c8cf72a53f8 │
└──────────────────────────────────────┘

SQL Syntax

CONNECTION_ID()

SQL Examples

SELECT CONNECTION_ID();

┌──────────────────────────────────────┐
│            connection_id()           │
├──────────────────────────────────────┤
│ 23cb06ec-583e-4eba-b790-7c8cf72a53f8 │
└──────────────────────────────────────┘

6.2 - CURRENT_CATALOG

Returns the name of the catalog currently in use for the session.

SQL Syntax

CURRENT_CATALOG()

SQL Examples

SELECT CURRENT_CATALOG();

┌───────────────────┐
│ current_catalog() │
├───────────────────┤
│ default           │
└───────────────────┘

6.3 - CURRENT_USER

Returns the user name and host name combination for the account that the server used to authenticate the current client. This account determines your access privileges. The return value is a string in the utf8 character set.

Analyze Syntax

func.current_user()

Analyze Examples

func.current_user()

┌─────────────────────┐
│ func.current_user() │
├─────────────────────┤
│ 'root'@'%'          │
└─────────────────────┘

SQL Syntax

CURRENT_USER()

SQL Examples

SELECT CURRENT_USER();

┌────────────────┐
│ current_user() │
├────────────────┤
│ 'root'@'%'     │
└────────────────┘

6.4 - DATABASE

Returns the name of the currently selected database. If no database is selected, then this function returns default.

Analyze Syntax

func.database()

Analyze Examples

func.database()

┌─────────────────┐
│ func.database() │
├─────────────────┤
│ default         │
└─────────────────┘

SQL Syntax

DATABASE()

SQL Examples

SELECT DATABASE();

┌────────────┐
│ database() │
├────────────┤
│ default    │
└────────────┘

6.5 - LAST_QUERY_ID

Returns the last query ID of query in current session, index can be (-1, 1, 1+2)..., out of range index will return empty string.

Analyze Syntax

func.last_query_id(<index>)

Analyze Examples

func.last_query_id(-1)

┌──────────────────────────────────────┐
│    func.last_query_id((- 1))         │
├──────────────────────────────────────┤
│ a6f615c6-5bad-4863-8558-afd01889448c │
└──────────────────────────────────────┘

SQL Syntax

LAST_QUERY_ID(<index>)

SQL Examples

SELECT LAST_QUERY_ID(-1);

┌──────────────────────────────────────┐
│         last_query_id((- 1))         │
├──────────────────────────────────────┤
│ a6f615c6-5bad-4863-8558-afd01889448c │
└──────────────────────────────────────┘

6.6 - VERSION

Returns the current version of PlaidCloud LakehouseQuery.

Analyze Syntax

func.version()

Analyze Examples

func.version()

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                           func.version()                                                  │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ PlaidCloud LakehouseQuery v1.2.252-nightly-193ed56304(rust-1.75.0-nightly-2023-12-12T22:07:25.371440000Z) │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

VERSION()

SQL Examples

SELECT VERSION();

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                version()                                                  │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ PlaidCloud LakehouseQuery v1.2.252-nightly-193ed56304(rust-1.75.0-nightly-2023-12-12T22:07:25.371440000Z) │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────┘

7 - Conversion Functions

This section provides reference information for the conversion functions in PlaidCloud Lakehouse.

Please note the following when converting a value from one type to another:

  • When converting from floating-point, decimal numbers, or strings to integers or decimal numbers with fractional parts, PlaidCloud Lakehouse rounds the values to the nearest integer. This is determined by the setting numeric_cast_option (defaults to 'rounding') which controls the behavior of numeric casting operations. When numeric_cast_option is explicitly set to 'truncating', PlaidCloud Lakehouse will truncate the decimal part, discarding any fractional values.

    SELECT CAST('0.6' AS DECIMAL(10, 0)), CAST(0.6 AS DECIMAL(10, 0)), CAST(1.5 AS INT);

┌──────────────────────────────────────────────────────────────────────────────────┐
│ cast('0.6' as decimal(10, 0)) │ cast(0.6 as decimal(10, 0)) │ cast(1.5 as int32) │
├───────────────────────────────┼─────────────────────────────┼────────────────────┤
│                             1 │                           1 │                  2 │
└──────────────────────────────────────────────────────────────────────────────────┘

SET numeric_cast_option = 'truncating';

SELECT CAST('0.6' AS DECIMAL(10, 0)), CAST(0.6 AS DECIMAL(10, 0)), CAST(1.5 AS INT);

┌──────────────────────────────────────────────────────────────────────────────────┐
│ cast('0.6' as decimal(10, 0)) │ cast(0.6 as decimal(10, 0)) │ cast(1.5 as int32) │
├───────────────────────────────┼─────────────────────────────┼────────────────────┤
│                             0 │                           0 │                  1 │
└──────────────────────────────────────────────────────────────────────────────────┘

    The table below presents a summary of numeric casting operations, highlighting the casting possibilities between different source and target numeric data types. Please note that, it specifies the requirement for String to Integer casting, where the source string must contain an integer value.

    Source TypeTarget Type
    StringDecimal
    FloatDecimal
    DecimalDecimal
    FloatInt
    DecimalInt
    String (Int)Int

  • PlaidCloud Lakehouse also offers a variety of functions for converting expressions into different date and time formats. For more information, see Date & Time Functions.

7.1 - BUILD_BITMAP

Converts an array of positive integers to a BITMAP value.

Analyze Syntax

func.build_bitmap( <expr> )

Analyze Examples

func.to_string(func.build_bitmap([1, 4, 5]))

┌───────────────────────────────────────────────┐
│ func.to_string(func.build_bitmap([1, 4, 5]))  │
├───────────────────────────────────────────────┤
│ 1,4,5                                         │
└───────────────────────────────────────────────┘

SQL Syntax

BUILD_BITMAP( <expr> )

SQL Examples

SELECT BUILD_BITMAP([1,4,5])::String;

┌─────────────────────────────────┐
│ build_bitmap([1, 4, 5])::string │
├─────────────────────────────────┤
│ 1,4,5                           │
└─────────────────────────────────┘

7.2 - CAST, ::

Converts a value from one data type to another. :: is an alias for CAST.

See also: TRY_CAST

Analyze Syntax

func.cast( <expr>, <data_type> )

Analyze Examples

func.cast(1, string), func.to_string(1)

┌───────────────────────────────────────────┐
│ func.cast(1, string) │ func.to_string(1)  │
├──────────────────────┼────────────────────┤
│ 1                    │ 1                  │
└───────────────────────────────────────────┘

SQL Syntax

CAST( <expr> AS <data_type> )

<expr>::<data_type>

SQL Examples

SELECT CAST(1 AS VARCHAR), 1::VARCHAR;

┌───────────────────────────────┐
│ cast(1 as string) │ 1::string │
├───────────────────┼───────────┤
│ 1                 │ 1         │
└───────────────────────────────┘

7.3 - TO_BINARY

Converts supported data types, including string, variant, bitmap, geometry, and geography, into their binary representation (hex format).

See also: TRY_TO_BINARY

Analyze Syntax

func.to_binary( <expr> )

Analyze Examples

func.to_binary('Databend')

┌───────────────────────────────┐
│ func.to_binary('Databend')    │
├───────────────────────────────┤
│ 4461746162656E64              │
└───────────────────────────────┘

SQL Syntax

TO_BINARY( <expr> )

SQL Examples

This example converts a string to binary:

SELECT TO_BINARY('Databend');

┌───────────────────────┐
│ to_binary('Databend') │
├───────────────────────┤
│ 4461746162656E64      │
└───────────────────────┘

This example converts JSON data to binary:

SELECT TO_BINARY(PARSE_JSON('{"key":"value", "number":123}')) AS binary_variant;

┌──────────────────────────────────────────────────────────────────────────┐
│                              binary_variant                              │
├──────────────────────────────────────────────────────────────────────────┤
│ 40000002100000031000000610000005200000026B65796E756D62657276616C7565507B │
└──────────────────────────────────────────────────────────────────────────┘

This example converts bitmap data to binary:

SELECT TO_BINARY(TO_BITMAP('10,20,30')) AS binary_bitmap;

┌──────────────────────────────────────────────────────────────────────┐
│                             binary_bitmap                            │
├──────────────────────────────────────────────────────────────────────┤
│ 0100000000000000000000003A3000000100000000000200100000000A0014001E00 │
└──────────────────────────────────────────────────────────────────────┘

This example converts geometry data (WKT format) to binary:

SELECT TO_BINARY(ST_GEOMETRYFROMWKT('SRID=4326;POINT(1.0 2.0)')) AS binary_geometry;

┌────────────────────────────────────────────────────┐
│                   binary_geometry                  │
├────────────────────────────────────────────────────┤
│ 0101000020E6100000000000000000F03F0000000000000040 │
└────────────────────────────────────────────────────┘

This example converts geography data (EWKT format) to binary:

SELECT TO_BINARY(ST_GEOGRAPHYFROMEWKT('SRID=4326;POINT(-122.35 37.55)')) AS binary_geography;

┌────────────────────────────────────────────────────┐
│                  binary_geography                  │
├────────────────────────────────────────────────────┤
│ 0101000020E61000006666666666965EC06666666666C64240 │
└────────────────────────────────────────────────────┘

7.4 - TO_BITMAP

Converts a value to BITMAP data type.

Analyze Syntax

func.to_bitmap( <expr> )

Analyze Examples

func.to_bitmap('1101')

┌─────────────────────────┐
│ func.to_bitmap('1101')  │
├─────────────────────────┤
│ <bitmap binary>         │
└─────────────────────────┘

SQL Syntax

TO_BITMAP( <expr> )

SQL Examples

SELECT TO_BITMAP('1101');

┌───────────────────┐
│ to_bitmap('1101') │
├───────────────────┤
│ <bitmap binary>   │
└───────────────────┘

7.5 - TO_BOOLEAN

Converts a value to BOOLEAN data type.

Analyze Syntax

func.to_boolean( <expr> )

Analyze Examples

func.to_boolean('true')

┌──────────────────────────┐
│ func.to_boolean('true')  │
├──────────────────────────┤
│ true                     │
└──────────────────────────┘

SQL Syntax

TO_BOOLEAN( <expr> )

SQL Examples

SELECT TO_BOOLEAN('true');

┌────────────────────┐
│ to_boolean('true') │
├────────────────────┤
│ true               │
└────────────────────┘

7.6 - TO_FLOAT32

Converts a value to FLOAT32 data type.

Analyze Syntax

func.to_float32( <expr> )

Analyze Examples

func.to_float32('1.2')

┌─────────────────────────┐
│ func.to_float32('1.2')  │
├─────────────────────────┤
│                     1.2 │
└─────────────────────────┘

SQL Syntax

TO_FLOAT32( <expr> )

SQL Examples

SELECT TO_FLOAT32('1.2');

┌───────────────────┐
│ to_float32('1.2') │
├───────────────────┤
│               1.2 │
└───────────────────┘

7.7 - TO_FLOAT64

Converts a value to FLOAT64 data type.

Analyze Syntax

func.to_float64( <expr> )

Analyze Examples

func.to_float64('1.2')

┌─────────────────────────┐
│ func.to_float64('1.2')  │
├─────────────────────────┤
│                     1.2 │
└─────────────────────────┘

SQL Syntax

TO_FLOAT64( <expr> )

SQL Examples

SELECT TO_FLOAT64('1.2');

┌───────────────────┐
│ to_float64('1.2') │
├───────────────────┤
│               1.2 │
└───────────────────┘

7.8 - TO_HEX

For a string argument str, TO_HEX() returns a hexadecimal string representation of str where each byte of each character in str is converted to two hexadecimal digits. The inverse of this operation is performed by the UNHEX() function.

For a numeric argument N, TO_HEX() returns a hexadecimal string representation of the value of N treated as a longlong (BIGINT) number.

Analyze Syntax

func.to_hex(<expr>)

Analyze Examples

func.to_hex('abc')

┌────────────────────┐
│ func.to_hex('abc') │
├────────────────────┤
│ 616263             │
└────────────────────┘

SQL Syntax

TO_HEX(<expr>)

Aliases

SQL Examples

SELECT HEX('abc'), TO_HEX('abc');

┌────────────────────────────┐
│ hex('abc') │ to_hex('abc') │
├────────────┼───────────────┤
│ 616263     │ 616263        │
└────────────────────────────┘

SELECT HEX(255), TO_HEX(255);

┌────────────────────────┐
│ hex(255) │ to_hex(255) │
├──────────┼─────────────┤
│ ff       │ ff          │
└────────────────────────┘

7.9 - TO_INT16

Converts a value to INT16 data type.

Analyze Syntax

func.to_int16( <expr> )

Analyze Examples

func.to_int16('123')

┌──────────────────────┐
│ func.to_int16('123') │
├──────────────────────┤
│                  123 │
└──────────────────────┘

SQL Syntax

TO_INT16( <expr> )

SQL Examples

SELECT TO_INT16('123');

┌─────────────────┐
│ to_int16('123') │
├─────────────────┤
│             123 │
└─────────────────┘

7.10 - TO_INT32

Converts a value to INT32 data type.

Analyze Syntax

func.to_int32( <expr> )

Analyze Examples

func.to_int32('123')

┌──────────────────────┐
│ func.to_int32('123') │
├──────────────────────┤
│                  123 │
└──────────────────────┘

SQL Syntax

TO_INT32( <expr> )

SQL Examples

SELECT TO_INT32('123');

┌─────────────────┐
│ to_int32('123') │
├─────────────────┤
│             123 │
└─────────────────┘

7.11 - TO_INT64

Converts a value to INT64 data type.

Analyze Syntax

func.to_int64( <expr> )

Analyze Examples

func.to_int64('123')

┌──────────────────────┐
│ func.to_int64('123') │
├──────────────────────┤
│                  123 │
└──────────────────────┘

SQL Syntax

TO_INT64( <expr> )

SQL Examples

SELECT TO_INT64('123');

┌─────────────────┐
│ to_int64('123') │
├─────────────────┤
│             123 │
└─────────────────┘

7.12 - TO_INT8

Converts a value to INT8 data type.

Analyze Syntax

func.to_int8( <expr> )

Analyze Examples

func.to_int8('123')

┌─────────────────────┐
│ func.to_int8('123') │
├─────────────────────┤
│                 123 │
└─────────────────────┘

SQL Syntax

TO_INT8( <expr> )

SQL Examples

SELECT TO_INT8('123');

┌────────────────┐
│ to_int8('123') │
│      UInt8     │
├────────────────┤
│            123 │
└────────────────┘

7.13 - TO_STRING

Converts a value to String data type, or converts a Date value to a specific string format. To customize the format of date and time in PlaidCloud Lakehouse, you can utilize specifiers. These specifiers allow you to define the desired format for date and time values. For a comprehensive list of supported specifiers, see Formatting Date and Time.

Analyze Syntax

func.to_string( '<expr>' )

Analyze Examples

func.date_format('1.23'), func.to_string('1.23'), func.to_text('1.23'), func.to_varchar('1.23'), func.json_to_string('1.23')

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.date_format('1.23') │ func.to_string('1.23') │ func.to_text('1.23') │ func.to_varchar('1.23') │ func.json_to_string('1.23') │
├──────────────────────────┼────────────────────────┼──────────────────────┼─────────────────────────┼─────────────────────────────┤
│ 1.23                     │ 1.23                   │ 1.23                 │ 1.23                    │ 1.23                        │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_STRING( '<expr>' )

TO_STRING( '<date>', '<format>' )

Aliases

Return Type

String.

SQL Examples

SELECT
  DATE_FORMAT('1.23'),
  TO_STRING('1.23'),
  TO_TEXT('1.23'),
  TO_VARCHAR('1.23'),
  JSON_TO_STRING('1.23');

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ date_format('1.23') │ to_string('1.23') │ to_text('1.23') │ to_varchar('1.23') │ json_to_string('1.23') │
├─────────────────────┼───────────────────┼─────────────────┼────────────────────┼────────────────────────┤
│ 1.23                │ 1.23              │ 1.23            │ 1.23               │ 1.23                   │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT
  DATE_FORMAT('["Cooking", "Reading"]' :: JSON),
  TO_STRING('["Cooking", "Reading"]' :: JSON),
  TO_TEXT('["Cooking", "Reading"]' :: JSON),
  TO_VARCHAR('["Cooking", "Reading"]' :: JSON),
  JSON_TO_STRING('["Cooking", "Reading"]' :: JSON);

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ date_format('["cooking", "reading"]'::variant) │ to_string('["cooking", "reading"]'::variant) │ to_text('["cooking", "reading"]'::variant) │ to_varchar('["cooking", "reading"]'::variant) │ json_to_string('["cooking", "reading"]'::variant) │
├────────────────────────────────────────────────┼──────────────────────────────────────────────┼────────────────────────────────────────────┼───────────────────────────────────────────────┼───────────────────────────────────────────────────┤
│ ["Cooking","Reading"]                          │ ["Cooking","Reading"]                        │ ["Cooking","Reading"]                      │ ["Cooking","Reading"]                         │ ["Cooking","Reading"]                             │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- With one argument, the function converts input to a string without validating as a date.
SELECT
  DATE_FORMAT('20223-12-25'),
  TO_STRING('20223-12-25'),
  TO_TEXT('20223-12-25'),
  TO_VARCHAR('20223-12-25'),
  JSON_TO_STRING('20223-12-25');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ date_format('20223-12-25') │ to_string('20223-12-25') │ to_text('20223-12-25') │ to_varchar('20223-12-25') │ json_to_string('20223-12-25') │
├────────────────────────────┼──────────────────────────┼────────────────────────┼───────────────────────────┼───────────────────────────────┤
│ 20223-12-25                │ 20223-12-25              │ 20223-12-25            │ 20223-12-25               │ 20223-12-25                   │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT
  DATE_FORMAT('2022-12-25', '%m/%d/%Y'),
  TO_STRING('2022-12-25', '%m/%d/%Y'),
  TO_TEXT('2022-12-25', '%m/%d/%Y'),
  TO_VARCHAR('2022-12-25', '%m/%d/%Y'),
  JSON_TO_STRING('2022-12-25', '%m/%d/%Y');

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ date_format('2022-12-25', '%m/%d/%y') │ to_string('2022-12-25', '%m/%d/%y') │ to_text('2022-12-25', '%m/%d/%y') │ to_varchar('2022-12-25', '%m/%d/%y') │ json_to_string('2022-12-25', '%m/%d/%y') │
├───────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────┼──────────────────────────────────────┼──────────────────────────────────────────┤
│ 12/25/2022                            │ 12/25/2022                          │ 12/25/2022                        │ 12/25/2022                           │ 12/25/2022                               │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

7.14 - TO_TEXT

Alias for TO_STRING.

7.15 - TO_UINT16

Converts a value to UINT16 data type.

Analyze Syntax

func.to_uint16( <expr> )

Analyze Examples

func.to_uint16('123')

┌───────────────────────┐
│ func.to_uint16('123') │
├───────────────────────┤
│                   123 │
└───────────────────────┘

SQL Syntax

TO_UINT16( <expr> )

SQL Examples

SELECT TO_UINT16('123');

┌──────────────────┐
│ to_uint16('123') │
├──────────────────┤
│              123 │
└──────────────────┘

7.16 - TO_UINT32

Converts a value to UINT32 data type.

Analyze Syntax

func.to_uint32( <expr> )

Analyze Examples

func.to_uint32('123')

┌───────────────────────┐
│ func.to_uint32('123') │
├───────────────────────┤
│                   123 │
└───────────────────────┘

SQL Syntax

TO_UINT32( <expr> )

SQL Examples

SELECT TO_UINT32('123');

┌──────────────────┐
│ to_uint32('123') │
├──────────────────┤
│              123 │
└──────────────────┘

7.17 - TO_UINT64

Converts a value to UINT64 data type.

Analyze Syntax

func.to_uint64( <expr> )

Analyze Examples

func.to_uint64('123')

┌───────────────────────┐
│ func.to_uint64('123') │
├───────────────────────┤
│                   123 │
└───────────────────────┘

SQL Syntax

TO_UINT64( <expr> )

SQL Examples

SELECT TO_UINT64('123');

┌──────────────────┐
│ to_uint64('123') │
├──────────────────┤
│              123 │
└──────────────────┘

7.18 - TO_UINT8

Converts a value to UINT8 data type.

Analyze Syntax

func.to_uint8( <expr> )

Analyze Examples

func.to_uint8('123')

┌──────────────────────┐
│ func.to_uint8('123') │
├──────────────────────┤
│                  123 │
└──────────────────────┘

SQL Syntax

TO_UINT8( <expr> )

SQL Examples

SELECT TO_UINT8('123');

┌─────────────────┐
│ to_uint8('123') │
├─────────────────┤
│             123 │
└─────────────────┘

7.19 - TO_VARCHAR

Alias for TO_STRING.

7.20 - TO_VARIANT

Converts a value to VARIANT data type.

Analyze Syntax

func.to_variant( <expr> )

Analyze Examples

func.to_variant(to_bitmap('100,200,300'))

┌───────────────────────────────────────────┐
│ func.to_variant(to_bitmap('100,200,300')) │
├───────────────────────────────────────────┤
│ [100,200,300]                             │
└───────────────────────────────────────────┘

SQL Syntax

TO_VARIANT( <expr> )

SQL Examples

SELECT TO_VARIANT(TO_BITMAP('100,200,300'));

┌──────────────────────────────────────┐
│ to_variant(to_bitmap('100,200,300')) │
├──────────────────────────────────────┤
│ [100,200,300]                        │
└──────────────────────────────────────┘

7.21 - TRY_CAST

Converts a value from one data type to another. Returns NULL on error.

See also: CAST

Analyze Syntax

func.try_cast( <expr>, <data_type> )

Analyze Examples

func.try_cast(1, string)

┌──────────────────────────┐
│ func.try_cast(1, string) │
├──────────────────────────┤
│ 1                        │
└──────────────────────────┘

SQL Syntax

TRY_CAST( <expr> AS <data_type> )

SQL Examples

SELECT TRY_CAST(1 AS VARCHAR);

┌───────────────────────┐
│ try_cast(1 as string) │
├───────────────────────┤
│ 1                     │
└───────────────────────┘

7.22 - TRY_TO_BINARY

An enhanced version of TO_BINARY that converts an input expression to a binary value, returning NULL if the conversion fails instead of raising an error.

See also: TO_BINARY

Analyze Syntax

func.try_to_binary( <expr> )

Analyze Examples

func.try_to_binary('Databend')

┌───────────────────────────────────────┐
│ func.try_to_binary('Databend')        │
├───────────────────────────────────────┤
│  4461746162656E64                     │
└───────────────────────────────────────┘

SQL Syntax

TRY_TO_BINARY( <expr> )

Examples

This example successfully converts the JSON data to binary:

SELECT TRY_TO_BINARY(PARSE_JSON('{"key":"value", "number":123}')) AS binary_variant_success;

┌──────────────────────────────────────────────────────────────────────────┐
│                              binary_variant                              │
├──────────────────────────────────────────────────────────────────────────┤
│ 40000002100000031000000610000005200000026B65796E756D62657276616C7565507B │
└──────────────────────────────────────────────────────────────────────────┘

This example demonstrates that the function fails to convert when the input is NULL:

SELECT TRY_TO_BINARY(PARSE_JSON(NULL)) AS binary_variant_invalid_json;

┌─────────────────────────────┐
│ binary_variant_invalid_json │
├─────────────────────────────┤
│ NULL                        │
└─────────────────────────────┘

8 - Date & Time Functions

This section provides reference information for the datetime-related functions in PlaidCloud Lakehouse.

Conversion Functions

Date Arithmetic Functions

Date Information Functions

Others

8.1 - ADD TIME INTERVAL

Add time interval function

Add a time interval to a date or timestamp, return the result of date or timestamp type.

Analyze Syntax

func.add_years(<exp0>, <expr1>)
func.add_quarters(<exp0>, <expr1>)
func.add_months(<exp0>, <expr1>)
func.add_days(<exp0>, <expr1>)
func.add_hours(<exp0>, <expr1>)
func.add_minutes(<exp0>, <expr1>)
func.add_seconds(<exp0>, <expr1>)

Analyze Examples

func.to_date(18875), func.add_years(func.to_date(18875), 2)
+---------------------------------+---------------------------------------------------+
| func.to_date(18875)             | func.add_years(func.to_date(18875), 2)            |
+---------------------------------+---------------------------------------------------+
| 2021-09-05                      | 2023-09-05                                        |
+---------------------------------+---------------------------------------------------+

func.to_date(18875), func.add_quarters(func.to_date(18875), 2)
+---------------------------------+---------------------------------------------------+
| func.to_date(18875)             | add_quarters(func.to_date(18875), 2)              |
+---------------------------------+---------------------------------------------------+
| 2021-09-05                      | 2022-03-05                                        |
+---------------------------------+---------------------------------------------------+

func.to_date(18875), func.add_months(func.to_date(18875), 2)
+---------------------------------+---------------------------------------------------+
| func.to_date(18875)             | func.add_months(func.to_date(18875), 2)           |
+---------------------------------+---------------------------------------------------+
| 2021-09-05                      | 2021-11-05                                        |
+---------------------------------+---------------------------------------------------+

func.to_date(18875), func.add_days(func.to_date(18875), 2)
+---------------------------------+---------------------------------------------------+
| func.to_date(18875)             | func.add_days(func.to_date(18875), 2)             |
+---------------------------------+---------------------------------------------------+
| 2021-09-05                      | 2021-09-07                                        |
+---------------------------------+---------------------------------------------------+

func.to_datetime(1630833797), func.add_hours(func.to_datetime(1630833797), 2)
+---------------------------------+---------------------------------------------------+
| func.to_datetime(1630833797)    | func.add_hours(func.to_datetime(1630833797), 2)   |
+---------------------------------+---------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 11:23:17.000000                        |
+---------------------------------+---------------------------------------------------+

func.to_datetime(1630833797), func.add_minutes(func.to_datetime(1630833797), 2)
+---------------------------------+---------------------------------------------------+
| func.to_datetime(1630833797)    | func.add_minutes(func.to_datetime(1630833797), 2) |
+---------------------------------+---------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 09:25:17.000000                        |
+---------------------------------+---------------------------------------------------+

func.to_datetime(1630833797), func.add_seconds(func.to_datetime(1630833797), 2)
+---------------------------------+---------------------------------------------------+
| func.to_datetime(1630833797)    | func.add_seconds(func.to_datetime(1630833797), 2) |
+---------------------------------+---------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 09:23:19.000000                        |
+---------------------------------+---------------------------------------------------+

SQL Syntax

ADD_YEARS(<exp0>, <expr1>)
ADD_QUARTERs(<exp0>, <expr1>)
ADD_MONTHS(<exp0>, <expr1>)
ADD_DAYS(<exp0>, <expr1>)
ADD_HOURS(<exp0>, <expr1>)
ADD_MINUTES(<exp0>, <expr1>)
ADD_SECONDS(<exp0>, <expr1>)

Return Type

DATE, TIMESTAMP, depends on the input.

SQL Examples

SELECT to_date(18875), add_years(to_date(18875), 2);
+----------------+------------------------------+
| to_date(18875) | add_years(to_date(18875), 2) |
+----------------+------------------------------+
| 2021-09-05     | 2023-09-05                   |
+----------------+------------------------------+

SELECT to_date(18875), add_quarters(to_date(18875), 2);
+----------------+---------------------------------+
| to_date(18875) | add_quarters(to_date(18875), 2) |
+----------------+---------------------------------+
| 2021-09-05     | 2022-03-05                      |
+----------------+---------------------------------+

SELECT to_date(18875), add_months(to_date(18875), 2);
+----------------+-------------------------------+
| to_date(18875) | add_months(to_date(18875), 2) |
+----------------+-------------------------------+
| 2021-09-05     | 2021-11-05                    |
+----------------+-------------------------------+

SELECT to_date(18875), add_days(to_date(18875), 2);
+----------------+-----------------------------+
| to_date(18875) | add_days(to_date(18875), 2) |
+----------------+-----------------------------+
| 2021-09-05     | 2021-09-07                  |
+----------------+-----------------------------+

SELECT to_datetime(1630833797), add_hours(to_datetime(1630833797), 2);
+----------------------------+---------------------------------------+
| to_datetime(1630833797)    | add_hours(to_datetime(1630833797), 2) |
+----------------------------+---------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 11:23:17.000000            |
+----------------------------+---------------------------------------+

SELECT to_datetime(1630833797), add_minutes(to_datetime(1630833797), 2);
+----------------------------+-----------------------------------------+
| to_datetime(1630833797)    | add_minutes(to_datetime(1630833797), 2) |
+----------------------------+-----------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 09:25:17.000000              |
+----------------------------+-----------------------------------------+

SELECT to_datetime(1630833797), add_seconds(to_datetime(1630833797), 2);
+----------------------------+-----------------------------------------+
| to_datetime(1630833797)    | add_seconds(to_datetime(1630833797), 2) |
+----------------------------+-----------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 09:23:19.000000              |
+----------------------------+-----------------------------------------+

8.2 - CURRENT_TIMESTAMP

Alias for NOW.

8.3 - DATE

Alias for TO_DATE.

8.4 - DATE DIFF

PlaidCloud Lakehouse does not provide a date_diff function yet, but it supports direct arithmetic operations on dates and times. For example, you can use the expression TO_DATE(NOW())-2 to obtain the date from two days ago.

This flexibility of directly manipulating dates and times in PlaidCloud Lakehouse makes it convenient and versatile for handling date and time computations. See an example below:

CREATE TABLE tasks (
  task_name VARCHAR(50),
  start_date DATE,
  end_date DATE
);

INSERT INTO tasks (task_name, start_date, end_date)
VALUES
  ('Task 1', '2023-06-15', '2023-06-20'),
  ('Task 2', '2023-06-18', '2023-06-25'),
  ('Task 3', '2023-06-20', '2023-06-23');

SELECT task_name, end_date - start_date AS duration
FROM tasks;

task_name|duration|
---------+--------+
Task 1   |       5|
Task 2   |       7|
Task 3   |       3|

8.5 - DATE_ADD

Add the time interval or date interval to the provided date or date with time (timestamp/datetime).

Analyze Syntax

func.date_add(<unit>, <value>,  <date_or_time_expr>)

Analyze Examples

func.date_add('YEAR', 1, func.to_date('2018-01-02'))
+------------------------------------------------------+
| func.date_add('YEAR', 1, func.to_date('2018-01-02')) |
+------------------------------------------------------+
| 2019-01-02                                           |
+------------------------------------------------------+

SQL Syntax

DATE_ADD(<unit>, <value>,  <date_or_time_expr>)

Arguments

ArgumentsDescription
<unit>Must be of the following values: YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE and SECOND
<value>This is the number of units of time that you want to add. For example, if you want to add 2 days, this will be 2.
<date_or_time_expr>A value of DATE or TIMESTAMP type

Return Type

The function returns a value of the same type as the <date_or_time_expr> argument.

SQL Examples

Query:

SELECT date_add(YEAR, 1, to_date('2018-01-02'));
+---------------------------------------------------+
| DATE_ADD(YEAR, INTERVAL 1, to_date('2018-01-02')) |
+---------------------------------------------------+
| 2019-01-02                                        |
+---------------------------------------------------+

8.6 - DATE_FORMAT

Alias for TO_STRING.

8.7 - DATE_PART

Retrieves the designated portion of a date, time, or timestamp.

See also: EXTRACT

Analyze Syntax

func.date_part(<unit>, <date_or_time_expr>)

Analyze Examples

func.now()           |
---------------------+
2023-10-16 02:09:28.0|

func.date_part('day', now())

func.date_part('day', now())|
----------------------------+
                         16 |

SQL Syntax

DATE_PART( YEAR | QUARTER | MONTH | WEEK | DAY | HOUR | MINUTE | SECOND | DOW | DOY, <date_or_time_expr> )
  • DOW: Day of Week.
  • DOY: Day of Year.

Return Type

Integer.

SQL Examples

SELECT NOW();

now()                |
---------------------+
2023-10-16 02:09:28.0|

SELECT DATE_PART(DAY, NOW());

date_part(day, now())|
---------------------+
                   16|

-- October 16, 2023, is a Monday
SELECT DATE_PART(DOW, NOW());

date_part(dow, now())|
---------------------+
                    1|

-- October 16, 2023, is the 289th day of the year
SELECT DATE_PART(DOY, NOW());

date_part(doy, now())|
---------------------+
                  289|

SELECT DATE_PART(MONTH, TO_DATE('2022-05-13'));

date_part(month, to_date('2022-05-13'))|
---------------------------------------+
                                      5|

8.8 - DATE_SUB

Subtract the time interval or date interval from the provided date or date with time (timestamp/datetime).

Analyze Syntax

func.date_sub(<unit>, <value>,  <date_or_time_expr>)

Analyze Examples

func.date_sub('YEAR', 1, func.to_date('2018-01-02'))
+------------------------------------------------------+
| func.date_sub('YEAR', 1, func.to_date('2018-01-02')) |
+------------------------------------------------------+
| 2017-01-02                                           |
+------------------------------------------------------+

SQL Syntax

DATE_SUB(<unit>, <value>,  <date_or_time_expr>)

Arguments

ArgumentsDescription
<unit>Must be of the following values: YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE and SECOND
<value>This is the number of units of time that you want to add. For example, if you want to add 2 days, this will be 2.
<date_or_time_expr>A value of DATE or TIMESTAMP type

Return Type

The function returns a value of the same type as the <date_or_time_expr> argument.

SQL Examples

Query:

SELECT date_sub(YEAR, 1, to_date('2018-01-02'));
+---------------------------------------------------+
| DATE_SUB(YEAR, INTERVAL 1, to_date('2018-01-02')) |
+---------------------------------------------------+
| 2017-01-02                                        |
+---------------------------------------------------+

8.9 - DATE_TRUNC

Truncates a date, time, or timestamp value to a specified precision. For example, if you truncate 2022-07-07 to MONTH, the result will be 2022-07-01; if you truncate 2022-07-07 01:01:01.123456 to SECOND, the result will be 2022-07-07 01:01:01.000000.

Analyze Syntax

func.date_sub(<precision>,  <date_or_time_expr>)

Analyze Examples

func.date_trunc('month', func.to_date('2022-07-07'))
+------------------------------------------------------+
| func.date_trunc('month', func.to_date('2022-07-07')) |
+------------------------------------------------------+
| 2022-07-01                                           |
+------------------------------------------------------+

SQL Syntax

DATE_TRUNC(<precision>, <date_or_time_expr>)

Arguments

ArgumentsDescription
<precision>Must be of the following values: YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE and SECOND
<date_or_time_expr>A value of DATE or TIMESTAMP type

Return Type

The function returns a value of the same type as the <date_or_time_expr> argument.

SQL Examples

select date_trunc(month, to_date('2022-07-07'));
+------------------------------------------+
| date_trunc(month, to_date('2022-07-07')) |
+------------------------------------------+
| 2022-07-01                               |
+------------------------------------------+

8.10 - DAY

Alias for TO_DAY_OF_MONTH.

8.11 - EXTRACT

Retrieves the designated portion of a date, time, or timestamp.

See also: DATE_PART

SQL Syntax

EXTRACT( YEAR | QUARTER | MONTH | WEEK | DAY | HOUR | MINUTE | SECOND | DOW | DOY FROM <date_or_time_expr> )
  • DOW: Day of the Week.
  • DOY: Day of Year.

Return Type

Integer.

SQL Examples

SELECT NOW();

now()                |
---------------------+
2023-10-16 02:09:28.0|

SELECT EXTRACT(DAY FROM NOW());

extract(day from now())|
-----------------------+
                     16|

-- October 16, 2023, is a Monday
SELECT EXTRACT(DOW FROM NOW());

extract(dow from now())|
-----------------------+
                      1|

-- October 16, 2023, is the 289th day of the year
SELECT EXTRACT(DOY FROM NOW());

extract(doy from now())|
-----------------------+
                    289|

SELECT EXTRACT(MONTH FROM TO_DATE('2022-05-13'));

extract(month from to_date('2022-05-13'))|
-----------------------------------------+
                                        5|

8.12 - LAST_DAY

Returns the last day of the specified interval (week, month, quarter, or year) based on the provided date or timestamp.

Analyze Syntax

func.last_day(<date_expression>, <interval>)

Analyze Examples

func.last_day(func.to_date('2024-11-13'), 'month')
+------------------------------------------------------+
| func.last_day(func.to_date('2024-11-13'), 'month')   |
+------------------------------------------------------+
| 2024-11-30                                           |
+------------------------------------------------------+

SQL Syntax

LAST_DAY(<date_expression>, <interval>)
ParameterDescription
<date_expression>A DATE or TIMESTAMP value to calculate the last day of the specified interval.
<interval>The interval type for which to find the last day. Accepted values are week, month, quarter, and year.

Return Type

Date.

SQL Examples

Let's say you want to determine the billing date, which is always the last day of the month, based on an arbitrary date of a transaction (e.g., 2024-11-13):

SELECT LAST_DAY(to_date('2024-11-13'), month) AS billing_date;

┌──────────────┐
│ billing_date │
├──────────────┤
│ 2024-11-30   │
└──────────────┘

8.13 - MONTH

Alias for TO_MONTH.

8.14 - MONTHS_BETWEEN

Returns the number of months between date1 and date2.

Analyze Syntax

func.months_between(<date1>, <date2>)

Analyze Examples

func.months_between(func.to_date('2024-03-15'), func.to_date('2024-02-15'))
+-------------------------------------------------------------------------------+
| func.months_between(func.to_date('2024-03-15'), func.to_date('2024-02-15'))   |
+-------------------------------------------------------------------------------+
| 1                                                                             |
+-------------------------------------------------------------------------------+

func.months_between(func.to_date('2024-02-15'), func.to_date('2024-03-15'))
+-------------------------------------------------------------------------------+
| func.months_between(func.to_date('2024-02-15'), func.to_date('2024-03-15'))   |
+-------------------------------------------------------------------------------+
| -1                                                                             |
+-------------------------------------------------------------------------------+

SQL Syntax

MONTHS_BETWEEN( <date1>, <date2> )

Arguments

date1 and date2 can be of DATE type, TIMESTAMP type, or a mix of both.

Return Type

The function returns a FLOAT value based on the following rules:

  • If date1 is earlier than date2, the function returns a negative value; otherwise, it returns a positive value.

    SELECT
    MONTHS_BETWEEN('2024-03-15'::DATE,
                '2024-02-15'::DATE),
    MONTHS_BETWEEN('2024-02-15'::DATE,
                '2024-03-15'::DATE);

-[ RECORD 1 ]-----------------------------------
months_between('2024-03-15'::date, '2024-02-15'::date): 1
months_between('2024-02-15'::date, '2024-03-15'::date): -1

  • If date1 and date2 fall on the same day of their respective months or both are the last day of their respective months, the result is an integer. Otherwise, the function calculates the fractional portion of the result based on a 31-day month.

    SELECT
    MONTHS_BETWEEN('2024-02-29'::DATE,
                '2024-01-29'::DATE),
    MONTHS_BETWEEN('2024-02-29'::DATE,
                '2024-01-31'::DATE);

-[ RECORD 1 ]-----------------------------------
months_between('2024-02-29'::date, '2024-01-29'::date): 1
months_between('2024-02-29'::date, '2024-01-31'::date): 1

SELECT
    MONTHS_BETWEEN('2024-08-05'::DATE,
                '2024-01-01'::DATE);

-[ RECORD 1 ]-----------------------------------
months_between('2024-08-05'::date, '2024-01-01'::date): 7.129032258064516

  • If date1 and date2 are the same date, the function ignores any time components and returns 0.

    SELECT
    MONTHS_BETWEEN('2024-08-05'::DATE,
                '2024-08-05'::DATE),
    MONTHS_BETWEEN('2024-08-05 02:00:00'::TIMESTAMP,
                '2024-08-05 01:00:00'::TIMESTAMP);

-[ RECORD 1 ]-----------------------------------
                            months_between('2024-08-05'::date, '2024-08-05'::date): 0
months_between('2024-08-05 02:00:00'::timestamp, '2024-08-05 01:00:00'::timestamp): 0

8.15 - NEXT_DAY

Returns the date of the upcoming specified day of the week after the given date or timestamp.

Analyze Syntax

func.next_day(date_expression>, <target_day>)

Analyze Examples

func.next_day(func.to_date('2024-11-13'), 'monday')
+------------------------------------------------------+
| func.next_day(func.to_date('2024-11-13'), 'monday')  |
+------------------------------------------------------+
| 2024-11-18                                           |
+------------------------------------------------------+

SQL Syntax

NEXT_DAY(<date_expression>, <target_day>)
ParameterDescription
<date_expression>A DATE or TIMESTAMP value to calculate the next occurrence of the specified day.
<target_day>The target day of the week to find the next occurrence of. Accepted values include monday, tuesday, wednesday, thursday, friday, saturday, and sunday.

Return Type

Date.

SQL Examples

To find the next Monday after a specific date, such as 2024-11-13:

SELECT NEXT_DAY(to_date('2024-11-13'), monday) AS next_monday;

┌─────────────┐
│ next_monday │
├─────────────┤
│ 2024-11-18  │
└─────────────┘

8.16 - NOW

Returns the current date and time.

Analyze Syntax

func.now()

Analyze Examples

┌─────────────────────────────────────────────────────────┐
│  func.current_timestamp()  │         func.now()         │
├────────────────────────────┼────────────────────────────┤
│ 2024-01-29 04:38:12.584359 │ 2024-01-29 04:38:12.584417 │
└─────────────────────────────────────────────────────────┘

SQL Syntax

NOW()

Return Type

TIMESTAMP

Aliases

SQL Examples

This example returns the current date and time:

SELECT CURRENT_TIMESTAMP(), NOW();

┌─────────────────────────────────────────────────────────┐
│     current_timestamp()    │            now()           │
├────────────────────────────┼────────────────────────────┤
│ 2024-01-29 04:38:12.584359 │ 2024-01-29 04:38:12.584417 │
└─────────────────────────────────────────────────────────┘

8.17 - PREVIOUS_DAY

Returns the date of the most recent specified day of the week before the given date or timestamp.

Analyze Syntax

func.next_day(date_expression>, <target_day>)

Analyze Examples

func.next_day(func.to_date('2024-11-13'), 'friday')
+------------------------------------------------------+
| func.next_day(func.to_date('2024-11-13'), 'friday')  |
+------------------------------------------------------+
| 2024-11-08                                           |
+------------------------------------------------------+

SQL Syntax

PREVIOUS_DAY(<date_expression>, <target_day>)
ParameterDescription
<date_expression>A DATE or TIMESTAMP value to calculate the previous occurrence of the specified day.
<target_day>The target day of the week to find the previous occurrence of. Accepted values include monday, tuesday, wednesday, thursday, friday, saturday, and sunday.

Return Type

Date.

SQL Examples

If you need to find the previous Friday before a given date, such as 2024-11-13:

SELECT PREVIOUS_DAY(to_date('2024-11-13'), friday) AS last_friday;

┌─────────────┐
│ last_friday │
├─────────────┤
│ 2024-11-08  │
└─────────────┘

8.18 - QUARTER

Alias for TO_QUARTER.

8.19 - STR_TO_DATE

Alias for TO_DATE.

8.20 - STR_TO_TIMESTAMP

Alias for TO_TIMESTAMP.

8.21 - SUBTRACT TIME INTERVAL

Subtract time interval function

Subtract time interval from a date or timestamp, return the result of date or timestamp type.

Analyze Syntax

func.subtract_years(<exp0>, <expr1>)
func.subtract_quarters(<exp0>, <expr1>)
func.subtract_months(<exp0>, <expr1>)
func.subtract_days(<exp0>, <expr1>)
func.subtract_hours(<exp0>, <expr1>)
func.subtract_minutes(<exp0>, <expr1>)
func.subtract_seconds(<exp0>, <expr1>)

Analyze Examples

func.to_date(18875), func.subtract_years(func.to_date(18875), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_date(18875)             | func.subtract_years(func.to_date(18875), 2)            |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05                      | 2019-09-05                                             |
+---------------------------------+--------------------------------------------------------+

func.to_date(18875), func.subtract_quarters(func.to_date(18875), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_date(18875)             | subtract_quarters(func.to_date(18875), 2)              |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05                      | 2021-03-05                                             |
+---------------------------------+--------------------------------------------------------+

func.to_date(18875), func.subtract_months(func.to_date(18875), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_date(18875)             | func.subtract_months(func.to_date(18875), 2)           |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05                      | 2021-07-05                                             |
+---------------------------------+--------------------------------------------------------+

func.to_date(18875), func.subtract_days(func.to_date(18875), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_date(18875)             | func.subtract_days(func.to_date(18875), 2)             |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05                      | 2021-09-03                                             |
+---------------------------------+--------------------------------------------------------+

func.to_datetime(1630833797), func.subtract_hours(func.to_datetime(1630833797), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_datetime(1630833797)    | func.subtract_hours(func.to_datetime(1630833797), 2)   |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 07:23:17.000000                             |
+---------------------------------+--------------------------------------------------------+

func.to_datetime(1630833797), func.subtract_minutes(func.to_datetime(1630833797), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_datetime(1630833797)    | func.subtract_minutes(func.to_datetime(1630833797), 2) |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 09:21:17.000000                             |
+---------------------------------+--------------------------------------------------------+

func.to_datetime(1630833797), func.subtract_seconds(func.to_datetime(1630833797), 2)
+---------------------------------+--------------------------------------------------------+
| func.to_datetime(1630833797)    | func.subtract_seconds(func.to_datetime(1630833797), 2) |
+---------------------------------+--------------------------------------------------------+
| 2021-09-05 09:23:17.000000      | 2021-09-05 09:23:15.000000                             |
+---------------------------------+--------------------------------------------------------+

SQL Syntax

SUBTRACT_YEARS(<exp0>, <expr1>)
SUBTRACT_QUARTERS(<exp0>, <expr1>)
SUBTRACT_MONTHS(<exp0>, <expr1>)
SUBTRACT_DAYS(<exp0>, <expr1>)
SUBTRACT_HOURS(<exp0>, <expr1>)
SUBTRACT_MINUTES(<exp0>, <expr1>)
SUBTRACT_SECONDS(<exp0>, <expr1>)

Return Type

DATE, TIMESTAMP depends on the input.

SQL Examples

SELECT to_date(18875), subtract_years(to_date(18875), 2);
+----------------+-----------------------------------+
| to_date(18875) | subtract_years(to_date(18875), 2) |
+----------------+-----------------------------------+
| 2021-09-05     | 2019-09-05                        |
+----------------+-----------------------------------+

SELECT to_date(18875), subtract_quarters(to_date(18875), 2);
+----------------+--------------------------------------+
| to_date(18875) | subtract_quarters(to_date(18875), 2) |
+----------------+--------------------------------------+
| 2021-09-05     | 2021-03-05                           |
+----------------+--------------------------------------+

SELECT to_date(18875), subtract_months(to_date(18875), 2);
+----------------+------------------------------------+
| to_date(18875) | subtract_months(to_date(18875), 2) |
+----------------+------------------------------------+
| 2021-09-05     | 2021-07-05                         |
+----------------+------------------------------------+

SELECT to_date(18875), subtract_days(to_date(18875), 2);
+----------------+----------------------------------+
| to_date(18875) | subtract_days(to_date(18875), 2) |
+----------------+----------------------------------+
| 2021-09-05     | 2021-09-03                       |
+----------------+----------------------------------+

SELECT to_datetime(1630833797), subtract_hours(to_datetime(1630833797), 2);
+----------------------------+--------------------------------------------+
| to_datetime(1630833797)    | subtract_hours(to_datetime(1630833797), 2) |
+----------------------------+--------------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 07:23:17.000000                 |
+----------------------------+--------------------------------------------+

SELECT to_datetime(1630833797), subtract_minutes(to_datetime(1630833797), 2);
+----------------------------+----------------------------------------------+
| to_datetime(1630833797)    | subtract_minutes(to_datetime(1630833797), 2) |
+----------------------------+----------------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 09:21:17.000000                   |
+----------------------------+----------------------------------------------+

SELECT to_datetime(1630833797), subtract_seconds(to_datetime(1630833797), 2);
+----------------------------+----------------------------------------------+
| to_datetime(1630833797)    | subtract_seconds(to_datetime(1630833797), 2) |
+----------------------------+----------------------------------------------+
| 2021-09-05 09:23:17.000000 | 2021-09-05 09:23:15.000000                   |
+----------------------------+----------------------------------------------+

8.22 - TIME_SLOT

Rounds the time to the half-hour.

Analyze Syntax

func.time_slot(<expr>)

Analyze Examples

func.time_slot('2023-11-12 09:38:18.165575')
┌───────────────────────────────-───-───-──────┐
│ func.time_slot('2023-11-12 09:38:18.165575') │
│                Timestamp                     │
├─────────────────────────────────-───-────────┤
│ 2023-11-12 09:30:00                          │
└─────────────────────────────────-───-────────┘

SQL Syntax

time_slot(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  time_slot('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────┐
│ time_slot('2023-11-12 09:38:18.165575') │
│                Timestamp                │
├─────────────────────────────────────────┤
│ 2023-11-12 09:30:00                     │
└─────────────────────────────────────────┘

8.23 - TIMESTAMP_DIFF

Calculates the difference between two timestamps and returns the result as an INTERVAL.

Analyze Syntax

func.timestamp_diff(<timestamp1>, <timestamp2>)

Analyze Examples

func.timestamp_diff(func.to_timestamp('2025-02-01'), func.to_timestamp('2025-01-01'))
+----------------------------------------------------------------------------------------+
| func.timestamp_diff(func.to_timestamp('2025-02-01'), func.to_timestamp('2025-01-01'))  |
+----------------------------------------------------------------------------------------+
| 744:00:00                                                                              |
+----------------------------------------------------------------------------------------+

SQL Syntax

TIMESTAMP_DIFF(<timestamp1>, <timestamp2>)

Return Type

INTERVAL (formatted as hours:minutes:seconds).

SQL Examples

This example shows that the time difference between February 1, 2025, and January 1, 2025, is 744 hours, corresponding to 31 days:

SELECT TIMESTAMP_DIFF('2025-02-01'::TIMESTAMP, '2025-01-01'::TIMESTAMP);

┌──────────────────────────────────────────────────────────────────┐
│ timestamp_diff('2025-02-01'::TIMESTAMP, '2025-01-01'::TIMESTAMP) │
├──────────────────────────────────────────────────────────────────┤
│ 744:00:00                                                        │
└──────────────────────────────────────────────────────────────────┘

8.24 - TIMEZONE

Returns the timezone for the current connection.

PlaidCloud Lakehouse uses UTC (Coordinated Universal Time) as the default timezone and allows you to change the timezone to your current geographic location. For the available values you can assign to the timezone setting, refer to https://docs.rs/chrono-tz/latest/chrono_tz/enum.Tz.html. See the examples below for details.

Analyze Syntax

func.timezone()

Analyze Examples

func.timezone()
┌─────────────────────┐
│ timezone            │
├─────────────────────┤
│ UTC                 │
└─────────────────────┘

SQL Syntax

SELECT TIMEZONE();

SQL Examples

-- Return the current timezone
SELECT TIMEZONE();
+-----------------+
| TIMEZONE('UTC') |
+-----------------+
| UTC             |
+-----------------+

-- Set the timezone to China Standard Time
SET timezone='Asia/Shanghai';

SELECT TIMEZONE();
+---------------------------+
| TIMEZONE('Asia/Shanghai') |
+---------------------------+
| Asia/Shanghai             |
+---------------------------+

8.25 - TO_DATE

Converts an expression to a date, including:

  • Converting a timestamp-format string to a date: Extracts a date from the given string.

  • Converting an integer to a date: Interprets the integer as the number of days before (for negative numbers) or after (for positive numbers) the Unix epoch (midnight on January 1, 1970). Please note that a Date value ranges from 1000-01-01 to 9999-12-31. PlaidCloud Lakehouse would return an error if you run "SELECT TO_DATE(9999999999999999999)".

  • Converting a string to a date using the specified format: The function takes two arguments, converting the first string to a date based on the format specified in the second string. To customize the date and time format in PlaidCloud Lakehouse, specifiers can be used. For a comprehensive list of supported specifiers, see Formatting Date and Time.

See also: TO_TIMESTAMP

Analyze Syntax

func.to_date('<timestamp_expr>')
func.to_date(<integer>)
func.to_date('<string>', '<format>')

Analyze Examples

func.typeof(func.to_date('2022-01-02')), func.typeof(func.str_to_date('2022-01-02'))

┌───────────────────────────────────────────────────────────────────────────────────────┐
│ func.typeof(func.to_date('2022-01-02')) │ func.typeof(func.str_to_date('2022-01-02')) │
├─────────────────────────────────────────┼─────────────────────────────────────────────┤
│ DATE                                    │ DATE                                        │
└───────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

-- Convert a timestamp-format string
TO_DATE('<timestamp_expr>')

-- Convert an integer
TO_DATE(<integer>)

-- Convert a string using the given format
TO_DATE('<string>', '<format>')

Aliases

Return Type

The function returns a date in the format "YYYY-MM-DD":

SELECT TYPEOF(TO_DATE('2022-01-02')), TYPEOF(STR_TO_DATE('2022-01-02'));

┌───────────────────────────────────────────────────────────────────┐
│ typeof(to_date('2022-01-02')) │ typeof(str_to_date('2022-01-02')) │
├───────────────────────────────┼───────────────────────────────────┤
│ DATE                          │ DATE                              │
└───────────────────────────────────────────────────────────────────┘

To convert the returned date back to a string, use the DATE_FORMAT function:

SELECT DATE_FORMAT(TO_DATE('2022-01-02')) AS dt, TYPEOF(dt);

┌─────────────────────────┐
│     dt     │ typeof(dt) │
├────────────┼────────────┤
│ 2022-01-02 │ VARCHAR    │
└─────────────────────────┘

SQL Examples

SQL Examples 1: Converting a Timestamp-Format String

SELECT TO_DATE('2022-01-02T01:12:00+07:00'), STR_TO_DATE('2022-01-02T01:12:00+07:00');

┌─────────────────────────────────────────────────────────────────────────────────┐
│ to_date('2022-01-02t01:12:00+07:00') │ str_to_date('2022-01-02t01:12:00+07:00') │
├──────────────────────────────────────┼──────────────────────────────────────────┤
│ 2022-01-01                           │ 2022-01-01                               │
└─────────────────────────────────────────────────────────────────────────────────┘

SELECT TO_DATE('2022-01-02'), STR_TO_DATE('2022-01-02');

┌───────────────────────────────────────────────────┐
│ to_date('2022-01-02') │ str_to_date('2022-01-02') │
├───────────────────────┼───────────────────────────┤
│ 2022-01-02            │ 2022-01-02                │
└───────────────────────────────────────────────────┘

SQL Examples 2: Converting an Integer

SELECT TO_DATE(1), STR_TO_DATE(1), TO_DATE(-1), STR_TO_DATE(-1);

┌───────────────────────────────────────────────────────────────────┐
│ to_date(1) │ str_to_date(1) │ to_date((- 1)) │ str_to_date((- 1)) │
│    Date    │      Date      │      Date      │        Date        │
├────────────┼────────────────┼────────────────┼────────────────────┤
│ 1970-01-02 │ 1970-01-02     │ 1969-12-31     │ 1969-12-31         │
└───────────────────────────────────────────────────────────────────┘

SQL Examples 3: Converting a String using the Given Format

SELECT TO_DATE('12/25/2022','%m/%d/%Y'), STR_TO_DATE('12/25/2022','%m/%d/%Y');

┌───────────────────────────────────────────────────────────────────────────┐
│ to_date('12/25/2022', '%m/%d/%y') │ str_to_date('12/25/2022', '%m/%d/%y') │
├───────────────────────────────────┼───────────────────────────────────────┤
│ 2022-12-25                        │ 2022-12-25                            │
└───────────────────────────────────────────────────────────────────────────┘

8.26 - TO_DATETIME

Alias for TO_TIMESTAMP.

8.27 - TO_DAY_OF_MONTH

Convert a date or date with time (timestamp/datetime) to a UInt8 number containing the number of the day of the month (1-31).

Analyze Syntax

func.to_day_of_month(<expr>)

Analyze Examples

func.now(), func.to_day_of_month(func.now()), func.day(func.now())

┌──────────────────────────────────────────────────────────────────────────────────────┐
│       func.now()           │ func.to_day_of_month(func.now()) │ func.day(func.now()) │
├────────────────────────────┼──────────────────────────────────┼──────────────────────┤
│ 2024-03-14 23:35:41.947962 │                               14 │                   14 │
└──────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_DAY_OF_MONTH(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Aliases

Return Type

TINYINT

SQL Examples

SELECT NOW(), TO_DAY_OF_MONTH(NOW()), DAY(NOW());

┌──────────────────────────────────────────────────────────────────┐
│            now()           │ to_day_of_month(now()) │ day(now()) │
├────────────────────────────┼────────────────────────┼────────────┤
│ 2024-03-14 23:35:41.947962 │                     14 │         14 │
└──────────────────────────────────────────────────────────────────┘

8.28 - TO_DAY_OF_WEEK

Converts a date or date with time (timestamp/datetime) to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).

Analyze Syntax

func.to_day_of_week(<expr>)

Analyze Examples

func.to_day_of_week('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_day_of_week('2023-11-12 09:38:18.165575')  │
│                     UInt8                          │
├────────────────────────────────────────────────────┤
│                                                  7 │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_DAY_OF_WEEK(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

``TINYINT`

SQL Examples


SELECT
    to_day_of_week('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────┐
│ to_day_of_week('2023-11-12 09:38:18.165575') │
│                     UInt8                    │
├──────────────────────────────────────────────┤
│                                            7 │
└──────────────────────────────────────────────┘

8.29 - TO_DAY_OF_YEAR

Convert a date or date with time (timestamp/datetime) to a UInt16 number containing the number of the day of the year (1-366).

Analyze Syntax

func.to_day_of_year(<expr>)

Analyze Examples

func.to_day_of_week('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_day_of_year('2023-11-12 09:38:18.165575')  │
│                     UInt8                          │
├────────────────────────────────────────────────────┤
│                                                316 │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_DAY_OF_YEAR(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

SMALLINT

SQL Examples

SELECT
    to_day_of_year('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────┐
│ to_day_of_year('2023-11-12 09:38:18.165575') │
│                    UInt16                    │
├──────────────────────────────────────────────┤
│                                          316 │
└──────────────────────────────────────────────┘

8.30 - TO_HOUR

Converts a date with time (timestamp/datetime) to a UInt8 number containing the number of the hour in 24-hour time (0-23). This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true – even in Moscow the clocks were twice changed at a different time).

Analyze Syntax

func.to_hour(<expr>)

Analyze Examples

func.to_hour('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_hour('2023-11-12 09:38:18.165575')         │
│                     UInt8                          │
├────────────────────────────────────────────────────┤
│                                                  9 │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_HOUR(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TINYINT

SQL Examples

SELECT
    to_hour('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────┐
│ to_hour('2023-11-12 09:38:18.165575') │
│                 UInt8                 │
├───────────────────────────────────────┤
│                                     9 │
└───────────────────────────────────────┘

8.31 - TO_MINUTE

Converts a date with time (timestamp/datetime) to a UInt8 number containing the number of the minute of the hour (0-59).

Analyze Syntax

func.to_minute(<expr>)

Analyze Examples

func.to_minute('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_minute('2023-11-12 09:38:18.165575')       │
│                     UInt8                          │
├────────────────────────────────────────────────────┤
│                                                 38 │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_MINUTE(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TINYINT

SQL Examples

SELECT
    to_minute('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────┐
│ to_minute('2023-11-12 09:38:18.165575') │
│                  UInt8                  │
├─────────────────────────────────────────┤
│                                      38 │
└─────────────────────────────────────────┘

8.32 - TO_MONDAY

Round down a date or date with time (timestamp/datetime) to the nearest Monday. Returns the date.

Analyze Syntax

func.to_monday(<expr>)

Analyze Examples

func.to_monday('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_monday('2023-11-12 09:38:18.165575')       │
│                      Date                          │
├────────────────────────────────────────────────────┤
│ 2023-11-06                                         │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_MONDAY(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
    to_monday('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────┐
│ to_monday('2023-11-12 09:38:18.165575') │
│                   Date                  │
├─────────────────────────────────────────┤
│ 2023-11-06                              │
└─────────────────────────────────────────┘

8.33 - TO_MONTH

Convert a date or date with time (timestamp/datetime) to a UInt8 number containing the month number (1-12).

Analyze Syntax

func.to_month(<expr>)

Analyze Examples

func.now(), func.to_month(func.now()), func.month(func.now())

┌─────────────────────────────────────────────────────────────────────────────────┐
│       func.now()           │ func.to_month(func.now()) │ func.month(func.now()) │
├────────────────────────────┼───────────────────────────┼────────────────────────┤
│ 2024-03-14 23:34:02.161291 │                         3 │                      3 │
└─────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_MONTH(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Aliases

Return Type

TINYINT

SQL Examples

SELECT NOW(), TO_MONTH(NOW()), MONTH(NOW());

┌─────────────────────────────────────────────────────────────┐
│            now()           │ to_month(now()) │ month(now()) │
├────────────────────────────┼─────────────────┼──────────────┤
│ 2024-03-14 23:34:02.161291 │               3 │            3 │
└─────────────────────────────────────────────────────────────┘

8.34 - TO_QUARTER

Retrieves the quarter (1, 2, 3, or 4) from a given date or timestamp.

Analyze Syntax

func.to_quarter(<expr>)

Analyze Examples

func.now(), func.to_quarter(func.now()), func.quarter(func.now())

┌─────────────────────────────────────────────────────────────────────────────────────┐
│       func.now()           │ func.to_quarter(func.now()) │ func.quarter(func.now()) │
├────────────────────────────┼─────────────────────────────┼──────────────────────────┤
│ 2024-03-14 23:32:52.743133 │                           3 │                        3 │
└─────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_QUARTER( <date_or_time_expr> )

Aliases

Return Type

Integer.

SQL Examples

SELECT NOW(), TO_QUARTER(NOW()), QUARTER(NOW());

┌─────────────────────────────────────────────────────────────────┐
│            now()           │ to_quarter(now()) │ quarter(now()) │
├────────────────────────────┼───────────────────┼────────────────┤
│ 2024-03-14 23:32:52.743133 │                 1 │              1 │
└─────────────────────────────────────────────────────────────────┘

8.35 - TO_SECOND

Converts a date with time (timestamp/datetime) to a UInt8 number containing the number of the second in the minute (0-59).

Analyze Syntax

func.to_second(<expr>)

Analyze Examples

func.to_second('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────┐
│ func.to_second('2023-11-12 09:38:18.165575') │
│                   UInt8                      │
├──────────────────────────────────────────────┤
│                                           18 │
└──────────────────────────────────────────────┘

SQL Syntax

TO_SECOND(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TINYINT

SQL Examples

SELECT
    to_second('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────┐
│ to_second('2023-11-12 09:38:18.165575') │
│                  UInt8                  │
├─────────────────────────────────────────┤
│                                      18 │
└─────────────────────────────────────────┘

8.36 - TO_START_OF_DAY

Rounds down a date with time (timestamp/datetime) to the start of the day.

Analyze Syntax

func.to_start_of_day(<expr>)

Analyze Examples

func.to_start_of_day('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ func.to_start_of_day('2023-11-12 09:38:18.165575') │
│                     Timestamp                      │
├────────────────────────────────────────────────────┤
│ 2023-11-12 00:00:00                                │
└────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_DAY( <expr> )

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_day('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────────────┐
│ to_start_of_day('2023-11-12 09:38:18.165575') │
│                   Timestamp                   │
├───────────────────────────────────────────────┤
│ 2023-11-12 00:00:00                           │
└───────────────────────────────────────────────┘

8.37 - TO_START_OF_FIFTEEN_MINUTES

Rounds down the date with time (timestamp/datetime) to the start of the fifteen-minute interval.

Analyze Syntax

func.to_start_of_fifteen_minutes(<expr>)

Analyze Examples

func.to_start_of_fifteen_minutes('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_fifteen_minutes('2023-11-12 09:38:18.165575') │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:30:00                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_FIFTEEN_MINUTES(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_fifteen_minutes('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────────────────────────┐
│ to_start_of_fifteen_minutes('2023-11-12 09:38:18.165575') │
│                         Timestamp                         │
├───────────────────────────────────────────────────────────┤
│ 2023-11-12 09:30:00                                       │
└───────────────────────────────────────────────────────────┘

8.38 - TO_START_OF_FIVE_MINUTES

Rounds down a date with time (timestamp/datetime) to the start of the five-minute interval.

Analyze Syntax

func.to_start_of_five_minutes(<expr>)

Analyze Examples

func.to_start_of_five_minutes('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_five_minutes('2023-11-12 09:38:18.165575')    │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:35:00                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_FIVE_MINUTES(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_five_minutes('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────┐
│ to_start_of_five_minutes('2023-11-12 09:38:18.165575') │
│                        Timestamp                       │
├────────────────────────────────────────────────────────┤
│ 2023-11-12 09:35:00                                    │
└────────────────────────────────────────────────────────┘

8.39 - TO_START_OF_HOUR

Rounds down a date with time (timestamp/datetime) to the start of the hour.

Analyze Syntax

func.to_start_of_hour(<expr>)

Analyze Examples

func.to_start_of_hour('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_hour('2023-11-12 09:38:18.165575')            │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:00:00                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_HOUR(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_hour('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────┐
│ to_start_of_hour('2023-11-12 09:38:18.165575') │
│                    Timestamp                   │
├────────────────────────────────────────────────┤
│ 2023-11-12 09:00:00                            │
└────────────────────────────────────────────────┘

8.40 - TO_START_OF_ISO_YEAR

Returns the first day of the ISO year for a date or a date with time (timestamp/datetime).

Analyze Syntax

func.to_start_of_iso_year(<expr>)

Analyze Examples

func.to_start_of_iso_year('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_iso_year('2023-11-12 09:38:18.165575')        │
│                          Date                                  │
├────────────────────────────────────────────────────────────────┤
│ 2023-01-02                                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_ISO_YEAR(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
  to_start_of_iso_year('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────┐
│ to_start_of_iso_year('2023-11-12 09:38:18.165575') │
│                        Date                        │
├────────────────────────────────────────────────────┤
│ 2023-01-02                                         │
└────────────────────────────────────────────────────┘

8.41 - TO_START_OF_MINUTE

Rounds down a date with time (timestamp/datetime) to the start of the minute.

Analyze Syntax

func.to_start_of_minute(<expr>)

Analyze Examples

func.to_start_of_minute('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_minute('2023-11-12 09:38:18.165575')          │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:38:00                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_MINUTE( <expr> )

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_minute('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────────┐
│ to_start_of_minute('2023-11-12 09:38:18.165575') │
│                     Timestamp                    │
├──────────────────────────────────────────────────┤
│ 2023-11-12 09:38:00                              │
└──────────────────────────────────────────────────┘

8.42 - TO_START_OF_MONTH

Rounds down a date or date with time (timestamp/datetime) to the first day of the month. Returns the date.

Analyze Syntax

func.to_start_of_month(<expr>)

Analyze Examples

func.to_start_of_month('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_month('2023-11-12 09:38:18.165575')           │
│                          Date                                  │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-01                                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_MONTH(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
  to_start_of_month('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────────────┐
│ to_start_of_month('2023-11-12 09:38:18.165575') │
│                       Date                      │
├─────────────────────────────────────────────────┤
│ 2023-11-01                                      │
└─────────────────────────────────────────────────┘

8.43 - TO_START_OF_QUARTER

Rounds down a date or date with time (timestamp/datetime) to the first day of the quarter. The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. Returns the date.

Analyze Syntax

func.to_start_of_quarter(<expr>)

Analyze Examples

func.to_start_of_quarter('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_quarter('2023-11-12 09:38:18.165575')         │
│                          Date                                  │
├────────────────────────────────────────────────────────────────┤
│ 2023-10-01                                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_QUARTER(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
  to_start_of_quarter('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────────────────┐
│ to_start_of_quarter('2023-11-12 09:38:18.165575') │
│                        Date                       │
├───────────────────────────────────────────────────┤
│ 2023-10-01                                        │
└───────────────────────────────────────────────────┘

8.44 - TO_START_OF_SECOND

Rounds down a date with time (timestamp/datetime) to the start of the second.

Analyze Syntax

func.to_start_of_second(<expr>)

Analyze Examples

func.to_start_of_second('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_second('2023-11-12 09:38:18.165575')          │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:38:18                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_SECOND(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_second('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────────┐
│ to_start_of_second('2023-11-12 09:38:18.165575') │
│                     Timestamp                    │
├──────────────────────────────────────────────────┤
│ 2023-11-12 09:38:18                              │
└──────────────────────────────────────────────────┘

8.45 - TO_START_OF_TEN_MINUTES

Rounds down a date with time (timestamp/datetime) to the start of the ten-minute interval.

Analyze Syntax

func.to_start_of_ten_minutes(<expr>)

Analyze Examples

func.to_start_of_ten_minutes('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_ten_minutes('2023-11-12 09:38:18.165575')     │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12 09:30:00                                            │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_TEN_MINUTES(<expr>)

Arguments

ArgumentsDescription
<expr>timestamp

Return Type

TIMESTAMP, returns date in “YYYY-MM-DD hh:mm:ss.ffffff” format.

SQL Examples

SELECT
  to_start_of_ten_minutes('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────────────────────┐
│ to_start_of_ten_minutes('2023-11-12 09:38:18.165575') │
│                       Timestamp                       │
├───────────────────────────────────────────────────────┤
│ 2023-11-12 09:30:00                                   │
└───────────────────────────────────────────────────────┘

8.46 - TO_START_OF_WEEK

Returns the first day of the week for a date or a date with time (timestamp/datetime). The first day of a week can be Sunday or Monday, which is specified by the argument mode.

Analyze Syntax

func.to_start_of_week(<expr>)

Analyze Examples

func.to_start_of_week('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_week('2023-11-12 09:38:18.165575')            │
│                          Date                                  │
├────────────────────────────────────────────────────────────────┤
│ 2023-11-12                                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_WEEK(<expr> [, mode])

Arguments

ArgumentsDescription
<expr>date/timestamp
[mode]Optional. If it is 0, the result is Sunday, otherwise, the result is Monday. The default value is 0

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
  to_start_of_week('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────┐
│ to_start_of_week('2023-11-12 09:38:18.165575') │
│                      Date                      │
├────────────────────────────────────────────────┤
│ 2023-11-12                                     │
└────────────────────────────────────────────────┘

8.47 - TO_START_OF_YEAR

Returns the first day of the year for a date or a date with time (timestamp/datetime).

Analyze Syntax

func.to_start_of_year(<expr>)

Analyze Examples

func.to_start_of_year('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_start_of_year('2023-11-12 09:38:18.165575')            │
│                          Date                                  │
├────────────────────────────────────────────────────────────────┤
│ 2023-01-01                                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_START_OF_YEAR(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT
  to_start_of_year('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────┐
│ to_start_of_year('2023-11-12 09:38:18.165575') │
│                      Date                      │
├────────────────────────────────────────────────┤
│ 2023-01-01                                     │
└────────────────────────────────────────────────┘

8.48 - TO_TIMESTAMP

TO_TIMESTAMP converts an expression to a date with time (timestamp/datetime).

The function can accept one or two arguments. If given one argument, the function extracts a date from the string. If the argument is an integer, the function interprets the integer as the number of seconds, milliseconds, or microseconds before (for a negative number) or after (for a positive number) the Unix epoch (midnight on January 1, 1970):

  • If the integer is less than 31,536,000,000, it is treated as seconds.
  • If the integer is greater than or equal to 31,536,000,000 and less than 31,536,000,000,000, it is treated as milliseconds.
  • If the integer is greater than or equal to 31,536,000,000,000, it is treated as microseconds.

If given two arguments, the function converts the first string to a timestamp based on the format specified in the second string. To customize the format of date and time in PlaidCloud Lakehouse, you can utilize specifiers. These specifiers allow you to define the desired format for date and time values. For a comprehensive list of supported specifiers, see Formatting Date and Time.

  • The output timestamp reflects your PlaidCloud Lakehouse timezone.
  • The timezone information must be included in the string you want to convert, otherwise NULL will be returned.

See also: TO_DATE

Analyze Syntax

func.to_timestamp(<expr>)

Analyze Examples

func.to_timestamp('2022-01-02T03:25:02.868894-07:00')

┌────────────────────────────────────────────────────────────────┐
│ func.to_timestamp('2022-01-02T03:25:02.868894-07:00')          │
│                          Timestamp                             │
├────────────────────────────────────────────────────────────────┤
│ 2022-01-02 10:25:02.868894                                     │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

-- Convert a string or integer to a timestamp
TO_TIMESTAMP(<expr>)

-- Convert a string to a timestamp using the given pattern
TO_TIMESTAMP(<expr, expr>)

Return Type

Returns a timestamp in the format "YYYY-MM-DD hh:mm:ss.ffffff". If the given string matches this format but does not have the time part, it is automatically extended to this pattern. The padding value is 0.

Aliases

SQL Examples

Given a String Argument

SELECT TO_TIMESTAMP('2022-01-02T03:25:02.868894-07:00');

---
2022-01-02 10:25:02.868894

SELECT TO_TIMESTAMP('2022-01-02 02:00:11');

---
2022-01-02 02:00:11.000000

SELECT TO_TIMESTAMP('2022-01-02T02:00:22');

---
2022-01-02 02:00:22.000000

SELECT TO_TIMESTAMP('2022-01-02T01:12:00-07:00');

---
2022-01-02 08:12:00.000000

SELECT TO_TIMESTAMP('2022-01-02T01');

---
2022-01-02 01:00:00.000000

Given an Integer Argument

SELECT TO_TIMESTAMP(1);

---
1970-01-01 00:00:01.000000

SELECT TO_TIMESTAMP(-1);

---
1969-12-31 23:59:59.000000

Given Two Arguments

SET GLOBAL timezone ='Japan';
SELECT TO_TIMESTAMP('2022 年 2 月 4 日、8 時 58 分 59 秒、タイムゾーン：+0900', '%Y年%m月%d日、%H時%M分%S秒、タイムゾーン：%z');

---
2022-02-04 08:58:59.000000

SET GLOBAL timezone ='America/Toronto';
SELECT TO_TIMESTAMP('2022 年 2 月 4 日、8 時 58 分 59 秒、タイムゾーン：+0900', '%Y年%m月%d日、%H時%M分%S秒、タイムゾーン：%z');

---
2022-02-03 18:58:59.000000

8.49 - TO_UNIX_TIMESTAMP

Converts a timestamp in a date/time format to a Unix timestamp format. A Unix timestamp represents the number of seconds that have elapsed since January 1, 1970, at 00:00:00 UTC.

Analyze Syntax

func.to_unix_timestamp(<expr>)

Analyze Examples

func.to_unix_timestamp('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────────────────────┐
│ func.to_unix_timestamp('2023-11-12 09:38:18.165575')           │
│                          UInt32                                │
├────────────────────────────────────────────────────────────────┤
│                                                     1699781898 │
└────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_UNIX_TIMESTAMP(<expr>)

Arguments

ArgumentsDescription
<expr>Timestamp

For more information about the timestamp data type, see Date & Time.

Return Type

BIGINT

SQL Examples

SELECT
  to_unix_timestamp('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────────────┐
│ to_unix_timestamp('2023-11-12 09:38:18.165575') │
│                      UInt32                     │
├─────────────────────────────────────────────────┤
│                                      1699781898 │
└─────────────────────────────────────────────────┘

8.50 - TO_WEEK_OF_YEAR

Calculates the week number within a year for a given date.

ISO week numbering works as follows: January 4th is always considered part of the first week. If January 1st is a Thursday, then the week that spans from Monday, December 29th, to Sunday, January 4th, is designated as ISO week 1. If January 1st falls on a Friday, then the week that goes from Monday, January 4th, to Sunday, January 10th, is marked as ISO week 1.

Analyze Syntax

func.to_week_of_year(<expr>)

Analyze Examples

func.now(), func.to_week_of_year(func.now()), func.week(func.now()), func.weekofyear(func.now())

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│       func.now()           │ func.to_week_of_year(func.now()) │ func.week(func.now()) │ func.weekofyear(func.now()) │
├────────────────────────────┼──────────────────────────────────┼───────────────────────┼─────────────────────────────┤
│ 2024-03-14 23:30:04.011624 │                               11 │                    11 │                          11 │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_WEEK_OF_YEAR(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Aliases

Return Type

Returns an integer that represents the week number within a year, with numbering ranging from 1 to 53.

SQL Examples

SELECT NOW(), TO_WEEK_OF_YEAR(NOW()), WEEK(NOW()), WEEKOFYEAR(NOW());

┌───────────────────────────────────────────────────────────────────────────────────────┐
│            now()           │ to_week_of_year(now()) │ week(now()) │ weekofyear(now()) │
├────────────────────────────┼────────────────────────┼─────────────┼───────────────────┤
│ 2024-03-14 23:30:04.011624 │                     11 │          11 │                11 │
└───────────────────────────────────────────────────────────────────────────────────────┘

8.51 - TO_YEAR

Converts a date or date with time (timestamp/datetime) to a UInt16 number containing the year number (AD).

Analyze Syntax

func.to_year(<expr>)

Analyze Examples

func.now(), func.to_year(func.now()), func.year(func.now())

┌───────────────────────────────────────────────────────────────────────────────┐
│       func.now()           │ func.to_year(func.now()) │ func.year(func.now()) │
├────────────────────────────┼──────────────────────────┼───────────────────────┤
│ 2024-03-14 23:37:03.895166 │                     2024 │                  2024 │
└───────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

TO_YEAR(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Aliases

Return Type

SMALLINT

SQL Examples

SELECT NOW(), TO_YEAR(NOW()), YEAR(NOW());

┌───────────────────────────────────────────────────────────┐
│            now()           │ to_year(now()) │ year(now()) │
├────────────────────────────┼────────────────┼─────────────┤
│ 2024-03-14 23:37:03.895166 │           2024 │        2024 │
└───────────────────────────────────────────────────────────┘

8.52 - TO_YYYYMM

Converts a date or date with time (timestamp/datetime) to a UInt32 number containing the year and month number.

Analyze Syntax

func.to_yyyymm(<expr>)

Analyze Examples

func.to_yyyymm('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────┐
│ func.to_yyyymm('2023-11-12 09:38:18.165575') │
│                   UInt32                     │
├──────────────────────────────────────────────┤
│                                       202311 │
└──────────────────────────────────────────────┘

SQL Syntax

TO_YYYYMM(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

INT, returns in YYYYMM format.

SQL Examples

SELECT
  to_yyyymm('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────┐
│ to_yyyymm('2023-11-12 09:38:18.165575') │
│                  UInt32                 │
├─────────────────────────────────────────┤
│                                  202311 │
└─────────────────────────────────────────┘

8.53 - TO_YYYYMMDD

Converts a date or date with time (timestamp/datetime) to a UInt32 number containing the year and month number (YYYY * 10000 + MM * 100 + DD).

Analyze Syntax

func.to_yyyymmdd(<expr>)

Analyze Examples

func.to_yyyymmdd('2023-11-12 09:38:18.165575')

┌────────────────────────────────────────────────┐
│ func.to_yyyymmdd('2023-11-12 09:38:18.165575') │
│                   UInt32                       │
├────────────────────────────────────────────────┤
│                                       20231112 │
└────────────────────────────────────────────────┘

SQL Syntax

TO_YYYYMMDD(<expr>)

Arguments

ArgumentsDescription
<expr>date/datetime

Return Type

INT, returns in YYYYMMDD format.

SQL Examples

SELECT
  to_yyyymmdd('2023-11-12 09:38:18.165575')

┌───────────────────────────────────────────┐
│ to_yyyymmdd('2023-11-12 09:38:18.165575') │
│                   UInt32                  │
├───────────────────────────────────────────┤
│                                  20231112 │
└───────────────────────────────────────────┘

8.54 - TO_YYYYMMDDHH

Formats a given date or timestamp into a string representation in the format "YYYYMMDDHH" (Year, Month, Day, Hour).

Analyze Syntax

func.to_yyyymmddhh(<expr>)

Analyze Examples

func.to_yyyymmddhh('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────────┐
│ func.to_yyyymmddhh('2023-11-12 09:38:18.165575') │
│                   UInt32                         │
├──────────────────────────────────────────────────┤
│                                       2023111209 │
└──────────────────────────────────────────────────┘

SQL Syntax

TO_YYYYMMDDHH(<expr>)

Arguments

ArgumentsDescription
<expr>date/datetime

Return Type

Returns an unsigned 64-bit integer (UInt64) in the format "YYYYMMDDHH".

SQL Examples

SELECT
  to_yyyymmddhh('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────────┐
│ to_yyyymmddhh('2023-11-12 09:38:18.165575') │
│                    UInt32                   │
├─────────────────────────────────────────────┤
│                                  2023111209 │
└─────────────────────────────────────────────┘

8.55 - TO_YYYYMMDDHHMMSS

Convert a date or date with time (timestamp/datetime) to a UInt64 number containing the year and month number (YYYY * 10000000000 + MM * 100000000 + DD * 1000000 + hh * 10000 + mm * 100 + ss).

Analyze Syntax

func.to_yyyymmddhhmmss(<expr>)

Analyze Examples

func.to_yyyymmddhhmmss('2023-11-12 09:38:18.165575')

┌──────────────────────────────────────────────────────┐
│ func.to_yyyymmddhhmmss('2023-11-12 09:38:18.165575') │
│                   UInt64                             │
├──────────────────────────────────────────────────────┤
│                                       20231112092818 │
└──────────────────────────────────────────────────────┘

SQL Syntax

TO_YYYYMMDDHHMMSS(<expr>)

Arguments

ArgumentsDescription
<expr>date/timestamp

Return Type

BIGINT, returns in YYYYMMDDhhmmss format.

SQL Examples

SELECT
  to_yyyymmddhhmmss('2023-11-12 09:38:18.165575')

┌─────────────────────────────────────────────────┐
│ to_yyyymmddhhmmss('2023-11-12 09:38:18.165575') │
│                      UInt64                     │
├─────────────────────────────────────────────────┤
│                                  20231112092818 │
└─────────────────────────────────────────────────┘

8.56 - TODAY

Returns current date.

Analyze Syntax

func.today()

Analyze Examples

func.today()
+--------------+
| func.today() |
+--------------+
| 2021-09-03   |
+--------------+

SQL Syntax

TODAY()

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT TODAY();
+------------+
| TODAY()    |
+------------+
| 2021-09-03 |
+------------+

8.57 - TOMORROW

Returns tomorrow date, same as today() + 1.

Analyze Syntax

func.tomorrow()

Analyze Examples

func.tomorrow()
+-----------------+
| func.tomorrow() |
+-----------------+
| 2021-09-03      |
+-----------------+

SQL Syntax

TOMORROW()

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT TOMORROW();
+------------+
| TOMORROW() |
+------------+
| 2021-09-04 |
+------------+

SELECT TODAY()+1;
+---------------+
| (TODAY() + 1) |
+---------------+
| 2021-09-04    |
+---------------+

8.58 - TRY_TO_DATETIME

Alias for TRY_TO_TIMESTAMP.

8.59 - TRY_TO_TIMESTAMP

A variant of TO_TIMESTAMP in PlaidCloud Lakehouse that, while performing the same conversion of an input expression to a timestamp, incorporates error-handling support by returning NULL if the conversion fails instead of raising an error.

See also: TO_TIMESTAMP

Analyze Syntax

func.try_to_timestamp(<expr>)

Analyze Examples

func.try_to_timestamp('2022-01-02 02:00:11'), func.try_to_datetime('2022-01-02 02:00:11'), func.try_to_timestamp('plaidcloud')
┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.try_to_timestamp('2022-01-02 02:00:11') │ func.try_to_datetime('2022-01-02 02:00:11') │ func.try_to_timestamp('plaidcloud') │
│                Timestamp                │                Timestamp                         │                                     │
├─────────────────────────────────────────┼──────────────────────────────────────────────────┤─────────────────────────────────────│
│ 2022-01-02 02:00:11                     │ 2022-01-02 02:00:11                              │ NULL                                │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

-- Convert a string or integer to a timestamp
TRY_TO_TIMESTAMP(<expr>)

-- Convert a string to a timestamp using the given pattern
TRY_TO_TIMESTAMP(<expr, expr>)

Aliases

SQL Examples

SELECT TRY_TO_TIMESTAMP('2022-01-02 02:00:11'), TRY_TO_DATETIME('2022-01-02 02:00:11');

┌──────────────────────────────────────────────────────────────────────────────────┐
│ try_to_timestamp('2022-01-02 02:00:11') │ try_to_datetime('2022-01-02 02:00:11') │
│                Timestamp                │                Timestamp               │
├─────────────────────────────────────────┼────────────────────────────────────────┤
│ 2022-01-02 02:00:11                     │ 2022-01-02 02:00:11                    │
└──────────────────────────────────────────────────────────────────────────────────┘

SELECT TRY_TO_TIMESTAMP('databend'), TRY_TO_DATETIME('databend');

┌────────────────────────────────────────────────────────────┐
│ try_to_timestamp('databend') │ try_to_datetime('databend') │
├──────────────────────────────┼─────────────────────────────┤
│ NULL                         │ NULL                        │
└────────────────────────────────────────────────────────────┘

8.60 - WEEK

Alias for TO_WEEK_OF_YEAR.

8.61 - WEEKOFYEAR

Alias for TO_WEEK_OF_YEAR.

8.62 - YEAR

Alias for TO_YEAR.

8.63 - YESTERDAY

Returns yesterday date, same as today() - 1.

Analyze Syntax

func.yesterday()

Analyze Examples

func.yesterday()
+------------------+
| func.yesterday() |
+------------------+
| 2021-09-02       |
+------------------+

SQL Syntax

YESTERDAY()

Return Type

DATE, returns date in “YYYY-MM-DD” format.

SQL Examples

SELECT YESTERDAY();
+-------------+
| YESTERDAY() |
+-------------+
| 2021-09-02  |
+-------------+

SELECT TODAY()-1;
+---------------+
| (TODAY() - 1) |
+---------------+
| 2021-09-02    |
+---------------+

9 - Dictionary Functions

This section provides reference information for dictionary functions in PlaidCloud Lakehouse.

9.1 - DICT_GET

Retrieves the value of a specified attribute from a dictionary using a provided key expression.

SQL Syntax

DICT_GET([db_name.]<dict_name>, '<attr_name>', <key_expr>)
ParameterDescription
dict_nameThe name of the dictionary.
attr_nameThe name of the attribute in the dictionary that you want to retrieve the value for.
key_exprThe key expression used to locate a specific entry in the dictionary. It represents the value of the dictionary's primary key to retrieve the corresponding data.

SQL Examples

10 - Geography Functions

This section provides reference information for the geography functions in PlaidCloud Lakehouse. These functions are based on the very innovate H3 system developed by Uber to better calculate geographic relationships. The explanation of H3 can be found here.

Coordinate Conversion

Hexagon Properties

Hexagon Relationships

Measurement

General Utility

10.1 - GEO_TO_H3

Returns the H3 index of the hexagon cell where the given location resides. Returning 0 means an error occurred.

Analyze Syntax

func.geo_to_h3(lon, lat, res)

Analyze Examples

func.geo_to_h3(37.79506683, 55.71290588, 15)

┌──────────────────────────────────────────────┐
│ func.geo_to_h3(37.79506683, 55.71290588, 15) │
├──────────────────────────────────────────────┤
│                           644325524701193974 │
└──────────────────────────────────────────────┘

SQL Syntax

GEO_TO_H3(lon, lat, res)

SQL Examples

SELECT GEO_TO_H3(37.79506683, 55.71290588, 15);

┌─────────────────────────────────────────┐
│ geo_to_h3(37.79506683, 55.71290588, 15) │
├─────────────────────────────────────────┤
│                      644325524701193974 │
└─────────────────────────────────────────┘

10.2 - GEOHASH_DECODE

Converts a Geohash-encoded string into latitude/longitude coordinates.

Analyze Syntax

func.geohash_decode('<geohashed-string\>')

Analyze Examples

func.geohash_decode('ezs42')

┌─────────────────────────────────┐
│  func.geohash_decode('ezs42')   │
├─────────────────────────────────┤
│ (-5.60302734375,42.60498046875) │
└─────────────────────────────────┘

SQL Syntax

GEOHASH_DECODE('<geohashed-string\>')

SQL Examples

SELECT GEOHASH_DECODE('ezs42');

┌─────────────────────────────────┐
│     geohash_decode('ezs42')     │
├─────────────────────────────────┤
│ (-5.60302734375,42.60498046875) │
└─────────────────────────────────┘

10.3 - GEOHASH_ENCODE

Converts a pair of latitude and longitude coordinates into a Geohash-encoded string.

Analyze Syntax

func.geohash_encode(lon, lat)

Analyze Examples

func.geohash_encode(-5.60302734375, 42.593994140625)

┌─────────────────────────────────────────────────────────┐
│ func.geohash_encode((- 5.60302734375), 42.593994140625) │
├─────────────────────────────────────────────────────────┤
│ ezs42d000000                                            │
└─────────────────────────────────────────────────────────┘

SQL Syntax

GEOHASH_ENCODE(lon, lat)

SQL Examples

SELECT GEOHASH_ENCODE(-5.60302734375, 42.593994140625);

┌────────────────────────────────────────────────────┐
│ geohash_encode((- 5.60302734375), 42.593994140625) │
├────────────────────────────────────────────────────┤
│ ezs42d000000                                       │
└────────────────────────────────────────────────────┘

10.4 - H3_CELL_AREA_M2

Returns the exact area of specific cell in square meters.

Analyze Syntax

func.h3_cell_area_m2(h3)

Analyze Examples

func.h3_cell_area_m2(599119489002373119)

┌──────────────────────────────────────────┐
│ func.h3_cell_area_m2(599119489002373119) │
├──────────────────────────────────────────┤
│                       127785582.60809991 │
└──────────────────────────────────────────┘

SQL Syntax

H3_CELL_AREA_M2(h3)

SQL Examples

SELECT H3_CELL_AREA_M2(599119489002373119);

┌─────────────────────────────────────┐
│ h3_cell_area_m2(599119489002373119) │
├─────────────────────────────────────┤
│                  127785582.60809991 │
└─────────────────────────────────────┘

10.5 - H3_CELL_AREA_RADS2

Returns the exact area of specific cell in square radians.

Analyze Syntax

func.h3_cell_area_rads2(h3)

Analyze Examples

func.h3_cell_area_rads2(599119489002373119)

┌─────────────────────────────────────────────┐
│ func.h3_cell_area_rads2(599119489002373119) │
├─────────────────────────────────────────────┤
│                     0.000003148224310427697 │
└─────────────────────────────────────────────┘

SQL Syntax

H3_CELL_AREA_RADS2(h3)

SQL Examples

SELECT H3_CELL_AREA_RADS2(599119489002373119);

┌────────────────────────────────────────┐
│ h3_cell_area_rads2(599119489002373119) │
├────────────────────────────────────────┤
│                0.000003148224310427697 │
└────────────────────────────────────────┘

10.6 - H3_DISTANCE

Returns the grid distance between the the given two H3 indexes.

Analyze Syntax

func.h3_distance(h3, a_h3)

Analyze Examples

func.h3_distance(599119489002373119, 599119491149856767)

┌──────────────────────────────────────────────────────────┐
│ func.h3_distance(599119489002373119, 599119491149856767) │
├──────────────────────────────────────────────────────────┤
│                                                        1 │
└──────────────────────────────────────────────────────────┘

SQL Syntax

H3_DISTANCE(h3, a_h3)

SQL Examples

SELECT H3_DISTANCE(599119489002373119, 599119491149856767);

┌─────────────────────────────────────────────────────┐
│ h3_distance(599119489002373119, 599119491149856767) │
├─────────────────────────────────────────────────────┤
│                                                   1 │
└─────────────────────────────────────────────────────┘

10.7 - H3_EDGE_ANGLE

Returns the average length of the H3 hexagon edge in grades.

Analyze Syntax

func.h3_edge_angle(res)

Analyze Examples

func.h3_edge_angle(10)

┌────────────────────────────┐
│   func.h3_edge_angle(10)   │
├────────────────────────────┤
│      0.0006822586214153981 │
└────────────────────────────┘

SQL Syntax

H3_EDGE_ANGLE(res)

SQL Examples

SELECT H3_EDGE_ANGLE(10);

┌───────────────────────┐
│   h3_edge_angle(10)   │
├───────────────────────┤
│ 0.0006822586214153981 │
└───────────────────────┘

10.8 - H3_EDGE_LENGTH_KM

Returns the average hexagon edge length in kilometers at the given resolution. Excludes pentagons.

Analyze Syntax

func.h3_edge_length_km(res)

Analyze Examples

func.h3_edge_length_km(1)

┌───────────────────────────┐
│ func.h3_edge_length_km(1) │
├───────────────────────────┤
│         483.0568390711111 │
└───────────────────────────┘

SQL Syntax

H3_EDGE_LENGTH_KM(res)

SQL Examples

SELECT H3_EDGE_LENGTH_KM(1);

┌──────────────────────┐
│ h3_edge_length_km(1) │
├──────────────────────┤
│    483.0568390711111 │
└──────────────────────┘

10.9 - H3_EDGE_LENGTH_M

Returns the average hexagon edge length in meters at the given resolution. Excludes pentagons.

Analyze Syntax

func.h3_edge_length(1)

Analyze Examples

func.h3_edge_length(1)

┌──────────────────────────┐
│ func.h3_edge_length_m(1) │
├──────────────────────────┤
│        483056.8390711111 │
└──────────────────────────┘

SQL Syntax

H3_EDGE_LENGTH_M(1)

SQL Examples

┌─────────────────────┐
│ h3_edge_length_m(1) │
├─────────────────────┤
│   483056.8390711111 │
└─────────────────────┘

10.10 - H3_EXACT_EDGE_LENGTH_KM

Computes the length of this directed edge, in kilometers.

Analyze Syntax

func.h3_exact_edge_length_km(h3)

Analyze Examples

func.h3_exact_edge_length_km(1319695429381652479)

┌───────────────────────────────────────────────────┐
│ func.h3_exact_edge_length_km(1319695429381652479) │
├───────────────────────────────────────────────────┤
│                                 8.267326832647143 │
└───────────────────────────────────────────────────┘

SQL Syntax

H3_EXACT_EDGE_LENGTH_KM(h3)

SQL Examples

SELECT H3_EXACT_EDGE_LENGTH_KM(1319695429381652479);

┌──────────────────────────────────────────────┐
│ h3_exact_edge_length_km(1319695429381652479) │
├──────────────────────────────────────────────┤
│                            8.267326832647143 │
└──────────────────────────────────────────────┘

10.11 - H3_EXACT_EDGE_LENGTH_M

Computes the length of this directed edge, in meters.

Analyze Syntax

func.h3_exact_edge_length_m(h3)

Analyze Examples

func.h3_exact_edge_length_m(1319695429381652479)

┌──────────────────────────────────────────────────┐
│ func.h3_exact_edge_length_m(1319695429381652479) │
├──────────────────────────────────────────────────┤
│                                8267.326832647143 │
└──────────────────────────────────────────────────┘

SQL Syntax

H3_EXACT_EDGE_LENGTH_M(h3)

SQL Examples

SELECT H3_EXACT_EDGE_LENGTH_M(1319695429381652479);

┌─────────────────────────────────────────────┐
│ h3_exact_edge_length_m(1319695429381652479) │
├─────────────────────────────────────────────┤
│                           8267.326832647143 │
└─────────────────────────────────────────────┘

10.12 - H3_EXACT_EDGE_LENGTH_RADS

Computes the length of this directed edge, in radians.

Analyze Syntax

func.h3_exact_edge_length_km(h3)

Analyze Examples

func.h3_exact_edge_length_km(1319695429381652479)

┌───────────────────────────────────────────────────┐
│ func.h3_exact_edge_length_km(1319695429381652479) │
├───────────────────────────────────────────────────┤
│                                 8.267326832647143 │
└───────────────────────────────────────────────────┘

SQL Syntax

H3_EXACT_EDGE_LENGTH_RADS(h3)

SQL Examples

SELECT H3_EXACT_EDGE_LENGTH_KM(1319695429381652479);

┌──────────────────────────────────────────────┐
│ h3_exact_edge_length_km(1319695429381652479) │
├──────────────────────────────────────────────┤
│                            8.267326832647143 │
└──────────────────────────────────────────────┘

10.13 - H3_GET_BASE_CELL

Returns the base cell number of the given H3 index.

Analyze Syntax

func.h3_get_base_cell(h3)

Analyze Examples

func.h3_get_base_cell(644325524701193974)

┌───────────────────────────────────────────┐
│ func.h3_get_base_cell(644325524701193974) │
├───────────────────────────────────────────┤
│                                         8 │
└───────────────────────────────────────────┘

SQL Syntax

H3_GET_BASE_CELL(h3)

SQL Examples

SELECT H3_GET_BASE_CELL(644325524701193974);

┌──────────────────────────────────────┐
│ h3_get_base_cell(644325524701193974) │
├──────────────────────────────────────┤
│                                    8 │
└──────────────────────────────────────┘

10.14 - H3_GET_DESTINATION_INDEX_FROM_UNIDIRECTIONAL_EDGE

Returns the destination hexagon index from the unidirectional edge H3Index.

Analyze Syntax

func.h3_get_destination_index_from_unidirectional_edge(h3)

Analyze Examples

func.h3_get_destination_index_from_unidirectional_edge(1248204388774707199)

┌─────────────────────────────────────────────────────────────────────────────┐
│ func.h3_get_destination_index_from_unidirectional_edge(1248204388774707199) │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                          599686043507097599 │
└─────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_DESTINATION_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)

SQL Examples

SELECT H3_GET_DESTINATION_INDEX_FROM_UNIDIRECTIONAL_EDGE(1248204388774707199);

┌────────────────────────────────────────────────────────────────────────┐
│ h3_get_destination_index_from_unidirectional_edge(1248204388774707199) │
├────────────────────────────────────────────────────────────────────────┤
│                                                     599686043507097599 │
└────────────────────────────────────────────────────────────────────────┘

10.15 - H3_GET_FACES

Finds all icosahedron faces intersected by the given H3 index. Faces are represented as integers from 0-19.

Analyze Syntax

func.h3_get_faces(h3)

Analyze Examples

func.h3_get_faces(599119489002373119)

┌───────────────────────────────────────┐
│ func.h3_get_faces(599119489002373119) │
├───────────────────────────────────────┤
│ [0,1,2,3,4]                           │
└───────────────────────────────────────┘

SQL Syntax

H3_GET_FACES(h3)

SQL Examples

SELECT H3_GET_FACES(599119489002373119);

┌──────────────────────────────────┐
│ h3_get_faces(599119489002373119) │
├──────────────────────────────────┤
│ [0,1,2,3,4]                      │
└──────────────────────────────────┘

10.16 - H3_GET_INDEXES_FROM_UNIDIRECTIONAL_EDGE

Returns the origin and destination hexagon indexes from the given unidirectional edge H3Index.

Analyze Syntax

func.h3_get_indexes_from_unidirectional_edge(h3)

Analyze Examples

func.h3_get_indexes_from_unidirectional_edge(1248204388774707199)

┌────────────────────────────────────────────────────────────────────┐
│ func.h3_get_indexes_from_unidirectional_edge(1248204388774707199)  │
├────────────────────────────────────────────────────────────────────┤
│ (599686042433355775,599686043507097599)                            │
└────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_INDEXES_FROM_UNIDIRECTIONAL_EDGE(h3)

SQL Examples

SELECT H3_GET_INDEXES_FROM_UNIDIRECTIONAL_EDGE(1248204388774707199);

┌──────────────────────────────────────────────────────────────┐
│ h3_get_indexes_from_unidirectional_edge(1248204388774707199) │
├──────────────────────────────────────────────────────────────┤
│ (599686042433355775,599686043507097599)                      │
└──────────────────────────────────────────────────────────────┘

10.17 - H3_GET_ORIGIN_INDEX_FROM_UNIDIRECTIONAL_EDGE

Returns the origin hexagon index from the unidirectional edge H3Index.

Analyze Syntax

func.h3_get_origin_index_from_unidirectional_edge(h3)

Analyze Examples

func.h3_get_origin_index_from_unidirectional_edge(1248204388774707199)

┌────────────────────────────────────────────────────────────────────────┐
│ func.h3_get_origin_index_from_unidirectional_edge(1248204388774707199) │
├────────────────────────────────────────────────────────────────────────┤
│                                                     599686042433355775 │
└────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_ORIGIN_INDEX_FROM_UNIDIRECTIONAL_EDGE(h3)

SQL Examples

SELECT H3_GET_ORIGIN_INDEX_FROM_UNIDIRECTIONAL_EDGE(1248204388774707199);

┌───────────────────────────────────────────────────────────────────┐
│ h3_get_origin_index_from_unidirectional_edge(1248204388774707199) │
├───────────────────────────────────────────────────────────────────┤
│                                                599686042433355775 │
└───────────────────────────────────────────────────────────────────┘

10.18 - H3_GET_RESOLUTION

Returns the resolution of the given H3 index.

Analyze Syntax

func.h3_get_resolution(h3)

Analyze Examples

func.h3_get_resolution(644325524701193974)

┌────────────────────────────────────────────┐
│ func.h3_get_resolution(644325524701193974) │
├────────────────────────────────────────────┤
│                                         15 │
└────────────────────────────────────────────┘

SQL Syntax

H3_GET_RESOLUTION(h3)

SQL Examples

SELECT H3_GET_RESOLUTION(644325524701193974);

┌───────────────────────────────────────┐
│ h3_get_resolution(644325524701193974) │
├───────────────────────────────────────┤
│                                    15 │
└───────────────────────────────────────┘

10.19 - H3_GET_UNIDIRECTIONAL_EDGE

Returns the edge between the given two H3 indexes.

Analyze Syntax

func.h3_get_unidirectional_edge(h3, a_h3)

Analyze Examples

func.h3_get_unidirectional_edge(644325524701193897, 644325524701193754)

┌─────────────────────────────────────────────────────────────────────────┐
│ func.h3_get_unidirectional_edge(644325524701193897, 644325524701193754) │
├─────────────────────────────────────────────────────────────────────────┤
│                                                     1581074247194257065 │
└─────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_UNIDIRECTIONAL_EDGE(h3, a_h3)

SQL Examples

SELECT H3_GET_UNIDIRECTIONAL_EDGE(644325524701193897, 644325524701193754);

┌────────────────────────────────────────────────────────────────────┐
│ h3_get_unidirectional_edge(644325524701193897, 644325524701193754) │
├────────────────────────────────────────────────────────────────────┤
│                                                1581074247194257065 │
└────────────────────────────────────────────────────────────────────┘

10.20 - H3_GET_UNIDIRECTIONAL_EDGE_BOUNDARY

Returns the coordinates defining the unidirectional edge.

Analyze Syntax

func.h3_get_unidirectional_edge_boundary(h3)

Analyze Examples

func.h3_get_unidirectional_edge_boundary(1248204388774707199)

┌─────────────────────────────────────────────────────────────────────────────────┐
│           func.h3_get_unidirectional_edge_boundary(1248204388774707199)         │
├─────────────────────────────────────────────────────────────────────────────────┤
│ [(37.42012867767778,-122.03773496427027),(37.33755608435298,-122.090428929044)] │
└─────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_UNIDIRECTIONAL_EDGE_BOUNDARY(h3)

SQL Examples

SELECT H3_GET_UNIDIRECTIONAL_EDGE_BOUNDARY(1248204388774707199);

┌─────────────────────────────────────────────────────────────────────────────────┐
│             h3_get_unidirectional_edge_boundary(1248204388774707199)            │
├─────────────────────────────────────────────────────────────────────────────────┤
│ [(37.42012867767778,-122.03773496427027),(37.33755608435298,-122.090428929044)] │
└─────────────────────────────────────────────────────────────────────────────────┘

10.21 - H3_GET_UNIDIRECTIONAL_EDGES_FROM_HEXAGON

Returns all of the unidirectional edges from the provided H3Index.

Analyze Syntax

func.h3_get_unidirectional_edges_from_hexagon(h3)

Analyze Examples

func.h3_get_unidirectional_edges_from_hexagon(644325524701193754)

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                              func.h3_get_unidirectional_edges_from_hexagon(644325524701193754)                            │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [1292843871042545178,1364901465080473114,1436959059118401050,1509016653156328986,1581074247194256922,1653131841232184858] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_GET_UNIDIRECTIONAL_EDGES_FROM_HEXAGON(h3)

SQL Examples

SELECT H3_GET_UNIDIRECTIONAL_EDGES_FROM_HEXAGON(644325524701193754);

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                h3_get_unidirectional_edges_from_hexagon(644325524701193754)                               │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [1292843871042545178,1364901465080473114,1436959059118401050,1509016653156328986,1581074247194256922,1653131841232184858] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

10.22 - H3_HEX_AREA_KM2

Returns the average hexagon area in square kilometers at the given resolution. Excludes pentagons.

Analyze Syntax

func.h3_area_km2(res)

Analyze Examples

func.h3_area_km2(1)

┌─────────────────────────┐
│ func.h3_hex_area_km2(1) │
├─────────────────────────┤
│       609788.4417941332 │
└─────────────────────────┘

SQL Syntax

H3_HEX_AREA_KM2(res)

SQL Examples

SELECT H3_HEX_AREA_KM2(1);

┌────────────────────┐
│ h3_hex_area_km2(1) │
├────────────────────┤
│  609788.4417941332 │
└────────────────────┘

10.23 - H3_HEX_AREA_M2

Returns the average hexagon area in square meters at the given resolution. Excludes pentagons.

Analyze Syntax

func.h3_hex_area_m2(res)

Analyze Examples

func.h3_hex_area_m2(1)

┌────────────────────────┐
│ func.h3_hex_area_m2(1) │
├────────────────────────┤
│      609788441794.1339 │
└────────────────────────┘

SQL Syntax

H3_HEX_AREA_M2(res)

SQL Examples

SELECT H3_HEX_AREA_M2(1);

┌───────────────────┐
│ h3_hex_area_m2(1) │
├───────────────────┤
│ 609788441794.1339 │
└───────────────────┘

10.24 - H3_HEX_RING

Returns the "hollow" ring of hexagons at exactly grid distance k from the given H3 index.

Analyze Syntax

func.h3_hex_ring(h3, k)

Analyze Examples

func.h3_hex_ring(599686042433355775, 2)

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                                 func.h3_hex_ring(599686042433355775, 2)                                                                                               │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [599686018811035647,599686034917163007,599686029548453887,599686032769679359,599686198125920255,599686040285872127,599686041359613951,599686039212130303,599686023106002943,599686027400970239,599686013442326527,599686012368584703] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_HEX_RING(h3, k)

SQL Examples

SELECT H3_HEX_RING(599686042433355775, 2);

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                                   h3_hex_ring(599686042433355775, 2)                                                                                                  │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [599686018811035647,599686034917163007,599686029548453887,599686032769679359,599686198125920255,599686040285872127,599686041359613951,599686039212130303,599686023106002943,599686027400970239,599686013442326527,599686012368584703] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

10.25 - H3_INDEXES_ARE_NEIGHBORS

Returns whether or not the provided H3 indexes are neighbors.

Analyze Syntax

func.h3_indexes_are_neighbors(h3, a_h3)

Analyze Examples

func.h3_indexes_are_neighbors(644325524701193974, 644325524701193897)

┌───────────────────────────────────────────────────────────────────────┐
│ func.h3_indexes_are_neighbors(644325524701193974, 644325524701193897) │
├───────────────────────────────────────────────────────────────────────┤
│ true                                                                  │
└───────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_INDEXES_ARE_NEIGHBORS(h3, a_h3)

SQL Examples

SELECT H3_INDEXES_ARE_NEIGHBORS(644325524701193974, 644325524701193897);

┌──────────────────────────────────────────────────────────────────┐
│ h3_indexes_are_neighbors(644325524701193974, 644325524701193897) │
├──────────────────────────────────────────────────────────────────┤
│ true                                                             │
└──────────────────────────────────────────────────────────────────┘

10.26 - H3_IS_PENTAGON

Checks if the given H3 index represents a pentagonal cell.

Analyze Syntax

func.h3_is_pentagon(h3)

Analyze Examples

func.h3_is_pentagon(599119489002373119)

┌─────────────────────────────────────────┐
│ func.h3_is_pentagon(599119489002373119) │
├─────────────────────────────────────────┤
│ true                                    │
└─────────────────────────────────────────┘

SQL Syntax

H3_IS_PENTAGON(h3)

SQL Examples

SELECT H3_IS_PENTAGON(599119489002373119);

┌────────────────────────────────────┐
│ h3_is_pentagon(599119489002373119) │
├────────────────────────────────────┤
│ true                               │
└────────────────────────────────────┘

10.27 - H3_IS_RES_CLASS_III

Checks if the given H3 index has a resolution with Class III orientation.

Analyze Syntax

func.h3_is_res_class_iii(h3)

Analyze Examples

func.h3_is_res_class_iii(635318325446452991)

┌──────────────────────────────────────────────┐
│ func.h3_is_res_class_iii(635318325446452991) │
├──────────────────────────────────────────────┤
│ true                                         │
└──────────────────────────────────────────────┘

SQL Syntax

H3_IS_RES_CLASS_III(h3)

SQL Examples

SELECT H3_IS_RES_CLASS_III(635318325446452991);

┌─────────────────────────────────────────┐
│ h3_is_res_class_iii(635318325446452991) │
├─────────────────────────────────────────┤
│ true                                    │
└─────────────────────────────────────────┘

10.28 - H3_IS_VALID

Checks if the given H3 index is valid.

Analyze Syntax

func.h3_is_valid(h3)

Analyze Examples

func.h3_is_valid(644325524701193974)

┌──────────────────────────────────────┐
│ func.h3_is_valid(644325524701193974) │
├──────────────────────────────────────┤
│ true                                 │
└──────────────────────────────────────┘

SQL Syntax

H3_IS_VALID(h3)

SQL Examples

SELECT H3_IS_VALID(644325524701193974);

┌─────────────────────────────────┐
│ h3_is_valid(644325524701193974) │
├─────────────────────────────────┤
│ true                            │
└─────────────────────────────────┘

10.29 - H3_K_RING

Returns an array containing the H3 indexes of the k-ring hexagons surrounding the input H3 index. Each element in this array is an H3 index.

Analyze Syntax

func.h3_k_ring(h3, k)

Analyze Examples

func.h3_k_ring(644325524701193974, 1)

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                  func.h3_k_ring(644325524701193974, 1)                                                 │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [644325524701193974,644325524701193899,644325524701193869,644325524701193970,644325524701193968,644325524701193972,644325524701193897] │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_K_RING(h3, k)

SQL Examples

SELECT H3_K_RING(644325524701193974, 1);

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                    h3_k_ring(644325524701193974, 1)                                                    │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [644325524701193974,644325524701193899,644325524701193869,644325524701193970,644325524701193968,644325524701193972,644325524701193897] │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

10.30 - H3_LINE

Returns the line of indexes between the given two H3 indexes.

Analyze Syntax

func.h3_line(h3, a_h3)

Analyze Examples

func.h3_line(599119489002373119, 599119491149856767)

┌──────────────────────────────────────────────────────┐
│ func.h3_line(599119489002373119, 599119491149856767) │
├──────────────────────────────────────────────────────┤
│ [599119489002373119,599119491149856767]              │
└──────────────────────────────────────────────────────┘

SQL Syntax

H3_LINE(h3, a_h3)

SQL Examples

SELECT H3_LINE(599119489002373119, 599119491149856767);

┌─────────────────────────────────────────────────┐
│ h3_line(599119489002373119, 599119491149856767) │
├─────────────────────────────────────────────────┤
│ [599119489002373119,599119491149856767]         │
└─────────────────────────────────────────────────┘

10.31 - H3_NUM_HEXAGONS

Returns the number of unique H3 indexes at the given resolution.

Analyze Syntax

func.h3_num_hexagons(res)

Analyze Examples

func.h3_num_hexagons(10)

┌──────────────────────────┐
│ func.h3_num_hexagons(10) │
├──────────────────────────┤
│              33897029882 │
└──────────────────────────┘

SQL Syntax

H3_NUM_HEXAGONS(res)

SQL Examples

SELECT H3_NUM_HEXAGONS(10);

┌─────────────────────┐
│ h3_num_hexagons(10) │
├─────────────────────┤
│         33897029882 │
└─────────────────────┘

10.32 - H3_TO_CENTER_CHILD

Returns the center child index at the specified resolution.

Analyze Syntax

func.h3_to_center_child(h3, res)

Analyze Examples

func.h3_to_center_child(599119489002373119, 15)

┌─────────────────────────────────────────────────┐
│ func.h3_to_center_child(599119489002373119, 15) │
├─────────────────────────────────────────────────┤
│                              644155484202336256 │
└─────────────────────────────────────────────────┘

SQL Syntax

H3_TO_CENTER_CHILD(h3, res)

SQL Examples

SELECT H3_TO_CENTER_CHILD(599119489002373119, 15);

┌────────────────────────────────────────────┐
│ h3_to_center_child(599119489002373119, 15) │
├────────────────────────────────────────────┤
│                         644155484202336256 │
└────────────────────────────────────────────┘

10.33 - H3_TO_CHILDREN

Returns the indexes contained by h3 at resolution child_res.

Analyze Syntax

func.h3_to_children(h3, child_res)

Analyze Examples

func.h3_to_children(635318325446452991, 14)

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                               func.h3_to_children(635318325446452991, 14)                                              │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [639821925073823431,639821925073823439,639821925073823447,639821925073823455,639821925073823463,639821925073823471,639821925073823479] │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_TO_CHILDREN(h3, child_res)

SQL Examples

SELECT H3_TO_CHILDREN(635318325446452991, 14);

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                 h3_to_children(635318325446452991, 14)                                                 │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [639821925073823431,639821925073823439,639821925073823447,639821925073823455,639821925073823463,639821925073823471,639821925073823479] │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

10.34 - H3_TO_GEO

Returns the longitude and latitude corresponding to the given H3 index.

Analyze Syntax

func.h3_to_geo(h3)

Analyze Examples

func.h3_to_geo(644325524701193974)

┌────────────────────────────────────────┐
│   func.h3_to_geo(644325524701193974)   │
├────────────────────────────────────────┤
│ (37.79506616830255,55.712902431456676) │
└────────────────────────────────────────┘

SQL Syntax

H3_TO_GEO(h3)

SQL Examples

SELECT H3_TO_GEO(644325524701193974);

┌────────────────────────────────────────┐
│      h3_to_geo(644325524701193974)     │
├────────────────────────────────────────┤
│ (37.79506616830255,55.712902431456676) │
└────────────────────────────────────────┘

10.35 - H3_TO_GEO_BOUNDARY

Returns an array containing the longitude and latitude coordinates of the vertices of the hexagon corresponding to the H3 index.

Analyze Syntax

func.h3_to_geo_boundary(h3)

Analyze Examples

func.h3_to_geo_boundary(644325524701193974)

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                                 func.h3_to_geo_boundary(644325524701193974)                                                                                               │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [(37.79505811173477,55.712900225355526),(37.79506506997187,55.71289713485417),(37.795073126539855,55.71289934095484),(37.795074224871684,55.71290463755745),(37.79506726663349,55.71290772805916),(37.79505921006456,55.712905521957914)] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

H3_TO_GEO_BOUNDARY(h3)

SQL Examples

SELECT H3_TO_GEO_BOUNDARY(644325524701193974);

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                                   h3_to_geo_boundary(644325524701193974)                                                                                                  │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ [(37.79505811173477,55.712900225355526),(37.79506506997187,55.71289713485417),(37.795073126539855,55.71289934095484),(37.795074224871684,55.71290463755745),(37.79506726663349,55.71290772805916),(37.79505921006456,55.712905521957914)] │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

10.36 - H3_TO_PARENT

Returns the parent index containing the h3 at resolution parent_res. Returning 0 means an error occurred.

Analyze Syntax

func.h3_to_parent(h3, parent_res)

Analyze Examples

func.h3_to_parent(635318325446452991, 12)

┌───────────────────────────────────────────┐
│ func.h3_to_parent(635318325446452991, 12) │
├───────────────────────────────────────────┤
│                        630814725819082751 │
└───────────────────────────────────────────┘

SQL Syntax

H3_TO_PARENT(h3, parent_res)

SQL Examples

SELECT H3_TO_PARENT(635318325446452991, 12);

┌──────────────────────────────────────┐
│ h3_to_parent(635318325446452991, 12) │
├──────────────────────────────────────┤
│                   630814725819082751 │
└──────────────────────────────────────┘

10.37 - H3_TO_STRING

Converts the representation of the given H3 index to the string representation.

Analyze Syntax

func.h3_to_string(h3)

Analyze Examples

func.h3_to_string(635318325446452991)

┌───────────────────────────────────────┐
│ func.h3_to_string(635318325446452991) │
├───────────────────────────────────────┤
│ 8d11aa6a38826ff                       │
└───────────────────────────────────────┘

SQL Syntax

H3_TO_STRING(h3)

SQL Examples

SELECT H3_TO_STRING(635318325446452991);

┌──────────────────────────────────┐
│ h3_to_string(635318325446452991) │
├──────────────────────────────────┤
│ 8d11aa6a38826ff                  │
└──────────────────────────────────┘

10.38 - H3_UNIDIRECTIONAL_EDGE_IS_VALID

Determines if the provided H3Index is a valid unidirectional edge index. Returns 1 if it's a unidirectional edge and 0 otherwise.

Analyze Syntax

func.h3_unidirectional_edge_is_valid(h3)

Analyze Examples

func.h3_unidirectional_edge_is_valid(1248204388774707199)

┌───────────────────────────────────────────────────────────┐
│ func.h3_unidirectional_edge_is_valid(1248204388774707199) │
├───────────────────────────────────────────────────────────┤
│ true                                                      │
└───────────────────────────────────────────────────────────┘

SQL Syntax

H3_UNIDIRECTIONAL_EDGE_IS_VALID(h3)

SQL Examples

SELECT H3_UNIDIRECTIONAL_EDGE_IS_VALID(1248204388774707199);

┌──────────────────────────────────────────────────────┐
│ h3_unidirectional_edge_is_valid(1248204388774707199) │
├──────────────────────────────────────────────────────┤
│ true                                                 │
└──────────────────────────────────────────────────────┘

10.39 - POINT_IN_POLYGON

Calculates whether a given point falls within the polygon formed by joining multiple points. A polygon is a closed shape connected by coordinate pairs in the order they appear. Changing the order of coordinate pairs can result in a different shape.

Analyze Syntax

func.point_in_polygon((x,y), [(a,b), (c,d), (e,f) ... ])

Analyze Examples

func.point_in_polygon((3., 3.), [(6, 0), (8, 4), (5, 8), (0, 2)])

┌─────────────────────────────────────────────────────────────────┐
│ func.point_in_polygon((3, 3), [(6, 0), (8, 4), (5, 8), (0, 2)]) │
├─────────────────────────────────────────────────────────────────┤
│                                                               1 │
└─────────────────────────────────────────────────────────────────┘

SQL Syntax

POINT_IN_POLYGON((x,y), [(a,b), (c,d), (e,f) ... ])

SQL Examples

SELECT POINT_IN_POLYGON((3., 3.), [(6, 0), (8, 4), (5, 8), (0, 2)]);

┌────────────────────────────────────────────────────────────┐
│ point_in_polygon((3, 3), [(6, 0), (8, 4), (5, 8), (0, 2)]) │
├────────────────────────────────────────────────────────────┤
│                                                          1 │
└────────────────────────────────────────────────────────────┘

10.40 - STRING_TO_H3

Converts the string representation to H3 (uint64) representation.

Analyze Syntax

func.string_to_h3(h3)

Analyze Examples

func.string_to_h3('8d11aa6a38826ff')

┌──────────────────────────────────────┐
│ func.string_to_h3('8d11aa6a38826ff') │
├──────────────────────────────────────┤
│                   635318325446452991 │
└──────────────────────────────────────┘

SQL Syntax

STRING_TO_H3(h3)

SQL Examples

SELECT STRING_TO_H3('8d11aa6a38826ff');

┌─────────────────────────────────┐
│ string_to_h3('8d11aa6a38826ff') │
├─────────────────────────────────┤
│              635318325446452991 │
└─────────────────────────────────┘

11 - Geometry Functions

This section provides reference information for geometry and distance functions in PlaidCloud Lakehouse.

11.1 - HAVERSINE

Calculates the great circle distance in kilometers between two points on the Earth’s surface, using the Haversine formula. The two points are specified by their latitude and longitude in degrees.

SQL Syntax

HAVERSINE(<lat1>, <lon1>, <lat2>, <lon2>)

Arguments

ArgumentsDescription
<lat1>The latitude of the first point.
<lon1>The longitude of the first point.
<lat2>The latitude of the second point.
<lon2>The longitude of the second point.

Return Type

Double.

SQL Examples

SELECT
  HAVERSINE(40.7127, -74.0059, 34.0500, -118.2500) AS distance

┌────────────────┐
│    distance    │
├────────────────┤
│ 3936.390533556 │
└────────────────┘

11.2 - ST_ASBINARY

Alias for ST_ASWKB.

11.3 - ST_ASEWKB

Converts a GEOMETRY object into a EWKB(extended well-known-binary) format representation.

SQL Syntax

ST_ASEWKB(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Binary.

SQL Examples

SELECT
  ST_ASEWKB(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_ewkb;

┌────────────────────────────────────────────────────────────────────────────────────────────┐
│                                        pipeline_ewkb                                       │
├────────────────────────────────────────────────────────────────────────────────────────────┤
│ 0102000020E61000000200000000000000006A18410000000060E3564100000000A07918410000000024ED5641 │
└────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT
  ST_ASEWKB(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;POINT(-122.35 37.55)'
    )
  ) AS pipeline_ewkb;

┌────────────────────────────────────────────────────┐
│                    pipeline_ewkb                   │
├────────────────────────────────────────────────────┤
│ 0101000020E61000006666666666965EC06666666666C64240 │
└────────────────────────────────────────────────────┘

11.4 - ST_ASEWKT

Converts a GEOMETRY object into a EWKT(extended well-known-text) format representation.

SQL Syntax

ST_ASEWKT(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

String.

SQL Examples

SELECT
  ST_ASEWKT(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_ewkt;

┌─────────────────────────────────────────────────────┐
│                    pipeline_ewkt                    │
├─────────────────────────────────────────────────────┤
│ SRID=4326;LINESTRING(400000 6000000,401000 6010000) │
└─────────────────────────────────────────────────────┘

SELECT
  ST_ASEWKT(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;POINT(-122.35 37.55)'
    )
  ) AS pipeline_ewkt;

┌────────────────────────────────┐
│          pipeline_ewkt         │
├────────────────────────────────┤
│ SRID=4326;POINT(-122.35 37.55) │
└────────────────────────────────┘

11.5 - ST_ASGEOJSON

Converts a GEOMETRY object into a GeoJSON representation.

SQL Syntax

ST_ASGEOJSON(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Variant.

SQL Examples

SELECT
  ST_ASGEOJSON(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geojson;

┌─────────────────────────────────────────────────────────────────────────┐
│                             pipeline_geojson                            │
├─────────────────────────────────────────────────────────────────────────┤
│ {"coordinates":[[400000,6000000],[401000,6010000]],"type":"LineString"} │
└─────────────────────────────────────────────────────────────────────────┘

11.6 - ST_ASTEXT

Alias for ST_ASWKT.

11.7 - ST_ASWKB

Converts a GEOMETRY object into a WKB(well-known-binary) format representation.

SQL Syntax

ST_ASWKB(<geometry>)

Aliases

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Binary.

SQL Examples

SELECT
  ST_ASWKB(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_wkb;

┌────────────────────────────────────────────────────────────────────────────────────┐
│                                    pipeline_wkb                                    │
├────────────────────────────────────────────────────────────────────────────────────┤
│ 01020000000200000000000000006A18410000000060E3564100000000A07918410000000024ED5641 │
└────────────────────────────────────────────────────────────────────────────────────┘

SELECT
  ST_ASBINARY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;POINT(-122.35 37.55)'
    )
  ) AS pipeline_wkb;

┌────────────────────────────────────────────┐
│                pipeline_wkb                │
├────────────────────────────────────────────┤
│ 01010000006666666666965EC06666666666C64240 │
└────────────────────────────────────────────┘

11.8 - ST_ASWKT

Converts a GEOMETRY object into a WKT(well-known-text) format representation.

SQL Syntax

ST_ASWKT(<geometry>)

Aliases

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

String.

SQL Examples

SELECT
  ST_ASWKT(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_wkt;

┌───────────────────────────────────────────┐
│                pipeline_wkt               │
├───────────────────────────────────────────┤
│ LINESTRING(400000 6000000,401000 6010000) │
└───────────────────────────────────────────┘

SELECT
  ST_ASTEXT(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;POINT(-122.35 37.55)'
    )
  ) AS pipeline_wkt;

┌──────────────────────┐
│     pipeline_wkt     │
├──────────────────────┤
│ POINT(-122.35 37.55) │
└──────────────────────┘

11.9 - ST_CONTAINS

Returns TRUE if the second GEOMETRY object is completely inside the first GEOMETRY object.

SQL Syntax

ST_CONTAINS(<geometry1>, <geometry2>)

Arguments

ArgumentsDescription
<geometry1>The argument must be an expression of type GEOMETRY object that is not a GeometryCollection.
<geometry2>The argument must be an expression of type GEOMETRY object that is not a GeometryCollection.

:::note

  • The function reports an error if the two input GEOMETRY objects have different SRIDs. :::

Return Type

Boolean.

SQL Examples

SELECT ST_CONTAINS(TO_GEOMETRY('POLYGON((-2 0, 0 2, 2 0, -2 0))'), TO_GEOMETRY('POLYGON((-1 0, 0 1, 1 0, -1 0))')) AS contains

┌──────────┐
│ contains │
├──────────┤
│ true     │
└──────────┘

SELECT ST_CONTAINS(TO_GEOMETRY('POLYGON((-2 0, 0 2, 2 0, -2 0))'), TO_GEOMETRY('LINESTRING(-1 1, 0 2, 1 1)')) AS contains

┌──────────┐
│ contains │
├──────────┤
│ false    │
└──────────┘

SELECT ST_CONTAINS(TO_GEOMETRY('POLYGON((-2 0, 0 2, 2 0, -2 0))'), TO_GEOMETRY('LINESTRING(-2 0, 0 0, 0 1)')) AS contains

┌──────────┐
│ contains │
├──────────┤
│ true     │
└──────────┘

11.10 - ST_DIMENSION

Return the dimension for a geometry object. The dimension of a GEOMETRY object is:

Geospatial Object TypeDimension
Point / MultiPoint0
LineString / MultiLineString1
Polygon / MultiPolygon2

SQL Syntax

ST_DIMENSION(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

UInt8.

SQL Examples

SELECT
  ST_DIMENSION(
    ST_GEOMETRYFROMWKT(
      'POINT(-122.306100 37.554162)'
    )
  ) AS pipeline_dimension;

┌────────────────────┐
│ pipeline_dimension │
├────────────────────┤
│                  0 │
└────────────────────┘

SELECT
  ST_DIMENSION(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(-124.20 42.00, -120.01 41.99)'
    )
  ) AS pipeline_dimension;

┌────────────────────┐
│ pipeline_dimension │
├────────────────────┤
│                  1 │
└────────────────────┘

SELECT
  ST_DIMENSION(
    ST_GEOMETRYFROMWKT(
      'POLYGON((-124.20 42.00, -120.01 41.99, -121.1 42.01, -124.20 42.00))'
    )
  ) AS pipeline_dimension;

┌────────────────────┐
│ pipeline_dimension │
├────────────────────┤
│                  2 │
└────────────────────┘

11.11 - ST_DISTANCE

Returns the minimum Euclidean distance between two GEOMETRY objects.

SQL Syntax

ST_DISTANCE(<geometry1>, <geometry2>)

Arguments

ArgumentsDescription
<geometry1>The argument must be an expression of type GEOMETRY and must contain a Point.
<geometry2>The argument must be an expression of type GEOMETRY and must contain a Point.

:::note

  • Returns NULL if one or more input points are NULL.
  • The function reports an error if the two input GEOMETRY objects have different SRIDs. :::

Return Type

Double.

SQL Examples

SELECT
  ST_DISTANCE(
    TO_GEOMETRY('POINT(0 0)'),
    TO_GEOMETRY('POINT(1 1)')
  ) AS distance

┌─────────────┐
│   distance  │
├─────────────┤
│ 1.414213562 │
└─────────────┘

11.12 - ST_ENDPOINT

Returns the last Point in a LineString.

SQL Syntax

ST_ENDPOINT(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY that represents a LineString.

Return Type

Geometry.

SQL Examples

SELECT
  ST_ENDPOINT(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(1 1, 2 2, 3 3, 4 4)'
    )
  ) AS pipeline_endpoint;

┌───────────────────┐
│ pipeline_endpoint │
├───────────────────┤
│ POINT(4 4)        │
└───────────────────┘

11.13 - ST_GEOHASH

Return the geohash for a GEOMETRY object. A geohash is a short base32 string that identifies a geodesic rectangle containing a location in the world. The optional precision argument specifies the precision of the returned geohash. For example, passing 5 for `precision returns a shorter geohash (5 characters long) that is less precise.

SQL Syntax

ST_GEOHASH(<geometry> [, <precision>])

Arguments

ArgumentsDescription
geometryThe argument must be an expression of type GEOMETRY.
[precision]Optional. specifies the precision of the returned geohash, default is 12.

Return Type

String.

SQL Examples

SELECT
  ST_GEOHASH(
    ST_GEOMETRYFROMWKT(
      'POINT(-122.306100 37.554162)'
    )
  ) AS pipeline_geohash;

┌──────────────────┐
│ pipeline_geohash │
├──────────────────┤
│ 9q9j8ue2v71y     │
└──────────────────┘

SELECT
  ST_GEOHASH(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;POINT(-122.35 37.55)'
    ),
    5
  ) AS pipeline_geohash;

┌──────────────────┐
│ pipeline_geohash │
├──────────────────┤
│ 9q8vx            │
└──────────────────┘

11.14 - ST_GEOM_POINT

Alias for ST_MAKEGEOMPOINT.

11.15 - ST_GEOMETRYFROMEWKB

Alias for ST_GEOMTRYFROMWKB.

11.16 - ST_GEOMETRYFROMEWKT

Alias for ST_GEOMTRYFROMWKT.

11.17 - ST_GEOMETRYFROMTEXT

Alias for ST_GEOMETRYFROMWKT.

11.18 - ST_GEOMETRYFROMWKB

Parses a WKB(well-known-binary) or EWKB(extended well-known-binary) input and returns a value of type GEOMETRY.

SQL Syntax

ST_GEOMETRYFROMWKB(<string>, [<srid>])
ST_GEOMETRYFROMWKB(<binary>, [<srid>])

Aliases

Arguments

ArgumentsDescription
<string>The argument must be a string expression in WKB or EWKB in hexadecimal format.
<binary>The argument must be a binary expression in WKB or EWKB format.
<srid>The integer value of the SRID to use.

Return Type

Geometry.

SQL Examples

SELECT
  ST_GEOMETRYFROMWKB(
    '0101000020797f000066666666a9cb17411f85ebc19e325641'
  ) AS pipeline_geometry;

┌────────────────────────────────────────┐
│            pipeline_geometry           │
├────────────────────────────────────────┤
│ SRID=32633;POINT(389866.35 5819003.03) │
└────────────────────────────────────────┘

SELECT
  ST_GEOMETRYFROMWKB(
    FROM_HEX('0101000020797f000066666666a9cb17411f85ebc19e325641'), 4326
  ) AS pipeline_geometry;

┌───────────────────────────────────────┐
│           pipeline_geometry           │
├───────────────────────────────────────┤
│ SRID=4326;POINT(389866.35 5819003.03) │
└───────────────────────────────────────┘

11.19 - ST_GEOMETRYFROMWKT

Parses a WKT(well-known-text) or EWKT(extended well-known-text) input and returns a value of type GEOMETRY.

SQL Syntax

ST_GEOMETRYFROMWKT(<string>, [<srid>])

Aliases

Arguments

ArgumentsDescription
<string>The argument must be a string expression in WKT or EWKT format.
<srid>The integer value of the SRID to use.

Return Type

Geometry.

SQL Examples

SELECT
  ST_GEOMETRYFROMWKT(
    'POINT(1820.12 890.56)'
  ) AS pipeline_geometry;

┌───────────────────────┐
│   pipeline_geometry   │
├───────────────────────┤
│ POINT(1820.12 890.56) │
└───────────────────────┘

SELECT
  ST_GEOMETRYFROMWKT(
    'POINT(1820.12 890.56)', 4326
  ) AS pipeline_geometry;

┌─────────────────────────────────┐
│        pipeline_geometry        │
│             Geometry            │
├─────────────────────────────────┤
│ SRID=4326;POINT(1820.12 890.56) │
└─────────────────────────────────┘

11.20 - ST_GEOMFROMEWKB

Alias for ST_GEOMTRYFROMWKB.

11.21 - ST_GEOMFROMEWKT

Alias for ST_GEOMTRYFROMWKT.

11.22 - ST_GEOMFROMGEOHASH

Returns a GEOMETRY object for the polygon that represents the boundaries of a geohash.

SQL Syntax

ST_GEOMFROMGEOHASH(<geohash>)

Arguments

ArgumentsDescription
<geohash>The argument must be a geohash.

Return Type

Geometry.

SQL Examples

SELECT
  ST_GEOMFROMGEOHASH(
    '9q60y60rhs'
  ) AS pipeline_geometry;

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                                                                     st_geomfromgeohash('9q60y60rhs')                                                                                     │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ POLYGON((-120.66230535507202 35.30029535293579,-120.66230535507202 35.30030071735382,-120.66229462623596 35.30030071735382,-120.66229462623596 35.30029535293579,-120.66230535507202 35.30029535293579)) │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

11.23 - ST_GEOMFROMTEXT

Alias for ST_GEOMTRYFROMWKT.

11.24 - ST_GEOMFROMWKB

Alias for ST_GEOMTRYFROMWKB.

11.25 - ST_GEOMFROMWKT

Alias for ST_GEOMTRYFROMWKT.

11.26 - ST_GEOMPOINTFROMGEOHASH

Returns a GEOMETRY object for the point that represents center of a geohash.

SQL Syntax

ST_GEOMPOINTFROMGEOHASH(<geohash>)

Arguments

ArgumentsDescription
<geohash>The argument must be a geohash.

Return Type

Geometry.

SQL Examples

SELECT
  ST_GEOMPOINTFROMGEOHASH(
    's02equ0'
  ) AS pipeline_geometry;

┌──────────────────────────────────────────────┐
│               pipeline_geometry              │
│                   Geometry                   │
├──────────────────────────────────────────────┤
│ POINT(1.0004425048828125 2.0001983642578125) │
└──────────────────────────────────────────────┘

11.27 - ST_LENGTH

Returns the Euclidean length of the LineString(s) in a GEOMETRY object.

SQL Syntax

ST_LENGTH(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY containing linestrings.

:::note

  • If <geometry> is not a LineString, MultiLineString, or GeometryCollection containing linestrings, returns 0.
  • If <geometry> is a GeometryCollection, returns the sum of the lengths of the linestrings in the collection. :::

Return Type

Double.

SQL Examples

SELECT
  ST_LENGTH(TO_GEOMETRY('POINT(1 1)')) AS length

┌─────────┐
│  length │
├─────────┤
│       0 │
└─────────┘

SELECT
  ST_LENGTH(TO_GEOMETRY('LINESTRING(0 0, 1 1)')) AS length

┌─────────────┐
│    length   │
├─────────────┤
│ 1.414213562 │
└─────────────┘

SELECT
  ST_LENGTH(
    TO_GEOMETRY('POLYGON((0 0, 0 1, 1 1, 1 0, 0 0))')
  ) AS length

┌─────────┐
│  length │
├─────────┤
│       0 │
└─────────┘

11.28 - ST_MAKE_LINE

Alias for ST_MAKELINE.

11.29 - ST_MAKEGEOMPOINT

Constructs a GEOMETRY object that represents a Point with the specified longitude and latitude.

SQL Syntax

ST_MAKEGEOMPOINT(<longitude>, <latitude>)

Aliases

Arguments

ArgumentsDescription
<longitude>A Double value that represents the longitude.
<latitude>A Double value that represents the latitude.

Return Type

Geometry.

SQL Examples

SELECT
  ST_MAKEGEOMPOINT(
    7.0, 8.0
  ) AS pipeline_point;

┌────────────────┐
│ pipeline_point │
├────────────────┤
│ POINT(7 8)     │
└────────────────┘

SELECT
  ST_MAKEGEOMPOINT(
    -122.3061, 37.554162
  ) AS pipeline_point;

┌────────────────────────────┐
│       pipeline_point       │
├────────────────────────────┤
│ POINT(-122.3061 37.554162) │
└────────────────────────────┘

11.30 - ST_MAKELINE

Constructs a GEOMETRY object that represents a line connecting the points in the input two GEOMETRY objects.

SQL Syntax

ST_MAKELINE(<geometry1>, <geometry2>)

Aliases

Arguments

ArgumentsDescription
<geometry1>A GEOMETRY object containing the points to connect. This object must be a Point, MultiPoint, or LineString.
<geometry2>A GEOMETRY object containing the points to connect. This object must be a Point, MultiPoint, or LineString.

Return Type

Geometry.

SQL Examples

SELECT
  ST_MAKELINE(
    ST_GEOMETRYFROMWKT(
      'POINT(-122.306100 37.554162)'
    ),
    ST_GEOMETRYFROMWKT(
      'POINT(-104.874173 56.714538)'
    )
  ) AS pipeline_line;

┌───────────────────────────────────────────────────────┐
│                     pipeline_line                     │
├───────────────────────────────────────────────────────┤
│ LINESTRING(-122.3061 37.554162,-104.874173 56.714538) │
└───────────────────────────────────────────────────────┘

11.31 - ST_MAKEPOLYGON

Constructs a GEOMETRY object that represents a Polygon without holes. The function uses the specified LineString as the outer loop.

SQL Syntax

ST_MAKEPOLYGON(<geometry>)

Aliases

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Geometry.

SQL Examples

SELECT
  ST_MAKEPOLYGON(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(0.0 0.0, 1.0 0.0, 1.0 2.0, 0.0 2.0, 0.0 0.0)'
    )
  ) AS pipeline_polygon;

┌────────────────────────────────┐
│        pipeline_polygon        │
├────────────────────────────────┤
│ POLYGON((0 0,1 0,1 2,0 2,0 0)) │
└────────────────────────────────┘

11.32 - ST_NPOINTS

Returns the number of points in a GEOMETRY object.

SQL Syntax

ST_NPOINTS(<geometry>)

Aliases

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY object.

Return Type

UInt8.

SQL Examples

SELECT ST_NPOINTS(TO_GEOMETRY('POINT(66 12)')) AS npoints

┌─────────┐
│ npoints │
├─────────┤
│       1 │
└─────────┘

SELECT ST_NPOINTS(TO_GEOMETRY('MULTIPOINT((45 21),(12 54))')) AS npoints

┌─────────┐
│ npoints │
├─────────┤
│       2 │
└─────────┘

SELECT ST_NPOINTS(TO_GEOMETRY('LINESTRING(40 60,50 50,60 40)')) AS npoints

┌─────────┐
│ npoints │
├─────────┤
│       3 │
└─────────┘

SELECT ST_NPOINTS(TO_GEOMETRY('MULTILINESTRING((1 1,32 17),(33 12,73 49,87.1 6.1))')) AS npoints

┌─────────┐
│ npoints │
├─────────┤
│       5 │
└─────────┘

SELECT ST_NPOINTS(TO_GEOMETRY('GEOMETRYCOLLECTION(POLYGON((-10 0,0 10,10 0,-10 0)),LINESTRING(40 60,50 50,60 40),POINT(99 11))')) AS npoints

┌─────────┐
│ npoints │
├─────────┤
│       8 │
└─────────┘

11.33 - ST_NUMPOINTS

Alias for ST_NPOINTS.

11.34 - ST_POINTN

Returns a Point at a specified index in a LineString.

SQL Syntax

ST_POINTN(<geometry>, <index>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY that represents a LineString.
<index>The index of the Point to return.

:::note The index is 1-based, and a negative index is uesed as the offset from the end of LineString. If index is out of bounds, the function returns an error. :::

Return Type

Geometry.

SQL Examples

SELECT
  ST_POINTN(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(1 1, 2 2, 3 3, 4 4)'
    ),
    1
  ) AS pipeline_pointn;

┌─────────────────┐
│ pipeline_pointn │
├─────────────────┤
│ POINT(1 1)      │
└─────────────────┘

SELECT
  ST_POINTN(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(1 1, 2 2, 3 3, 4 4)'
    ),
    -2
  ) AS pipeline_pointn;

┌─────────────────┐
│ pipeline_pointn │
├─────────────────┤
│ POINT(3 3)      │
└─────────────────┘

11.35 - ST_POLYGON

Alias for ST_MAKEPOLYGON.

11.36 - ST_SETSRID

Returns a GEOMETRY object that has its SRID (spatial reference system identifier) set to the specified value. This Function only change the SRID without affecting the coordinates of the object. If you also need to change the coordinates to match the new SRS (spatial reference system), use ST_TRANSFORM instead.

SQL Syntax

ST_SETSRID(<geometry>, <srid>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY object.
<srid>The SRID integer to set in the returned GEOMETRY object.

Return Type

Geometry.

SQL Examples

SET GEOMETRY_OUTPUT_FORMAT = 'EWKT'

SELECT ST_SETSRID(TO_GEOMETRY('POINT(13 51)'), 4326) AS geometry

┌────────────────────────┐
│        geometry        │
├────────────────────────┤
│ SRID=4326;POINT(13 51) │
└────────────────────────┘

11.37 - ST_SRID

Returns the SRID (spatial reference system identifier) of a GEOMETRY object.

SQL Syntax

ST_SRID(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

INT32.

:::note If the Geometry don't have a SRID, a default value 4326 will be returned. :::

SQL Examples

SELECT
  ST_SRID(
    TO_GEOMETRY(
      'POINT(-122.306100 37.554162)',
      1234
    )
  ) AS pipeline_srid;

┌───────────────┐
│ pipeline_srid │
├───────────────┤
│          1234 │
└───────────────┘

SELECT
  ST_SRID(
    ST_MAKEGEOMPOINT(
      37.5, 45.5
    )
  ) AS pipeline_srid;

┌───────────────┐
│ pipeline_srid │
├───────────────┤
│          4326 │
└───────────────┘

11.38 - ST_STARTPOINT

Returns the first Point in a LineString.

SQL Syntax

ST_STARTPOINT(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY that represents a LineString.

Return Type

Geometry.

SQL Examples

SELECT
  ST_STARTPOINT(
    ST_GEOMETRYFROMWKT(
      'LINESTRING(1 1, 2 2, 3 3, 4 4)'
    )
  ) AS pipeline_endpoint;

┌───────────────────┐
│ pipeline_endpoint │
├───────────────────┤
│ POINT(1 1)        │
└───────────────────┘

11.39 - ST_TRANSFORM

Converts a GEOMETRY object from one spatial reference system (SRS) to another. If you just need to change the SRID without changing the coordinates (e.g. if the SRID was incorrect), use ST_SETSRID instead.

SQL Syntax

ST_TRANSFORM(<geometry> [, <from_srid>], <to_srid>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY object.
<from_srid>Optional SRID identifying the current SRS of the input GEOMETRY object, if this argument is omitted, use the SRID specified in the input GEOMETRY object.
<to_srid>The SRID that identifies the SRS to use, transforms the input GEOMETRY object to a new object that uses this SRS.

Return Type

Geometry.

SQL Examples

SET GEOMETRY_OUTPUT_FORMAT = 'EWKT'

SELECT ST_TRANSFORM(ST_GEOMFROMWKT('POINT(389866.35 5819003.03)', 32633), 3857) AS transformed_geom

┌───────────────────────────────────────────────┐
│                transformed_geom               │
├───────────────────────────────────────────────┤
│ SRID=3857;POINT(1489140.093766 6892872.19868) │
└───────────────────────────────────────────────┘

SELECT ST_TRANSFORM(ST_GEOMFROMWKT('POINT(4.500212 52.161170)'), 4326, 28992) AS transformed_geom

┌──────────────────────────────────────────────┐
│               transformed_geom               │
├──────────────────────────────────────────────┤
│ SRID=28992;POINT(94308.670475 464038.168827) │
└──────────────────────────────────────────────┘

11.40 - ST_X

Returns the longitude (X coordinate) of a Point represented by a GEOMETRY object.

SQL Syntax

ST_X(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY and must contain a Point.

Return Type

Double.

SQL Examples

SELECT
  ST_X(
    ST_MAKEGEOMPOINT(
      37.5, 45.5
    )
  ) AS pipeline_x;

┌────────────┐
│ pipeline_x │
├────────────┤
│       37.5 │
└────────────┘

11.41 - ST_XMAX

Returns the maximum longitude (X coordinate) of all points contained in the specified GEOMETRY object.

SQL Syntax

ST_XMAX(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Double.

SQL Examples

SELECT
  ST_XMAX(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(40 10),LINESTRING(10 10,20 20,10 40),POINT EMPTY)'
    )
  ) AS pipeline_xmax;

┌───────────────┐
│ pipeline_xmax │
├───────────────┤
│            40 │
└───────────────┘

SELECT
  ST_XMAX(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(40 10),LINESTRING(10 10,20 20,10 40),POLYGON((40 40,20 45,45 30,40 40)))'
    )
  ) AS pipeline_xmax;

┌───────────────┐
│ pipeline_xmax │
├───────────────┤
│            45 │
└───────────────┘

11.42 - ST_XMIN

Returns the minimum longitude (X coordinate) of all points contained in the specified GEOMETRY object.

SQL Syntax

ST_XMIN(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Double.

SQL Examples

SELECT
  ST_XMIN(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(180 10),LINESTRING(20 10,30 20,40 40),POINT EMPTY)'
    )
  ) AS pipeline_xmin;

┌───────────────┐
│ pipeline_xmin │
├───────────────┤
│            20 │
└───────────────┘

SELECT
  ST_XMIN(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(40 10),LINESTRING(20 10,30 20,10 40),POLYGON((40 40,20 45,45 30,40 40)))'
    )
  ) AS pipeline_xmin;

┌───────────────┐
│ pipeline_xmin │
├───────────────┤
│            10 │
└───────────────┘

11.43 - ST_Y

Returns the latitude (Y coordinate) of a Point represented by a GEOMETRY object.

SQL Syntax

ST_Y(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY and must contain a Point.

Return Type

Double.

SQL Examples

SELECT
  ST_Y(
    ST_MAKEGEOMPOINT(
      37.5, 45.5
    )
  ) AS pipeline_y;

┌────────────┐
│ pipeline_y │
├────────────┤
│       45.5 │
└────────────┘

11.44 - ST_YMAX

Returns the maximum latitude (Y coordinate) of all points contained in the specified GEOMETRY object.

SQL Syntax

ST_YMAX(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Double.

SQL Examples

SELECT
  ST_YMAX(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(180 50),LINESTRING(10 10,20 20,10 40),POINT EMPTY)'
    )
  ) AS pipeline_ymax;

┌───────────────┐
│ pipeline_ymax │
├───────────────┤
│            50 │
└───────────────┘

SELECT
  ST_YMAX(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(40 10),LINESTRING(10 10,20 20,10 40),POLYGON((40 40,20 45,45 30,40 40)))'
    )
  ) AS pipeline_ymax;

┌───────────────┐
│ pipeline_ymax │
├───────────────┤
│            45 │
└───────────────┘

11.45 - ST_YMIN

Returns the minimum latitude (Y coordinate) of all points contained in the specified GEOMETRY object.

SQL Syntax

ST_YMIN(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

Double.

SQL Examples

SELECT
  ST_YMIN(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(-180 -10),LINESTRING(-179 0, 179 30),POINT EMPTY)'
    )
  ) AS pipeline_ymin;

┌───────────────┐
│ pipeline_ymin │
├───────────────┤
│           -10 │
└───────────────┘

SELECT
  ST_YMIN(
    TO_GEOMETRY(
      'GEOMETRYCOLLECTION(POINT(180 0),LINESTRING(-60 -30, 60 30),POLYGON((40 40,20 45,45 30,40 40)))'
    )
  ) AS pipeline_ymin;

┌───────────────┐
│ pipeline_ymin │
├───────────────┤
│           -30 │
└───────────────┘

11.46 - TO_GEOMETRY

Parses an input and returns a value of type GEOMETRY.

TRY_TO_GEOMETRY returns a NULL value if an error occurs during parsing.

SQL Syntax

TO_GEOMETRY(<string>, [<srid>])
TO_GEOMETRY(<binary>, [<srid>])
TO_GEOMETRY(<variant>, [<srid>])
TRY_TO_GEOMETRY(<string>, [<srid>])
TRY_TO_GEOMETRY(<binary>, [<srid>])
TRY_TO_GEOMETRY(<variant>, [<srid>])

Arguments

ArgumentsDescription
<string>The argument must be a string expression in WKT, EWKT, WKB or EWKB in hexadecimal format, GeoJSON format.
<binary>The argument must be a binary expression in WKB or EWKB format.
<variant>The argument must be a JSON OBJECT in GeoJSON format.
<srid>The integer value of the SRID to use.

Return Type

Geometry.

SQL Examples

SELECT
  TO_GEOMETRY(
    'POINT(1820.12 890.56)'
  ) AS pipeline_geometry;

┌───────────────────────┐
│   pipeline_geometry   │
├───────────────────────┤
│ POINT(1820.12 890.56) │
└───────────────────────┘

SELECT
  TO_GEOMETRY(
    '0101000020797f000066666666a9cb17411f85ebc19e325641', 4326
  ) AS pipeline_geometry;

┌───────────────────────────────────────┐
│           pipeline_geometry           │
├───────────────────────────────────────┤
│ SRID=4326;POINT(389866.35 5819003.03) │
└───────────────────────────────────────┘

SELECT
  TO_GEOMETRY(
    FROM_HEX('0101000020797f000066666666a9cb17411f85ebc19e325641'), 4326
  ) AS pipeline_geometry;

┌───────────────────────────────────────┐
│           pipeline_geometry           │
├───────────────────────────────────────┤
│ SRID=4326;POINT(389866.35 5819003.03) │
└───────────────────────────────────────┘

SELECT
  TO_GEOMETRY(
    '{"coordinates":[[389866,5819003],[390000,5830000]],"type":"LineString"}'
  ) AS pipeline_geometry;

┌───────────────────────────────────────────┐
│             pipeline_geometry             │
├───────────────────────────────────────────┤
│ LINESTRING(389866 5819003,390000 5830000) │
└───────────────────────────────────────────┘

SELECT
  TO_GEOMETRY(
    PARSE_JSON('{"coordinates":[[389866,5819003],[390000,5830000]],"type":"LineString"}')
  ) AS pipeline_geometry;

┌───────────────────────────────────────────┐
│             pipeline_geometry             │
├───────────────────────────────────────────┤
│ LINESTRING(389866 5819003,390000 5830000) │
└───────────────────────────────────────────┘

11.47 - TO_STRING

Converts a GEOMETRY object into a String representation. The display format of the output data is controlled by the geometry_output_format setting, which contains the following types:

ParameterDescription
GeoJSON (default)The GEOMETRY result is rendered as a JSON object in GeoJSON format.
WKTThe GEOMETRY result is rendered as a String in WKT format.
WKBThe GEOMETRY result is rendered as a Binary in WKB format.
EWKTThe GEOMETRY result is rendered as a String in EWKT format.
EWKBThe GEOMETRY result is rendered as a Binary in EWKB format.

SQL Syntax

TO_STRING(<geometry>)

Arguments

ArgumentsDescription
<geometry>The argument must be an expression of type GEOMETRY.

Return Type

String.

SQL Examples

SET geometry_output_format='GeoJSON';

SELECT
  TO_GEOMETRY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geometry;

┌────────────────────────────────────────────────────────────────────────────┐
│                              pipeline_geometry                             │
├────────────────────────────────────────────────────────────────────────────┤
│ {"type": "LineString", "coordinates": [[400000,6000000],[401000,6010000]]} │
└────────────────────────────────────────────────────────────────────────────┘

SET geometry_output_format='WKT';

SELECT
  TO_GEOMETRY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geometry;

┌───────────────────────────────────────────┐
│             pipeline_geometry             │
├───────────────────────────────────────────┤
│ LINESTRING(400000 6000000,401000 6010000) │
└───────────────────────────────────────────┘

SET geometry_output_format='EWKT';

SELECT
  TO_GEOMETRY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geometry;

┌─────────────────────────────────────────────────────┐
│                  pipeline_geometry                  │
├─────────────────────────────────────────────────────┤
│ SRID=4326;LINESTRING(400000 6000000,401000 6010000) │
└─────────────────────────────────────────────────────┘

SET geometry_output_format='WKB';

SELECT
  TO_GEOMETRY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geometry;

┌────────────────────────────────────────────────────────────────────────────────────┐
│                                  pipeline_geometry                                 │
├────────────────────────────────────────────────────────────────────────────────────┤
│ 01020000000200000000000000006A18410000000060E3564100000000A07918410000000024ED5641 │
└────────────────────────────────────────────────────────────────────────────────────┘

SET geometry_output_format='EWKB';

SELECT
  TO_GEOMETRY(
    ST_GEOMETRYFROMWKT(
      'SRID=4326;LINESTRING(400000 6000000, 401000 6010000)'
    )
  ) AS pipeline_geometry;

┌────────────────────────────────────────────────────────────────────────────────────────────┐
│                                      pipeline_geometry                                     │
├────────────────────────────────────────────────────────────────────────────────────────────┤
│ 0102000020E61000000200000000000000006A18410000000060E3564100000000A07918410000000024ED5641 │
└────────────────────────────────────────────────────────────────────────────────────────────┘

12 - Hash Functions

This section provides reference information for the Hash functions in PlaidCloud Lakehouse.

12.1 - BLAKE3

Calculates a BLAKE3 256-bit checksum for a string. The value is returned as a string of 64 hexadecimal digits or NULL if the argument was NULL.

Analyze Syntax

func.blake3(<expr>)

Analyze Examples

func.blake3('1234567890')

+------------------------------------------------------------------+
| func.blake3('1234567890')                                        |
+------------------------------------------------------------------+
| d12e417e04494572b561ba2c12c3d7f9e5107c4747e27b9a8a54f8480c63e841 |
+------------------------------------------------------------------+

SQL Syntax

BLAKE3(<expr>)

SQL Examples

SELECT BLAKE3('1234567890');

┌──────────────────────────────────────────────────────────────────┐
│                       blake3('1234567890')                       │
├──────────────────────────────────────────────────────────────────┤
│ d12e417e04494572b561ba2c12c3d7f9e5107c4747e27b9a8a54f8480c63e841 │
└──────────────────────────────────────────────────────────────────┘

12.2 - CITY64WITHSEED

Calculates a City64WithSeed 64-bit hash for a string.

Analyze Syntax

func.city64withseed(<expr1>, <expr2>)

Analyze Examples

func.city64withseed('1234567890', 12)

+---------------------------------------+
| func.city64withseed('1234567890', 12) |
+---------------------------------------+
|                  10660895976650300430 |
+---------------------------------------+

SQL Syntax

CITY64WITHSEED(<expr1>, <expr2>)

SQL Examples

SELECT CITY64WITHSEED('1234567890', 12);

┌──────────────────────────────────┐
│ city64withseed('1234567890', 12) │
├──────────────────────────────────┤
│             10660895976650300430 │
└──────────────────────────────────┘

12.3 - MD5

Calculates an MD5 128-bit checksum for a string. The value is returned as a string of 32 hexadecimal digits or NULL if the argument was NULL.

Analyze Syntax

func.md5(<expr>)

Analyze Examples

func.md5('1234567890')

+------------------------------------------+
| func.md5('1234567890')                   |
+------------------------------------------+
| e807f1fcf82d132f9bb018ca6738a19f         |
+------------------------------------------+

SQL Syntax

MD5(<expr>)

SQL Examples

SELECT MD5('1234567890');

┌──────────────────────────────────┐
│         md5('1234567890')        │
├──────────────────────────────────┤
│ e807f1fcf82d132f9bb018ca6738a19f │
└──────────────────────────────────┘

12.4 - SHA

Calculates an SHA-1 160-bit checksum for the string, as described in RFC 3174 (Secure Hash Algorithm). The value is returned as a string of 40 hexadecimal digits or NULL if the argument was NULL.

Analyze Syntax

func.sha(<expr>)

Analyze Examples

func.sha('1234567890')

+------------------------------------------+
| func.sha('1234567890')                   |
+------------------------------------------+
| 01b307acba4f54f55aafc33bb06bbbf6ca803e9a |
+------------------------------------------+

SQL Syntax

SHA(<expr>)

Aliases

SQL Examples

SELECT SHA('1234567890'), SHA1('1234567890');

┌─────────────────────────────────────────────────────────────────────────────────────┐
│             sha('1234567890')            │            sha1('1234567890')            │
├──────────────────────────────────────────┼──────────────────────────────────────────┤
│ 01b307acba4f54f55aafc33bb06bbbf6ca803e9a │ 01b307acba4f54f55aafc33bb06bbbf6ca803e9a │
└─────────────────────────────────────────────────────────────────────────────────────┘

12.5 - SHA1

Alias for SHA.

12.6 - SHA2

Calculates the SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384, and SHA-512). If the hash length is not one of the permitted values, the return value is NULL. Otherwise, the function result is a hash value containing the desired number of bits as a string of hexadecimal digits.

Analyze Syntax

func.sha2(<expr>, <expr>)

Analyze Examples

func.sha2('1234567890', 0)

+------------------------------------------------------------------+
| func.sha2('1234567890', 0))                                      |
+------------------------------------------------------------------+
| c775e7b757ede630cd0aa1113bd102661ab38829ca52a6422ab782862f268646 |
+------------------------------------------------------------------+

SQL Syntax

SHA2(<expr>, <expr>)

SQL Examples

SELECT SHA2('1234567890', 0);

┌──────────────────────────────────────────────────────────────────┐
│                       sha2('1234567890', 0)                      │
├──────────────────────────────────────────────────────────────────┤
│ c775e7b757ede630cd0aa1113bd102661ab38829ca52a6422ab782862f268646 │
└──────────────────────────────────────────────────────────────────┘

12.7 - SIPHASH

Alias for SIPHASH64.

12.8 - SIPHASH64

Produces a 64-bit SipHash hash value.

Analyze Syntax

func.siphash64(<expr>)

Analyze Examples

func.siphash64('1234567890')

+-------------------------------+
| func.siphash64('1234567890')  |
+-------------------------------+
|          18110648197875983073 |
+-------------------------------+

SQL Syntax

SIPHASH64(<expr>)

Aliases

SQL Examples

SELECT SIPHASH('1234567890'), SIPHASH64('1234567890');

┌─────────────────────────────────────────────────┐
│ siphash('1234567890') │ siphash64('1234567890') │
├───────────────────────┼─────────────────────────┤
│  18110648197875983073 │    18110648197875983073 │
└─────────────────────────────────────────────────┘

12.9 - XXHASH32

Calculates an xxHash32 32-bit hash value for a string. The value is returned as a UInt32 or NULL if the argument was NULL.

Analyze Syntax

func.xxhash32(<expr>)

Analyze Examples

func.xxhash32('1234567890')

+-----------------------------+
| func.xxhash32('1234567890') |
+-----------------------------+
|                  3896585587 |
+-----------------------------+

SQL Syntax

XXHASH32(<expr>)

SQL Examples

SELECT XXHASH32('1234567890');

┌────────────────────────┐
│ xxhash32('1234567890') │
├────────────────────────┤
│             3896585587 │
└────────────────────────┘

12.10 - XXHASH64

Calculates an xxHash64 64-bit hash value for a string. The value is returned as a UInt64 or NULL if the argument was NULL.

Analyze Syntax

func.xxhash64(<expr>)

Analyze Examples

func.xxhash64('1234567890')

+-----------------------------+
| func.xxhash64('1234567890') |
+-----------------------------+
|        12237639266330420150 |
+-----------------------------+

SQL Syntax

XXHASH64(<expr>)

SQL Examples

SELECT XXHASH64('1234567890');

┌────────────────────────┐
│ xxhash64('1234567890') │
├────────────────────────┤
│   12237639266330420150 │
└────────────────────────┘

13 - Interval Functions

This section provides reference information for interval functions in PlaidCloud Lakehouse.

13.1 - EPOCH

Alias for TO_SECONDS.

13.2 - TO_CENTURIES

Converts a specified number of centuries into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_centuries(<centuries>)

Analyze Examples

func.to_centuries(2)
+------------------------------------------------------+
| func.to_centuries(2)                                 |
+------------------------------------------------------+
| 200 years                                            |
+------------------------------------------------------+

SQL Syntax

TO_CENTURIES(<centuries>)

Return Type

Interval (represented in years).

SQL Examples

SELECT TO_CENTURIES(2), TO_CENTURIES(0), TO_CENTURIES(-2);

┌───────────────────────────────────────────────────────┐
│ to_centuries(2) │ to_centuries(0) │ to_centuries(- 2) │
├─────────────────┼─────────────────┼───────────────────┤
│ 200 years       │ 00:00:00        │ -200 years        │
└───────────────────────────────────────────────────────┘

13.3 - TO_DAYS

Converts a specified number of days into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_days(<days>)

Analyze Examples

func.to_days(2)
+------------------------------------------------------+
| func.to_days(2)                                      |
+------------------------------------------------------+
| 200 days                                             |
+------------------------------------------------------+

SQL Syntax

TO_DAYS(<days>)

Return Type

Interval (represented in days).

SQL Examples

SELECT TO_DAYS(2), TO_DAYS(0), TO_DAYS(-2);

┌────────────────────────────────────────┐
│ to_days(2) │ to_days(0) │ to_days(- 2) │
├────────────┼────────────┼──────────────┤
│ 2 days     │ 00:00:00   │ -2 days      │
└────────────────────────────────────────┘

13.4 - TO_DECADES

Converts a specified number of decades into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_decades(<decades>)

Analyze Examples

func.to_decades(2)
+------------------------------------------------------+
| func.to_decades(2)                                   |
+------------------------------------------------------+
| 20 years                                             |
+------------------------------------------------------+

SQL Syntax

TO_DECADES(<decades>)

Return Type

Interval (represented in years).

SQL Examples

SELECT TO_DECADES(2), TO_DECADES(0), TO_DECADES((- 2));

┌─────────────────────────────────────────────────┐
│ to_decades(2) │ to_decades(0) │ to_decades(- 2) │
├───────────────┼───────────────┼─────────────────┤
│ 20 years      │ 00:00:00      │ -20 years       │
└─────────────────────────────────────────────────┘

13.5 - TO_HOURS

Converts a specified number of hours into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_hours(<hours>)

Analyze Examples

func.to_hours(2)
+------------------------------------------------------+
| func.to_hours(2)                                     |
+------------------------------------------------------+
| 2:00:00                                              |
+------------------------------------------------------+

SQL Syntax

TO_HOURS(<hours>)

Return Type

Interval (in the format hh:mm:ss).

SQL Examples

SELECT TO_HOURS(2), TO_HOURS(0), TO_HOURS((- 2));

┌───────────────────────────────────────────┐
│ to_hours(2) │ to_hours(0) │ to_hours(- 2) │
├─────────────┼─────────────┼───────────────┤
│ 2:00:00     │ 00:00:00    │ -2:00:00      │
└───────────────────────────────────────────┘

13.6 - TO_MICROSECONDS

Converts a specified number of microseconds into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_microseconds(<microseconds>)

Analyze Examples

func.to_microseconds(2)
+------------------------------------------------------+
| func.to_microseconds(2)                              |
+------------------------------------------------------+
| 0:00:00.000002                                       |
+------------------------------------------------------+

SQL Syntax

TO_MICROSECONDS(<microseconds>)

Return Type

Interval (in the format hh:mm:ss.sssssss).

SQL Examples

SELECT TO_MICROSECONDS(2), TO_MICROSECONDS(0), TO_MICROSECONDS((- 2));

┌────────────────────────────────────────────────────────────────┐
│ to_microseconds(2) │ to_microseconds(0) │ to_microseconds(- 2) │
├────────────────────┼────────────────────┼──────────────────────┤
│ 0:00:00.000002     │ 00:00:00           │ -0:00:00.000002      │
└────────────────────────────────────────────────────────────────┘

13.7 - TO_MILLENNIA

Converts a specified number of millennia into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_millennia(<millennia>)

Analyze Examples

func.to_millennia(2)
+------------------------------------------------------+
| func.to_millennia(2)                                 |
+------------------------------------------------------+
| 2000 years                                            |
+------------------------------------------------------+

SQL Syntax

TO_MILLENNIA(<millennia>)

Return Type

Interval (represented in years).

SQL Examples

SELECT TO_MILLENNIA(2), TO_MILLENNIA(0), TO_MILLENNIA((- 2));

┌───────────────────────────────────────────────────────┐
│ to_millennia(2) │ to_millennia(0) │ to_millennia(- 2) │
├─────────────────┼─────────────────┼───────────────────┤
│ 2000 years      │ 00:00:00        │ -2000 years       │
└───────────────────────────────────────────────────────┘

13.8 - TO_MILLISECONDS

Converts a specified number of milliseconds into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_milliseconds(<milliseconds>)

Analyze Examples

func.to_milliseconds(2)
+------------------------------------------------------+
| func.to_milliseconds(2)                              |
+------------------------------------------------------+
|  0:00:00.002                                         |
+------------------------------------------------------+

SQL Syntax

TO_MILLISECONDS(<milliseconds>)

Return Type

Interval (in the format hh:mm:ss.sss).

SQL Examples

SELECT TO_MILLISECONDS(2), TO_MILLISECONDS(0), TO_MILLISECONDS((- 2));

┌────────────────────────────────────────────────────────────────┐
│ to_milliseconds(2) │ to_milliseconds(0) │ to_milliseconds(- 2) │
├────────────────────┼────────────────────┼──────────────────────┤
│ 0:00:00.002        │ 00:00:00           │ -0:00:00.002         │
└────────────────────────────────────────────────────────────────┘

13.9 - TO_MINUTES

Converts a specified number of minutes into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_minutes(<minutes>)

Analyze Examples

func.to_minutes(2)
+------------------------------------------------------+
| func.to_minutes(2)                                   |
+------------------------------------------------------+
| 0:02:00                                              |
+------------------------------------------------------+

SQL Syntax

TO_MINUTES(<minutes>)

Return Type

Interval (in the format hh:mm:ss).

SQL Examples

SELECT TO_MINUTES(2), TO_MINUTES(0), TO_MINUTES((- 2));

┌─────────────────────────────────────────────────┐
│ to_minutes(2) │ to_minutes(0) │ to_minutes(- 2) │
├───────────────┼───────────────┼─────────────────┤
│ 0:02:00       │ 00:00:00      │ -0:02:00        │
└─────────────────────────────────────────────────┘

13.10 - TO_MONTHS

Converts a specified number of months into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_months(<months>)

Analyze Examples

func.to_months(2)
+------------------------------------------------------+
| func.to_months(2)                                    |
+------------------------------------------------------+
| 2 months                                             |
+------------------------------------------------------+

SQL Syntax

TO_MONTHS(<months>)

Return Type

Interval (represented in months).

SQL Examples

SELECT TO_MONTHS(2), TO_MONTHS(0), TO_MONTHS((- 2));

┌──────────────────────────────────────────────┐
│ to_months(2) │ to_months(0) │ to_months(- 2) │
├──────────────┼──────────────┼────────────────┤
│ 2 months     │ 00:00:00     │ -2 months      │
└──────────────────────────────────────────────┘

13.11 - TO_SECONDS

Converts a specified number of seconds into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_seconds(<seconds>)

Analyze Examples

func.to_seconds(2)
+------------------------------------------------------+
| func.to_seconds(2)                                   |
+------------------------------------------------------+
| 0:00:02                                              |
+------------------------------------------------------+

SQL Syntax

TO_SECONDS(<seconds>)

Aliases

Return Type

Interval (in the format hh:mm:ss).

sQL Examples

SELECT TO_SECONDS(2), TO_SECONDS(0), TO_SECONDS((- 2));

┌─────────────────────────────────────────────────┐
│ to_seconds(2) │ to_seconds(0) │ to_seconds(- 2) │
├───────────────┼───────────────┼─────────────────┤
│ 0:00:02       │ 00:00:00      │ -0:00:02        │
└─────────────────────────────────────────────────┘

13.12 - TO_WEEKS

Converts a specified number of weeks into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_weeks(<weeks>)

Analyze Examples

func.to_weeks(2)
+------------------------------------------------------+
| func.to_weeks(2)                                     |
+------------------------------------------------------+
| 14 days                                              |
+------------------------------------------------------+

SQL Syntax

TO_WEEKS(<weeks>)

Return Type

Interval (represented in days).

sQL Examples

SELECT TO_WEEKS(2), TO_WEEKS(0), TO_WEEKS((- 2));

┌───────────────────────────────────────────┐
│ to_weeks(2) │ to_weeks(0) │ to_weeks(- 2) │
├─────────────┼─────────────┼───────────────┤
│ 14 days     │ 00:00:00    │ -14 days      │
└───────────────────────────────────────────┘

13.13 - TO_YEARS

Converts a specified number of years into an Interval type.

  • Accepts positive integers, zero, and negative integers as input.

Analyze Syntax

func.to_years(<years>)

Analyze Examples

func.to_years(2)
+------------------------------------------------------+
| func.to_years(2)                                     |
+------------------------------------------------------+
| 2 years                                              |
+------------------------------------------------------+

SQL Syntax

TO_YEARS(<years>)

Return Type

Interval (represented in years).

SQL Examples

SELECT TO_YEARS(2), TO_YEARS(0), TO_YEARS((- 2));

┌───────────────────────────────────────────┐
│ to_years(2) │ to_years(0) │ to_years(- 2) │
├─────────────┼─────────────┼───────────────┤
│ 2 years     │ 00:00:00    │ -2 years      │
└───────────────────────────────────────────┘

14 - IP Address Functions

This section provides reference information for the IP address-related functions in PlaidCloud Lakehouse.

14.1 - INET_ATON

Converts an IPv4 address to a 32-bit integer.

Analyze Syntax

func.inet_aton(<ip>)

Analyze Examples

func.inet_aton('1.2.3.4')

┌───────────────────────────────┐
│ func.inet_aton('1.2.3.4')     │
├───────────────────────────────┤
│                      16909060 │
└───────────────────────────────┘

SQL Syntax

INET_ATON(<ip>)

Aliases

Return Type

Integer.

SQL Examples

SELECT IPV4_STRING_TO_NUM('1.2.3.4'), INET_ATON('1.2.3.4');

┌──────────────────────────────────────────────────────┐
│ ipv4_string_to_num('1.2.3.4') │ inet_aton('1.2.3.4') │
├───────────────────────────────┼──────────────────────┤
│                      16909060 │             16909060 │
└──────────────────────────────────────────────────────┘

14.2 - INET_NTOA

Converts a 32-bit integer to an IPv4 address.

Analyze Syntax

func.inet_ntoa(<int32>)

Analyze Examples

SELECT func.inet_ntoa(16909060)

┌──────────────────────────────┐
│ func.inet_ntoa(16909060)     │
├──────────────────────────────┤
│ 1.2.3.4                      │
└──────────────────────────────┘

SQL Syntax

INET_NOTA( <int32> )

Aliases

Return Type

String.

SQL Examples

SELECT IPV4_NUM_TO_STRING(16909060), INET_NTOA(16909060);

┌────────────────────────────────────────────────────┐
│ ipv4_num_to_string(16909060) │ inet_ntoa(16909060) │
├──────────────────────────────┼─────────────────────┤
│ 1.2.3.4                      │ 1.2.3.4             │
└────────────────────────────────────────────────────┘

14.3 - IPV4_NUM_TO_STRING

Alias for INET_NTOA.

14.4 - IPV4_STRING_TO_NUM

Alias for INET_ATON.

14.5 - TRY_INET_ATON

try_inet_aton function is used to take the dotted-quad representation of an IPv4 address as a string and returns the numeric value of the given IP address in form of an integer.

Analyze Syntax

func.try_inet_aton(<str>)

Analyze Examples

func.try_inet_aton('10.0.5.9')

┌────────────────────────────────┐
│ func.try_inet_aton('10.0.5.9') │
├────────────────────────────────┤
│                      167773449 │
└────────────────────────────────┘

SQL Syntax

TRY_INET_ATON( <str> )

Aliases

Return Type

Integer.

SQL Examples

SELECT TRY_INET_ATON('10.0.5.9'), TRY_IPV4_STRING_TO_NUM('10.0.5.9');

┌────────────────────────────────────────────────────────────────┐
│ try_inet_aton('10.0.5.9') │ try_ipv4_string_to_num('10.0.5.9') │
│           UInt32          │               UInt32               │
├───────────────────────────┼────────────────────────────────────┤
│                 167773449 │                          167773449 │
└────────────────────────────────────────────────────────────────┘

14.6 - TRY_INET_NTOA

Takes an IPv4 address in network byte order and then returns the address as a dotted-quad string representation.

Analyze Syntax

func.try_inet_ntoa(<integer>)

Analyze Examples

func.try_inet_ntoaA(167773449)

┌───────────────────────────────┐
│ func.try_inet_ntoa(167773449) │
├───────────────────────────────┤
│ 10.0.5.9                      │
└───────────────────────────────┘

SQL Syntax

TRY_INET_NTOA( <integer> )

Aliases

Return Type

String.

SQL Examples

SELECT TRY_INET_NTOA(167773449), TRY_IPV4_NUM_TO_STRING(167773449);

┌──────────────────────────────────────────────────────────────┐
│ try_inet_ntoa(167773449) │ try_ipv4_num_to_string(167773449) │
├──────────────────────────┼───────────────────────────────────┤
│ 10.0.5.9                 │ 10.0.5.9                          │
└──────────────────────────────────────────────────────────────┘

14.7 - TRY_IPV4_NUM_TO_STRING

Alias for TRY_INET_NTOA.

14.8 - TRY_IPV4_STRING_TO_NUM

Alias for TRY_INET_ATON.

15 - Map Functions

This section provides reference information for map functions in PlaidCloud Lakehouse.

15.1 - MAP_CAT

Returns the concatenatation of two MAPs.

SQL Syntax

MAP_CAT( <map1>, <map2> )

Arguments

ArgumentsDescription
<map1>The source MAP.
<map2>The MAP to be appended to map1.

:::note

  • If both map1 and map2 have a value with the same key, then the output map contains the value from map2.
  • If either argument is NULL, the function returns NULL without reporting any error. :::

Return Type

Map.

SQL Examples

SELECT MAP_CAT({'a':1,'b':2,'c':3}, {'c':5,'d':6});
┌─────────────────────────────────────────────┐
│ map_cat({'a':1,'b':2,'c':3}, {'c':5,'d':6}) │
├─────────────────────────────────────────────┤
│ {'a':1,'b':2,'c':5,'d':6}                   │
└─────────────────────────────────────────────┘

15.2 - MAP_CONTAINS_KEY

Determines whether the specified MAP contains the specified key.

SQL Syntax

MAP_CONTAINS_KEY( <map>, <key> )

Arguments

ArgumentsDescription
<map>The map to be searched.
<key>The key to find.

Return Type

Boolean.

SQL Examples

SELECT MAP_CONTAINS_KEY({'a':1,'b':2,'c':3}, 'c');
┌────────────────────────────────────────────┐
│ map_contains_key({'a':1,'b':2,'c':3}, 'c') │
├────────────────────────────────────────────┤
│ true                                       │
└────────────────────────────────────────────┘

SELECT MAP_CONTAINS_KEY({'a':1,'b':2,'c':3}, 'x');
┌────────────────────────────────────────────┐
│ map_contains_key({'a':1,'b':2,'c':3}, 'x') │
├────────────────────────────────────────────┤
│ false                                      │
└────────────────────────────────────────────┘

15.3 - MAP_DELETE

Returns an existing MAP with one or more keys removed.

SQL Syntax

MAP_DELETE( <map>, <key1> [, <key2>, ... ] )
MAP_DELETE( <map>, <array> )

Arguments

ArgumentsDescription
<map>The MAP that contains the KEY to remove.
<keyN>The KEYs to be omitted from the returned MAP.
<array>The Array of KEYs to be omitted from the returned MAP.

:::note

  • The types of the key expressions and the keys in the map must be the same.
  • Key values not found in the map will be ignored. :::

Return Type

Map.

SQL Examples

SELECT MAP_DELETE({'a':1,'b':2,'c':3}, 'a', 'c');
┌───────────────────────────────────────────┐
│ map_delete({'a':1,'b':2,'c':3}, 'a', 'c') │
├───────────────────────────────────────────┤
│ {'b':2}                                   │
└───────────────────────────────────────────┘

SELECT MAP_DELETE({'a':1,'b':2,'c':3}, ['a', 'b']);
┌─────────────────────────────────────────────┐
│ map_delete({'a':1,'b':2,'c':3}, ['a', 'b']) │
├─────────────────────────────────────────────┤
│ {'c':3}                                     │
└─────────────────────────────────────────────┘

15.4 - MAP_FILTER

Filters key-value pairs from a map using a lambda expression to define the condition.

SQL Syntax

MAP_FILTER(<map>, (<key>, <value>) -> <condition>)

Return Type

Returns a map that includes only the key-value pairs meeting the condition specified by the lambda expression.

SQL Examples

This example returns a map containing only the products with stock quantities below 10:

SELECT MAP_FILTER({101:15, 102:8, 103:12, 104:5}, (product_id, stock) -> (stock < 10)) AS low_stock_products;

┌────────────────────┐
│ low_stock_products │
├────────────────────┤
│ {102:8,104:5}      │
└────────────────────┘

15.5 - MAP_INSERT

Returns a new MAP consisting of the input MAP with a new key-value pair inserted (an existing key updated with a new value).

SQL Syntax

MAP_INSERT( <map>, <key>, <value> [, <updateFlag> ] )

Arguments

ArgumentsDescription
<map>The input MAP.
<key>The new key to insert into the MAP.
<value>The new value to insert into the MAP.
<updateFlag>The boolean flag indicates whether an existing key can be overwritten. The default is FALSE.

Return Type

Map.

SQL Examples

SELECT MAP_INSERT({'a':1,'b':2,'c':3}, 'd', 4);
┌─────────────────────────────────────────┐
│ map_insert({'a':1,'b':2,'c':3}, 'd', 4) │
├─────────────────────────────────────────┤
│ {'a':1,'b':2,'c':3,'d':4}               │
└─────────────────────────────────────────┘

SELECT MAP_INSERT({'a':1,'b':2,'c':3}, 'a', 5, true);
┌───────────────────────────────────────────────┐
│ map_insert({'a':1,'b':2,'c':3}, 'a', 5, TRUE) │
├───────────────────────────────────────────────┤
│ {'a':5,'b':2,'c':3}                           │
└───────────────────────────────────────────────┘

15.6 - MAP_KEYS

Returns the keys in a map.

SQL Syntax

MAP_KEYS( <map> )

Arguments

ArgumentsDescription
<map>The input map.

Return Type

Array.

SQL Examples

SELECT MAP_KEYS({'a':1,'b':2,'c':3});

┌───────────────────────────────┐
│ map_keys({'a':1,'b':2,'c':3}) │
├───────────────────────────────┤
│ ['a','b','c']                 │
└───────────────────────────────┘

15.7 - MAP_PICK

Returns a new MAP containing the specified key-value pairs from an existing MAP.

SQL Syntax

MAP_PICK( <map>, <key1> [, <key2>, ... ] )
MAP_PICK( <map>, <array> )

Arguments

ArgumentsDescription
<map>The input MAP.
<keyN>The KEYs to be included from the returned MAP.
<array>The Array of KEYs to be included from the returned MAP.

:::note

  • The types of the key expressions and the keys in the map must be the same.
  • Key values not found in the map will be ignored. :::

Return Type

Map.

SQL Examples

SELECT MAP_PICK({'a':1,'b':2,'c':3}, 'a', 'c');
┌─────────────────────────────────────────┐
│ map_pick({'a':1,'b':2,'c':3}, 'a', 'c') │
├─────────────────────────────────────────┤
│ {'a':1,'c':3}                           │
└─────────────────────────────────────────┘

SELECT MAP_PICK({'a':1,'b':2,'c':3}, ['a', 'b']);
┌───────────────────────────────────────────┐
│ map_pick({'a':1,'b':2,'c':3}, ['a', 'b']) │
├───────────────────────────────────────────┤
│ {'a':1,'b':2}                             │
└───────────────────────────────────────────┘

15.8 - MAP_SIZE

Returns the size of a MAP.

SQL Syntax

MAP_SIZE( <map> )

Arguments

ArgumentsDescription
<map>The input map.

Return Type

UInt64.

SQL Examples

SELECT MAP_SIZE({'a':1,'b':2,'c':3});

┌───────────────────────────────┐
│ map_size({'a':1,'b':2,'c':3}) │
├───────────────────────────────┤
│ 3                             │
└───────────────────────────────┘

15.9 - MAP_TRANSFORM_KEYS

Applies a transformation to each key in a map using a lambda expression.

SQL Syntax

MAP_TRANSFORM_KEYS(<map>, (<key>, <value>) -> <key_transformation>)

Return Type

Returns a map with the same values as the input map but with keys modified according to the specified lambda transformation.

SQL Examples

This example adds 1,000 to each product ID, creating a new map with updated keys while keeping the associated prices the same:

SELECT MAP_TRANSFORM_KEYS({101: 29.99, 102: 45.50, 103: 15.00}, (product_id, price) -> product_id + 1000) AS updated_product_ids;

┌────────────────────────────────────┐
│         updated_product_ids        │
├────────────────────────────────────┤
│ {1101:29.99,1102:45.50,1103:15.00} │
└────────────────────────────────────┘

15.10 - MAP_TRANSFORM_VALUES

Applies a transformation to each value in a map using a lambda expression.

SQL Syntax

MAP_TRANSFORM_VALUES(<map>, (<key>, <value>) -> <value_transformation>)

Return Type

Returns a map with the same keys as the input map but with values modified according to the specified lambda transformation.

SQL Examples

This example reduces each product's price by 10%, while the product IDs (keys) remain unchanged:

SELECT MAP_TRANSFORM_VALUES({101: 100.0, 102: 150.0, 103: 200.0}, (product_id, price) -> price * 0.9) AS discounted_prices;

┌───────────────────────────────────┐
│         discounted_prices         │
├───────────────────────────────────┤
│ {101:90.00,102:135.00,103:180.00} │
└───────────────────────────────────┘

15.11 - MAP_VALUES

Returns the values in a map.

SQL Syntax

MAP_VALUES( <map> )

Arguments

ArgumentsDescription
<map>The input map.

Return Type

Array.

SQL Examples

SELECT MAP_VALUES({'a':1,'b':2,'c':3});

┌─────────────────────────────────┐
│ map_values({'a':1,'b':2,'c':3}) │
├─────────────────────────────────┤
│ [1,2,3]                         │
└─────────────────────────────────┘

16 - Numeric Functions

This section provides reference information for the numeric functions in PlaidCloud Lakehouse.

16.1 - ABS

Returns the absolute value of x.

Analyze Syntax

func.abs( <x> )

Analyze Examples

func.abs((- 5))

┌─────────────────┐
│ func.abs((- 5)) │
├─────────────────┤
│               5 │
└─────────────────┘

SQL Syntax

ABS( <x> )

SQL Examples

SELECT ABS(-5);

┌────────────┐
│ abs((- 5)) │
├────────────┤
│          5 │
└────────────┘

16.2 - ACOS

Returns the arc cosine of x, that is, the value whose cosine is x. Returns NULL if x is not in the range -1 to 1.

Analyze Syntax

func.abs( <x> )

Analyze Examples

func.abs(1)

┌──────────────┐
│ func.acos(1) │
├──────────────┤
│            0 │
└──────────────┘

SQL Syntax

ACOS( <x> )

SQL Examples

SELECT ACOS(1);

┌─────────┐
│ acos(1) │
├─────────┤
│       0 │
└─────────┘

16.3 - ADD

Alias for PLUS.

16.4 - ASIN

Returns the arc sine of x, that is, the value whose sine is x. Returns NULL if x is not in the range -1 to 1.

Analyze Syntax

func.asin( <x> )

Analyze Examples

func.asin(0.2)

┌────────────────────┐
│   func.asin(0.2)   │
├────────────────────┤
│ 0.2013579207903308 │
└────────────────────┘

SQL Syntax

ASIN( <x> )

SQL Examples

SELECT ASIN(0.2);

┌────────────────────┐
│      asin(0.2)     │
├────────────────────┤
│ 0.2013579207903308 │
└────────────────────┘

16.5 - ATAN

Returns the arc tangent of x, that is, the value whose tangent is x.

Analyze Syntax

func.atan( <x> )

Analyze Examples

func.atan(-2)

┌─────────────────────┐
│  func.atan((- 2))   │
├─────────────────────┤
│ -1.1071487177940906 │
└─────────────────────┘

SQL Syntax

ATAN( <x> )

SQL Examples

SELECT ATAN(-2);

┌─────────────────────┐
│     atan((- 2))     │
├─────────────────────┤
│ -1.1071487177940906 │
└─────────────────────┘

16.6 - ATAN2

Returns the arc tangent of the two variables x and y. It is similar to calculating the arc tangent of y / x, except that the signs of both arguments are used to determine the quadrant of the result. ATAN(y, x) is a synonym for ATAN2(y, x).

Analyze Syntax

func.atan2( <y, x> )

Analyze Examples

func.atan2((- 2), 2)

┌─────────────────────┐
│ func.atan2((- 2), 2)│
├─────────────────────┤
│ -0.7853981633974483 │
└─────────────────────┘

SQL Syntax

ATAN2( <y, x> )

SQL Examples

SELECT ATAN2(-2, 2);

┌─────────────────────┐
│   atan2((- 2), 2)   │
├─────────────────────┤
│ -0.7853981633974483 │
└─────────────────────┘

16.7 - CBRT

Returns the cube root of a nonnegative number x.

Analyze Syntax

func.cbrt( <x> )

Analyze Examples

func.cbrt(27)

┌───────────────┐
│ func.cbrt(27) │
├───────────────┤
│             3 │
└───────────────┘

SQL Syntax

CBRT( <x> )

SQL Examples

SELECT CBRT(27);

┌──────────┐
│ cbrt(27) │
├──────────┤
│        3 │
└──────────┘

16.8 - CEIL

Rounds the number up.

Analyze Syntax

func.ceil( <x> )

Analyze Examples

func.ceil((- 1.23))

┌─────────────────────┐
│ func.ceil((- 1.23)) │
├─────────────────────┤
│                  -1 │ 
└─────────────────────┘

SQL Syntax

CEIL( <x> )

Aliases

SQL Examples

SELECT CEILING(-1.23), CEIL(-1.23);

┌────────────────────────────────────┐
│ ceiling((- 1.23)) │ ceil((- 1.23)) │
├───────────────────┼────────────────┤
│                -1 │             -1 │
└────────────────────────────────────┘

16.9 - CEILING

Alias for CEIL.

16.10 - COS

Returns the cosine of x, where x is given in radians.

Analyze Syntax

func.cos( <x> )

Analyze Examples

func.cos(func.pi())

┌─────────────────────┐
│ func.cos(func.pi()) │
├─────────────────────┤
│                  -1 │
└─────────────────────┘

SQL Syntax

COS( <x> )

SQL Examples

SELECT COS(PI());

┌───────────┐
│ cos(pi()) │
├───────────┤
│        -1 │
└───────────┘

16.11 - COT

Returns the cotangent of x, where x is given in radians.

Analyze Syntax

func.cot( <x> )

Analyze Examples

func.cot(12) 

┌─────────────────────┐
│    func.cot(12)     │
├─────────────────────┤
│ -1.5726734063976895 │
└─────────────────────┘

SQL Syntax

COT( <x> )

SQL Examples

SELECT COT(12);

┌─────────────────────┐
│       cot(12)       │
├─────────────────────┤
│ -1.5726734063976895 │
└─────────────────────┘

16.12 - CRC32

Returns the CRC32 checksum of x, where 'x' is expected to be a string and (if possible) is treated as one if it is not.

Analyze Syntax

func.crc32( '<x>' )

Analyze Examples

func.crc32('databend')

┌────────────────────────┐
│ func.crc32('databend') │
├────────────────────────┤
│             1177678456 │
└────────────────────────┘

SQL Syntax

CRC32( '<x>' )

SQL Examples

SELECT CRC32('databend');

┌───────────────────┐
│ crc32('databend') │
├───────────────────┤
│        1177678456 │
└───────────────────┘

16.13 - DEGREES

Returns the argument x, converted from radians to degrees, where x is given in radians.

Analyze Syntax

func.degrees( <x> )

Analyze Examples

func.degrees(func.pi())

┌─────────────────────────┐
│ func.degrees(func.pi()) │
├─────────────────────────┤
│                     180 │
└─────────────────────────┘

SQL Syntax

DEGREES( <x> )

SQL Examples

SELECT DEGREES(PI());

┌───────────────┐
│ degrees(pi()) │
├───────────────┤
│           180 │
└───────────────┘

16.14 - DIV

Returns the quotient by dividing the first number by the second one, rounding down to the closest smaller integer. Equivalent to the division operator //.

See also:

SQL Syntax

func.div(<numerator>, <denominator>)

Analyze Examples

# Equivalent to the division operator "//"
func.div(6.1, 2)

┌───────────────────────────────┐
│ func.div(6.1, 2) │ (6.1 // 2) │
├──────────────────┼────────────┤
│                3 │          3 │
└───────────────────────────────┘

# Error when divided by 0
error: APIError: ResponseError with 1006: divided by zero while evaluating function `div(6.1, 0)`

Analyze Syntax

<number1> DIV <number2>

Aliases

SQL Examples

-- Equivalent to the division operator "//"
SELECT 6.1 DIV 2, 6.1//2;

┌──────────────────────────┐
│ (6.1 div 2) │ (6.1 // 2) │
├─────────────┼────────────┤
│           3 │          3 │
└──────────────────────────┘

SELECT 6.1 DIV 2, INTDIV(6.1, 2), 6.1 DIV NULL;

┌───────────────────────────────────────────────┐
│ (6.1 div 2) │ intdiv(6.1, 2) │ (6.1 div null) │
├─────────────┼────────────────┼────────────────┤
│           3 │              3 │ NULL           │
└───────────────────────────────────────────────┘

-- Error when divided by 0
root@localhost:8000/default> SELECT 6.1 DIV 0;
error: APIError: ResponseError with 1006: divided by zero while evaluating function `div(6.1, 0)`

16.15 - DIV0

Returns the quotient by dividing the first number by the second one. Returns 0 if the second number is 0.

See also:

Analyze Syntax

func.div0(<numerator>, <denominator>)

Analyze Examples

func.div0(20, 6), func.div0(20, 0), func.div0(20, null)

┌─────────────────────────────────────────────────────────────┐
│  func.div0(20, 6)  │ func.div0(20, 0) │ func.div0(20, null) │
├────────────────────┼──────────────────┼─────────────────────┤
│ 3.3333333333333335 │                0 │ NULL                │
└─────────────────────────────────────────────────────────────┘

SQL Syntax

DIV0(<number1>, <number2>)

SQL Examples

SELECT
  DIV0(20, 6),
  DIV0(20, 0),
  DIV0(20, NULL);

┌───────────────────────────────────────────────────┐
│     div0(20, 6)    │ div0(20, 0) │ div0(20, null) │
├────────────────────┼─────────────┼────────────────┤
│ 3.3333333333333335 │           0 │ NULL           │
└───────────────────────────────────────────────────┘

16.16 - DIVNULL

Returns the quotient by dividing the first number by the second one. Returns NULL if the second number is 0 or NULL.

See also:

Analyze Syntax

func.divnull(<numerator>, <denominator>)

Analyze Examples

func.divnull(20, 6), func.divnull(20, 0), func.divnull(20, null)

┌───────────────────────────────────────────────────────────────────┐
│ func.divnull(20, 6)│ func.divnull(20, 0) │ func.divnull(20, null) │
├────────────────────┼─────────────────────┼────────────────────────┤
│ 3.3333333333333335 │ NULL                │ NULL                   │
└───────────────────────────────────────────────────────────────────┘

SQL Syntax

DIVNULL(<number1>, <number2>)

SQL Examples

SELECT
  DIVNULL(20, 6),
  DIVNULL(20, 0),
  DIVNULL(20, NULL);

┌─────────────────────────────────────────────────────────┐
│   divnull(20, 6)   │ divnull(20, 0) │ divnull(20, null) │
├────────────────────┼────────────────┼───────────────────┤
│ 3.3333333333333335 │ NULL           │ NULL              │
└─────────────────────────────────────────────────────────┘

16.17 - EXP

Returns the value of e (the base of natural logarithms) raised to the power of x.

Analyze Syntax

func.exp( <x> )

Analyze Examples

func.exp(2)

┌──────────────────┐
│   func.exp(2)    │
├──────────────────┤
│ 7.38905609893065 │
└──────────────────┘

SQL Syntax

EXP( <x> )

SQL Examples

SELECT EXP(2);

┌──────────────────┐
│      exp(2)      │
├──────────────────┤
│ 7.38905609893065 │
└──────────────────┘

16.18 - FACTORIAL

Returns the factorial logarithm of x. If x is less than or equal to 0, the function returns 0.

Analyze Syntax

func.factorial( <x> )

Analyze Examples

func.factorial(5)

┌───────────────────┐
│ func.factorial(5) │
├───────────────────┤
│               120 │
└───────────────────┘

SQL Syntax

FACTORIAL( <x> )

SQL Examples

SELECT FACTORIAL(5);

┌──────────────┐
│ factorial(5) │
├──────────────┤
│          120 │
└──────────────┘

16.19 - FLOOR

Rounds the number down.

Analyze Syntax

func.floor( <x> )

Analyze Examples

func.floor(1.23)

┌──────────────────┐
│ func.floor(1.23) │
├──────────────────┤
│                1 │
└──────────────────┘

SQL Syntax

FLOOR( <x> )

SQL Examples

SELECT FLOOR(1.23);

┌─────────────┐
│ floor(1.23) │
├─────────────┤
│           1 │
└─────────────┘

16.20 - INTDIV

Alias for DIV.

16.21 - LN

Returns the natural logarithm of x; that is, the base-e logarithm of x. If x is less than or equal to 0.0E0, the function returns NULL.

Analyze Syntax

func.ln( <x> )

Analyze Examples

func.ln(2)

┌────────────────────┐
│     func.ln(2)     │
├────────────────────┤
│ 0.6931471805599453 │
└────────────────────┘

SQL Syntax

LN( <x> )

SQL Examples

SELECT LN(2);

┌────────────────────┐
│        ln(2)       │
├────────────────────┤
│ 0.6931471805599453 │
└────────────────────┘

16.22 - LOG(b, x)

Returns the base-b logarithm of x. If x is less than or equal to 0.0E0, the function returns NULL.

Analyze Syntax

func.log( <b, x> )

Analyze Examples

func.log(2, 65536)

┌────────────────────┐
│ func.log(2, 65536) │
├────────────────────┤
│                 16 │
└────────────────────┘

SQL Syntax

LOG( <b, x> )

SQL Examples

SELECT LOG(2, 65536);

┌───────────────┐
│ log(2, 65536) │
├───────────────┤
│            16 │
└───────────────┘

16.23 - LOG(x)

Returns the natural logarithm of x. If x is less than or equal to 0.0E0, the function returns NULL.

Analyze Syntax

 func.log( <x> )

Analyze Examples

 func.log(2)

┌────────────────────┐
│    func.log(2)     │
├────────────────────┤
│ 0.6931471805599453 │
└────────────────────┘

SQL Syntax

LOG( <x> )

SQL Examples

SELECT LOG(2);

┌────────────────────┐
│       log(2)       │
├────────────────────┤
│ 0.6931471805599453 │
└────────────────────┘

16.24 - LOG10

Returns the base-10 logarithm of x. If x is less than or equal to 0.0E0, the function returns NULL.

Analyze Syntax

func.log10( <x> )

Analyze Examples

func.log10(100)

┌─────────────────┐
│ func.log10(100) │
├─────────────────┤
│               2 │
└─────────────────┘

SQL Syntax

LOG10( <x> )

SQL Examples

SELECT LOG10(100);

┌────────────┐
│ log10(100) │
├────────────┤
│          2 │
└────────────┘

16.25 - LOG2

Returns the base-2 logarithm of x. If x is less than or equal to 0.0E0, the function returns NULL.

Analyze Syntax

func.log2( <x> )

Analyze Examples

func.log2(65536)

┌──────────────────┐
│ func.log2(65536) │
├──────────────────┤
│               16 │
└──────────────────┘

SQL Syntax

LOG2( <x> )

SQL Examples

SELECT LOG2(65536);

┌─────────────┐
│ log2(65536) │
├─────────────┤
│          16 │
└─────────────┘

16.26 - MINUS

Negates a numeric value.

Analyze Syntax

func.minus( <x> )

Analyze Examples

func.minus(func.pi())

┌─────────────────────────┐
│  func.minus(func.pi())  │
├─────────────────────────┤
│      -3.141592653589793 │
└─────────────────────────┘

SQL Syntax

MINUS( <x> )

Aliases

SQL Examples

SELECT MINUS(PI()), NEG(PI()), NEGATE(PI()), SUBTRACT(PI());

┌───────────────────────────────────────────────────────────────────────────────────┐
│     minus(pi())    │      neg(pi())     │    negate(pi())    │   subtract(pi())   │
├────────────────────┼────────────────────┼────────────────────┼────────────────────┤
│ -3.141592653589793 │ -3.141592653589793 │ -3.141592653589793 │ -3.141592653589793 │
└───────────────────────────────────────────────────────────────────────────────────┘

16.27 - MOD

Alias for MODULO.

16.28 - MODULO

Returns the remainder of x divided by y. If y is 0, it returns an error.

Analyze Syntax

func.modulo( <x>, <y> )

Analyze Examples

func.modulo(9, 2)

┌───────────────────┐
│ func.modulo(9, 2) │
├───────────────────┤
│                 1 │
└───────────────────┘

SQL Syntax

MODULO( <x>, <y> )

Aliases

SQL Examples

SELECT MOD(9, 2), MODULO(9, 2);

┌──────────────────────────┐
│ mod(9, 2) │ modulo(9, 2) │
├───────────┼──────────────┤
│         1 │            1 │
└──────────────────────────┘

16.29 - NEG

Alias for MINUS.

16.30 - NEGATE

Alias for MINUS.

16.31 - PI

Returns the value of π as a floating-point value.

Analyze Syntax

func.pi()

Analyze Examples

func.pi()

┌───────────────────┐
│     func.pi()     │
├───────────────────┤
│ 3.141592653589793 │
└───────────────────┘

SQL Syntax

PI()

SQL Examples

SELECT PI();

┌───────────────────┐
│        pi()       │
├───────────────────┤
│ 3.141592653589793 │
└───────────────────┘

16.32 - PLUS

Calculates the sum of two numeric or decimal values.

Analyze Syntax

func.plus(<number1>, <number2>)

Analyze Examples

func.plus(1, 2.3)

┌────────────────────┐
│  func.plus(1, 2.3) │
├────────────────────┤
│ 3.3                │
└────────────────────┘

SQL Syntax

PLUS(<number1>, <number2>)

Aliases

SQL Examples

SELECT ADD(1, 2.3), PLUS(1, 2.3);

┌───────────────────────────────┐
│  add(1, 2.3)  │  plus(1, 2.3) │
├───────────────┼───────────────┤
│ 3.3           │ 3.3           │
└───────────────────────────────┘

16.33 - POW

Returns the value of x to the power of y.

Analyze Syntax

func.pow( <x, y> )

Analyze Examples

func.pow(-2, 2)

┌────────────────────┐
│ func.pow((- 2), 2) │
├────────────────────┤
│                  4 │ 
└────────────────────┘

SQL Syntax

POW( <x, y> )

Aliases

SQL Examples

SELECT POW(-2, 2), POWER(-2, 2);

┌─────────────────────────────────┐
│ pow((- 2), 2) │ power((- 2), 2) │
├───────────────┼─────────────────┤
│             4 │               4 │
└─────────────────────────────────┘

16.34 - POWER

Alias for POW.

16.35 - RADIANS

Returns the argument x, converted from degrees to radians.

Analyze Syntax

func.radians( <x> )

Analyze Examples

func.radians(90)

┌────────────────────┐
│  func.radians(90)  │
├────────────────────┤
│ 1.5707963267948966 │
└────────────────────┘

SQL Syntax

RADIANS( <x> )

SQL Examples

SELECT RADIANS(90);

┌────────────────────┐
│     radians(90)    │
├────────────────────┤
│ 1.5707963267948966 │
└────────────────────┘

16.36 - RAND()

Returns a random floating-point value v in the range 0 <= v < 1.0. To obtain a random integer R in the range i <= R < j, use the expression FLOOR(i + RAND() * (j − i)).

Analyze Syntax

func.rand()

Analyze Examples

func.rand()

┌────────────────────┐
│    func.rand()     │
├────────────────────┤
│ 0.5191511074382174 │
└────────────────────┘

SQL Syntax

RAND()

SQL Examples

SELECT RAND();

┌────────────────────┐
│       rand()       │
├────────────────────┤
│ 0.5191511074382174 │
└────────────────────┘

16.37 - RAND(n)

Returns a random floating-point value v in the range 0 <= v < 1.0. To obtain a random integer R in the range i <= R < j, use the expression FLOOR(i + RAND() * (j − i)). Argument n is used as the seed value. For equal argument values, RAND(n) returns the same value each time , and thus produces a repeatable sequence of column values.

Analyze Syntax

func.rand( <n>)

Analyze Examples

func.rand(1)

┌────────────────────┐
│    func.rand(1)    │
├────────────────────┤
│ 0.7133693869548766 │
└────────────────────┘

SQL Syntax

RAND( <n>)

SQL Examples

SELECT RAND(1);

┌────────────────────┐
│       rand(1)      │
├────────────────────┤
│ 0.7133693869548766 │
└────────────────────┘

16.38 - ROUND

Rounds the argument x to d decimal places. The rounding algorithm depends on the data type of x. d defaults to 0 if not specified. d can be negative to cause d digits left of the decimal point of the value x to become zero. The maximum absolute value for d is 30; any digits in excess of 30 (or -30) are truncated.

When using this function's result in calculations, be aware of potential precision issues due to its return data type being DOUBLE, which may affect final accuracy:

SELECT ROUND(4/7, 4) - ROUND(3/7, 4); -- Result: 0.14280000000000004
SELECT ROUND(4/7, 4)::DECIMAL(8,4) - ROUND(3/7, 4)::DECIMAL(8,4); -- Result: 0.1428

Analyze Syntax

func.round( <x, d> )

Analyze Examples

func.round(0.123, 2)

┌──────────────────────┐
│ func.round(0.123, 2) │
├──────────────────────┤
│                 0.12 │
└──────────────────────┘

SQL Syntax

ROUND( <x, d> )

SQL Examples

SELECT ROUND(0.123, 2);

┌─────────────────┐
│ round(0.123, 2) │
├─────────────────┤
│ 0.12            │
└─────────────────┘

16.39 - SIGN

Returns the sign of the argument as -1, 0, or 1, depending on whether x is negative, zero, or positive or NULL if the argument was NULL.

Analyze Syntax

func.sign( <x> )

Analyze Examples

func.sign(0)

┌──────────────┐
│ func.sign(0) │
├──────────────┤
│            0 │
└──────────────┘

SQL Syntax

SIGN( <x> )

SQL Examples

SELECT SIGN(0);

┌─────────┐
│ sign(0) │
├─────────┤
│       0 │
└─────────┘

16.40 - SIN

Returns the sine of x, where x is given in radians.

Analyze Syntax

func.sin( <x> )

Analyze Examples

func.sin(90)

┌────────────────────┐
│    func.sin(90)    │
├────────────────────┤
│ 0.8939966636005579 │
└────────────────────┘

SQL Syntax

SIN( <x> )

SQL Examples

SELECT SIN(90);

┌────────────────────┐
│       sin(90)      │
├────────────────────┤
│ 0.8939966636005579 │
└────────────────────┘

16.41 - SQRT

Returns the square root of a nonnegative number x. Returns Nan for negative input.

Analyze Syntax

func.sqrt( <x> )

Analyze Examples

func.sqrt(4)

┌──────────────┐
│ func.sqrt(4) │
├──────────────┤
│            2 │
└──────────────┘

SQL Syntax

SQRT( <x> )

SQL Examples

SELECT SQRT(4);

┌─────────┐
│ sqrt(4) │
├─────────┤
│       2 │
└─────────┘

16.42 - SUBTRACT

Alias for MINUS.

16.43 - TAN

Returns the tangent of x, where x is given in radians.

Analyze Syntax

func.tan( <x> )

Analyze Examples

func.tan(90)

┌────────────────────┐
│    func.tan(90)    │
├────────────────────┤
│ -1.995200412208242 │
└────────────────────┘

SQL Syntax

TAN( <x> )

SQL Examples

SELECT TAN(90);

┌────────────────────┐
│       tan(90)      │
├────────────────────┤
│ -1.995200412208242 │
└────────────────────┘

16.44 - TRUNCATE

Returns the number x, truncated to d decimal places. If d is 0, the result has no decimal point or fractional part. d can be negative to cause d digits left of the decimal point of the value x to become zero. The maximum absolute value for d is 30; any digits in excess of 30 (or -30) are truncated.

Analyze Syntax

func.truncate( <x, d> )

Analyze Examples

func.truncate(1.223, 1)

┌─────────────────────────┐
│ func.truncate(1.223, 1) │
├─────────────────────────┤
│                     1.2 │
└─────────────────────────┘

SQL Syntax

TRUNCATE( <x, d> )

SQL Examples

SELECT TRUNCATE(1.223, 1);

┌────────────────────┐
│ truncate(1.223, 1) │
├────────────────────┤
│ 1.2                │
└────────────────────┘

17 - Other Functions

Type Conversion Functions

Utility Functions

Others

17.1 - ASSUME_NOT_NULL

Results in an equivalent non-Nullable value for a Nullable type. In case the original value is NULL the result is undetermined.

Analyze Syntax

func.assume_not_null(<x>)

Analyze Examples

With a table like:

┌────────────────────┐
│        x  │   y    │
├────────────────────┤
│         1 │   NULL │
│         2 │      3 │
└────────────────────┘

func.assume_not_null(y)
┌─────────────────────────┐
│ func.assume_not_null(y) │
├─────────────────────────┤
│                       0 │
│                       3 │
└─────────────────────────┘

SQL Syntax

ASSUME_NOT_NULL(<x>)

Aliases

Return Type

Returns the original datatype from the non-Nullable type; Returns the embedded non-Nullable datatype for Nullable type.

SQL Examples

CREATE TABLE default.t_null ( x int,  y int null);

INSERT INTO default.t_null values (1, null), (2, 3);

SELECT ASSUME_NOT_NULL(y), REMOVE_NULLABLE(y) FROM t_null;

┌─────────────────────────────────────────┐
│ assume_not_null(y) │ remove_nullable(y) │
├────────────────────┼────────────────────┤
│                  0 │                  0 │
│                  3 │                  3 │
└─────────────────────────────────────────┘

17.2 - EXISTS

The exists condition is used in combination with a subquery and is considered "to be met" if the subquery returns at least one row.

SQL Syntax

WHERE EXISTS ( <subquery> );

SQL Examples

SELECT number FROM numbers(5) AS A WHERE exists (SELECT * FROM numbers(3) WHERE number=1); 
+--------+
| number |
+--------+
|      0 |
|      1 |
|      2 |
|      3 |
|      4 |
+--------+

17.3 - GROUPING

Returns a bit mask indicating which GROUP BY expressions are not included in the current grouping set. Bits are assigned with the rightmost argument corresponding to the least-significant bit; each bit is 0 if the corresponding expression is included in the grouping criteria of the grouping set generating the current result row, and 1 if it is not included.

SQL Syntax

GROUPING ( expr [, expr, ...] )

Arguments

Grouping sets items.

Return Type

UInt32.

SQL Examples

select a, b, grouping(a), grouping(b), grouping(a,b), grouping(b,a) from t group by grouping sets ((a,b),(a),(b), ()) ;
+------+------+-------------+-------------+----------------+----------------+
| a    | b    | grouping(a) | grouping(b) | grouping(a, b) | grouping(b, a) |
+------+------+-------------+-------------+----------------+----------------+
| NULL | A    |           1 |           0 |              2 |              1 |
| a    | NULL |           0 |           1 |              1 |              2 |
| b    | A    |           0 |           0 |              0 |              0 |
| NULL | NULL |           1 |           1 |              3 |              3 |
| a    | A    |           0 |           0 |              0 |              0 |
| b    | B    |           0 |           0 |              0 |              0 |
| b    | NULL |           0 |           1 |              1 |              2 |
| a    | B    |           0 |           0 |              0 |              0 |
| NULL | B    |           1 |           0 |              2 |              1 |
+------+------+-------------+-------------+----------------+----------------+

17.4 - HUMANIZE_NUMBER

Returns a readable number.

Analyze Syntax

func.humanize_number(x);

Analyze Examples

func.humanize_number(1000 * 1000)
+-------------------------------------+
| func.humanize_number((1000 * 1000)) |
+-------------------------------------+
| 1 million                           |
+-------------------------------------+

SQL Syntax

HUMANIZE_NUMBER(x);

Arguments

ArgumentsDescription
xThe numerical size.

Return Type

String.

SQL Examples

SELECT HUMANIZE_NUMBER(1000 * 1000)
+-------------------------+
| HUMANIZE_NUMBER((1000 * 1000)) |
+-------------------------+
| 1 million               |
+-------------------------+

17.5 - HUMANIZE_SIZE

Returns the readable size with a suffix(KiB, MiB, etc).

Analyze Syntax

func.humanize_size(x);

Analyze Examples

func.humanize_size(1024 * 1024)
+----------------------------------------+
| func.func.humanize_size((1024 * 1024)) |
+----------------------------------------+
| 1 MiB                                  |
+----------------------------------------+

SQL Syntax

HUMANIZE_SIZE(x);

Arguments

ArgumentsDescription
xThe numerical size.

Return Type

String.

SQL Examples

SELECT HUMANIZE_SIZE(1024 * 1024)
+-------------------------+
| HUMANIZE_SIZE((1024 * 1024)) |
+-------------------------+
| 1 MiB                    |
+-------------------------+

17.6 - IGNORE

By using insert ignore statement, the rows with invalid data that cause the error are ignored and the rows with valid data are inserted into the table.

SQL Syntax

INSERT ignore INTO TABLE(column_list)
VALUES( value_list),
      ( value_list),
      ...

17.7 - REMOVE_NULLABLE

Alias for ASSUME_NOT_NULL.

17.8 - TO_NULLABLE

Converts a value to its nullable equivalent.

When you apply this function to a value, it checks if the value is already able to hold NULL values or not. If the value is already able to hold NULL values, the function will return the value without making any changes.

However, if the value is not able to hold NULL values, the TO_NULLABLE function will modify the value to make it able to hold NULL values. It does this by wrapping the value in a structure that can hold NULL values, which means the value can now hold NULL values in the future.

Analyze Syntax

func.to_nullable(x);

Analyze Examples

func.typeof(3), func.to_nullable(3), func.typeof(func.to_nullable(3))

func.typeof(3)   | func.to_nullable(3) | func.typeof(func.to_nullable(3)) |
-----------------+---------------------+----------------------------------+
TINYINT UNSIGNED |                   3 | TINYINT UNSIGNED NULL            |

SQL Syntax

TO_NULLABLE(x);

Arguments

ArgumentsDescription
xThe original value.

Return Type

Returns a value of the same data type as the input value, but wrapped in a nullable container if the input value is not already nullable.

SQL Examples

SELECT typeof(3), TO_NULLABLE(3), typeof(TO_NULLABLE(3));

typeof(3)       |to_nullable(3)|typeof(to_nullable(3))|
----------------+--------------+----------------------+
TINYINT UNSIGNED|             3|TINYINT UNSIGNED NULL |

17.9 - TYPEOF

TYPEOF function is used to return the name of a data type.

Analyze Syntax

func.typeof( <expr> )

Analyze Examples

func.typeof(1)
+------------------+
| func.typeof(1)   |
+------------------+
| INT              |
+------------------+

SQL Syntax

TYPEOF( <expr> )

Arguments

ArgumentsDescription
<expr>Any expression.
This may be a column name, the result of another function, or a math operation.

Return Type

String

SQL Examples

SELECT typeof(1::INT);
+------------------+
| typeof(1::Int32) |
+------------------+
| INT              |
+------------------+

18 - Search Functions

This section provides reference information for search functions in PlaidCloud Lakehouse.

18.1 - MATCH

Searches for documents containing specified keywords. Please note that the MATCH function can only be used in a WHERE clause.

:::info Databend's MATCH function is inspired by Elasticsearch's MATCH. :::

SQL Syntax

MATCH( '<columns>', '<keywords>'[, '<options>'] )
ParameterDescription
<columns>A comma-separated list of column names in the table to search for the specified keywords, with optional weighting using the syntax (^), which allows assigning different weights to each column, influencing the importance of each column in the search.
<keywords>The keywords to match against the specified columns in the table. This parameter can also be used for suffix matching, where the search term followed by an asterisk (*) can match any number of characters or words.
<options>A set of configuration options, separated by semicolons ;, that customize the search behavior. See the table below for details.
OptionDescriptionExampleExplanation
fuzzinessAllows matching terms within a specified Levenshtein distance. fuzziness can be set to 1 or 2.SELECT id, score(), content FROM t WHERE match(content, 'box', 'fuzziness=1');When matching the query term "box", fuzziness=1 allows matching terms like "fox", since "box" and "fox" have a Levenshtein distance of 1.
operatorSpecifies how multiple query terms are combined. Can be set to OR (default) or AND. OR returns results containing any of the query terms, while AND returns results containing all query terms.SELECT id, score(), content FROM t WHERE match(content, 'action works', 'fuzziness=1;operator=AND');With operator=AND, the query requires both "action" and "works" to be present in the results. Due to fuzziness=1, it matches terms like "Actions" and "words", so "Actions speak louder than words" is returned.
lenientControls whether errors are reported when the query text is invalid. Defaults to false. If set to true, no error is reported, and an empty result set is returned if the query text is invalid.SELECT id, score(), content FROM t WHERE match(content, '()', 'lenient=true');If the query text () is invalid, setting lenient=true prevents an error from being thrown and returns an empty result set instead.

SQL Examples

CREATE TABLE test(title STRING, body STRING);

CREATE INVERTED INDEX idx ON test(title, body);

INSERT INTO test VALUES
('The Importance of Reading', 'Reading is a crucial skill that opens up a world of knowledge and imagination.'),
('The Benefits of Exercise', 'Exercise is essential for maintaining a healthy lifestyle.'),
('The Power of Perseverance', 'Perseverance is the key to overcoming obstacles and achieving success.'),
('The Art of Communication', 'Effective communication is crucial in everyday life.'),
('The Impact of Technology on Society', 'Technology has revolutionized our society in countless ways.');

-- Retrieve documents where the 'title' column matches 'art power'
SELECT * FROM test WHERE MATCH('title', 'art power');

┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                  body                                  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────┤
│ The Power of Perseverance │ Perseverance is the key to overcoming obstacles and achieving success. │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                   │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains values that start with 'The' followed by any characters
SELECT * FROM test WHERE MATCH('title', 'The*')

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                title                │                                      body                                      │
│           Nullable(String)          │                                Nullable(String)                                │
├─────────────────────────────────────┼────────────────────────────────────────────────────────────────────────────────┤
│ The Importance of Reading           │ Reading is a crucial skill that opens up a world of knowledge and imagination. │
│ The Benefits of Exercise            │ Exercise is essential for maintaining a healthy lifestyle.                     │
│ The Power of Perseverance           │ Perseverance is the key to overcoming obstacles and achieving success.         │
│ The Art of Communication            │ Effective communication is crucial in everyday life.                           │
│ The Impact of Technology on Society │ Technology has revolutionized our society in countless ways.                   │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where either the 'title' or 'body' column matches 'knowledge technology'
SELECT *, score() FROM test WHERE MATCH('title, body', 'knowledge technology');

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                title                │                                      body                                      │  score()  │
├─────────────────────────────────────┼────────────────────────────────────────────────────────────────────────────────┼───────────┤
│ The Importance of Reading           │ Reading is a crucial skill that opens up a world of knowledge and imagination. │ 1.1550591 │
│ The Impact of Technology on Society │ Technology has revolutionized our society in countless ways.                   │ 2.6830134 │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where either the 'title' or 'body' column matches 'knowledge technology', with weighted importance on both columns
SELECT *, score() FROM test WHERE MATCH('title^5, body^1.2', 'knowledge technology');

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                title                │                                      body                                      │  score()  │
├─────────────────────────────────────┼────────────────────────────────────────────────────────────────────────────────┼───────────┤
│ The Importance of Reading           │ Reading is a crucial skill that opens up a world of knowledge and imagination. │ 1.3860708 │
│ The Impact of Technology on Society │ Technology has revolutionized our society in countless ways.                   │ 7.8053584 │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'body' column contains both "knowledge" and "imagination" (allowing for minor typos).
SELECT * FROM test WHERE MATCH('body', 'knowledg imaginatio', 'fuzziness = 1; operator = AND');

-[ RECORD 1 ]-----------------------------------
title: The Importance of Reading
 body: Reading is a crucial skill that opens up a world of knowledge and imagination.

18.2 - QUERY

Searches for documents satisfying a specified query expression. Please note that the QUERY function can only be used in a WHERE clause.

:::info Databend's QUERY function is inspired by Elasticsearch's QUERY. :::

SQL Syntax

QUERY( '<query_expr>'[, '<options>'] )

The query expression supports the following syntaxes. Please note that <keyword> can also be used for suffix matching, where the search term followed by an asterisk (*) can match any number of characters or words.

SyntaxDescriptionExamples
<column>:<keyword>Matches documents where the specified column contains the specified keyword.QUERY('title:power')
<column>:IN [<keyword1>, <keyword2>...]Matches documents where the specified column contains any of the keywords listed within the square brackets.QUERY('title:IN [power, art]')
<column>:<keyword> AND / OR <keyword>Matches documents where the specified column contains both or either of the specified keywords. In queries with both AND and OR, AND operations are prioritized over OR, meaning that 'a AND b OR c' is read as '(a AND b) OR c'.QUERY('title:power AND art')
<column>:+<keyword> -<keyword>Matches documents where the specified positive keyword exists in the specified column and excludes documents where the specified negative keyword exists.QUERY('title:+the -reading')
<column>:"<phrase>"Matches documents where the specified column contains the exact specified phrase.QUERY('title:"Benefits of Exercise"')
<column>:<keyword>^<boost> <column>:<keyword>^<boost>Matches documents where the specified keyword exists in the specified columns with the specified boosts to increase their relevance in the search. This syntax allows setting different weights for multiple columns to influence the search relevance.QUERY('title:art^5 body:reading^1.2')
OptionDescriptionExampleExplanation
fuzzinessAllows matching terms within a specified Levenshtein distance. fuzziness can be set to 1 or 2.SELECT id, score(), content FROM t WHERE query('content:box', 'fuzziness=1');When matching the query term "box", fuzziness=1 allows matching terms like "fox", since "box" and "fox" have a Levenshtein distance of 1.
operatorSpecifies how multiple query terms are combined. Can be set to OR (default) or AND. OR returns results containing any of the query terms, while AND returns results containing all query terms.SELECT id, score(), content FROM t WHERE query('content:action works', 'fuzziness=1;operator=AND');With operator=AND, the query requires both "action" and "works" to be present in the results. Due to fuzziness=1, it matches terms like "Actions" and "words", so "Actions speak louder than words" is returned.
lenientControls whether errors are reported when the query text is invalid. Defaults to false. If set to true, no error is reported, and an empty result set is returned if the query text is invalid.SELECT id, score(), content FROM t WHERE query('content:()', 'lenient=true');If the query text () is invalid, setting lenient=true prevents an error from being thrown and returns an empty result set instead.

SQL Examples

CREATE TABLE test(title STRING, body STRING);

CREATE INVERTED INDEX idx ON test(title, body);

INSERT INTO test VALUES
('The Importance of Reading', 'Reading is a crucial skill that opens up a world of knowledge and imagination.'),
('The Benefits of Exercise', 'Exercise is essential for maintaining a healthy lifestyle.'),
('The Power of Perseverance', 'Perseverance is the key to overcoming obstacles and achieving success.'),
('The Art of Communication', 'Effective communication is crucial in everyday life.'),
('The Impact of Technology on Society', 'Technology has revolutionized our society in countless ways.');

-- Retrieve documents where the 'title' column contains the keyword 'power'
SELECT * FROM test WHERE QUERY('title:power');

┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                  body                                  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────┤
│ The Power of Perseverance │ Perseverance is the key to overcoming obstacles and achieving success. │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains values that start with 'The' followed by any characters
SELECT * FROM test WHERE QUERY('title:The*');

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                title                │                                      body                                      │
├─────────────────────────────────────┼────────────────────────────────────────────────────────────────────────────────┤
│ The Importance of Reading           │ Reading is a crucial skill that opens up a world of knowledge and imagination. │
│ The Benefits of Exercise            │ Exercise is essential for maintaining a healthy lifestyle.                     │
│ The Power of Perseverance           │ Perseverance is the key to overcoming obstacles and achieving success.         │
│ The Art of Communication            │ Effective communication is crucial in everyday life.                           │
│ The Impact of Technology on Society │ Technology has revolutionized our society in countless ways.                   │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains either the keyword 'power' or 'art'
SELECT * FROM test WHERE QUERY('title:power OR art');

┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                  body                                  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────┤
│ The Power of Perseverance │ Perseverance is the key to overcoming obstacles and achieving success. │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                   │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT * FROM test WHERE QUERY('title:IN [power, art]')

┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                  body                                  │
│      Nullable(String)     │                            Nullable(String)                            │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────┤
│ The Power of Perseverance │ Perseverance is the key to overcoming obstacles and achieving success. │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                   │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains the positive keyword 'the' but not 'reading'
SELECT * FROM test WHERE QUERY('title:+the -reading');

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                title                │                                  body                                  │
├─────────────────────────────────────┼────────────────────────────────────────────────────────────────────────┤
│ The Benefits of Exercise            │ Exercise is essential for maintaining a healthy lifestyle.             │
│ The Power of Perseverance           │ Perseverance is the key to overcoming obstacles and achieving success. │
│ The Art of Communication            │ Effective communication is crucial in everyday life.                   │
│ The Impact of Technology on Society │ Technology has revolutionized our society in countless ways.           │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains the exact phrase 'Benefits of Exercise'
SELECT * FROM test WHERE QUERY('title:"Benefits of Exercise"');

┌───────────────────────────────────────────────────────────────────────────────────────┐
│           title          │                            body                            │
├──────────────────────────┼────────────────────────────────────────────────────────────┤
│ The Benefits of Exercise │ Exercise is essential for maintaining a healthy lifestyle. │
└───────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains the keyword 'art' with a boost of 5 and the 'body' column contains the keyword 'reading' with a boost of 1.2
SELECT *, score() FROM test WHERE QUERY('title:art^5 body:reading^1.2');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                      body                                      │  score()  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────────────┼───────────┤
│ The Importance of Reading │ Reading is a crucial skill that opens up a world of knowledge and imagination. │ 1.3860708 │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                           │ 7.1992116 │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'body' column contains both "knowledge" and "imagination" (allowing for minor typos).
SELECT * FROM test WHERE QUERY('body:knowledg OR imaginatio', 'fuzziness = 1; operator = AND');

-[ RECORD 1 ]-----------------------------------
title: The Importance of Reading
 body: Reading is a crucial skill that opens up a world of knowledge and imagination.

18.3 - SCORE

Returns the relevance of the query string. The higher the score, the more relevant the data. Please note that SCORE function can only be used with the QUERY or MATCH function.

:::info Databend's SCORE function is inspired by Elasticsearch's SCORE. :::

SQL Syntax

SCORE()

SQL Examples

CREATE TABLE test(title STRING, body STRING);

CREATE INVERTED INDEX idx ON test(title, body);

INSERT INTO test VALUES
('The Importance of Reading', 'Reading is a crucial skill that opens up a world of knowledge and imagination.'),
('The Benefits of Exercise', 'Exercise is essential for maintaining a healthy lifestyle.'),
('The Power of Perseverance', 'Perseverance is the key to overcoming obstacles and achieving success.'),
('The Art of Communication', 'Effective communication is crucial in everyday life.'),
('The Impact of Technology on Society', 'Technology has revolutionized our society in countless ways.');

-- Retrieve documents where the 'title' column contains the keyword 'art' with a boost of 5 and the 'body' column contains the keyword 'reading' with a boost of 1.2, along with their relevance scores
SELECT *, score() FROM test WHERE QUERY('title:art^5 body:reading^1.2');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                      body                                      │  score()  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────────────┼───────────┤
│ The Importance of Reading │ Reading is a crucial skill that opens up a world of knowledge and imagination. │ 1.3860708 │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                           │ 7.1992116 │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve documents where the 'title' column contains the keyword 'reading' with a boost of 5 and the 'body' column contains the keyword 'everyday' with a boost of 1.2, along with their relevance scores
SELECT *, score() FROM test WHERE MATCH('title^5, body^1.2', 'reading everyday');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           title           │                                      body                                      │  score()  │
├───────────────────────────┼────────────────────────────────────────────────────────────────────────────────┼───────────┤
│ The Importance of Reading │ Reading is a crucial skill that opens up a world of knowledge and imagination. │  8.585282 │
│ The Art of Communication  │ Effective communication is crucial in everyday life.                           │ 1.8575745 │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

19 - Semi-Structured Functions

This section provides reference information for the semi-structured data functions in PlaidCloud Lakehouse.

JSON Parsing, Conversion & Type Checking:

JSON Query and Extraction:

JSON Data Manipulation:

Object Operations:

Type Conversion:

19.1 - AS_<type>

Strict casting VARIANT values to other data types. If the input data type is not VARIANT, the output is NULL. If the type of value in the VARIANT does not match the output value, the output is NULL.

Analyze Syntax

func.as_boolean( <variant> )
func.as_integer( <variant> )
func.as_float( <variant> )
func.as_string( <variant> )
func.as_array( <variant> )
func.as_object( <variant> )

SQL Syntax

AS_BOOLEAN( <variant> )
AS_INTEGER( <variant> )
AS_FLOAT( <variant> )
AS_STRING( <variant> )
AS_ARRAY( <variant> )
AS_OBJECT( <variant> )

Arguments

ArgumentsDescription
<variant>The VARIANT value

Return Type

  • AS_BOOLEAN: BOOLEAN
  • AS_INTEGER: BIGINT
  • AS_FLOAT: DOUBLE
  • AS_STRING: VARCHAR
  • AS_ARRAY: Variant contains Array
  • AS_OBJECT: Variant contains Object

SQL Examples

SELECT as_boolean(parse_json('true'));
+--------------------------------+
| as_boolean(parse_json('true')) |
+--------------------------------+
| 1                              |
+--------------------------------+

SELECT as_integer(parse_json('123'));
+-------------------------------+
| as_integer(parse_json('123')) |
+-------------------------------+
| 123                           |
+-------------------------------+

SELECT as_float(parse_json('12.34'));
+-------------------------------+
| as_float(parse_json('12.34')) |
+-------------------------------+
| 12.34                         |
+-------------------------------+

SELECT as_string(parse_json('"abc"'));
+--------------------------------+
| as_string(parse_json('"abc"')) |
+--------------------------------+
| abc                            |
+--------------------------------+

SELECT as_array(parse_json('[1,2,3]'));
+---------------------------------+
| as_array(parse_json('[1,2,3]')) |
+---------------------------------+
| [1,2,3]                         |
+---------------------------------+

SELECT as_object(parse_json('{"k":"v","a":"b"}'));
+--------------------------------------------+
| as_object(parse_json('{"k":"v","a":"b"}')) |
+--------------------------------------------+
| {"k":"v","a":"b"}                          |
+--------------------------------------------+

19.2 - CHECK_JSON

Checks the validity of a JSON document. If the input string is a valid JSON document or a NULL, the output is NULL. If the input cannot be translated to a valid JSON value, the output string contains the error message.

Analyze Syntax

func.check_json(<expr>)

Analyze Example

func.check_json('[1,2,3]');
+----------------------------+
| func.check_json('[1,2,3]') |
+----------------------------+
| NULL                       |
+----------------------------+

SQL Syntax

CHECK_JSON( <expr> )

Arguments

ArgumentsDescription
<expr>An expression of string type

Return Type

String

SQL Examples

SELECT check_json('[1,2,3]');
+-----------------------+
| check_json('[1,2,3]') |
+-----------------------+
| NULL                  |
+-----------------------+

SELECT check_json('{"key":"val"}');
+-----------------------------+
| check_json('{"key":"val"}') |
+-----------------------------+
| NULL                        |
+-----------------------------+

SELECT check_json('{"key":');
+----------------------------------------------+
| check_json('{"key":')                        |
+----------------------------------------------+
| EOF while parsing a value at line 1 column 7 |
+----------------------------------------------+

19.3 - FLATTEN

Transforms nested JSON data into a tabular format, where each element or field is represented as a separate row.

SQL Syntax

[LATERAL] FLATTEN ( INPUT => <expr> [ , PATH => <expr> ]
                                    [ , OUTER => TRUE | FALSE ]
                                    [ , RECURSIVE => TRUE | FALSE ]
                                    [ , MODE => 'OBJECT' | 'ARRAY' | 'BOTH' ] )
Parameter / KeywordDescriptionDefault
INPUTSpecifies the JSON or array data to flatten.-
PATHSpecifies the path to the array or object within the input data to flatten.-
OUTERIf set to TRUE, rows with zero results will still be included in the output, but the values in the KEY, INDEX, and VALUE columns of those rows will be set to NULL.FALSE
RECURSIVEIf set to TRUE, the function will continue to flatten nested elements.FALSE
MODEControls whether to flatten only objects ('OBJECT'), only arrays ('ARRAY'), or both ('BOTH').'BOTH'
LATERALLATERAL is an optional keyword used to reference columns defined to the left of the LATERAL keyword within the FROM clause. LATERAL enables cross-referencing between the preceding table expressions and the function.-

Output

The following table describes the output columns of the FLATTEN function:

ColumnDescription
SEQA unique sequence number associated with the input.
KEYKey to the expanded value. If the flattened element does not contain a key, it's set to NULL.
PATHPath to the flattened element.
INDEXIf the element is an array, this column contains its index; otherwise, it's set to NULL.
VALUEValue of the flattened element.
THISThis column identifies the element currently being flattened.

SQL Examples

SQL Examples 1: Demonstrating PATH, OUTER, RECURSIVE, and MODE Parameters

This example demonstrates the behavior of the FLATTEN function with respect to the PATH, OUTER, RECURSIVE, and MODE parameters.

SELECT
  *
FROM
  FLATTEN (
    INPUT => PARSE_JSON (
      '{"name": "John", "languages": ["English", "Spanish", "French"], "address": {"city": "New York", "state": "NY"}}'
    )
  );

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   seq  │        key       │       path       │       index      │               value              │                                                  this                                                 │
├────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────────────────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────┤
│      1 │ address          │ address          │             NULL │ {"city":"New York","state":"NY"} │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
│      1 │ languages        │ languages        │             NULL │ ["English","Spanish","French"]   │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
│      1 │ name             │ name             │             NULL │ "John"                           │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- PATH helps in selecting elements at a specific path from the original JSON data.
SELECT
  *
FROM
  FLATTEN (
    INPUT => PARSE_JSON (
      '{"name": "John", "languages": ["English", "Spanish", "French"], "address": {"city": "New York", "state": "NY"}}'
    ),
    PATH => 'languages'
  );

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   seq  │        key       │       path       │       index      │       value       │              this              │
├────────┼──────────────────┼──────────────────┼──────────────────┼───────────────────┼────────────────────────────────┤
│      1 │ NULL             │ languages[0]     │                0 │ "English"         │ ["English","Spanish","French"] │
│      1 │ NULL             │ languages[1]     │                1 │ "Spanish"         │ ["English","Spanish","French"] │
│      1 │ NULL             │ languages[2]     │                2 │ "French"          │ ["English","Spanish","French"] │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- RECURSIVE enables recursive flattening of nested structures.
SELECT
  *
FROM
  FLATTEN (
    INPUT => PARSE_JSON (
      '{"name": "John", "languages": ["English", "Spanish", "French"], "address": {"city": "New York", "state": "NY"}}'
    ),
    RECURSIVE => TRUE
  );

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   seq  │        key       │       path       │       index      │               value              │                                                  this                                                 │
├────────┼──────────────────┼──────────────────┼──────────────────┼──────────────────────────────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────┤
│      1 │ address          │ address          │             NULL │ {"city":"New York","state":"NY"} │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
│      1 │ city             │ address.city     │             NULL │ "New York"                       │ {"city":"New York","state":"NY"}                                                                      │
│      1 │ state            │ address.state    │             NULL │ "NY"                             │ {"city":"New York","state":"NY"}                                                                      │
│      1 │ languages        │ languages        │             NULL │ ["English","Spanish","French"]   │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
│      1 │ NULL             │ languages[0]     │                0 │ "English"                        │ ["English","Spanish","French"]                                                                        │
│      1 │ NULL             │ languages[1]     │                1 │ "Spanish"                        │ ["English","Spanish","French"]                                                                        │
│      1 │ NULL             │ languages[2]     │                2 │ "French"                         │ ["English","Spanish","French"]                                                                        │
│      1 │ name             │ name             │             NULL │ "John"                           │ {"address":{"city":"New York","state":"NY"},"languages":["English","Spanish","French"],"name":"John"} │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘


-- MODE specifies whether only objects ('OBJECT'), only arrays ('ARRAY'), or both ('BOTH') should be flattened.
-- In this example, MODE => 'ARRAY' is used, which means that only arrays within the JSON data will be flattened.
SELECT
  *
FROM
  FLATTEN (
    INPUT => PARSE_JSON (
      '{"name": "John", "languages": ["English", "Spanish", "French"], "address": {"city": "New York", "state": "NY"}}'
    ),
    MODE => 'ARRAY'
  );

---


-- OUTER determines the inclusion of zero-row expansions in the output.
-- In this first example, OUTER => TRUE is used with an empty JSON array, which results in zero-row expansions. 
-- Rows are included in the output even when there are no values to flatten.
SELECT
  *
FROM
  FLATTEN (INPUT => PARSE_JSON ('[]'), OUTER => TRUE);

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   seq  │        key       │       path       │       index      │       value       │        this       │
├────────┼──────────────────┼──────────────────┼──────────────────┼───────────────────┼───────────────────┤
│      1 │ NULL             │ NULL             │             NULL │ NULL              │ NULL              │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- In this second example, OUTER is omitted, and the output shows how rows with zero results are not included when OUTER is not specified.
SELECT
  *
FROM
  FLATTEN (INPUT => PARSE_JSON ('[]'));

SQL Examples 2: Demonstrating LATERAL FLATTEN

This example demonstrates the behavior of the FLATTEN function when used in conjunction with the LATERAL keyword.

-- Create a table for Tim Hortons transactions with multiple items
CREATE TABLE tim_hortons_transactions (
    transaction_id INT,
    customer_id INT,
    items VARIANT
);

-- Insert data for Tim Hortons transactions with multiple items
INSERT INTO tim_hortons_transactions (transaction_id, customer_id, items)
VALUES
    (101, 1, parse_json('[{"item":"coffee", "price":2.50}, {"item":"donut", "price":1.20}]')),
    (102, 2, parse_json('[{"item":"bagel", "price":1.80}, {"item":"muffin", "price":2.00}]')),
    (103, 3, parse_json('[{"item":"timbit_assortment", "price":5.00}]'));

-- Show Tim Hortons transactions with multiple items using LATERAL FLATTEN
SELECT
    t.transaction_id,
    t.customer_id,
    f.value:item::STRING AS purchased_item,
    f.value:price::FLOAT AS price
FROM
    tim_hortons_transactions t,
    LATERAL FLATTEN(input => t.items) f;

┌───────────────────────────────────────────────────────────────────────────┐
│  transaction_id │   customer_id   │   purchased_item  │       price       │
├─────────────────┼─────────────────┼───────────────────┼───────────────────┤
│             101 │               1 │ coffee            │               2.5 │
│             101 │               1 │ donut             │               1.2 │
│             102 │               2 │ bagel             │               1.8 │
│             102 │               2 │ muffin            │                 2 │
│             103 │               3 │ timbit_assortment │                 5 │
└───────────────────────────────────────────────────────────────────────────┘

-- Find maximum, minimum, and average prices of the purchased items
SELECT
    MAX(f.value:price::FLOAT) AS max_price,
    MIN(f.value:price::FLOAT) AS min_price,
    AVG(f.value:price::FLOAT) AS avg_price
FROM
    tim_hortons_transactions t,
    LATERAL FLATTEN(input => t.items) f;

┌───────────────────────────────────────────────────────────┐
│     max_price     │     min_price     │     avg_price     │
├───────────────────┼───────────────────┼───────────────────┤
│                 5 │               1.2 │               2.5 │
└───────────────────────────────────────────────────────────┘

19.4 - GET

Extracts value from a Variant that contains ARRAY by index, or a Variant that contains OBJECT by field_name. The value is returned as a Variant or NULL if either of the arguments is NULL.

GET applies case-sensitive matching to field_name. For case-insensitive matching, use GET_IGNORE_CASE.

Analyze Syntax

func.get(<variant>, <index>)

or

func.get(<variant>, <field_name>)

Analyze Example

func.get(func.parse_json('[2.71, 3.14]'), 0);
+----------------------------------------------+
| func.get(func.parse_json('[2.71, 3.14]'), 0) |
+----------------------------------------------+
| 2.71                                         |
+----------------------------------------------+


func.get(func.parse_json('{"aa":1, "aA":2, "Aa":3}'), 'aa');
+-------------------------------------------------------------+
| func.get(func.parse_json('{"aa":1, "aA":2, "Aa":3}'), 'aa') |
+-------------------------------------------------------------+
| 1                                                           |
+-------------------------------------------------------------+

SQL Syntax

GET( <variant>, <index> )

GET( <variant>, <field_name> )

Arguments

ArgumentsDescription
<variant>The VARIANT value that contains either an ARRAY or an OBJECT
<index>The Uint32 value specifies the position of the value in ARRAY
<field_name>The String value specifies the key in a key-value pair of OBJECT

Return Type

VARIANT

SQL Examples

SELECT get(parse_json('[2.71, 3.14]'), 0);
+------------------------------------+
| get(parse_json('[2.71, 3.14]'), 0) |
+------------------------------------+
| 2.71                               |
+------------------------------------+

SELECT get(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'aa');
+---------------------------------------------------+
| get(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'aa') |
+---------------------------------------------------+
| 1                                                 |
+---------------------------------------------------+

SELECT get(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA');
+---------------------------------------------------+
| get(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA') |
+---------------------------------------------------+
| NULL                                              |
+---------------------------------------------------+

19.5 - GET_IGNORE_CASE

Extracts value from a VARIANT that contains OBJECT by the field_name. The value is returned as a Variant or NULL if either of the arguments is NULL.

GET_IGNORE_CASE is similar to GET but applies case-insensitive matching to field names. First match the exact same field name, if not found, match the case-insensitive field name alphabetically.

Analyze Syntax

func.get_ignore_Case(<variant>, <field_name>)

Analyze Example

func.get_ignore_case(func.parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA')
+-------------------------------------------------------------------------+
| func.get_ignore_case(func.parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA') |
+-------------------------------------------------------------------------+
| 3                                                                       |
+-------------------------------------------------------------------------+

SQL Syntax

GET_IGNORE_CASE( <variant>, <field_name> )

Arguments

ArgumentsDescription
<variant>The VARIANT value that contains either an ARRAY or an OBJECT
<field_name>The String value specifies the key in a key-value pair of OBJECT

Return Type

VARIANT

SQL Examples

SELECT get_ignore_case(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA');
+---------------------------------------------------------------+
| get_ignore_case(parse_json('{"aa":1, "aA":2, "Aa":3}'), 'AA') |
+---------------------------------------------------------------+
| 3                                                             |
+---------------------------------------------------------------+

19.6 - GET_PATH

Extracts value from a VARIANT by path_name. The value is returned as a Variant or NULL if either of the arguments is NULL.

GET_PATH is equivalent to a chain of GET functions, path_name consists of a concatenation of field names preceded by periods (.), colons (:) or index operators ([index]). The first field name does not require the leading identifier to be specified.

Analyze Syntax

func.get_path(<variant>, <path_name>)

Analyze Example

func.get_path(func.parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k4')
+---------------------------------------------------------------------------------+
| func.get_path(func.parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k4') |
+---------------------------------------------------------------------------------+
| 4                                                                               |
+---------------------------------------------------------------------------------+

SQL Syntax

GET_PATH( <variant>, <path_name> )

Arguments

ArgumentsDescription
<variant>The VARIANT value that contains either an ARRAY or an OBJECT
<path_name>The String value that consists of a concatenation of field names

Return Type

VARIANT

SQL Examples

SELECT get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k1[0]');
+-----------------------------------------------------------------------+
| get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k1[0]') |
+-----------------------------------------------------------------------+
| 0                                                                     |
+-----------------------------------------------------------------------+

SELECT get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2:k3');
+-----------------------------------------------------------------------+
| get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2:k3') |
+-----------------------------------------------------------------------+
| 3                                                                     |
+-----------------------------------------------------------------------+

SELECT get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k4');
+-----------------------------------------------------------------------+
| get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k4') |
+-----------------------------------------------------------------------+
| 4                                                                     |
+-----------------------------------------------------------------------+

SELECT get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k5');
+-----------------------------------------------------------------------+
| get_path(parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k5') |
+-----------------------------------------------------------------------+
| NULL                                                                  |
+-----------------------------------------------------------------------+

19.7 - IS_ARRAY

Checks if the input value is a JSON array. Please note that a JSON array is not the same as the ARRAY data type. A JSON array is a data structure commonly used in JSON, representing an ordered collection of values enclosed within square brackets [ ]. It is a flexible format for organizing and exchanging various data types, including strings, numbers, booleans, objects, and nulls.

[
  "Apple",
  42,
  true,
  {"name": "John", "age": 30, "isStudent": false},
  [1, 2, 3],
  null
]

Analyze Syntax

func.is_array(<expr>)

Analyze Example


func.is_array(func.parse_json('true')), func.is_array(func.parse_json('[1,2,3]'))
┌────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_array(func.parse_json('true')) │ func.is_array(func.parse_json('[1,2,3]')) │
├────────────────────────────────────────┼───────────────────────────────────────────┤
│ false                                  │ true                                      │
└────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_ARRAY( <expr> )

Return Type

Returns true if the input value is a JSON array, and false otherwise.

SQL Examples

SELECT
  IS_ARRAY(PARSE_JSON('true')),
  IS_ARRAY(PARSE_JSON('[1,2,3]'));

┌────────────────────────────────────────────────────────────────┐
│ is_array(parse_json('true')) │ is_array(parse_json('[1,2,3]')) │
├──────────────────────────────┼─────────────────────────────────┤
│ false                        │ true                            │
└────────────────────────────────────────────────────────────────┘

19.8 - IS_BOOLEAN

Checks if the input JSON value is a boolean.

Analyze Syntax

func.is_boolean(<expr>)

Analyze Example


func.is_boolean(func.parse_json('true')), func.is_boolean(func.parse_json('[1,2,3]'))
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_boolean(func.parse_json('true')) │ func.is_boolean(func.parse_json('[1,2,3]')) │
├──────────────────────────────────────────┼─────────────────────────────────────────────┤
│ true                                     │ false                                       │
└────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_BOOLEAN( <expr> )

Return Type

Returns true if the input JSON value is a boolean, and false otherwise.

SQL Examples

SELECT
  IS_BOOLEAN(PARSE_JSON('true')),
  IS_BOOLEAN(PARSE_JSON('[1,2,3]'));

┌────────────────────────────────────────────────────────────────────┐
│ is_boolean(parse_json('true')) │ is_boolean(parse_json('[1,2,3]')) │
├────────────────────────────────┼───────────────────────────────────┤
│ true                           │ false                             │
└────────────────────────────────────────────────────────────────────┘

19.9 - IS_FLOAT

Checks if the input JSON value is a float.

Analyze Syntax

func.is_float(<expr>)

Analyze Example


func.is_float(func.parse_json('1.23')), func.is_float(func.parse_json('[1,2,3]'))
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_float(func.parse_json('1.23'))   │ func.is_float(func.parse_json('[1,2,3]'))   │
├──────────────────────────────────────────┼─────────────────────────────────────────────┤
│ true                                     │ false                                       │
└────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_FLOAT( <expr> )

Return Type

Returns true if the input JSON value is a float, and false otherwise.

SQL Examples

SELECT
  IS_FLOAT(PARSE_JSON('1.23')),
  IS_FLOAT(PARSE_JSON('[1,2,3]'));

┌────────────────────────────────────────────────────────────────┐
│ is_float(parse_json('1.23')) │ is_float(parse_json('[1,2,3]')) │
├──────────────────────────────┼─────────────────────────────────┤
│ true                         │ false                           │
└────────────────────────────────────────────────────────────────┘

19.10 - IS_INTEGER

Checks if the input JSON value is an integer.

Analyze Syntax

func.is_integer(<expr>)

Analyze Example


func.is_integer(func.parse_json('123')), func.is_integer(func.parse_json('[1,2,3]'))
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_integer(func.parse_json('123'))  │ func.is_integer(func.parse_json('[1,2,3]')) │
├──────────────────────────────────────────┼─────────────────────────────────────────────┤
│ true                                     │ false                                       │
└────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_INTEGER( <expr> )

Return Type

Returns true if the input JSON value is an integer, and false otherwise.

SQL Examples

SELECT
  IS_INTEGER(PARSE_JSON('123')),
  IS_INTEGER(PARSE_JSON('[1,2,3]'));

┌───────────────────────────────────────────────────────────────────┐
│ is_integer(parse_json('123')) │ is_integer(parse_json('[1,2,3]')) │
├───────────────────────────────┼───────────────────────────────────┤
│ true                          │ false                             │
└───────────────────────────────────────────────────────────────────┘

19.11 - IS_NULL_VALUE

Checks whether the input value is a JSON null. Please note that this function examines JSON null, not SQL NULL. To check if a value is SQL NULL, use IS_NULL.

{
  "name": "John",
  "age": null
}

Analyze Syntax

func.is_null_value(<expr>)

Analyze Example


func.is_null_value(func.get_path(func.parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k5'))
┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_null_value(func.get_path(func.parse_json('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}'), 'k2.k5')) │
├─────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ true                                                                                                │
└─────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_NULL_VALUE( <expr> )

Return Type

Returns true if the input value is a JSON null, and false otherwise.

SQL Examples

SELECT
  IS_NULL_VALUE(PARSE_JSON('{"name":"John", "age":null}') :age), --JSON null
  IS_NULL(NULL); --SQL NULL

┌──────────────────────────────────────────────────────────────────────────────┐
│ is_null_value(parse_json('{"name":"john", "age":null}'):age) │ is_null(null) │
├──────────────────────────────────────────────────────────────┼───────────────┤
│ true                                                         │ true          │
└──────────────────────────────────────────────────────────────────────────────┘

19.12 - IS_OBJECT

Checks if the input value is a JSON object.

Analyze Syntax

func.is_object(<expr>)

Analyze Example


func.is_object(func.parse_json('{"a":"b"}')), func.is_object(func.parse_json('["a","b","c"]'))
┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_object(func.parse_json('{"a":"b"}'))  │ func.is_object(func.parse_json('["a","b","c"]')) │
├───────────────────────────────────────────────┼──────────────────────────────────────────────────┤
│ true                                          │ false                                            │
└──────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_OBJECT( <expr> )

Return Type

Returns true if the input JSON value is a JSON object, and false otherwise.

SQL Examples

SELECT
  IS_OBJECT(PARSE_JSON('{"a":"b"}')), -- JSON Object
  IS_OBJECT(PARSE_JSON('["a","b","c"]')); --JSON Array

┌─────────────────────────────────────────────────────────────────────────────┐
│ is_object(parse_json('{"a":"b"}')) │ is_object(parse_json('["a","b","c"]')) │
├────────────────────────────────────┼────────────────────────────────────────┤
│ true                               │ false                                  │
└─────────────────────────────────────────────────────────────────────────────┘

19.13 - IS_STRING

Checks if the input JSON value is a string.

Analyze Syntax

func.is_string(<expr>)

Analyze Example


func.is_string(func.parse_json('"abc"')), func.is_string(func.parse_json('123'))
┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.is_string(func.parse_json('"abc"'))      │ func.is_string(func.parse_json('123'))           │
├───────────────────────────────────────────────┼──────────────────────────────────────────────────┤
│ true                                          │ false                                            │
└──────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

IS_STRING( <expr> )

Return Type

Returns true if the input JSON value is a string, and false otherwise.

SQL Examples

SELECT
  IS_STRING(PARSE_JSON('"abc"')),
  IS_STRING(PARSE_JSON('123'));

┌───────────────────────────────────────────────────────────────┐
│ is_string(parse_json('"abc"')) │ is_string(parse_json('123')) │
├────────────────────────────────┼──────────────────────────────┤
│ true                           │ false                        │
└───────────────────────────────────────────────────────────────┘

19.14 - JQ

The JQ function is a set-returning SQL function that allows you to apply jq filters to JSON data stored in Variant columns. With this function, you can process JSON data by applying a specified jq filter, returning the results as a set of rows.

SQL Syntax

JQ (<jq_expression>, <json_data>)
ParameterDescription
jq_expressionA jq filter expression that defines how to process and transform JSON data using the jq syntax. This expression can specify how to select, modify, and manipulate data within JSON objects and arrays. For information on the syntax, filters, and functions supported by jq, please refer to the jq Manual.
json_dataThe JSON-formatted input that you want to process or transform using the jq filter expression. It can be a JSON object, array, or any valid JSON data structure.

Return Type

The JQ function returns a set of JSON values, where each value corresponds to an element of the transformed or extracted result based on the <jq_expression>.

SQL Examples

To start, we create a table named customer_data with columns for id and profile, where profile is a JSON type to store user information:

CREATE TABLE customer_data (
    id INT,
    profile JSON
);

INSERT INTO customer_data VALUES
    (1, '{"name": "Alice", "age": 30, "city": "New York"}'),
    (2, '{"name": "Bob", "age": 25, "city": "Los Angeles"}'),
    (3, '{"name": "Charlie", "age": 35, "city": "Chicago"}');

This example extracts specific fields from the JSON data:

SELECT
    id,
    jq('.name', profile) AS customer_name
FROM
    customer_data;

┌─────────────────────────────────────┐
│        id       │   customer_name   │
├─────────────────┼───────────────────┤
│               1 │ "Alice"           │
│               2 │ "Bob"             │
│               3 │ "Charlie"         │
└─────────────────────────────────────┘

This example selects the user ID and the age incremented by 1 for each user:

SELECT
    id,
    jq('.age + 1', profile) AS updated_age
FROM
    customer_data;

┌─────────────────────────────────────┐
│        id       │    updated_age    │
├─────────────────┼───────────────────┤
│               1 │ 31                │
│               2 │ 26                │
│               3 │ 36                │
└─────────────────────────────────────┘

This example converts city names to uppercase:

SELECT
    id,
    jq('.city | ascii_upcase', profile) AS city_uppercase
FROM
    customer_data;

┌─────────────────────────────────────┐
│        id       │   city_uppercase  │
├─────────────────┼───────────────────┤
│               1 │ "NEW YORK"        │
│               2 │ "LOS ANGELES"     │
│               3 │ "CHICAGO"         │
└─────────────────────────────────────┘

19.15 - JSON_ARRAY

Creates a JSON array with specified values.

Analyze Syntax

func.json_array(value1[, value2[, ...]])

Analyze Example


func.json_array('fruits', func.json_array('apple', 'banana', 'orange'), func.json_object('price', 1.2, 'quantity', 3)) |
-----------------------------------------------------------------------------------------------------------------------+
["fruits",["apple","banana","orange"],{"price":1.2,"quantity":3}]                                                      |

SQL Syntax

JSON_ARRAY(value1[, value2[, ...]])

Return Type

JSON array.

SQL Examples

SQL Examples 1: Creating JSON Array with Constant Values or Expressions

SELECT JSON_ARRAY('PlaidCloud Lakehouse', 3.14, NOW(), TRUE, NULL);

json_array('databend', 3.14, now(), true, null)         |
--------------------------------------------------------+
["PlaidCloud Lakehouse",3.14,"2023-09-06 07:23:55.399070",true,null]|

SELECT JSON_ARRAY('fruits', JSON_ARRAY('apple', 'banana', 'orange'), JSON_OBJECT('price', 1.2, 'quantity', 3));

json_array('fruits', json_array('apple', 'banana', 'orange'), json_object('price', 1.2, 'quantity', 3))|
-------------------------------------------------------------------------------------------------------+
["fruits",["apple","banana","orange"],{"price":1.2,"quantity":3}]                                      |

SQL Examples 2: Creating JSON Array from Table Data

CREATE TABLE products (
    ProductName VARCHAR(255),
    Price DECIMAL(10, 2)
);

INSERT INTO products (ProductName, Price)
VALUES
    ('Apple', 1.2),
    ('Banana', 0.5),
    ('Orange', 0.8);

SELECT JSON_ARRAY(ProductName, Price) FROM products;

json_array(productname, price)|
------------------------------+
["Apple",1.2]                 |
["Banana",0.5]                |
["Orange",0.8]                |

19.16 - JSON_ARRAY_APPLY

Alias for JSON_ARRAY_TRANSFORM.

19.17 - JSON_ARRAY_DISTINCT

Removes duplicate elements from a JSON array and returns an array with only distinct elements.

SQL Syntax

JSON_ARRAY_DISTINCT(<json_array>)

Return Type

JSON array.

SQL Examples

SELECT JSON_ARRAY_DISTINCT('["apple", "banana", "apple", "orange", "banana"]'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_distinct('["apple", "banana", "apple", "orange", "banana"]'::VARIANT): ["apple","banana","orange"]

19.18 - JSON_ARRAY_ELEMENTS

Extracts the elements from a JSON array, returning them as individual rows in the result set. JSON_ARRAY_ELEMENTS does not recursively expand nested arrays; it treats them as single elements.

Analyze Syntax

func.json_array_elements(<json_string>)

Analyze Example


func.json_array_elements(func.parse_json('[ \n  {"product": "laptop", "brand": "apple", "price": 1500},\n  {"product": "smartphone", "brand": "samsung", "price": 800},\n  {"product": "headphones", "brand": "sony", "price": 150}\n]'))
┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.json_array_elements(func.parse_json('[ \n  {"product": "laptop", "brand": "apple", "price": 1500},\n  {"product": "smartphone", "brand": "samsung", "price": 800},\n  {"product": "headphones", "brand": "sony", "price": 150}\n]')) │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ {"brand":"Apple","price":1500,"product":"Laptop"}                                                                                                                                                                                         │
│ {"brand":"Samsung","price":800,"product":"Smartphone"}                                                                                                                                                                                    │
│ {"brand":"Sony","price":150,"product":"Headphones"}                                                                                                                                                                                       │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_ARRAY_ELEMENTS(<json_string>)

Return Type

JSON_ARRAY_ELEMENTS returns a set of VARIANT values, each representing an element extracted from the input JSON array.

SQL Examples

-- Extract individual elements from a JSON array containing product information
SELECT
  JSON_ARRAY_ELEMENTS(
    PARSE_JSON (
      '[ 
  {"product": "Laptop", "brand": "Apple", "price": 1500},
  {"product": "Smartphone", "brand": "Samsung", "price": 800},
  {"product": "Headphones", "brand": "Sony", "price": 150}
]'
    )
  );

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ json_array_elements(parse_json('[ \n  {"product": "laptop", "brand": "apple", "price": 1500},\n  {"product": "smartphone", "brand": "samsung", "price": 800},\n  {"product": "headphones", "brand": "sony", "price": 150}\n]')) │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ {"brand":"Apple","price":1500,"product":"Laptop"}                                                                                                                                                                               │
│ {"brand":"Samsung","price":800,"product":"Smartphone"}                                                                                                                                                                          │
│ {"brand":"Sony","price":150,"product":"Headphones"}                                                                                                                                                                             │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Display data types of the extracted elements
SELECT
  TYPEOF (
    JSON_ARRAY_ELEMENTS(
      PARSE_JSON (
        '[ 
  {"product": "Laptop", "brand": "Apple", "price": 1500},
  {"product": "Smartphone", "brand": "Samsung", "price": 800},
  {"product": "Headphones", "brand": "Sony", "price": 150}
]'
      )
    )
  );

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ typeof(json_array_elements(parse_json('[ \n  {"product": "laptop", "brand": "apple", "price": 1500},\n  {"product": "smartphone", "brand": "samsung", "price": 800},\n  {"product": "headphones", "brand": "sony", "price": 150}\n]'))) │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ VARIANT NULL                                                                                                                                                                                                                            │
│ VARIANT NULL                                                                                                                                                                                                                            │
│ VARIANT NULL                                                                                                                                                                                                                            │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

19.19 - JSON_ARRAY_EXCEPT

Returns a new JSON array containing the elements from the first JSON array that are not present in the second JSON array.

SQL Syntax

JSON_ARRAY_EXCEPT(<json_array1>, <json_array2>)

Return Type

JSON array.

SQL Examples

SELECT JSON_ARRAY_EXCEPT(
    '["apple", "banana", "orange"]'::JSON,  
    '["banana", "grapes"]'::JSON         
);

-[ RECORD 1 ]-----------------------------------
json_array_except('["apple", "banana", "orange"]'::VARIANT, '["banana", "grapes"]'::VARIANT): ["apple","orange"]

-- Return an empty array because all elements in the first array are present in the second array.
SELECT json_array_except('["apple", "banana", "orange"]'::VARIANT, '["apple", "banana", "orange"]'::VARIANT)

-[ RECORD 1 ]-----------------------------------
json_array_except('["apple", "banana", "orange"]'::VARIANT, '["apple", "banana", "orange"]'::VARIANT): []

19.20 - JSON_ARRAY_FILTER

Filters elements from a JSON array based on a specified Lambda expression, returning only the elements that satisfy the condition. For more information about Lambda expression, see Lambda Expressions.

SQL Syntax

JSON_ARRAY_FILTER(<json_array>, <lambda_expression>)

Return Type

JSON array.

SQL Examples

This example filters the array to return only the strings that start with the letter a, resulting in ["apple", "avocado"]:

SELECT JSON_ARRAY_FILTER(
    ['apple', 'banana', 'avocado', 'grape']::JSON,
    d -> d::String LIKE 'a%'
);

-[ RECORD 1 ]-----------------------------------
json_array_filter(['apple', 'banana', 'avocado', 'grape']::VARIANT, d -> d::STRING LIKE 'a%'): ["apple","avocado"]

19.21 - JSON_ARRAY_INSERT

Inserts a value into a JSON array at the specified index and returns the updated JSON array.

SQL Syntax

JSON_ARRAY_INSERT(<json_array>, <index>, <json_value>)
ParameterDescription
<json_array>The JSON array to modify.
<index>The position at which the value will be inserted. Positive indices insert at the specified position or append if out of range; negative indices insert from the end or at the beginning if out of range.
<json_value>The JSON value to insert into the array.

Return Type

JSON array.

SQL Examples

When the <index> is a non-negative integer, the new element is inserted at the specified position, and existing elements are shifted to the right.

-- The new element is inserted at position 0 (the beginning of the array), shifting all original elements to the right
SELECT JSON_ARRAY_INSERT('["task1", "task2", "task3"]'::VARIANT, 0, '"new_task"'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_insert('["task1", "task2", "task3"]'::VARIANT, 0, '"new_task"'::VARIANT): ["new_task","task1","task2","task3"]

-- The new element is inserted at position 1, between task1 and task2
SELECT JSON_ARRAY_INSERT('["task1", "task2", "task3"]'::VARIANT, 1, '"new_task"'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_insert('["task1", "task2", "task3"]'::VARIANT, 1, '"new_task"'::VARIANT): ["task1","new_task","task2","task3"]

-- If the index exceeds the length of the array, the new element is appended at the end of the array
SELECT JSON_ARRAY_INSERT('["task1", "task2", "task3"]'::VARIANT, 6, '"new_task"'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_insert('["task1", "task2", "task3"]'::VARIANT, 6, '"new_task"'::VARIANT): ["task1","task2","task3","new_task"]

A negative <index> counts from the end of the array, with -1 representing the position before the last element, -2 before the second last, and so on.

-- The new element is inserted just before the last element (task3)
SELECT JSON_ARRAY_INSERT('["task1", "task2", "task3"]'::VARIANT, -1, '"new_task"'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_insert('["task1", "task2", "task3"]'::VARIANT, - 1, '"new_task"'::VARIANT): ["task1","task2","new_task","task3"]

-- Since the negative index exceeds the array’s length, the new element is inserted at the beginning
SELECT JSON_ARRAY_INSERT('["task1", "task2", "task3"]'::VARIANT, -6, '"new_task"'::VARIANT);

-[ RECORD 1 ]-----------------------------------
json_array_insert('["task1", "task2", "task3"]'::VARIANT, - 6, '"new_task"'::VARIANT): ["new_task","task1","task2","task3"]

19.22 - JSON_ARRAY_INTERSECTION

Returns the common elements between two JSON arrays.

SQL Syntax

JSON_ARRAY_INTERSECTION(<json_array1>, <json_array2>)

Return Type

JSON array.

SQL Examples

-- Find the intersection of two JSON arrays
SELECT json_array_intersection('["Electronics", "Books", "Toys"]'::JSON, '["Books", "Fashion", "Electronics"]'::JSON);

-[ RECORD 1 ]-----------------------------------
json_array_intersection('["Electronics", "Books", "Toys"]'::VARIANT, '["Books", "Fashion", "Electronics"]'::VARIANT): ["Electronics","Books"]

-- Find the intersection of the result from the first query with a third JSON array using an iterative approach
SELECT json_array_intersection(
    json_array_intersection('["Electronics", "Books", "Toys"]'::JSON, '["Books", "Fashion", "Electronics"]'::JSON),
    '["Electronics", "Books", "Clothing"]'::JSON
);

-[ RECORD 1 ]-----------------------------------
json_array_intersection(json_array_intersection('["Electronics", "Books", "Toys"]'::VARIANT, '["Books", "Fashion", "Electronics"]'::VARIANT), '["Electronics", "Books", "Clothing"]'::VARIANT): ["Electronics","Books"]

19.23 - JSON_ARRAY_MAP

Alias for JSON_ARRAY_TRANSFORM.

19.24 - JSON_ARRAY_OVERLAP

Checks if there is any overlap between two JSON arrays and returns true if there are common elements; otherwise, it returns false.

SQL Syntax

JSON_ARRAY_OVERLAP(<json_array1>, <json_array2>)

Return Type

The function returns a boolean value:

  • true if there is at least one common element between the two JSON arrays,
  • false if there are no common elements.

SQL Examples

SELECT json_array_overlap(
    '["apple", "banana", "cherry"]'::JSON,  
    '["banana", "kiwi", "mango"]'::JSON
);

-[ RECORD 1 ]-----------------------------------
json_array_overlap('["apple", "banana", "cherry"]'::VARIANT, '["banana", "kiwi", "mango"]'::VARIANT): true


SELECT json_array_overlap(
    '["grape", "orange"]'::JSON,  
    '["apple", "kiwi"]'::JSON     
);

-[ RECORD 1 ]-----------------------------------
json_array_overlap('["grape", "orange"]'::VARIANT, '["apple", "kiwi"]'::VARIANT): false

19.25 - JSON_ARRAY_REDUCE

Reduces a JSON array to a single value by applying a specified Lambda expression. For more information about Lambda expression, see Lambda Expressions.

SQL Syntax

JSON_ARRAY_REDUCE(<json_array>, <lambda_expression>)

SQL Examples

This example multiplies all the elements in the array (2 _ 3 _ 4):

SELECT JSON_ARRAY_REDUCE(
    [2, 3, 4]::JSON,
    (acc, d) -> acc::Int * d::Int
);

-[ RECORD 1 ]-----------------------------------
json_array_reduce([2, 3, 4]::VARIANT, (acc, d) -> acc::Int32 * d::Int32): 24

19.26 - JSON_ARRAY_TRANSFORM

Transforms each element of a JSON array using a specified transformation Lambda expression. For more information about Lambda expression, see Lambda Expressions.

SQL Syntax

JSON_ARRAY_TRANSFORM(<json_array>, <lambda_expression>)

Aliases

Return Type

JSON array.

SQL Examples

In this example, each numeric element in the array is multiplied by 10, transforming the original array into [10, 20, 30, 40]:

SELECT JSON_ARRAY_TRANSFORM(
    [1, 2, 3, 4]::JSON,
    data -> (data::Int * 10)
);

-[ RECORD 1 ]-----------------------------------
json_array_transform([1, 2, 3, 4]::VARIANT, data -> data::Int32 * 10): [10,20,30,40]

19.27 - JSON_EACH

Extracts key-value pairs from a JSON object, breaking down the structure into individual rows in the result set. Each row represents a distinct key-value pair derived from the input JSON expression.

Analyze Syntax

func.json_each(<json_string>)

Analyze Example


┌────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.json_each(func.parse_json('{"name": "john", "age": 25, "isstudent": false, "grades": [90, 85, 92]}')) │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ ('age','25')                                                                                               │
│ ('grades','[90,85,92]')                                                                                    │
│ ('isStudent','false')                                                                                      │
│ ('name','"John"')                                                                                          │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_EACH(<json_string>)

Return Type

JSON_EACH returns a set of tuples, each consisting of a STRING key and a corresponding VARIANT value.

SQL Examples

-- Extract key-value pairs from a JSON object representing information about a person
SELECT
  JSON_EACH(
    PARSE_JSON (
      '{"name": "John", "age": 25, "isStudent": false, "grades": [90, 85, 92]}'
    )
  );


┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
│ json_each(parse_json('{"name": "john", "age": 25, "isstudent": false, "grades": [90, 85, 92]}')) │
├──────────────────────────────────────────────────────────────────────────────────────────────────┤
│ ('age','25')                                                                                     │
│ ('grades','[90,85,92]')                                                                          │
│ ('isStudent','false')                                                                            │
│ ('name','"John"')                                                                                │
└──────────────────────────────────────────────────────────────────────────────────────────────────┘

-- Display data types of the extracted values
SELECT
  TYPEOF (
    JSON_EACH(
      PARSE_JSON (
        '{"name": "John", "age": 25, "isStudent": false, "grades": [90, 85, 92]}'
      )
    )
  );

┌──────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ typeof(json_each(parse_json('{"name": "john", "age": 25, "isstudent": false, "grades": [90, 85, 92]}'))) │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ TUPLE(STRING, VARIANT) NULL                                                                              │
│ TUPLE(STRING, VARIANT) NULL                                                                              │
│ TUPLE(STRING, VARIANT) NULL                                                                              │
│ TUPLE(STRING, VARIANT) NULL                                                                              │
└──────────────────────────────────────────────────────────────────────────────────────────────────────────┘

19.28 - JSON_EXTRACT_PATH_TEXT

Extracts value from a Json string by path_name. The value is returned as a String or NULL if either of the arguments is NULL. This function is equivalent to to_varchar(GET_PATH(PARSE_JSON(JSON), PATH_NAME)).

Analyze Syntax

func.json_extract_path_text(<expr>, <path_name>)

Analyze Example

func.json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k4')
+------------------------------------------------------------------------------+
| func.json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k4') |
+------------------------------------------------------------------------------+
| 4                                                                            |
+------------------------------------------------------------------------------+

SQL Syntax

JSON_EXTRACT_PATH_TEXT( <expr>, <path_name> )

Arguments

ArgumentsDescription
<expr>The Json String value
<path_name>The String value that consists of a concatenation of field names

Return Type

String

SQL Examples

SELECT json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k1[0]');
+-------------------------------------------------------------------------+
| json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k1[0]') |
+-------------------------------------------------------------------------+
| 0                                                                       |
+-------------------------------------------------------------------------+

SELECT json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2:k3');
+-------------------------------------------------------------------------+
| json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2:k3') |
+-------------------------------------------------------------------------+
| 3                                                                       |
+-------------------------------------------------------------------------+

SELECT json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k4');
+-------------------------------------------------------------------------+
| json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k4') |
+-------------------------------------------------------------------------+
| 4                                                                       |
+-------------------------------------------------------------------------+

SELECT json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k5');
+-------------------------------------------------------------------------+
| json_extract_path_text('{"k1":[0,1,2], "k2":{"k3":3,"k4":4}}', 'k2.k5') |
+-------------------------------------------------------------------------+
| NULL                                                                    |
+-------------------------------------------------------------------------+

19.29 - JSON_MAP_FILTER

Filters key-value pairs in a JSON object based on a specified condition, defined using a lambda expression.

SQL Syntax

JSON_MAP_FILTER(<json_object>, (<key>, <value>) -> <condition>)

Return Type

Returns a JSON object with only the key-value pairs that satisfy the specified condition.

SQL Examples

This example extracts only the "status": "active" key-value pair from the JSON object, filtering out the other fields:

SELECT JSON_MAP_FILTER('{"status":"active", "user":"admin", "time":"2024-11-01"}'::VARIANT, (k, v) -> k = 'status') AS filtered_metadata;

┌─────────────────────┐
│  filtered_metadata  │
├─────────────────────┤
│ {"status":"active"} │
└─────────────────────┘

19.30 - JSON_MAP_TRANSFORM_KEYS

Applies a transformation to each key in a JSON object using a lambda expression.

SQL Syntax

JSON_MAP_TRANSFORM_KEYS(<json_object>, (<key>, <value>) -> <key_transformation>)

Return Type

Returns a JSON object with the same values as the input JSON object, but with keys modified according to the specified lambda transformation.

SQL Examples

This example appends "_v1" to each key, creating a new JSON object with modified keys:

SELECT JSON_MAP_TRANSFORM_KEYS('{"name":"John", "role":"admin"}'::VARIANT, (k, v) -> CONCAT(k, '_v1')) AS versioned_metadata;

┌──────────────────────────────────────┐
│          versioned_metadata          │
├──────────────────────────────────────┤
│ {"name_v1":"John","role_v1":"admin"} │
└──────────────────────────────────────┘

19.31 - JSON_MAP_TRANSFORM_VALUES

Applies a transformation to each value in a JSON object using a lambda expression.

SQL Syntax

JSON_MAP_TRANSFORM_VALUES(<json_object>, (<key>, <value>) -> <value_transformation>)

Return Type

Returns a JSON object with the same keys as the input JSON object, but with values modified according to the specified lambda transformation.

SQL Examples

This example appends " - Special Offer" to each product description:

SELECT JSON_MAP_TRANSFORM_VALUES('{"product1":"laptop", "product2":"phone"}'::VARIANT, (k, v) -> CONCAT(v, ' - Special Offer')) AS promo_descriptions;

┌──────────────────────────────────────────────────────────────────────────┐
│                            promo_descriptions                            │
├──────────────────────────────────────────────────────────────────────────┤
│ {"product1":"laptop - Special Offer","product2":"phone - Special Offer"} │
└──────────────────────────────────────────────────────────────────────────┘

19.32 - JSON_OBJECT_DELETE

Deletes specified keys from a JSON object and returns the modified object. If a specified key doesn't exist in the object, it is ignored.

SQL Syntax

json_object_delete(<json_object>, <key1> [, <key2>, ...])

Parameters

ParameterDescription
json_objectA JSON object (VARIANT type) from which to delete keys.
key1, key2, ...One or more string literals representing the keys to be deleted from the object.

Return Type

Returns a VARIANT containing the modified JSON object with specified keys removed.

SQL Examples

Delete a single key:

SELECT json_object_delete('{"a":1,"b":2,"c":3}'::VARIANT, 'a');
-- Result: {"b":2,"c":3}

Delete multiple keys:

SELECT json_object_delete('{"a":1,"b":2,"d":4}'::VARIANT, 'a', 'c');
-- Result: {"b":2,"d":4}

Delete a non-existent key (key is ignored):

SELECT json_object_delete('{"a":1,"b":2}'::VARIANT, 'x');
-- Result: {"a":1,"b":2}

19.33 - JSON_OBJECT_INSERT

Inserts or updates a key-value pair in a JSON object.

SQL Syntax

JSON_OBJECT_INSERT(<json_object>, <key>, <value>[, <update_flag>])
ParameterDescription
<json_object>The input JSON object.
<key>The key to be inserted or updated.
<value>The value to assign to the key.
<update_flag>A boolean flag that controls whether to replace the value if the specified key already exists in the JSON object. If true, the function replaces the value if the key already exists. If false (or omitted), an error occurs if the key exists.

Return Type

Returns the updated JSON object.

SQL Examples

This example demonstrates how to insert a new key 'c' with the value 3 into the existing JSON object:

SELECT JSON_OBJECT_INSERT('{"a":1,"b":2,"d":4}'::variant, 'c', 3);

┌────────────────────────────────────────────────────────────┐
│ json_object_insert('{"a":1,"b":2,"d":4}'::VARIANT, 'c', 3) │
├────────────────────────────────────────────────────────────┤
│ {"a":1,"b":2,"c":3,"d":4}                                  │
└────────────────────────────────────────────────────────────┘

This example shows how to update the value of an existing key 'a' from 1 to 10 using the update flag set to true, allowing the key's value to be replaced:

SELECT JSON_OBJECT_INSERT('{"a":1,"b":2,"d":4}'::variant, 'a', 10, true);

┌───────────────────────────────────────────────────────────────────┐
│ json_object_insert('{"a":1,"b":2,"d":4}'::VARIANT, 'a', 10, TRUE) │
├───────────────────────────────────────────────────────────────────┤
│ {"a":10,"b":2,"d":4}                                              │
└───────────────────────────────────────────────────────────────────┘

This example demonstrates an error that occurs when trying to insert a value for an existing key 'a' without specifying the update flag set to true:

SELECT JSON_OBJECT_INSERT('{"a":1,"b":2,"d":4}'::variant, 'a', 10);

error: APIError: ResponseError with 1006: ObjectDuplicateKey while evaluating function `json_object_insert('{"a":1,"b":2,"d":4}', 'a', 10)` in expr `json_object_insert('{"a":1,"b":2,"d":4}', 'a', 10)`

19.34 - JSON_OBJECT_KEEP_NULL

Creates a JSON object with keys and values.

  • The arguments are zero or more key-value pairs(where keys are strings, and values are of any type).
  • If a key is NULL, the key-value pair is omitted from the resulting object. However, if a value is NULL, the key-value pair will be kept.
  • The keys must be distinct from each other, and their order in the resulting JSON might be different from the order you specify.
  • TRY_JSON_OBJECT_KEEP_NULL returns a NULL value if an error occurs when building the object.

See also: JSON_OBJECT

SQL Syntax

JSON_OBJECT_KEEP_NULL(key1, value1[, key2, value2[, ...]])

TRY_JSON_OBJECT_KEEP_NULL(key1, value1[, key2, value2[, ...]])

Return Type

JSON object.

SQL Examples

SELECT JSON_OBJECT_KEEP_NULL();
┌─────────────────────────┐
│ json_object_keep_null() │
├─────────────────────────┤
│ {}                      │
└─────────────────────────┘

SELECT JSON_OBJECT_KEEP_NULL('a', 3.14, 'b', 'xx', 'c', NULL);
┌────────────────────────────────────────────────────────┐
│ json_object_keep_null('a', 3.14, 'b', 'xx', 'c', null) │
├────────────────────────────────────────────────────────┤
│ {"a":3.14,"b":"xx","c":null}                           │
└────────────────────────────────────────────────────────┘

SELECT JSON_OBJECT_KEEP_NULL('fruits', ['apple', 'banana', 'orange'], 'vegetables', ['carrot', 'celery']);
┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ json_object_keep_null('fruits', ['apple', 'banana', 'orange'], 'vegetables', ['carrot', 'celery']) │
├────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ {"fruits":["apple","banana","orange"],"vegetables":["carrot","celery"]}                            │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT JSON_OBJECT_KEEP_NULL('key');
  |
1 | SELECT JSON_OBJECT_KEEP_NULL('key')
  |        ^^^^^^^^^^^^^^^^^^ The number of keys and values must be equal while evaluating function `json_object_keep_null('key')`


SELECT TRY_JSON_OBJECT_KEEP_NULL('key');
┌──────────────────────────────────┐
│ try_json_object_keep_null('key') │
├──────────────────────────────────┤
│ NULL                             │
└──────────────────────────────────┘

19.35 - JSON_OBJECT_KEYS

Returns an Array containing the list of keys in the input Variant OBJECT.

Analyze Syntax

func.json_object_keys(<variant>)

Analyze Example

func.json_object_keys(func.parse_json(parse_json('{"a": 1, "b": [1,2,3]}')), func.json_object_keys(func.parse_json(parse_json('{"b": [2,3,4]}'))
┌─────────────────────────────────────────────────────────────────┐
│       id       │  json_object_keys(var) │ json_object_keys(var) │
├────────────────┼────────────────────────┼───────────────────────┤
│              1 │ ["a","b"]              │ ["a","b"]             │
│              2 │ ["b"]                  │ ["b"]                 │
└─────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_OBJECT_KEYS(<variant>)

Arguments

ArgumentsDescription
<variant>The VARIANT value that contains an OBJECT

Aliases

Return Type

Array<String>

SQL Examples

CREATE TABLE IF NOT EXISTS objects_test1(id TINYINT, var VARIANT);

INSERT INTO
  objects_test1
VALUES
  (1, parse_json('{"a": 1, "b": [1,2,3]}'));

INSERT INTO
  objects_test1
VALUES
  (2, parse_json('{"b": [2,3,4]}'));

SELECT
  id,
  object_keys(var),
  json_object_keys(var)
FROM
  objects_test1;

┌────────────────────────────────────────────────────────────┐
│       id       │  object_keys(var) │ json_object_keys(var) │
├────────────────┼───────────────────┼───────────────────────┤
│              1 │ ["a","b"]         │ ["a","b"]             │
│              2 │ ["b"]             │ ["b"]                 │
└────────────────────────────────────────────────────────────┘

19.36 - JSON_OBJECT_PICK

Creates a new JSON object containing only the specified keys from the input JSON object. If a specified key doesn't exist in the input object, it is omitted from the result.

SQL Syntax

json_object_pick(<json_object>, <key1> [, <key2>, ...])

Parameters

ParameterDescription
json_objectA JSON object (VARIANT type) from which to pick keys.
key1, key2, ...One or more string literals representing the keys to be included in the result object.

Return Type

Returns a VARIANT containing a new JSON object with only the specified keys and their corresponding values.

SQL Examples

Pick a single key:

SELECT json_object_pick('{"a":1,"b":2,"c":3}'::VARIANT, 'a');
-- Result: {"a":1}

Pick multiple keys:

SELECT json_object_pick('{"a":1,"b":2,"d":4}'::VARIANT, 'a', 'b');
-- Result: {"a":1,"b":2}

Pick with non-existent key (non-existent keys are ignored):

SELECT json_object_pick('{"a":1,"b":2,"d":4}'::VARIANT, 'a', 'c');
-- Result: {"a":1}

19.37 - JSON_PATH_EXISTS

Checks whether a specified path exists in JSON data.

Analyze Syntax

func.json_path_exists(<json_data>, <json_path_expression)

Analyze Example

func.json_path_exists(parse_json('{"a": 1, "b": 2}'), '$.a ? (@ == 1)'), func.json_path_exists(parse_json('{"a": 1, "b": 2}'), '$.a ? (@ > 1)')
┌─────────────────────────────┐
│       Item 1   │  Item 2    │
├────────────────┼────────────┤
│       True     │  False     │
└─────────────────────────────┘

SQL Syntax

JSON_PATH_EXISTS(<json_data>, <json_path_expression>)

  • json_data: Specifies the JSON data you want to search within. It can be a JSON object or an array.

  • json_path_expression: Specifies the path, starting from the root of the JSON data represented by $, that you want to check within the JSON data. You can also include conditions within the expression, using @ to refer to the current node or element being evaluated, to filter the results.

Return Type

The function returns:

  • true if the specified JSON path (and conditions if any) exists within the JSON data.
  • false if the specified JSON path (and conditions if any) does not exist within the JSON data.
  • NULL if either the json_data or json_path_expression is NULL or invalid.

SQL Examples

SELECT JSON_PATH_EXISTS(parse_json('{"a": 1, "b": 2}'), '$.a ? (@ == 1)');

----
true


SELECT JSON_PATH_EXISTS(parse_json('{"a": 1, "b": 2}'), '$.a ? (@ > 1)');

----
false

SELECT JSON_PATH_EXISTS(NULL, '$.a');

----
NULL

SELECT JSON_PATH_EXISTS(parse_json('{"a": 1, "b": 2}'), NULL);

----
NULL

19.38 - JSON_PATH_MATCH

Checks whether a specified JSON path expression matches certain conditions within a JSON data. Please note that the @@ operator is synonymous with this function. For more information, see JSON Operators.

Analyze Syntax

func.json_path_match(<json_data>, <json_path_expression)

Analyze Example

func.json_path_match(func.parse_json('{"a":1,"b":[1,2,3]}'), '$.a == 1')
┌──────────────────────────────────────────────────────────────────────────┐
│ func.json_path_match(func.parse_json('{"a":1,"b":[1,2,3]}'), '$.a == 1') │
├──────────────────────────────────────────────────────────────────────────┤
│ true                                                                     │
└──────────────────────────────────────────────────────────────────────────┘

func.json_path_match(func.parse_json('{"a":1,"b":[1,2,3]}'), '$.b[0] > 1')
┌────────────────────────────────────────────────────────────────────────────┐
│ func.json_path_match(func.parse_json('{"a":1,"b":[1,2,3]}'), '$.b[0] > 1') │
├────────────────────────────────────────────────────────────────────────────┤
│ false                                                                      │
└────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_PATH_MATCH(<json_data>, <json_path_expression>)
  • json_data: Specifies the JSON data you want to examine. It can be a JSON object or an array.
  • json_path_expression: Specifies the conditions to be checked within the JSON data. This expression describes the specific path or criteria to be matched, such as verifying whether specific field values in the JSON structure meet certain conditions. The $ symbol represents the root of the JSON data. It is used to start the path expression and indicates the top-level object in the JSON structure.

Return Type

The function returns:

  • true if the specified JSON path expression matches the conditions within the JSON data.
  • false if the specified JSON path expression does not match the conditions within the JSON data.
  • NULL if either json_data or json_path_expression is NULL or invalid.

SQL Examples

-- Check if the value at JSON path $.a is equal to 1
SELECT JSON_PATH_MATCH(parse_json('{"a":1,"b":[1,2,3]}'), '$.a == 1');

┌────────────────────────────────────────────────────────────────┐
│ json_path_match(parse_json('{"a":1,"b":[1,2,3]}'), '$.a == 1') │
├────────────────────────────────────────────────────────────────┤
│ true                                                           │
└────────────────────────────────────────────────────────────────┘

-- Check if the first element in the array at JSON path $.b is greater than 1
SELECT JSON_PATH_MATCH(parse_json('{"a":1,"b":[1,2,3]}'), '$.b[0] > 1');

┌──────────────────────────────────────────────────────────────────┐
│ json_path_match(parse_json('{"a":1,"b":[1,2,3]}'), '$.b[0] > 1') │
├──────────────────────────────────────────────────────────────────┤
│ false                                                            │
└──────────────────────────────────────────────────────────────────┘

-- Check if any element in the array at JSON path $.b
-- from the second one to the last are greater than or equal to 2
SELECT JSON_PATH_MATCH(parse_json('{"a":1,"b":[1,2,3]}'), '$.b[1 to last] >= 2');

┌───────────────────────────────────────────────────────────────────────────┐
│ json_path_match(parse_json('{"a":1,"b":[1,2,3]}'), '$.b[1 to last] >= 2') │
├───────────────────────────────────────────────────────────────────────────┤
│ true                                                                      │
└───────────────────────────────────────────────────────────────────────────┘

-- NULL is returned if either the json_data or json_path_expression is NULL or invalid.
SELECT JSON_PATH_MATCH(parse_json('{"a":1,"b":[1,2,3]}'), NULL);

┌──────────────────────────────────────────────────────────┐
│ json_path_match(parse_json('{"a":1,"b":[1,2,3]}'), null) │
├──────────────────────────────────────────────────────────┤
│ NULL                                                     │
└──────────────────────────────────────────────────────────┘

SELECT JSON_PATH_MATCH(NULL, '$.a == 1');

┌───────────────────────────────────┐
│ json_path_match(null, '$.a == 1') │
├───────────────────────────────────┤
│ NULL                              │
└───────────────────────────────────┘

19.39 - JSON_PATH_QUERY

Get all JSON items returned by JSON path for the specified JSON value.

Analyze Syntax

func.json_path_query(<variant>, <path_name>)

Analyze Example

table.name, func.json_path_query(table.details, '$.features.*').alias('all_features')

+------------+--------------+
| name       | all_features |
+------------+--------------+
| Laptop     | "16GB"       |
| Laptop     | "512GB"      |
| Smartphone | "4GB"        |
| Smartphone | "128GB"      |
| Headphones | "20h"        |
| Headphones | "5.0"        |
+------------+--------------+

SQL Syntax

JSON_PATH_QUERY(<variant>, '<path_name>')

Return Type

VARIANT

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE products (
    name VARCHAR,
    details VARIANT
);

INSERT INTO products (name, details)
VALUES ('Laptop', '{"brand": "Dell", "colors": ["Black", "Silver"], "price": 1200, "features": {"ram": "16GB", "storage": "512GB"}}'),
       ('Smartphone', '{"brand": "Apple", "colors": ["White", "Black"], "price": 999, "features": {"ram": "4GB", "storage": "128GB"}}'),
       ('Headphones', '{"brand": "Sony", "colors": ["Black", "Blue", "Red"], "price": 150, "features": {"battery": "20h", "bluetooth": "5.0"}}');

Query Demo: Extracting All Features from Product Details

SELECT
    name,
    JSON_PATH_QUERY(details, '$.features.*') AS all_features
FROM
    products;

Result

+------------+--------------+
| name       | all_features |
+------------+--------------+
| Laptop     | "16GB"       |
| Laptop     | "512GB"      |
| Smartphone | "4GB"        |
| Smartphone | "128GB"      |
| Headphones | "20h"        |
| Headphones | "5.0"        |
+------------+--------------+

19.40 - JSON_PATH_QUERY_ARRAY

Get all JSON items returned by JSON path for the specified JSON value and wrap a result into an array.

Analyze Syntax

func.json_path_query_array(<variant>, <path_name>)

Analyze Example

table.name, func.json_path_query_array(table.details, '$.features.*').alias('all_features')

   name     |     all_features
------------+-----------------------
 Laptop     | ["16GB", "512GB"]
 Smartphone | ["4GB", "128GB"]
 Headphones | ["20h", "5.0"]

SQL Syntax

JSON_PATH_QUERY_ARRAY(<variant>, '<path_name>')

Return Type

VARIANT

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE products (
    name VARCHAR,
    details VARIANT
);

INSERT INTO products (name, details)
VALUES ('Laptop', '{"brand": "Dell", "colors": ["Black", "Silver"], "price": 1200, "features": {"ram": "16GB", "storage": "512GB"}}'),
       ('Smartphone', '{"brand": "Apple", "colors": ["White", "Black"], "price": 999, "features": {"ram": "4GB", "storage": "128GB"}}'),
       ('Headphones', '{"brand": "Sony", "colors": ["Black", "Blue", "Red"], "price": 150, "features": {"battery": "20h", "bluetooth": "5.0"}}');

Query Demo: Extracting All Features from Product Details as an Array

SELECT
    name,
    JSON_PATH_QUERY_ARRAY(details, '$.features.*') AS all_features
FROM
    products;

Result

   name    |         all_features
-----------+-----------------------
 Laptop    | ["16GB", "512GB"]
 Smartphone | ["4GB", "128GB"]
 Headphones | ["20h", "5.0"]

19.41 - JSON_PATH_QUERY_FIRST

Get the first JSON item returned by JSON path for the specified JSON value.

Analyze Syntax

func.json_path_query_first(<variant>, <path_name>)

Analyze Example

table.name, func.json_path_query_first(table.details, '$.features.*').alias('first_feature')

+------------+---------------+
| name       | first_feature |
+------------+---------------+
| Laptop     | "16GB"        |
| Laptop     | "16GB"        |
| Smartphone | "4GB"         |
| Smartphone | "4GB"         |
| Headphones | "20h"         |
| Headphones | "20h"         |
+------------+---------------+

SQL Syntax

JSON_PATH_QUERY_FIRST(<variant>, '<path_name>')

Return Type

VARIANT

SQL Examples

Create a Table and Insert Sample Data

CREATE TABLE products (
    name VARCHAR,
    details VARIANT
);

INSERT INTO products (name, details)
VALUES ('Laptop', '{"brand": "Dell", "colors": ["Black", "Silver"], "price": 1200, "features": {"ram": "16GB", "storage": "512GB"}}'),
       ('Smartphone', '{"brand": "Apple", "colors": ["White", "Black"], "price": 999, "features": {"ram": "4GB", "storage": "128GB"}}'),
       ('Headphones', '{"brand": "Sony", "colors": ["Black", "Blue", "Red"], "price": 150, "features": {"battery": "20h", "bluetooth": "5.0"}}');

Query Demo: Extracting the First Feature from Product Details

SELECT
    name,
    JSON_PATH_QUERY(details, '$.features.*') AS all_features,
    JSON_PATH_QUERY_FIRST(details, '$.features.*') AS first_feature
FROM
    products;

Result

+------------+--------------+---------------+
| name       | all_features | first_feature |
+------------+--------------+---------------+
| Laptop     | "16GB"       | "16GB"        |
| Laptop     | "512GB"      | "16GB"        |
| Smartphone | "4GB"        | "4GB"         |
| Smartphone | "128GB"      | "4GB"         |
| Headphones | "20h"        | "20h"         |
| Headphones | "5.0"        | "20h"         |
+------------+--------------+---------------+

19.42 - JSON_PRETTY

Formats JSON data, making it more readable and presentable. It automatically adds indentation, line breaks, and other formatting to the JSON data for better visual representation.

Analyze Syntax

func.json_pretty(<json_string>)

Analyze Example

func.json_pretty(func.parse_json('{"person": {"name": "bob", "age": 25}, "location": "city"}'))

┌─────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.json_pretty(func.parse_json('{"person": {"name": "bob", "age": 25}, "location": "city"}')) │
│                                         String                                                  │
├─────────────────────────────────────────────────────────────────────────────────────────────────┤
│ {                                                                                               │
│   "location": "City",                                                                           │
│   "person": {                                                                                   │
│     "age": 25,                                                                                  │
│     "name": "Bob"                                                                               │
│   }                                                                                             │
│ }                                                                                               │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_PRETTY(<json_string>)

Return Type

String.

SQL Examples

SELECT JSON_PRETTY(PARSE_JSON('{"name":"Alice","age":30}'));

---
┌──────────────────────────────────────────────────────┐
│ json_pretty(parse_json('{"name":"alice","age":30}')) │
│                        String                        │
├──────────────────────────────────────────────────────┤
│ {                                                    │
│   "age": 30,                                         │
│   "name": "Alice"                                    │
│ }                                                    │
└──────────────────────────────────────────────────────┘

SELECT JSON_PRETTY(PARSE_JSON('{"person": {"name": "Bob", "age": 25}, "location": "City"}'));

---
┌───────────────────────────────────────────────────────────────────────────────────────┐
│ json_pretty(parse_json('{"person": {"name": "bob", "age": 25}, "location": "city"}')) │
│                                         String                                        │
├───────────────────────────────────────────────────────────────────────────────────────┤
│ {                                                                                     │
│   "location": "City",                                                                 │
│   "person": {                                                                         │
│     "age": 25,                                                                        │
│     "name": "Bob"                                                                     │
│   }                                                                                   │
│ }                                                                                     │
└───────────────────────────────────────────────────────────────────────────────────────┘

19.43 - JSON_STRIP_NULLS

Removes all properties with null values from a JSON object.

Analyze Syntax

func.json_strip_nulls(<json_string>)

Analyze Example

func.json_strip_nulls(func.parse_json('{"name": "alice", "age": 30, "city": null}'))

┌─────────────────────────────────────────────────────────────────────────────────────────────────┐
│ func.json_strip_nulls(func.parse_json('{"name": "alice", "age": 30, "city": null}'))            │
│                                         String                                                  │
├─────────────────────────────────────────────────────────────────────────────────────────────────┤
│ {"age":30,"name":"Alice"}                                                                       │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

JSON_STRIP_NULLS(<json_string>)

Return Type

Returns a value of the same type as the input JSON value.

SQL Examples

SELECT JSON_STRIP_NULLS(PARSE_JSON('{"name": "Alice", "age": 30, "city": null}'));

json_strip_nulls(parse_json('{"name": "alice", "age": 30, "city": null}'))|
--------------------------------------------------------------------------+
{"age":30,"name":"Alice"}                                                 |

19.44 - JSON_TO_STRING

Alias for TO_STRING.

19.45 - JSON_TYPEOF

Returns the type of the main-level of a JSON structure.

Analyze Syntax

func.json_typeof(<json_string>)

Analyze Example

func.json_typeof(func.parse_json('null'))|
-----------------------------------------+
null                                     |

--
func.json_typeof(func.parse_json('true'))|
-----------------------------------------+
boolean                                  |

--
func.json_typeof(func.parse_json('"plaidcloud"'))|
-----------------------------------------------+
string                                         |

--
func.json_typeof(func.parse_json('-1.23'))|
------------------------------------------+
number                                    |

--
func.json_typeof(func.parse_json('[1,2,3]'))|
--------------------------------------------+
array                                       |

--
func.json_typeof(func.parse_json('{"name": "alice", "age": 30}'))|
-----------------------------------------------------------------+
object                                                           |

SQL Syntax

JSON_TYPEOF(<json_string>)

Return Type

The return type of the json_typeof function (or similar) is a string that indicates the data type of the parsed JSON value. The possible return values are: 'null', 'boolean', 'string', 'number', 'array', and 'object'.

SQL Examples

-- Parsing a JSON value that is NULL
SELECT JSON_TYPEOF(PARSE_JSON(NULL));

--
func.json_typeof(func.parse_json(null))|
-----------------------------+
                             |

-- Parsing a JSON value that is the string 'null'
SELECT JSON_TYPEOF(PARSE_JSON('null'));

--
func.json_typeof(func.parse_json('null'))|
-------------------------------+
null                           |

SELECT JSON_TYPEOF(PARSE_JSON('true'));

--
func.json_typeof(func.parse_json('true'))|
-------------------------------+
boolean                        |

SELECT JSON_TYPEOF(PARSE_JSON('"PlaidCloud Lakehouse"'));

--
func.json_typeof(func.parse_json('"databend"'))|
-------------------------------------+
string                               |


SELECT JSON_TYPEOF(PARSE_JSON('-1.23'));

--
func.json_typeof(func.parse_json('-1.23'))|
--------------------------------+
number                          |

SELECT JSON_TYPEOF(PARSE_JSON('[1,2,3]'));

--
func.json_typeof(func.parse_json('[1,2,3]'))|
----------------------------------+
array                             |

SELECT JSON_TYPEOF(PARSE_JSON('{"name": "Alice", "age": 30}'));

--
func.json_typeof(func.parse_json('{"name": "alice", "age": 30}'))|
-------------------------------------------------------+
object                                                 |

19.46 - OBJECT_KEYS

Alias for JSON_OBJECT_KEYS.

19.47 - PARSE_JSON

Interprets input JSON string, producing a VARIANT value

parse_json and try_parse_json interprets an input string as a JSON document, producing a VARIANT value.

try_parse_json returns a NULL value if an error occurs during parsing.

Analyze Syntax

func.parse_json(<json_string>)

or

func.try_parse_json(<json_string>)

Analyze Example

func.parse_json('[-1, 12, 289, 2188, false]')

+-----------------------------------------------+
| func.parse_json('[-1, 12, 289, 2188, false]') |
+-----------------------------------------------+
| [-1,12,289,2188,false]                        |
+-----------------------------------------------+

func.try_parse_json('{ "x" : "abc", "y" : false, "z": 10} ')

+--------------------------------------------------------------+
| func.try_parse_json('{ "x" : "abc", "y" : false, "z": 10} ') |
+--------------------------------------------------------------+
| {"x":"abc","y":false,"z":10}                                 |
+--------------------------------------------------------------+

SQL Syntax

PARSE_JSON(<expr>)
TRY_PARSE_JSON(<expr>)

Arguments

ArgumentsDescription
<expr>An expression of string type (e.g. VARCHAR) that holds valid JSON information.

Return Type

VARIANT

SQL Examples

SELECT parse_json('[-1, 12, 289, 2188, false]');
+------------------------------------------+
| parse_json('[-1, 12, 289, 2188, false]') |
+------------------------------------------+
| [-1,12,289,2188,false]                   |
+------------------------------------------+

SELECT try_parse_json('{ "x" : "abc", "y" : false, "z": 10} ');
+---------------------------------------------------------+
| try_parse_json('{ "x" : "abc", "y" : false, "z": 10} ') |
+---------------------------------------------------------+
| {"x":"abc","y":false,"z":10}                            |
+---------------------------------------------------------+

20 - Sequence Functions

This section provides reference information for sequence functions in PlaidCloud Lakehouse.

20.1 - NEXTVAL

Retrieves the next value from a sequence.

SQL Syntax

NEXTVAL(<sequence_name>)

Return Type

Integer.

SQL Examples

This example demonstrates how the NEXTVAL function works with a sequence:

CREATE SEQUENCE my_seq;

SELECT
  NEXTVAL(my_seq),
  NEXTVAL(my_seq),
  NEXTVAL(my_seq);

┌─────────────────────────────────────────────────────┐
│ nextval(my_seq) │ nextval(my_seq) │ nextval(my_seq) │
├─────────────────┼─────────────────┼─────────────────┤
│               1 │               2 │               3 │
└─────────────────────────────────────────────────────┘

This example showcases how sequences and the NEXTVAL function are employed to automatically generate and assign unique identifiers to rows in a table.

-- Create a new sequence named staff_id_seq
CREATE SEQUENCE staff_id_seq;

-- Create a new table named staff with columns for staff_id, name, and department
CREATE TABLE staff (
    staff_id INT,
    name VARCHAR(50),
    department VARCHAR(50)
);

-- Insert a new row into the staff table, using the next value from the staff_id_seq sequence for the staff_id column
INSERT INTO staff (staff_id, name, department)
VALUES (NEXTVAL(staff_id_seq), 'John Doe', 'HR');

-- Insert another row into the staff table, using the next value from the staff_id_seq sequence for the staff_id column
INSERT INTO staff (staff_id, name, department)
VALUES (NEXTVAL(staff_id_seq), 'Jane Smith', 'Finance');

SELECT * FROM staff;

┌───────────────────────────────────────────────────────┐
│     staff_id    │       name       │    department    │
├─────────────────┼──────────────────┼──────────────────┤
│               2 │ Jane Smith       │ Finance          │
│               1 │ John Doe         │ HR               │
└───────────────────────────────────────────────────────┘

21 - String Functions

This section provides reference information for the string-related functions in PlaidCloud Lakehouse.

String Manipulation:

String Information:

Case Conversion:

Regular Expressions:

Encoding and Decoding:

Miscellaneous:

21.1 - ASCII

Returns the numeric value of the leftmost character of the string str.

Analyze Syntax

func.ascii(<expr>)

Analyze Examples

func.ascii('2')
+-----------------+
| func.ascii('2') |
+-----------------+
|              50 |
+-----------------+

SQL Syntax

ASCII(<expr>)

Arguments

ArgumentsDescription
<expr>The string.

Return Type

TINYINT

SQL Examples

SELECT ASCII('2');
+------------+
| ASCII('2') |
+------------+
|         50 |
+------------+

21.2 - BIN

Returns a string representation of the binary value of N.

Analyze Syntax

func.bin(<expr>)

Analyze Examples

func.bin(12)
+--------------+
| func.bin(12) |
+--------------+
| 1100         |
+--------------+

SQL Syntax

BIN(<expr>)

Arguments

ArgumentsDescription
<expr>The number.

Return Type

VARCHAR

SQL Examples

SELECT BIN(12);
+---------+
| BIN(12) |
+---------+
| 1100    |
+---------+

21.3 - BIT_LENGTH

Return the length of a string in bits.

Analyze Syntax

func.bit_length(<expr>)

Analyze Examples

func.bit_length('Word')
+-------------------------+
| func.bit_length('Word') |
+-------------------------+
|                      32 |
+-------------------------+

SQL Syntax

BIT_LENGTH(<expr>)

Arguments

ArgumentsDescription
<expr>The string.

Return Type

BIGINT

SQL Examples

SELECT BIT_LENGTH('Word');
+----------------------------+
| SELECT BIT_LENGTH('Word'); |
+----------------------------+
| 32                         |
+----------------------------+

21.4 - CHAR

Return the character for each integer passed.

Analyze Syntax

func.char(N,...)

Analyze Examples

func.char(77,121,83,81,76)
+-----------------------------+
| func.char(77,121,83,81,76) |
+-----------------------------+
| 4D7953514C                  |
+-----------------------------+

SQL Syntax

CHAR(N, ...)

Arguments

ArgumentsDescription
NNumeric Column

Return Type

BINARY

SQL Examples

This example shows both the binary value returned as well as the string representation.

SELECT CHAR(77,121,83,81,76) as a, a::String;
┌────────────────────────┐
│      a     │ a::string │
│   Binary   │   String  │
├────────────┼───────────┤
│ 4D7953514C │ MySQL     │
└────────────────────────┘

21.5 - CHAR_LENGTH

Alias for LENGTH.

21.6 - CHARACTER_LENGTH

Alias for LENGTH.

21.7 - CONCAT

Returns the string that results from concatenating the arguments. May have one or more arguments. If all arguments are nonbinary strings, the result is a nonbinary string. If the arguments include any binary strings, the result is a binary string. A numeric argument is converted to its equivalent nonbinary string form.

Analyze Syntax

func.concat(<expr1>, ...)

Analyze Examples

func.concat('data', 'bend')
+-----------------------------+
| func.concat('data', 'bend') |
+-----------------------------+
| databend                    |
+-----------------------------+

SQL Syntax

CONCAT(<expr1>, ...)

Arguments

ArgumentsDescription
<expr1>string

Return Type

A VARCHAR data type value Or NULL data type.

SQL Examples

SELECT CONCAT('data', 'bend');
+------------------------+
| concat('data', 'bend') |
+------------------------+
| databend               |
+------------------------+

SELECT CONCAT('data', NULL, 'bend');
+------------------------------+
| CONCAT('data', NULL, 'bend') |
+------------------------------+
|                         NULL |
+------------------------------+

SELECT CONCAT('14.3');
+----------------+
| concat('14.3') |
+----------------+
| 14.3           |
+----------------+

21.8 - CONCAT_WS

CONCAT_WS() stands for Concatenate With Separator and is a special form of CONCAT(). The first argument is the separator for the rest of the arguments. The separator is added between the strings to be concatenated. The separator can be a string, as can the rest of the arguments. If the separator is NULL, the result is NULL.

CONCAT_WS() does not skip empty strings. However, it does skip any NULL values after the separator argument.

Analyze Syntax

func.concat_ws(<separator>, <expr1>, ...)

Analyze Examples

func.concat_ws(',', 'data', 'fuse', 'labs', '2021')
+-----------------------------------------------------+
| func.concat_ws(',', 'data', 'fuse', 'labs', '2021') |
+-----------------------------------------------------+
| data,fuse,labs,2021                                 |
+-----------------------------------------------------+

SQL Syntax

CONCAT_WS(<separator>, <expr1>, ...)

Arguments

ArgumentsDescription
<separator>string column
<expr1>value column

Return Type

A VARCHAR data type value Or NULL data type.

SQL Examples

SELECT CONCAT_WS(',', 'data', 'fuse', 'labs', '2021');
+------------------------------------------------+
| CONCAT_WS(',', 'data', 'fuse', 'labs', '2021') |
+------------------------------------------------+
| data,fuse,labs,2021                            |
+------------------------------------------------+

SELECT CONCAT_WS(',', 'data', NULL, 'bend');
+--------------------------------------+
| CONCAT_WS(',', 'data', NULL, 'bend') |
+--------------------------------------+
| data,bend                            |
+--------------------------------------+


SELECT CONCAT_WS(',', 'data', NULL, NULL, 'bend');
+--------------------------------------------+
| CONCAT_WS(',', 'data', NULL, NULL, 'bend') |
+--------------------------------------------+
| data,bend                                  |
+--------------------------------------------+


SELECT CONCAT_WS(NULL, 'data', 'fuse', 'labs');
+-----------------------------------------+
| CONCAT_WS(NULL, 'data', 'fuse', 'labs') |
+-----------------------------------------+
|                                    NULL |
+-----------------------------------------+

SELECT CONCAT_WS(',', NULL);
+----------------------+
| CONCAT_WS(',', NULL) |
+----------------------+
|                      |
+----------------------+

21.9 - FROM_BASE64

Takes a string encoded with the base-64 encoded rules nd returns the decoded result as a binary. The result is NULL if the argument is NULL or not a valid base-64 string.

Analyze Syntax

func.from_base64(<expr>)

Analyze Examples

func.from_base64('YWJj')
+--------------------------+
| func.from_base64('YWJj') |
+--------------------------+
| abc                      |
+--------------------------+

SQL Syntax

FROM_BASE64(<expr>)

Arguments

ArgumentsDescription
<expr>The string value.

Return Type

BINARY

SQL Examples

SELECT TO_BASE64('abc'), FROM_BASE64(TO_BASE64('abc')) as b, b::String;
┌───────────────────────────────────────┐
│ to_base64('abc') │    b   │ b::string │
│      String      │ Binary │   String  │
├──────────────────┼────────┼───────────┤
│ YWJj             │ 616263 │ abc       │
└───────────────────────────────────────┘

21.10 - FROM_HEX

Alias for UNHEX.

21.11 - HEX

Alias for TO_HEX.

21.12 - INSERT

Returns the string str, with the substring beginning at position pos and len characters long replaced by the string newstr. Returns the original string if pos is not within the length of the string. Replaces the rest of the string from position pos if len is not within the length of the rest of the string. Returns NULL if any argument is NULL.

Analyze Syntax

func.insert(<str>, <pos>, <len>, <newstr>)

Analyze Examples

func.insert('Quadratic', 3, 4, 'What')
+----------------------------------------+
| func.insert('Quadratic', 3, 4, 'What') |
+----------------------------------------+
| QuWhattic                              |
+----------------------------------------+

SQL Syntax

INSERT(<str>, <pos>, <len>, <newstr>)

Arguments

ArgumentsDescription
<str>The string.
<pos>The position.
<len>The length.
<newstr>The new string.

Return Type

VARCHAR

SQL Examples

SELECT INSERT('Quadratic', 3, 4, 'What');
+-----------------------------------+
| INSERT('Quadratic', 3, 4, 'What') |
+-----------------------------------+
| QuWhattic                         |
+-----------------------------------+

SELECT INSERT('Quadratic', -1, 4, 'What');
+---------------------------------------+
| INSERT('Quadratic', (- 1), 4, 'What') |
+---------------------------------------+
| Quadratic                             |
+---------------------------------------+

SELECT INSERT('Quadratic', 3, 100, 'What');
+-------------------------------------+
| INSERT('Quadratic', 3, 100, 'What') |
+-------------------------------------+
| QuWhat                              |
+-------------------------------------+

+--------------------------------------------+--------+
| INSERT('123456789', number, number, 'aaa') | number |
+--------------------------------------------+--------+
| 123456789                                  |      0 |
| aaa23456789                                |      1 |
| 1aaa456789                                 |      2 |
| 12aaa6789                                  |      3 |
| 123aaa89                                   |      4 |
| 1234aaa                                    |      5 |
| 12345aaa                                   |      6 |
| 123456aaa                                  |      7 |
| 1234567aaa                                 |      8 |
| 12345678aaa                                |      9 |
| 123456789                                  |     10 |
| 123456789                                  |     11 |
| 123456789                                  |     12 |
+--------------------------------------------+--------+

21.13 - INSTR

Returns the position of the first occurrence of substring substr in string str. This is the same as the two-argument form of LOCATE(), except that the order of the arguments is reversed.

Analyze Syntax

func.instr(<str>, <substr>)

Analyze Examples

func.instr('foobarbar', 'bar')
+--------------------------------+
| func.instr('foobarbar', 'bar') |
+--------------------------------+
|                              4 |
+--------------------------------+

SQL Syntax

INSTR(<str>, <substr>)

Arguments

ArgumentsDescription
<str>The string.
<substr>The substring.

Return Type

BIGINT

SQL Examples

SELECT INSTR('foobarbar', 'bar');
+---------------------------+
| INSTR('foobarbar', 'bar') |
+---------------------------+
|                         4 |
+---------------------------+

SELECT INSTR('xbar', 'foobar');
+-------------------------+
| INSTR('xbar', 'foobar') |
+-------------------------+
|                       0 |
+-------------------------+

21.14 - JARO_WINKLER

Calculates the Jaro-Winkler distance between two strings. It is commonly used for measuring the similarity between strings, with values ranging from 0.0 (completely dissimilar) to 1.0 (identical strings).

Analyze Syntax

func.jaro_winkler(<string1>, <string2>)

Analyze Examples

func.jaro_winkler('databend', 'Databend')
+-------------------------------------------+
| func.jaro_winkler('databend', 'Databend') |
+-------------------------------------------+
|                        0.9166666666666666 |
+-------------------------------------------+

SQL Syntax

JARO_WINKLER(<string1>, <string2>)

SQL Examples

The JARO_WINKLER function returns a FLOAT64 value representing the similarity between the two input strings. The return value follows these rules:

  • Similarity Range: The result ranges from 0.0 (completely dissimilar) to 1.0 (identical).

    SELECT JARO_WINKLER('databend', 'Databend') AS similarity;

┌────────────────────┐
│     similarity     │
├────────────────────┤
│ 0.9166666666666666 │
└────────────────────┘

SELECT JARO_WINKLER('databend', 'database') AS similarity;

┌────────────┐
│ similarity │
├────────────┤
│        0.9 │
└────────────┘

  • NULL Handling: If either string1 or string2 is NULL, the result is NULL.

    SELECT JARO_WINKLER('databend', NULL) AS similarity;

┌────────────┐
│ similarity │
├────────────┤
│ NULL       │
└────────────┘

  • Empty Strings:

    • Comparing two empty strings returns 1.0.
    SELECT JARO_WINKLER('', '') AS similarity;

┌────────────┐
│ similarity │
├────────────┤
│          1 │
└────────────┘
    • Comparing an empty string with a non-empty string returns 0.0.
    SELECT JARO_WINKLER('databend', '') AS similarity;

┌────────────┐
│ similarity │
├────────────┤
│          0 │
└────────────┘

21.15 - LCASE

Alias for LOWER.

21.16 - LEFT

Returns the leftmost len characters from the string str, or NULL if any argument is NULL.

Analyze Syntax

func.left(<str>, <len>)

Analyze Examples

func.left('foobarbar', 5)
+---------------------------+
| func.left('foobarbar', 5) |
+---------------------------+
| fooba                     |
+---------------------------+

SQL Syntax

LEFT(<str>, <len>);

Arguments

ArgumentsDescription
<str>The main string from where the character to be extracted
<len>The count of characters

Return Type

VARCHAR

SQL Examples

SELECT LEFT('foobarbar', 5);
+----------------------+
| LEFT('foobarbar', 5) |
+----------------------+
| fooba                |
+----------------------+

21.17 - LENGTH

Returns the length of a given input string or binary value. In the case of strings, the length represents the count of characters, with each UTF-8 character considered as a single character. For binary data, the length corresponds to the number of bytes.

Analyze Syntax

func.length(<expr>)

Analyze Examples

func.length('Hello')
+----------------------+
| func.length('Hello') |
+----------------------+
|                    5 |
+----------------------+

SQL Syntax

LENGTH(<expr>)

Aliases

Return Type

BIGINT

SQL Examples

SELECT LENGTH('Hello'), LENGTH_UTF8('Hello'), CHAR_LENGTH('Hello'), CHARACTER_LENGTH('Hello');

┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ length('hello') │ length_utf8('hello') │ char_length('hello') │ character_length('hello') │
├─────────────────┼──────────────────────┼──────────────────────┼───────────────────────────┤
│               5 │                    5 │                    5 │                         5 │
└───────────────────────────────────────────────────────────────────────────────────────────┘

21.18 - LENGTH_UTF8

Alias for LENGTH.

21.19 - LIKE

Pattern matching using an SQL pattern. Returns 1 (TRUE) or 0 (FALSE). If either expr or pat is NULL, the result is NULL.

Analyze Syntax

<column>.like('plaid%')

Analyze Examples

my_clothes.like('plaid%)
+-----------------+
| my_clothes      |
+-----------------+
| plaid pants     |
| plaid hat       |
| plaid shirt     |
+-----------------+

SQL Syntax

<expr> LIKE <pattern>

SQL Examples

SELECT name, category FROM system.functions WHERE name like 'tou%' ORDER BY name;
+----------+------------+
| name     | category   |
+----------+------------+
| touint16 | conversion |
| touint32 | conversion |
| touint64 | conversion |
| touint8  | conversion |
+----------+------------+

21.20 - LOCATE

The first syntax returns the position of the first occurrence of substring substr in string str. The second syntax returns the position of the first occurrence of substring substr in string str, starting at position pos. Returns 0 if substr is not in str. Returns NULL if any argument is NULL.

Analyze Syntax

func.locate(<substr>, <str>, <pos>)

Analyze Examples

func.locate('bar', 'foobarbar')
+------------------------------------+
| func.locate('bar', 'foobarbar') |
+------------------------------------+
|                                  5 |
+------------------------------------+

func.locate('bar', 'foobarbar', 5)
+------------------------------------+
| func.locate('bar', 'foobarbar', 5) |
+------------------------------------+
|                                  7 |
+------------------------------------+

SQL Syntax

LOCATE(<substr>, <str>)
LOCATE(<substr>, <str>, <pos>)

Arguments

ArgumentsDescription
<substr>The substring.
<str>The string.
<pos>The position.

Return Type

BIGINT

SQL Examples

SELECT LOCATE('bar', 'foobarbar')
+----------------------------+
| LOCATE('bar', 'foobarbar') |
+----------------------------+
|                          4 |
+----------------------------+

SELECT LOCATE('xbar', 'foobar')
+--------------------------+
| LOCATE('xbar', 'foobar') |
+--------------------------+
|                        0 |
+--------------------------+

SELECT LOCATE('bar', 'foobarbar', 5)
+-------------------------------+
| LOCATE('bar', 'foobarbar', 5) |
+-------------------------------+
|                             7 |
+-------------------------------+

21.21 - LOWER

Returns a string with all characters changed to lowercase.

Analyze Syntax

func.lower(<str>)

Analyze Examples

func.lower('Hello, PlaidCloud!')
+----------------------------------+
| func.lower('Hello, PlaidCloud!') |
+----------------------------------+
| hello, plaidcloud!               |
+----------------------------------+

SQL Syntax

LOWER(<str>)

Aliases

Return Type

VARCHAR

SQL Examples

SELECT LOWER('Hello, Databend!'), LCASE('Hello, Databend!');

┌───────────────────────────────────────────────────────┐
│ lower('hello, databend!') │ lcase('hello, databend!') │
├───────────────────────────┼───────────────────────────┤
│ hello, databend!          │ hello, databend!          │
└───────────────────────────────────────────────────────┘

21.22 - LPAD

Returns the string str, left-padded with the string padstr to a length of len characters. If str is longer than len, the return value is shortened to len characters.

Analyze Syntax

func.lpad(<str>, <len>, <padstr>)

Analyze Examples

func.lpad('hi',4,'??')
+------------------------+
| func.lpad('hi',4,'??') |
+------------------------+
| ??hi                   |
+------------------------+

func.lpad('hi',1,'??')
+------------------------+
| func.lpad('hi',1,'??') |
+------------------------+
| h                      |
+------------------------+

SQL Syntax

LPAD(<str>, <len>, <padstr>)

Arguments

ArgumentsDescription
<str>The string.
<len>The length.
<padstr>The pad string.

Return Type

VARCHAR

SQL Examples

SELECT LPAD('hi',4,'??');
+---------------------+
| LPAD('hi', 4, '??') |
+---------------------+
| ??hi                |
+---------------------+

SELECT LPAD('hi',1,'??');
+---------------------+
| LPAD('hi', 1, '??') |
+---------------------+
| h                   |
+---------------------+

21.23 - LTRIM

Removes all occurrences of any character present in the specified trim string from the left side of the string.

See also:

Analyze Syntax

func.ltrim(<string>, <trim_string>)

Analyze Examples

func.ltrim('xxdatabend', 'x')
+--------------------------------+
| func.ltrim('xxdatabend', 'x')  |
+--------------------------------+
| databend                       |
+--------------------------------+

SQL Syntax

LTRIM(<string>, <trim_string>)

SQL Examples

SELECT LTRIM('xxdatabend', 'xx'), LTRIM('xxdatabend', 'xy');

┌───────────────────────────────────────────────────────┐
│ ltrim('xxdatabend', 'xx') │ ltrim('xxdatabend', 'xy') │
├───────────────────────────┼───────────────────────────┤
│ databend                  │ databend                  │
└───────────────────────────────────────────────────────┘

21.24 - MID

Alias for SUBSTR.

21.25 - NOT LIKE

Pattern not matching using an SQL pattern. Returns 1 (TRUE) or 0 (FALSE). If either expr or pat is NULL, the result is NULL.

Analyze Syntax

<column>.not_like(<pattern>)

Analyze Examples

my_clothes.not_like('%pants)
+-----------------+
| my_clothes      |
+-----------------+
| plaid pants XL  |
| plaid hat       |
| plaid shirt     |
+-----------------+

SQL Syntax

<expr> NOT LIKE <pattern>

SQL Examples

SELECT name, category FROM system.functions WHERE name like 'tou%' AND name not like '%64' ORDER BY name;
+----------+------------+
| name     | category   |
+----------+------------+
| touint16 | conversion |
| touint32 | conversion |
| touint8  | conversion |
+----------+------------+

21.26 - NOT REGEXP

Returns 1 if the string expr doesn't match the regular expression specified by the pattern pat, 0 otherwise.

Analyze Syntax

not_(<column>.regexp_match(<pattern>))

Analyze Examples


With an input table of:
+-----------------+
| my_clothes      |
+-----------------+
| plaid pants     |
| plaid hat       |
| plaid shirt     |
| shoes           |
+-----------------+

not_(my_clothes.regexp_match('p*'))
+-------------------------------------+
| not_(my_clothes.regexp_match('p*')) |
+-------------------------------------+
| false                               |
| false                               |
| false                               |
| true                                |
+-------------------------------------+

SQL Syntax

<expr> NOT REGEXP <pattern>

SQL Examples

SELECT 'databend' NOT REGEXP 'd*';
+------------------------------+
| ('databend' not regexp 'd*') |
+------------------------------+
|                            0 |
+------------------------------+

21.27 - NOT RLIKE

Returns 1 if the string expr doesn't match the regular expression specified by the pattern pat, 0 otherwise.

Analyze Syntax

not_(<column>.regexp_match(<pattern>))

Analyze Examples


With an input table of:
+-----------------+
| my_clothes      |
+-----------------+
| plaid pants     |
| plaid hat       |
| plaid shirt     |
| shoes           |
+-----------------+

not_(my_clothes.regexp_match('p*'))
+-------------------------------------+
| not_(my_clothes.regexp_match('p*')) |
+-------------------------------------+
| false                               |
| false                               |
| false                               |
| true                                |
+-------------------------------------+

SQL Syntax

<expr> NOT RLIKE <pattern>

SQL Examples

SELECT 'databend' not rlike 'd*';
+-----------------------------+
| ('databend' not rlike 'd*') |
+-----------------------------+
|                           0 |
+-----------------------------+

21.28 - OCT

Returns a string representation of the octal value of N.

Analyze Syntax

func.oct(<expr>)

Analyze Examples

func.oct(12)
+-----------------+
| func.oct(12)    |
+-----------------+
| 014             |
+-----------------+

SQL Syntax

OCT(<expr>)

SQL Examples

SELECT OCT(12);
+---------+
| OCT(12) |
+---------+
| 014     |
+---------+

21.29 - OCTET_LENGTH

OCTET_LENGTH() is a synonym for LENGTH().

Analyze Syntax

func.octet_length(<str>)

Analyze Examples

func.octet_length('databend')
+-------------------------------+
| func.octet_length('databend') |
+-------------------------------+
|                             8 |
+-------------------------------+

SQL Syntax

OCTET_LENGTH(<str>)

SQL Examples

SELECT OCTET_LENGTH('databend');
+--------------------------+
| OCTET_LENGTH('databend') |
+--------------------------+
|                        8 |
+--------------------------+

21.30 - ORD

If the leftmost character is not a multibyte character, ORD() returns the same value as the ASCII() function.

If the leftmost character of the string str is a multibyte character, returns the code for that character, calculated from the numeric values of its constituent bytes using this formula:

  (1st byte code)
+ (2nd byte code * 256)
+ (3rd byte code * 256^2) ...

Analyze Syntax

func.ord(<str>)

Analyze Examples

func.ord('2')
+----------------+
| func.ord('2)   |
+----------------+
|             50 |
+----------------+

SQL Syntax

ORD(<str>)

Arguments

ArgumentsDescription
<str>The string.

Return Type

BIGINT

SQL Examples

SELECT ORD('2')
+--------+
| ORD(2) |
+--------+
|     50 |
+--------+

21.31 - POSITION

POSITION(substr IN str) is a synonym for LOCATE(substr,str). Returns the position of the first occurrence of substring substr in string str. Returns 0 if substr is not in str. Returns NULL if any argument is NULL.

Analyze Syntax

func.position(<substr>, <str>)

Analyze Examples

func.position('bar', 'foobarbar')
+-----------------------------------+
| func.position('bar', 'foobarbar') |
+-----------------------------------+
|                                 4 |
+-----------------------------------+

SQL Syntax

POSITION(<substr> IN <str>)

Arguments

ArgumentsDescription
<substr>The substring.
<str>The string.

Return Type

BIGINT

SQL Examples

SELECT POSITION('bar' IN 'foobarbar')
+----------------------------+
| POSITION('bar' IN 'foobarbar') |
+----------------------------+
|                          4 |
+----------------------------+

SELECT POSITION('xbar' IN 'foobar')
+--------------------------+
| POSITION('xbar' IN 'foobar') |
+--------------------------+
|                        0 |
+--------------------------+

21.32 - QUOTE

Quotes a string to produce a result that can be used as a properly escaped data value in an SQL statement.

Analyze Syntax

func.quote(<str>)

Analyze Examples

func.quote('Don\'t')
+----------------------+
| func.quote('Don\'t') |
+----------------------+
|  Don\'t!             |
+----------------------+

SQL Syntax

QUOTE(<str>)

SQL Examples

SELECT QUOTE('Don\'t!');
+-----------------+
| QUOTE('Don't!') |
+-----------------+
| Don\'t!         |
+-----------------+

SELECT QUOTE(NULL);
+-------------+
| QUOTE(NULL) |
+-------------+
|        NULL |
+-------------+

21.33 - REGEXP

Returns true if the string <expr> matches the regular expression specified by the <pattern>, false otherwise.

Analyze Syntax

<column>.regexp_match(<pattern>)

Analyze Examples


With an input table of:
+-----------------+
| my_clothes      |
+-----------------+
| plaid pants     |
| plaid hat       |
| plaid shirt     |
| shoes           |
+-----------------+

my_clothes.regexp_match('p*')
+-------------------------------+
| my_clothes.regexp_match('p*') |
+-------------------------------+
| true                          |
| true                          |
| true                          |
| false                         |
+-------------------------------+

SQL Syntax

<expr> REGEXP <pattern>

Aliases

SQL Examples

SELECT 'databend' REGEXP 'd*', 'databend' RLIKE 'd*';

┌────────────────────────────────────────────────────┐
│ ('databend' regexp 'd*') │ ('databend' rlike 'd*') │
├──────────────────────────┼─────────────────────────┤
│ true                     │ true                    │
└────────────────────────────────────────────────────┘

21.34 - REGEXP_INSTR

Returns the starting index of the substring of the string expr that matches the regular expression specified by the pattern pat, 0 if there is no match. If expr or pat is NULL, the return value is NULL. Character indexes begin at 1.

Analyze Syntax

func.regexp_instr(<expr>, <pat[, pos[, occurrence[, return_option[, match_type]]]]>)

Analyze Examples

func.regexp_instr('dog cat dog', 'dog')
+-----------------------------------------+
| func.regexp_instr('dog cat dog', 'dog') |
+-----------------------------------------+
|                                       1 |
+-----------------------------------------+

SQL Syntax

REGEXP_INSTR(<expr>, <pat[, pos[, occurrence[, return_option[, match_type]]]]>)

Arguments

ArgumentsDescription
exprThe string expr that to be matched
patThe regular expression
posOptional. The position in expr at which to start the search. If omitted, the default is 1.
occurrenceOptional. Which occurrence of a match to search for. If omitted, the default is 1.
return_optionOptional. Which type of position to return. If this value is 0, REGEXP_INSTR() returns the position of the matched substring's first character. If this value is 1, REGEXP_INSTR() returns the position following the matched substring. If omitted, the default is 0.
match_typeOptional. A string that specifies how to perform matching. The meaning is as described for REGEXP_LIKE().

Return Type

A number data type value.

SQL Examples

SELECT REGEXP_INSTR('dog cat dog', 'dog');
+------------------------------------+
| REGEXP_INSTR('dog cat dog', 'dog') |
+------------------------------------+
|                                  1 |
+------------------------------------+

SELECT REGEXP_INSTR('dog cat dog', 'dog', 2);
+---------------------------------------+
| REGEXP_INSTR('dog cat dog', 'dog', 2) |
+---------------------------------------+
|                                     9 |
+---------------------------------------+

SELECT REGEXP_INSTR('aa aaa aaaa', 'a{2}');
+-------------------------------------+
| REGEXP_INSTR('aa aaa aaaa', 'a{2}') |
+-------------------------------------+
|                                   1 |
+-------------------------------------+

SELECT REGEXP_INSTR('aa aaa aaaa', 'a{4}');
+-------------------------------------+
| REGEXP_INSTR('aa aaa aaaa', 'a{4}') |
+-------------------------------------+
|                                   8 |
+-------------------------------------+

21.35 - REGEXP_LIKE

REGEXP_LIKE function is used to check that whether the string matches the regular expression.

Analyze Syntax

func.regexp_like(<expr>, <pat[, match_type]>)

Analyze Examples

func.regexp_like('a', '^[a-d]')
+---------------------------------+
| func.regexp_like('a', '^[a-d]') |
+---------------------------------+
|                               1 |
+---------------------------------+

SQL Syntax

REGEXP_LIKE(<expr>, <pat[, match_type]>)

Arguments

ArgumentsDescription
<expr>The string expr that to be matched
<pat>The regular expression
[match_type]Optional. match_type argument is a string that specifying how to perform matching

match_type may contain any or all the following characters:

  • c: Case-sensitive matching.
  • i: Case-insensitive matching.
  • m: Multiple-line mode. Recognize line terminators within the string. The default behavior is to match line terminators only at the start and end of the string expression.
  • n: The . character matches line terminators. The default is for . matching to stop at the end of a line.
  • u: Unix-only line endings. Not be supported now.

Return Type

BIGINT Returns 1 if the string expr matches the regular expression specified by the pattern pat, 0 otherwise. If expr or pat is NULL, the return value is NULL.

SQL Examples

SELECT REGEXP_LIKE('a', '^[a-d]');
+----------------------------+
| REGEXP_LIKE('a', '^[a-d]') |
+----------------------------+
|                          1 |
+----------------------------+

SELECT REGEXP_LIKE('abc', 'ABC');
+---------------------------+
| REGEXP_LIKE('abc', 'ABC') |
+---------------------------+
|                         1 |
+---------------------------+

SELECT REGEXP_LIKE('abc', 'ABC', 'c');
+--------------------------------+
| REGEXP_LIKE('abc', 'ABC', 'c') |
+--------------------------------+
|                              0 |
+--------------------------------+

SELECT REGEXP_LIKE('new*\n*line', 'new\\*.\\*line');
+-------------------------------------------+
| REGEXP_LIKE('new*
*line', 'new\*.\*line') |
+-------------------------------------------+
|                                         0 |
+-------------------------------------------+

SELECT REGEXP_LIKE('new*\n*line', 'new\\*.\\*line', 'n');
+------------------------------------------------+
| REGEXP_LIKE('new*
*line', 'new\*.\*line', 'n') |
+------------------------------------------------+
|                                              1 |
+------------------------------------------------+

21.36 - REGEXP_REPLACE

Replaces occurrences in the string expr that match the regular expression specified by the pattern pat with the replacement string repl, and returns the resulting string. If expr, pat, or repl is NULL, the return value is NULL.

Analyze Syntax

func.regexp_replace(<expr>, <pat>, <repl[, pos[, occurrence[, match_type]]]>)

Analyze Examples

func.regexp_replace('a b c', 'b', 'X')
+----------------------------------------+
| func.regexp_replace('a b c', 'b', 'X') |
+----------------------------------------+
| a X c                                  |
+----------------------------------------+

SQL Syntax

REGEXP_REPLACE(<expr>, <pat>, <repl[, pos[, occurrence[, match_type]]]>)

Arguments

ArgumentsDescription
exprThe string expr that to be matched
patThe regular expression
replThe replacement string
posOptional. The position in expr at which to start the search. If omitted, the default is 1.
occurrenceOptional. Which occurrence of a match to replace. If omitted, the default is 0 (which means "replace all occurrences").
match_typeOptional. A string that specifies how to perform matching. The meaning is as described for REGEXP_LIKE().

Return Type

VARCHAR

SQL Examples

SELECT REGEXP_REPLACE('a b c', 'b', 'X');
+-----------------------------------+
| REGEXP_REPLACE('a b c', 'b', 'X') |
+-----------------------------------+
| a X c                             |
+-----------------------------------+

SELECT REGEXP_REPLACE('abc def ghi', '[a-z]+', 'X', 1, 3);
+----------------------------------------------------+
| REGEXP_REPLACE('abc def ghi', '[a-z]+', 'X', 1, 3) |
+----------------------------------------------------+
| abc def X                                          |
+----------------------------------------------------+

SELECT REGEXP_REPLACE('周 周周 周周周', '周+', 'X', 3, 2);
+-----------------------------------------------------------+
| REGEXP_REPLACE('周 周周 周周周', '周+', 'X', 3, 2)        |
+-----------------------------------------------------------+
| 周 周周 X                                                 |
+-----------------------------------------------------------+

21.37 - REGEXP_SUBSTR

Returns the substring of the string expr that matches the regular expression specified by the pattern pat, NULL if there is no match. If expr or pat is NULL, the return value is NULL.

Analyze Syntax

func.regexp_substr(<expr>, <pat[, pos[, occurrence[, match_type]]]>)

Analyze Examples

func.regexp_substr('abc def ghi', '[a-z]+')
+---------------------------------------------+
| func.regexp_substr('abc def ghi', '[a-z]+') |
+---------------------------------------------+
| abc                                         |
+---------------------------------------------+

SQL Syntax

REGEXP_SUBSTR(<expr>, <pat[, pos[, occurrence[, match_type]]]>)

Arguments

ArgumentsDescription
exprThe string expr that to be matched
patThe regular expression
posOptional. The position in expr at which to start the search. If omitted, the default is 1.
occurrenceOptional. Which occurrence of a match to search for. If omitted, the default is 1.
match_typeOptional. A string that specifies how to perform matching. The meaning is as described for REGEXP_LIKE().

Return Type

VARCHAR

SQL Examples

SELECT REGEXP_SUBSTR('abc def ghi', '[a-z]+');
+----------------------------------------+
| REGEXP_SUBSTR('abc def ghi', '[a-z]+') |
+----------------------------------------+
| abc                                    |
+----------------------------------------+

SELECT REGEXP_SUBSTR('abc def ghi', '[a-z]+', 1, 3);
+----------------------------------------------+
| REGEXP_SUBSTR('abc def ghi', '[a-z]+', 1, 3) |
+----------------------------------------------+
| ghi                                          |
+----------------------------------------------+

SELECT REGEXP_SUBSTR('周 周周 周周周 周周周周', '周+', 2, 3);
+------------------------------------------------------------------+
| REGEXP_SUBSTR('周 周周 周周周 周周周周', '周+', 2, 3)            |
+------------------------------------------------------------------+
| 周周周周                                                         |
+------------------------------------------------------------------+

21.38 - REPEAT

Returns a string consisting of the string str repeated count times. If count is less than 1, returns an empty string. Returns NULL if str or count are NULL.

Analyze Syntax

func.repeat(<str>, <count>)

Analyze Examples

func.repeat(<str>, <count>)
+-------------------------+
| func.repeat('plaid', 3) |
+-------------------------+
| plaidplaidplaid         |
+-------------------------+

SQL Syntax

REPEAT(<str>, <count>)

Arguments

ArgumentsDescription
<str>The string.
<count>The number.

SQL Examples

SELECT REPEAT('databend', 3);
+--------------------------+
| REPEAT('databend', 3)    |
+--------------------------+
| databenddatabenddatabend |
+--------------------------+

SELECT REPEAT('databend', 0);
+-----------------------+
| REPEAT('databend', 0) |
+-----------------------+
|                       |
+-----------------------+

SELECT REPEAT('databend', NULL);
+--------------------------+
| REPEAT('databend', NULL) |
+--------------------------+
|                     NULL |
+--------------------------+

21.39 - REPLACE

Returns the string str with all occurrences of the string from_str replaced by the string to_str.

Analyze Syntax

func.replace(<str>, <from_str>, <to_str>)

Analyze Examples

func.replace(<str>, <from_str>, <to_str>)
+--------------------------------------+
| func.replace('plaidCloud', 'p', 'P') |
+--------------------------------------+
| PlaidCloud                           |
+--------------------------------------+

SQL Syntax

REPLACE(<str>, <from_str>, <to_str>)

Arguments

ArgumentsDescription
<str>The string.
<from_str>The from string.
<to_str>The to string.

Return Type

VARCHAR

SQL Examples

SELECT REPLACE('www.mysql.com', 'w', 'Ww');
+-------------------------------------+
| REPLACE('www.mysql.com', 'w', 'Ww') |
+-------------------------------------+
| WwWwWw.mysql.com                    |
+-------------------------------------+

21.40 - REVERSE

Returns the string str with the order of the characters reversed.

Analyze Syntax

func.reverse(<str>)

Analyze Examples

func.reverse('abc')
+----------------------+
| func..reverse('abc') |
+----------------------+
| cba                  |
+----------------------+

SQL Syntax

REVERSE(<str>)

Arguments

ArgumentsDescription
<str>The string value.

Return Type

VARCHAR

SQL Examples

SELECT REVERSE('abc');
+----------------+
| REVERSE('abc') |
+----------------+
| cba            |
+----------------+

21.41 - RIGHT

Returns the rightmost len characters from the string str, or NULL if any argument is NULL.

Analyze Syntax

func.right(<str>, <len>)

Analyze Examples

func.right('foobarbar', 4)
+----------------------------+
| func.right('foobarbar', 4) |
+----------------------------+
| rbar                       |
+----------------------------+

SQL Syntax

RIGHT(<str>, <len>);

Arguments

ArgumentsDescription
<str>The main string from where the character to be extracted
<len>The count of characters

Return Type

VARCHAR

SQL Examples

SELECT RIGHT('foobarbar', 4);
+-----------------------+
| RIGHT('foobarbar', 4) |
+-----------------------+
| rbar                  |
+-----------------------+

21.42 - RLIKE

Alias for REGEXP.

21.43 - RPAD

Returns the string str, right-padded with the string padstr to a length of len characters. If str is longer than len, the return value is shortened to len characters.

Analyze Syntax

func.rpad(<str>, <len>, <padstr>)

Analyze Examples

func.rpad('hi',5,'?')
+-----------------------+
| func.rpad('hi',5,'?') |
+-----------------------+
| hi???                 |
+-----------------------+

func.rpad('hi',1,'?')
+-----------------------+
| func.rpad('hi',1,'?') |
+-----------------------+
| h                     |
+-----------------------+

SQL Syntax

RPAD(<str>, <len>, <padstr>)

Arguments

ArgumentsDescription
<str>The string.
<len>The length.
<padstr>The pad string.

Return Type

VARCHAR

SQL Examples

SELECT RPAD('hi',5,'?');
+--------------------+
| RPAD('hi', 5, '?') |
+--------------------+
| hi???              |
+--------------------+

SELECT RPAD('hi',1,'?');
+--------------------+
| RPAD('hi', 1, '?') |
+--------------------+
| h                  |
+--------------------+

21.44 - RTRIM

Removes all occurrences of any character present in the specified trim string from the right side of the string.

See also:

Analyze Syntax

func.rtrim(<string>, <trim_string>)

Analyze Examples

func.rtrim('databend'xx, 'x')
+--------------------------------+
| func.rtrim('databendxx', 'x')  |
+--------------------------------+
| databend                       |
+--------------------------------+

SQL Syntax

RTRIM(<string>, <trim_string>)

SQL Examples

SELECT RTRIM('databendxx', 'x'), RTRIM('databendxx', 'xy');

┌──────────────────────────────────────────────────────┐
│ rtrim('databendxx', 'x') │ rtrim('databendxx', 'xy') │
├──────────────────────────┼───────────────────────────┤
│ databend                 │ databend                  │
└──────────────────────────────────────────────────────┘

21.45 - SOUNDEX

Generates the Soundex code for a string.

  • A Soundex code consists of a letter followed by three numerical digits. PlaidCloud Lakehouse's implementation returns more than 4 digits, but you can SUBSTR the result to get a standard Soundex code.
  • All non-alphabetic characters in the string are ignored.
  • All international alphabetic characters outside the A-Z range are ignored unless they're the first letter.

See also: SOUNDS LIKE

Analyze Syntax

func.soundex(<str>)

Analyze Examples

func.soundex('PlaidCloud Lakehouse')
+--------------------------------------+
| func.soundex('PlaidCloud Lakehouse') |
+--------------------------------------+
| D153                                 |
+--------------------------------------+

SQL Syntax

SOUNDEX(<str>)

Arguments

ArgumentsDescription
strThe string.

Return Type

Returns a code of type VARCHAR or a NULL value.

SQL Examples

SELECT SOUNDEX('PlaidCloud Lakehouse');

---
D153

-- All non-alphabetic characters in the string are ignored.
SELECT SOUNDEX('PlaidCloud Lakehouse!');

---
D153

-- All international alphabetic characters outside the A-Z range are ignored unless they're the first letter.
SELECT SOUNDEX('PlaidCloud Lakehouse，你好');

---
D153

SELECT SOUNDEX('你好，PlaidCloud Lakehouse');

---
你3153

-- SUBSTR the result to get a standard Soundex code.
SELECT SOUNDEX('databend cloud'),SUBSTR(SOUNDEX('databend cloud'),1,4);

soundex('databend cloud')|substring(soundex('databend cloud') from 1 for 4)|
-------------------------+-------------------------------------------------+
D153243                  |D153                                             |

SELECT SOUNDEX(NULL);
+-------------------------------------+
| `SOUNDEX(NULL)`                     |
+-------------------------------------+
| <null>                              |
+-------------------------------------+

21.46 - SOUNDS LIKE

Compares the pronunciation of two strings by their Soundex codes. Soundex is a phonetic algorithm that produces a code representing the pronunciation of a string, allowing for approximate matching of strings based on their pronunciation rather than their spelling. PlaidCloud Lakehouse offers the SOUNDEX function that allows you to get the Soundex code from a string.

SOUNDS LIKE is frequently employed in the WHERE clause of SQL queries to narrow down rows using fuzzy string matching, such as for names and addresses, see Filtering Rows in Examples.

Analyze Syntax

func.sounds_like(<str1>, <str2>)

Analyze Examples

func..sounds_like('Monday', 'Sunday')
+---------------------------------------+
| func..sounds_like('Monday', 'Sunday') |
+---------------------------------------+
|                                     0 |
+---------------------------------------+

SQL Syntax

<str1> SOUNDS LIKE <str2>

Arguments

ArgumentsDescription
str1, 2The strings you compare.

Return Type

Return a Boolean value of 1 if the Soundex codes for the two strings are the same (which means they sound alike) and 0 otherwise.

SQL Examples

Comparing Strings

SELECT 'two' SOUNDS LIKE 'too'
----
1

SELECT CONCAT('A', 'B') SOUNDS LIKE 'AB';
----
1

SELECT 'Monday' SOUNDS LIKE 'Sunday';
----
0

Filtering Rows

SELECT * FROM  employees;

id|first_name|last_name|age|
--+----------+---------+---+
 0|John      |Smith    | 35|
 0|Mark      |Smythe   | 28|
 0|Johann    |Schmidt  | 51|
 0|Eric      |Doe      | 30|
 0|Sue       |Johnson  | 45|


SELECT * FROM  employees
WHERE  first_name SOUNDS LIKE 'John';

id|first_name|last_name|age|
--+----------+---------+---+
 0|John      |Smith    | 35|
 0|Johann    |Schmidt  | 51|

21.47 - SPACE

Returns a string consisting of N blank space characters.

Analyze Syntax

func.space(<n>)

Analyze Examples

func.space(20)
+-----------------+
| func.space(20)  |
+-----------------+
|                 |
+-----------------+

SQL Syntax

SPACE(<n>);

Arguments

ArgumentsDescription
<n>The number of spaces

Return Type

String data type value.

SQL Examples

SELECT SPACE(20)
+----------------------+
| SPACE(20)            |
+----------------------+
|                      |
+----------------------+

21.48 - SPLIT

Splits a string using a specified delimiter and returns the resulting parts as an array.

See also: SPLIT_PART

Analyze Syntax

func.split('<input_string>', '<delimiter>')

Analyze Examples

func.split('PlaidCloud Lakehouse', ' ')
+-----------------------------------------+
| func.split('PlaidCloud Lakehouse', ' ') |
+-----------------------------------------+
| ['PlaidCloud Lakehouse']          |
+-----------------------------------------+

SQL Syntax

SPLIT('<input_string>', '<delimiter>')

Return Type

Array of strings. SPLIT returns NULL when either the input string or the delimiter is NULL.

SQL Examples

-- Use a space as the delimiter
-- SPLIT returns an array with two parts.
SELECT SPLIT('PlaidCloud Lakehouse', ' ');

split('PlaidCloud Lakehouse', ' ')|
----------------------------------+
['PlaidCloud','Lakehouse']        |

-- Use an empty string as the delimiter or a delimiter that does not exist in the input string
-- SPLIT returns an array containing the entire input string as a single part.
SELECT SPLIT('PlaidCloud Lakehouse', '');

split('databend cloud', '')|
----------------------------------+
['PlaidCloud Lakehouse']    |

SELECT SPLIT('PlaidCloud Lakehouse', ',');

split('databend cloud', ',')|
----------------------------------+
['PlaidCloud Lakehouse']    |

-- Use '	' (tab) as the delimiter
-- SPLIT returns an array with timestamp, log level, and message.

SELECT SPLIT('2023-10-19 15:30:45	INFO	Log message goes here', '	');

split('2023-10-19 15:30:45\tinfo\tlog message goes here', '\t')|
---------------------------------------------------------------+
['2023-10-19 15:30:45','INFO','Log message goes here']         |

21.49 - SPLIT_PART

Splits a string using a specified delimiter and returns the specified part.

See also: SPLIT

Analyze Syntax

func.split_part('<input_string>', '<delimiter>', '<position>')

Analyze Examples

func.split_part('PlaidCloud Lakehouse', ' ', 1)
+-------------------------------------------------+
| func.split_part('PlaidCloud Lakehouse', ' ', 1) |
+-------------------------------------------------+
| PlaidCloud                                      |
+-------------------------------------------------+

SQL Syntax

SPLIT_PART('<input_string>', '<delimiter>', '<position>')

The position argument specifies which part to return. It uses a 1-based index but can also accept positive, negative, or zero values:

  • If position is a positive number, it returns the part at the position from the left to the right, or NULL if it doesn't exist.
  • If position is a negative number, it returns the part at the position from the right to the left, or NULL if it doesn't exist.
  • If position is 0, it is treated as 1, effectively returning the first part of the string.

Return Type

String. SPLIT_PART returns NULL when either the input string, the delimiter, or the position is NULL.

SQL Examples

-- Use a space as the delimiter
-- SPLIT_PART returns a specific part.
SELECT SPLIT_PART('PlaidCloud Lakehouse', ' ', 1);

split_part('PlaidCloud Lakehouse', ' ', 1)|
------------------------------------------+
PlaidCloud Lakehouse                      |

-- Use an empty string as the delimiter or a delimiter that does not exist in the input string
-- SPLIT_PART returns the entire input string.
SELECT SPLIT_PART('PlaidCloud Lakehouse', '', 1);

split_part('PlaidCloud Lakehouse', '', 1)|
-----------------------------------+
PlaidCloud Lakehouse                     |

SELECT SPLIT_PART('PlaidCloud Lakehouse', ',', 1);

split_part('PlaidCloud Lakehouse', ',', 1)|
------------------------------------+
PlaidCloud Lakehouse                      |

-- Use '    ' (tab) as the delimiter
-- SPLIT_PART returns individual fields.
SELECT SPLIT_PART('2023-10-19 15:30:45   INFO   Log message goes here', '   ', 3);

split_part('2023-10-19 15:30:45   info   log message goes here', '   ', 3)|
--------------------------------------------------------------------------+
Log message goes here                                                     |

-- SPLIT_PART returns an empty string as the specified part does not exist at all.
SELECT SPLIT_PART('2023-10-19 15:30:45   INFO   Log message goes here', '   ', 4);

split_part('2023-10-19 15:30:45   info   log message goes here', '   ', 4)|
--------------------------------------------------------------------------+
                                                                          |

21.50 - STRCMP

Returns 0 if the strings are the same, -1 if the first argument is smaller than the second, and 1 otherwise.

Analyze Syntax

func.strcmp(<expr1> ,<expr2>)

Analyze Examples

func.strcmp('text', 'text2')
+------------------------------+
| func.strcmp('text', 'text2') |
+------------------------------+
|                           -1 |
+------------------------------+

func.strcmp('text2', 'text')
+------------------------------+
| func.strcmp('text2', 'text') |
+------------------------------+
|                            1 |
+------------------------------+

func.strcmp('text', 'text')
+------------------------------+
| func.strcmp('text', 'text')  |
+------------------------------+
|                            0 |
+------------------------------+

SQL Syntax

STRCMP(<expr1> ,<expr2>)

Arguments

ArgumentsDescription
<expr1>The string.
<expr2>The string.

Return Type

BIGINT

SQL Examples

SELECT STRCMP('text', 'text2');
+-------------------------+
| STRCMP('text', 'text2') |
+-------------------------+
|                      -1 |
+-------------------------+

SELECT STRCMP('text2', 'text');
+-------------------------+
| STRCMP('text2', 'text') |
+-------------------------+
|                       1 |
+-------------------------+

SELECT STRCMP('text', 'text');
+------------------------+
| STRCMP('text', 'text') |
+------------------------+
|                      0 |
+------------------------+

21.51 - SUBSTR

Extracts a string containing a specific number of characters from a particular position of a given string.

  • The forms without a len argument return a substring from string str starting at position pos.
  • The forms with a len argument return a substring len characters long from string str, starting at position pos.

It is also possible to use a negative value for pos. In this case, the beginning of the substring is pos characters from the end of the string, rather than the beginning. A negative value may be used for pos in any of the forms of this function. A value of 0 for pos returns an empty string. The position of the first character in the string from which the substring is to be extracted is reckoned as 1.

Analyze Syntax

func.substr(<str>, <pos>, <len>)

Analyze Examples

func.substr('Quadratically', 5, 6)
+------------------------------------+
| func.substr('Quadratically', 5, 6) |
+------------------------------------+
| ratica                             |
+------------------------------------+

SQL Syntax

SUBSTR(<str>, <pos>)

SUBSTR(<str>, <pos>, <len>)

Arguments

ArgumentsDescription
<str>The main string from where the character to be extracted
<pos>The position (starting from 1) the substring to start at. If negative, counts from the end
<len>The maximum length of the substring to extract

Aliases

Return Type

VARCHAR

SQL Examples

SELECT
  SUBSTRING('Quadratically', 5),
  SUBSTR('Quadratically', 5),
  MID('Quadratically', 5);

┌─────────────────────────────────────────────────────────────────────────────────────────────────┐
│ substring('quadratically' from 5) │ substring('quadratically' from 5) │ mid('quadratically', 5) │
├───────────────────────────────────┼───────────────────────────────────┼─────────────────────────┤
│ ratically                         │ ratically                         │ ratically               │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT
  SUBSTRING('Quadratically', 5, 6),
  SUBSTR('Quadratically', 5, 6),
  MID('Quadratically', 5, 6);

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ substring('quadratically' from 5 for 6) │ substring('quadratically' from 5 for 6) │ mid('quadratically', 5, 6) │
├─────────────────────────────────────────┼─────────────────────────────────────────┼────────────────────────────┤
│ ratica                                  │ ratica                                  │ ratica                     │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

21.52 - SUBSTRING

Alias for SUBSTR.

21.53 - TO_BASE64

Converts the string argument to base-64 encoded form and returns the result as a character string. If the argument is not a string, it is converted to a string before conversion takes place. The result is NULL if the argument is NULL.

Analyze Syntax

func.to_base64(<v>)

Analyze Examples

func.to_base64('abc')
+-----------------------+
| func.to_base64('abc') |
+-----------------------+
| YWJj                  |
+-----------------------+

SQL Syntax

TO_BASE64(<v>)

Arguments

ArgumentsDescription
<v>The value.

Return Type

VARCHAR

SQL Examples

SELECT TO_BASE64('abc');
+------------------+
| TO_BASE64('abc') |
+------------------+
| YWJj             |
+------------------+

21.54 - TRANSLATE

Transforms a given string by replacing specific characters with corresponding replacements, as defined by the provided mapping.

Analyze Syntax

func.translate('<inputString>', '<charactersToReplace>', '<replacementCharacters>')

Analyze Examples

func.translate('databend', 'de', 'DE')
+----------------------------------------+
| func.translate('databend', 'de', 'DE') |
+----------------------------------------+
| DatabEnD                               |
+----------------------------------------+

SQL Syntax

TRANSLATE('<inputString>', '<charactersToReplace>', '<replacementCharacters>')
ParameterDescription
<inputString>The input string to be transformed.
<charactersToReplace>The string containing characters to be replaced in the input string.
<replacementCharacters>The string containing replacement characters corresponding to those in <charactersToReplace>.

SQL Examples

-- Replace 'd' with '$' in 'databend'
SELECT TRANSLATE('databend', 'd', '$');

---
$ataben$

-- Replace 'd' with 'D' in 'databend'
SELECT TRANSLATE('databend', 'd', 'D');

---
DatabenD

-- Replace 'd' with 'D' and 'e' with 'E' in 'databend'
SELECT TRANSLATE('databend', 'de', 'DE');

---
DatabEnD

-- Remove 'd' from 'databend'
SELECT TRANSLATE('databend', 'd', '');

---
ataben

21.55 - TRIM

Returns the string without leading or trailing occurrences of the specified remove string. If remove string is omitted, spaces are removed.

The Analyze function automatically trims both leading and trailing spaces.

Analyze Syntax

func.trim(str)

Analyze Examples

func.trim('   plaidcloud   ')
+--------------------------------+
| func.trim('   plaidcloud   ') |
+--------------------------------+
| 'plaidcloud'                   |
+--------------------------------+

SQL Syntax

TRIM([{BOTH | LEADING | TRAILING} [remstr] FROM ] str)

SQL Examples

Please note that ALL the examples in this section will return the string 'databend'.

The following example removes the leading and trailing string 'xxx' from the string 'xxxdatabendxxx':

SELECT TRIM(BOTH 'xxx' FROM 'xxxdatabendxxx');

The following example removes the leading string 'xxx' from the string 'xxxdatabend':

SELECT TRIM(LEADING 'xxx' FROM 'xxxdatabend' );

The following example removes the trailing string 'xxx' from the string 'databendxxx':

SELECT TRIM(TRAILING 'xxx' FROM 'databendxxx' );

If no remove string is specified, the function removes all leading and trailing spaces. The following examples remove the leading and/or trailing spaces:

SELECT TRIM('   databend   ');
SELECT TRIM('   databend');
SELECT TRIM('databend   ');

21.56 - TRIM_BOTH

Removes all occurrences of the specified trim string from the beginning, end, or both sides of the string.

See also: TRIM

Analyze Syntax

func.trim_both(<string>, <trim_string>)

Analyze Examples

func.trim_both('xxdatabendxx', 'x')
+--------------------------------------+
| func.trim_both('xxdatabendxx', 'x')  |
+--------------------------------------+
| databend                             |
+--------------------------------------+

SQL Syntax

TRIM_BOTH(<string>, <trim_string>)

SQL Examples

SELECT TRIM_BOTH('xxdatabendxx', 'xxx'), TRIM_BOTH('xxdatabendxx', 'xx'), TRIM_BOTH('xxdatabendxx', 'x');

┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ trim_both('xxdatabendxx', 'xxx') │ trim_both('xxdatabendxx', 'xx') │ trim_both('xxdatabendxx', 'x') │
├──────────────────────────────────┼─────────────────────────────────┼────────────────────────────────┤
│ xxdatabendxx                     │ databend                        │ databend                       │
└─────────────────────────────────────────────────────────────────────────────────────────────────────┘

21.57 - TRIM_LEADING

Removes all occurrences of the specified trim string from the beginning of the string.

See also:

Analyze Syntax

func.trim_leading(<string>, <trim_string>)

Analyze Examples

func.trim_leading('xxdatabendxx', 'x')
+------------------------------------------+
| func.trim_leading('xxdatabendxx', 'x')   |
+------------------------------------------+
| databendxx                               |
+------------------------------------------+

SQL Syntax

TRIM_LEADING(<string>, <trim_string>)

SQL Examples

SELECT TRIM_LEADING('xxdatabend', 'xxx'), TRIM_LEADING('xxdatabend', 'xx'), TRIM_LEADING('xxdatabend', 'x');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ trim_leading('xxdatabend', 'xxx') │ trim_leading('xxdatabend', 'xx') │ trim_leading('xxdatabend', 'x') │
├───────────────────────────────────┼──────────────────────────────────┼─────────────────────────────────┤
│ xxdatabend                        │ databend                         │ databend                        │
└────────────────────────────────────────────────────────────────────────────────────────────────────────┘

21.58 - TRIM_TRAILING

Removes all occurrences of the specified trim string from the end of the string.

See also:

Analyze Syntax

func.trim_trailing(<string>, <trim_string>)

Analyze Examples

func.trim_trailing('xxdatabendxx', 'x')
+------------------------------------------+
| func.trim_trailing('xxdatabendxx', 'x')  |
+------------------------------------------+
| xxdatabend                               |
+------------------------------------------+

SQL Syntax

TRIM_TRAILING(<string>, <trim_string>)

SQL Examples

SELECT TRIM_TRAILING('databendxx', 'xxx'), TRIM_TRAILING('databendxx', 'xx'), TRIM_TRAILING('databendxx', 'x');

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ trim_trailing('databendxx', 'xxx') │ trim_trailing('databendxx', 'xx') │ trim_trailing('databendxx', 'x') │
├────────────────────────────────────┼───────────────────────────────────┼──────────────────────────────────┤
│ databendxx                         │ databend                          │ databend                         │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────┘

21.59 - UCASE

Alias for UPPER.

21.60 - UNHEX

For a string argument str, UNHEX(str) interprets each pair of characters in the argument as a hexadecimal number and converts it to the byte represented by the number. The return value is a binary string.

Analyze Syntax

func.unhex(<expr>)

Analyze Examples

func.unhex('6461746162656e64')
+--------------------------------+
| func.unhex('6461746162656e64') |
+--------------------------------+
| 6461746162656E64               |
+--------------------------------+

SQL Syntax

UNHEX(<expr>)

Aliases

SQL Examples

SELECT UNHEX('6461746162656e64') as c1, typeof(c1),UNHEX('6461746162656e64')::varchar as c2, typeof(c2), FROM_HEX('6461746162656e64');

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│           c1              │     typeof(c1)         │       c2         |    typeof(c2)     |   from_hex('6461746162656e64')  |
├───────────────────────────┼────────────────────────|──────────────────┤───────────────────|─────────────────────────────────┤
│ 6461746162656E64          │      binary            │      databend    |    varchar        |   6461746162656E64              |
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

SELECT UNHEX(HEX('string')), unhex(HEX('string'))::varchar;

┌──────────────────────────────────────────────────────┐
│ unhex(hex('string')) │ unhex(hex('string'))::varchar │
├──────────────────────┼───────────────────────────────┤
│ 737472696E67         │ string                        │
└──────────────────────────────────────────────────────┘

21.61 - UPPER

Returns a string with all characters changed to uppercase.

Analyze Syntax

func.unhex(<expr>)

Analyze Examples

func.upper('hello, plaidcloud lakehouse!')
+--------------------------------------------+
| func.upper('hello, plaidcloud lakehouse!') |
+--------------------------------------------+
| 'HELLO, PLAIDCLOUD LAKEHOUSE!'             |
+--------------------------------------------+

SQL Syntax

UPPER(<str>)

Aliases

Return Type

VARCHAR

SQL Examples

SELECT UPPER('hello, databend!'), UCASE('hello, databend!');

┌───────────────────────────────────────────────────────┐
│ upper('hello, databend!') │ ucase('hello, databend!') │
├───────────────────────────┼───────────────────────────┤
│ HELLO, DATABEND!          │ HELLO, DATABEND!          │
└───────────────────────────────────────────────────────┘

22 - System Functions

This section provides reference information for the system-related functions in PlaidCloud Lakehouse.

List of Functions:

22.1 - CLUSTERING_INFORMATION

Returns clustering information of a table.

SQL Syntax

CLUSTERING_INFORMATION('<database_name>', '<table_name>')

SQL Examples

CREATE TABLE mytable(a int, b int) CLUSTER BY(a+1);

INSERT INTO mytable VALUES(1,1),(3,3);
INSERT INTO mytable VALUES(2,2),(5,5);
INSERT INTO mytable VALUES(4,4);

SELECT * FROM CLUSTERING_INFORMATION('default','mytable')\G
*************************** 1. row ***************************
            cluster_key: ((a + 1))
      total_block_count: 3
   constant_block_count: 1
unclustered_block_count: 0
       average_overlaps: 1.3333
          average_depth: 2.0
  block_depth_histogram: {"00002":3}
ParameterDescription
cluster_keyThe defined cluster key.
total_block_countThe current count of blocks.
constant_block_countThe count of blocks where min/max values are equal, meaning each block contains only one (group of) cluster_key value.
unclustered_block_countThe count of blocks that have not yet been clustered.
average_overlapsThe average ratio of overlapping blocks within a given range.
average_depthThe average depth of overlapping partitions for the cluster key.
block_depth_histogramThe number of partitions at each depth level. A higher concentration of partitions at lower depths indicates more effective table clustering.

22.2 - FUSE_BLOCK

Returns the block information of the latest or specified snapshot of a table. For more information about what is block in PlaidCloud Lakehouse, see What are Snapshot, Segment, and Block?.

The command returns the location information of each parquet file referenced by a snapshot. This enables downstream applications to access and consume the data stored in the files.

See Also:

SQL Syntax

FUSE_BLOCK('<database_name>', '<table_name>'[, '<snapshot_id>'])

SQL Examples

CREATE TABLE mytable(c int);
INSERT INTO mytable values(1);
INSERT INTO mytable values(2); 

SELECT * FROM FUSE_BLOCK('default', 'mytable');

---
+----------------------------------+----------------------------+----------------------------------------------------+------------+----------------------------------------------------+-------------------+
| snapshot_id                      | timestamp                  | block_location                                     | block_size | bloom_filter_location                              | bloom_filter_size |
+----------------------------------+----------------------------+----------------------------------------------------+------------+----------------------------------------------------+-------------------+
| 51e84b56458f44269b05a059b364a659 | 2022-09-15 07:14:14.137268 | 1/7/_b/39a6dbbfd9b44ad5a8ec8ab264c93cf5_v0.parquet |          4 | 1/7/_i/39a6dbbfd9b44ad5a8ec8ab264c93cf5_v1.parquet |               221 |
| 51e84b56458f44269b05a059b364a659 | 2022-09-15 07:14:14.137268 | 1/7/_b/d0ee9688c4d24d6da86acd8b0d6f4fad_v0.parquet |          4 | 1/7/_i/d0ee9688c4d24d6da86acd8b0d6f4fad_v1.parquet |               219 |
+----------------------------------+----------------------------+----------------------------------------------------+------------+----------------------------------------------------+-------------------+

22.3 - FUSE_COLUMN

Returns the column information of the latest or specified snapshot of a table. For more information about what is block in PlaidCloud Lakehouse, see What are Snapshot, Segment, and Block?.

See Also:

SQL Syntax

FUSE_COLUMN('<database_name>', '<table_name>'[, '<snapshot_id>'])

SQL Examples

CREATE TABLE mytable(c int);
INSERT INTO mytable values(1);
INSERT INTO mytable values(2);

SELECT * FROM FUSE_COLUMN('default', 'mytable');

---
+----------------------------------+----------------------------+---------------------------------------------------------+------------+-----------+-----------+-------------+-------------+-----------+--------------+------------------+
| snapshot_id                      | timestamp                  | block_location                                          | block_size | file_size | row_count | column_name | column_type | column_id | block_offset | bytes_compressed |
+----------------------------------+----------------------------+---------------------------------------------------------+------------+-----------+-----------+-------------+-------------+-----------+--------------+------------------+
| 3faefc1a9b6a48f388a8b59228dd06c1 | 2023-07-18 03:06:30.276502 | 1/118746/_b/44df130c207745cb858928135d39c1c0_v2.parquet |          4 |       196 |         1 | c           | Int32       |         0 |            8 |               14 |
| 3faefc1a9b6a48f388a8b59228dd06c1 | 2023-07-18 03:06:30.276502 | 1/118746/_b/b6f8496d7e3f4f62a89c09572840cf70_v2.parquet |          4 |       196 |         1 | c           | Int32       |         0 |            8 |               14 |
+----------------------------------+----------------------------+---------------------------------------------------------+------------+-----------+-----------+-------------+-------------+-----------+--------------+------------------+

22.4 - FUSE_ENCODING

Returns the encoding types applied to a specific column within a table. It helps you understand how data is compressed and stored in a native format within the table.

SQL Syntax

FUSE_ENCODING('<database_name>', '<table_name>', '<column_name>')

The function returns a result set with the following columns:

ColumnData TypeDescription
VALIDITY_SIZENullable(UInt32)The size of a bitmap value that indicates whether each row in the column has a non-null value. This bitmap is used to track the presence or absence of null values in the column's data.
COMPRESSED_SIZEUInt32The size of the column data after compression.
UNCOMPRESSED_SIZEUInt32The size of the column data before applying encoding.
LEVEL_ONEStringThe primary or initial encoding applied to the column.
LEVEL_TWONullable(String)A secondary or recursive encoding method applied to the column after the initial encoding.

SQL Examples

-- Create a table with an integer column 'c' and apply 'Lz4' compression
CREATE TABLE t(c INT) STORAGE_FORMAT = 'native' COMPRESSION = 'lz4';

-- Insert data into the table.
INSERT INTO t SELECT number FROM numbers(2048);

-- Analyze the encoding for column 'c' in table 't'
SELECT LEVEL_ONE, LEVEL_TWO, COUNT(*) 
FROM FUSE_ENCODING('default', 't', 'c') 
GROUP BY LEVEL_ONE, LEVEL_TWO;

level_one   |level_two|count(*)|
------------+---------+--------+
DeltaBitpack|         |       1|

--  Insert 2,048 rows with the value 1 into the table 't'
INSERT INTO t (c)
SELECT 1
FROM numbers(2048);

SELECT LEVEL_ONE, LEVEL_TWO, COUNT(*) 
FROM FUSE_ENCODING('default', 't', 'c') 
GROUP BY LEVEL_ONE, LEVEL_TWO;

level_one   |level_two|count(*)|
------------+---------+--------+
OneValue    |         |       1|
DeltaBitpack|         |       1|

22.5 - FUSE_SEGMENT

Returns the segment information of a specified table snapshot. For more information about what is segment in PlaidCloud Lakehouse, see What are Snapshot, Segment, and Block?.

See Also:

SQL Syntax

FUSE_SEGMENT('<database_name>', '<table_name>','<snapshot_id>')

SQL Examples

CREATE TABLE mytable(c int);
INSERT INTO mytable values(1);
INSERT INTO mytable values(2); 

-- Obtain a snapshot ID
SELECT snapshot_id FROM FUSE_SNAPSHOT('default', 'mytable') limit 1;

---
+----------------------------------+
| snapshot_id                      |
+----------------------------------+
| 82c572947efa476892bd7c0635158ba2 |
+----------------------------------+

SELECT * FROM FUSE_SEGMENT('default', 'mytable', '82c572947efa476892bd7c0635158ba2');

---
+----------------------------------------------------+----------------+-------------+-----------+--------------------+------------------+
| file_location                                      | format_version | block_count | row_count | bytes_uncompressed | bytes_compressed |
+----------------------------------------------------+----------------+-------------+-----------+--------------------+------------------+
| 1/319/_sg/d35fe7bf99584301b22e8f6a8a9c97f9_v1.json |              1 |           1 |         1 |                  4 |              184 |
| 1/319/_sg/c261059d47c840e1b749222dabb4b2bb_v1.json |              1 |           1 |         1 |                  4 |              184 |
+----------------------------------------------------+----------------+-------------+-----------+--------------------+------------------+

22.6 - FUSE_SNAPSHOT

Returns the snapshot information of a table. For more information about what is snapshot in PlaidCloud Lakehouse, see What are Snapshot, Segment, and Block?.

See Also:

SQL Syntax

FUSE_SNAPSHOT('<database_name>', '<table_name>')

SQL Examples

CREATE TABLE mytable(a int, b int) CLUSTER BY(a+1);

INSERT INTO mytable VALUES(1,1),(3,3);
INSERT INTO mytable VALUES(2,2),(5,5);
INSERT INTO mytable VALUES(4,4);

SELECT * FROM FUSE_SNAPSHOT('default','mytable');

---
| snapshot_id                      | snapshot_location                                          | format_version | previous_snapshot_id             | segment_count | block_count | row_count | bytes_uncompressed | bytes_compressed | index_size | timestamp                  |
|----------------------------------|------------------------------------------------------------|----------------|----------------------------------|---------------|-------------|-----------|--------------------|------------------|------------|----------------------------|
| a13d211b7421432898a3786848b8ced3 | 670655/783287/_ss/a13d211b7421432898a3786848b8ced3_v1.json | 1              | \N                               | 1             | 1           | 2         | 16                 | 290              | 363        | 2022-09-19 14:51:52.860425 |
| cf08e6af6c134642aeb76bc81e6e7580 | 670655/783287/_ss/cf08e6af6c134642aeb76bc81e6e7580_v1.json | 1              | a13d211b7421432898a3786848b8ced3 | 2             | 2           | 4         | 32                 | 580              | 726        | 2022-09-19 14:52:15.282943 |
| 1bd4f68b831a402e8c42084476461aa1 | 670655/783287/_ss/1bd4f68b831a402e8c42084476461aa1_v1.json | 1              | cf08e6af6c134642aeb76bc81e6e7580 | 3             | 3           | 5         | 40                 | 862              | 1085       | 2022-09-19 14:52:20.284347 |

22.7 - FUSE_STATISTIC

Returns the estimated number of distinct values of each column in a table.

SQL Syntax

FUSE_STATISTIC('<database_name>', '<table_name>')

SQL Examples

You're most likely to use this function together with ANALYZE TABLE <table_name> to generate and check the statistical information of a table. For more explanations and examples, see OPTIMIZE TABLE.

22.8 - FUSE_TIME_TRAVEL_SIZE

Calculates the storage size of historical data (for Time Travel) for tables.

SQL Syntax

-- Calculate historical data size for all tables in all databases
SELECT ...
FROM fuse_time_travel_size();

-- Calculate historical data size for all tables in a specified database
SELECT ...
FROM fuse_time_travel_size('<database_name>');

-- Calculate historical data size for a specified table in a specified database
SELECT ...
FROM fuse_time_travel_size('<database_name>', '<table_name>'));

Output

The function returns a result set with the following columns:

ColumnDescription
database_nameThe name of the database where the table is located.
table_nameThe name of the table.
is_droppedIndicates whether the table has been dropped (true for dropped tables, false otherwise).
time_travel_sizeThe total storage size of historical data (for Time Travel) for the table, in bytes.
latest_snapshot_sizeThe storage size of the latest snapshot of the table, in bytes.
data_retention_period_in_hoursThe retention period for Time Travel data in hours (NULL means using the default retention policy).
errorAny error encountered while retrieving the storage size (NULL if no errors occurred).

SQL Examples

This example calculates the historical data for all tables in the default database:

SELECT * FROM fuse_time_travel_size('default')

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ database_name │ table_name │ is_dropped │ time_travel_size │ latest_snapshot_size │ data_retention_period_in_hours │       error      │
├───────────────┼────────────┼────────────┼──────────────────┼──────────────────────┼────────────────────────────────┼──────────────────┤
│ default       │ books      │ true       │             2810 │                 1490 │                           NULL │ NULL             │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

23 - Table Functions

This section provides reference information for the table-related functions in PlaidCloud Lakehouse.

23.1 - GENERATE_SERIES

Generates a dataset starting from a specified point, ending at another specified point, and optionally with an incrementing value. The GENERATE_SERIES function works with the following data types:

  • Integer
  • Date
  • Timestamp

Analyze Syntax

func.generate_series(<start>, <stop>[, <step_interval>])

Analyze Examples

func.generate_series(1, 10, 2);

generate_series|
---------------+
              1|
              3|
              5|
              7|
              9|

SQL Syntax

GENERATE_SERIES(<start>, <stop>[, <step_interval>])

Arguments

ArgumentDescription
startThe starting value, representing the first number, date, or timestamp in the sequence.
stopThe ending value, representing the last number, date, or timestamp in the sequence.
step_intervalThe step interval, determining the difference between adjacent values in the sequence. For integer sequences, the default value is 1. For date sequences, the default step interval is 1 day. For timestamp sequences, the default step interval is 1 microsecond.

Return Type

Returns a list containing a continuous sequence of numeric values, dates, or timestamps from start to stop.

SQL Examples

SQL Examples 1: Generating Numeric, Date, and Timestamp Data

SELECT * FROM GENERATE_SERIES(1, 10, 2);

generate_series|
---------------+
              1|
              3|
              5|
              7|
              9|

SELECT * FROM GENERATE_SERIES('2023-03-20'::date, '2023-03-27'::date);

generate_series|
---------------+
     2023-03-20|
     2023-03-21|
     2023-03-22|
     2023-03-23|
     2023-03-24|
     2023-03-25|
     2023-03-26|
     2023-03-27|

SELECT * FROM GENERATE_SERIES('2023-03-26 00:00'::timestamp, '2023-03-27 12:00'::timestamp, 86400000000);

generate_series    |
-------------------+
2023-03-26 00:00:00|
2023-03-27 00:00:00|

SQL Examples 2: Filling Query Result Gaps

This example uses the GENERATE_SERIES function and left join operator to handle gaps in query results caused by missing information in specific ranges.

CREATE TABLE t_metrics (
  date Date,
  value INT
);

INSERT INTO t_metrics VALUES
  ('2020-01-01', 200),
  ('2020-01-01', 300),
  ('2020-01-04', 300),
  ('2020-01-04', 300),
  ('2020-01-05', 400),
  ('2020-01-10', 700);

SELECT date, SUM(value), COUNT() FROM t_metrics GROUP BY date ORDER BY date;

date      |sum(value)|count()|
----------+----------+-------+
2020-01-01|       500|      2|
2020-01-04|       600|      2|
2020-01-05|       400|      1|
2020-01-10|       700|      1|

To close the gaps between January 1st and January 10th, 2020, use the following query:

SELECT t.date, COALESCE(SUM(t_metrics.value), 0), COUNT(t_metrics.value)
FROM generate_series(
  '2020-01-01'::Date,
  '2020-01-10'::Date
) AS t(date)
LEFT JOIN t_metrics ON t_metrics.date = t.date
GROUP BY t.date ORDER BY t.date;

date      |coalesce(sum(t_metrics.value), 0)|count(t_metrics.value)|
----------+---------------------------------+----------------------+
2020-01-01|                              500|                     2|
2020-01-02|                                0|                     0|
2020-01-03|                                0|                     0|
2020-01-04|                              600|                     2|
2020-01-05|                              400|                     1|
2020-01-06|                                0|                     0|
2020-01-07|                                0|                     0|
2020-01-08|                                0|                     0|
2020-01-09|                                0|                     0|
2020-01-10|                              700|                     1|

23.2 - INFER_SCHEMA

Automatically detects the file metadata schema and retrieves the column definitions.

SQL Syntax

INFER_SCHEMA(
  LOCATION => '{ internalStage | externalStage }'
  [ PATTERN => '<regex_pattern>']
)

Where:

internalStage

internalStage ::= @<internal_stage_name>[/<path>]

externalStage

externalStage ::= @<external_stage_name>[/<path>]

PATTERN = 'regex_pattern'

A PCRE2-based regular expression pattern string, enclosed in single quotes, specifying the file names to match. Click here to see an example. For PCRE2 syntax, see http://www.pcre.org/current/doc/html/pcre2syntax.html.

SQL Examples

Generate a parquet file in a stage:

CREATE STAGE infer_parquet FILE_FORMAT = (TYPE = PARQUET);
COPY INTO @infer_parquet FROM (SELECT * FROM numbers(10)) FILE_FORMAT = (TYPE = PARQUET);

LIST @infer_parquet;
+-------------------------------------------------------+------+------------------------------------+-------------------------------+---------+
| name                                                  | size | md5                                | last_modified                 | creator |
+-------------------------------------------------------+------+------------------------------------+-------------------------------+---------+
| data_e0fd9cba-f45c-4c43-aa07-d6d87d134378_0_0.parquet |  258 | "7DCC9FFE04EA1F6882AED2CF9640D3D4" | 2023-02-09 05:21:52.000 +0000 | NULL    |
+-------------------------------------------------------+------+------------------------------------+-------------------------------+---------+

infer_schema

SELECT * FROM INFER_SCHEMA(location => '@infer_parquet/data_e0fd9cba-f45c-4c43-aa07-d6d87d134378_0_0.parquet');
+-------------+-----------------+----------+----------+
| column_name | type            | nullable | order_id |
+-------------+-----------------+----------+----------+
| number      | BIGINT UNSIGNED |        0 |        0 |
+-------------+-----------------+----------+----------+

infer_schema with Pattern Matching

SELECT * FROM infer_schema(location => '@infer_parquet/', pattern => '.*parquet');
+-------------+-----------------+----------+----------+
| column_name | type            | nullable | order_id |
+-------------+-----------------+----------+----------+
| number      | BIGINT UNSIGNED |        0 |        0 |
+-------------+-----------------+----------+----------+

Create a Table From Parquet File

The infer_schema can only display the schema of a parquet file and cannot create a table from it.

To create a table from a parquet file:

CREATE TABLE mytable AS SELECT * FROM @infer_parquet/ (pattern=>'.*parquet') LIMIT 0;

DESC mytable;
+--------+-----------------+------+---------+-------+
| Field  | Type            | Null | Default | Extra |
+--------+-----------------+------+---------+-------+
| number | BIGINT UNSIGNED | NO   | 0       |       |
+--------+-----------------+------+---------+-------+

23.3 - INSPECT_PARQUET

Retrieves a table of comprehensive metadata from a staged Parquet file, including the following columns:

ColumnDescription
created_byThe entity or source responsible for creating the Parquet file
num_columnsThe number of columns in the Parquet file
num_rowsThe total number of rows or records in the Parquet file
num_row_groupsThe count of row groups within the Parquet file
serialized_sizeThe size of the Parquet file on disk (compressed)
max_row_groups_size_compressedThe size of the largest row group (compressed)
max_row_groups_size_uncompressedThe size of the largest row group (uncompressed)

SQL Syntax

INSPECT_PARQUET('@<path-to-file>')

SQL Examples

This example retrieves the metadata from a staged sample Parquet file named books.parquet. The file contains two records:

Transaction Processing,Jim Gray,1992
Readings in Database Systems,Michael Stonebraker,2004

-- Show the staged file
LIST @my_internal_stage;

┌──────────────────────────────────────────────────────────────────────────────────────────────┐
│      name     │  size  │        md5       │         last_modified         │      creator     │
├───────────────┼────────┼──────────────────┼───────────────────────────────┼──────────────────┤
│ books.parquet │    998 │ NULL             │ 2023-04-19 19:34:51.303 +0000 │ NULL             │
└──────────────────────────────────────────────────────────────────────────────────────────────┘

-- Retrieve metadata from the staged file
SELECT * FROM INSPECT_PARQUET('@my_internal_stage/books.parquet');

┌────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│             created_by             │ num_columns │ num_rows │ num_row_groups │ serialized_size │ max_row_groups_size_compressed │ max_row_groups_size_uncompressed │
├────────────────────────────────────┼─────────────┼──────────┼────────────────┼─────────────────┼────────────────────────────────┼──────────────────────────────────┤
│ parquet-cpp version 1.5.1-SNAPSHOT │           3 │        2 │              1 │             998 │                            332 │                              320 │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

23.4 - LIST_STAGE

Lists files in a stage. This allows you to filter files in a stage based on their extensions and obtain comprehensive details about each file. The function is similar to the DDL command LIST STAGE FILES, but provides you the flexibility to retrieve specific file information with the SELECT statement, such as file name, size, MD5 hash, last modified timestamp, and creator, rather than all file information.

SQL Syntax

LIST_STAGE(
  LOCATION => '{ internalStage | externalStage | userStage }'
  [ PATTERN => '<regex_pattern>']
)

Where:

internalStage

internalStage ::= @<internal_stage_name>[/<path>]

externalStage

externalStage ::= @<external_stage_name>[/<path>]

userStage

userStage ::= @~[/<path>]

PATTERN

See COPY INTO table.

SQL Examples

SELECT * FROM list_stage(location => '@my_stage/', pattern => '.*[.]log');
+----------------+------+------------------------------------+-------------------------------+---------+
|      name      | size |                md5                 |         last_modified         | creator |
+----------------+------+------------------------------------+-------------------------------+---------+
| 2023/meta.log  |  475 | "4208ff530b252236e14b3cd797abdfbd" | 2023-04-19 20:23:24.000 +0000 | NULL    |
| 2023/query.log | 1348 | "1c6654b207472c277fc8c6207c035e18" | 2023-04-19 20:23:24.000 +0000 | NULL    |
+----------------+------+------------------------------------+-------------------------------+---------+

-- Equivalent to the following statement:
LIST @my_stage PATTERN = '.log';

23.5 - RESULT_SCAN

Returns the result set of a previous command in same session as if the result was a table.

SQL Syntax

RESULT_SCAN( { '<query_id>' | LAST_QUERY_ID() } )

SQL Examples

Create a simple table:

CREATE TABLE t1(a int);

Insert some values;

INSERT INTO t1(a) VALUES (1), (2), (3);

result_scan

SELECT * FROM t1 ORDER BY a;
+-------+
|   a   |
+-------+
|   1   |
+-------+
|   2   |
+-------+
|   3   |
+-------+

SELECT * FROM RESULT_SCAN(LAST_QUERY_ID()) ORDER BY a;
+-------+
|   a   |
+-------+
|   1   |
+-------+
|   2   |
+-------+
|   3   |
+-------+

23.6 - SHOW_GRANTS

Lists privileges explicitly granted to a user, to a role, or on a specific object.

SQL Syntax

SHOW_GRANTS('role', '<role_name>')
SHOW_GRANTS('user', '<user_name>')
SHOW_GRANTS('stage', '<stage_name>')
SHOW_GRANTS('udf', '<udf_name>')
SHOW_GRANTS('table', '<table_name>', '<catalog_name>', '<db_name>')
SHOW_GRANTS('database', '<db_name>', '<catalog_name>')

Configuring enable_expand_roles Setting

The enable_expand_roles setting controls whether the SHOW_GRANTS function expands role inheritance when displaying privileges.

  • enable_expand_roles=1 (default):

    • SHOW_GRANTS recursively expands inherited privileges, meaning that if a role has been granted another role, it will display all the inherited privileges.
    • Users will also see all privileges granted through their assigned roles.

  • enable_expand_roles=0:

    • SHOW_GRANTS only displays privileges that are directly assigned to the specified role or user.
    • However, the result will still include GRANT ROLE statements to indicate role inheritance.

For example, role a has the SELECT privilege on t1, and role b has the SELECT privilege on t2:

SELECT grants FROM show_grants('role', 'a') ORDER BY object_id;

┌──────────────────────────────────────────────────────┐
│                        grants                        │
├──────────────────────────────────────────────────────┤
│ GRANT SELECT ON 'default'.'default'.'t1' TO ROLE `a` │
└──────────────────────────────────────────────────────┘

SELECT grants FROM show_grants('role', 'b') ORDER BY object_id;

┌──────────────────────────────────────────────────────┐
│                        grants                        │
├──────────────────────────────────────────────────────┤
│ GRANT SELECT ON 'default'.'default'.'t2' TO ROLE `b` │
└──────────────────────────────────────────────────────┘

If you grant role b to role a and check the grants on role a again, you can see than the SELECT privilege on t2 is now included in role a:

GRANT ROLE b TO ROLE a;

SELECT grants FROM show_grants('role', 'a') ORDER BY object_id;

┌──────────────────────────────────────────────────────┐
│                        grants                        │
├──────────────────────────────────────────────────────┤
│ GRANT SELECT ON 'default'.'default'.'t1' TO ROLE `a` │
│ GRANT SELECT ON 'default'.'default'.'t2' TO ROLE `a` │
└──────────────────────────────────────────────────────┘

If you set enable_expand_roles to 0 and check the grants on role a again, the result will show the GRANT ROLE statement instead of listing the specific privileges inherited from role b:

SET enable_expand_roles=0;

SELECT grants FROM show_grants('role', 'a') ORDER BY object_id;

┌──────────────────────────────────────────────────────┐
│                        grants                        │
├──────────────────────────────────────────────────────┤
│ GRANT SELECT ON 'default'.'default'.'t1' TO ROLE `a` │
│ GRANT ROLE b to ROLE `a`                             │
│ GRANT ROLE public to ROLE `a`                        │
└──────────────────────────────────────────────────────┘

SQL Examples

This example illustrates how to list privileges granted to a user, a role, and on a specific object.

-- Create a new user
CREATE USER 'user1' IDENTIFIED BY 'password';

-- Create a new role
CREATE ROLE analyst;

-- Grant the analyst role to the user
GRANT ROLE analyst TO 'user1';

-- Create a stage
CREATE STAGE my_stage;

-- Grant privileges on the stage to the role
GRANT READ ON STAGE my_stage TO ROLE analyst;

-- List privileges granted to the user
SELECT * FROM SHOW_GRANTS('user', 'user1');

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ privileges │ object_name │     object_id    │ grant_to │  name  │                    grants                   │
├────────────┼─────────────┼──────────────────┼──────────┼────────┼─────────────────────────────────────────────┤
│ Read       │ my_stage    │             NULL │ USER     │ user1  │ GRANT Read ON STAGE my_stage TO 'user1'@'%' │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- List privileges granted to the role
SELECT * FROM SHOW_GRANTS('role', 'analyst');

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ privileges │ object_name │     object_id    │ grant_to │   name  │                     grants                     │
├────────────┼─────────────┼──────────────────┼──────────┼─────────┼────────────────────────────────────────────────┤
│ Read       │ my_stage    │             NULL │ ROLE     │ analyst │ GRANT Read ON STAGE my_stage TO ROLE `analyst` │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

-- List privileges granted on the stage
SELECT * FROM SHOW_GRANTS('stage', 'my_stage');

┌─────────────────────────────────────────────────────────────────────────────────────┐
│ privileges │ object_name │     object_id    │ grant_to │   name  │      grants      │
├────────────┼─────────────┼──────────────────┼──────────┼─────────┼──────────────────┤
│ Read       │ my_stage    │             NULL │ ROLE     │ analyst │                  │
└─────────────────────────────────────────────────────────────────────────────────────┘

23.7 - STREAM_STATUS

Provides information about the status of a specified stream, yielding a single-column result (has_data) that can take on values of true or false:

  • true: Indicates that the stream might contain change data capture records.
  • false: Indicates that the stream currently does not contain any change data capture records.

:::note The presence of a true in the result (has_data) does not ensure the definite existence of change data capture records. Other operations, such as performing a table compact operation, could also lead to a true value even when there are no actual change data capture records. :::

:::note When using STREAM_STATUS in tasks, you must include the database name when referencing the stream (e.g., STREAM_STATUS('mydb.stream_name')). :::

SQL Syntax

SELECT * FROM STREAM_STATUS('<database_name>.<stream_name>');
-- OR
SELECT * FROM STREAM_STATUS('<stream_name>');  -- Uses current database

SQL Examples

-- Create a table 't' with a column 'c'
CREATE TABLE t (c int);

-- Create a stream 's' on the table 't'
CREATE STREAM s ON TABLE t;

-- Check the initial status of the stream 's'
SELECT * FROM STREAM_STATUS('s');

-- The result should be 'false' indicating no change data capture records initially
┌──────────┐
│ has_data │
├──────────┤
│ false    │
└──────────┘

-- Insert a value into the table 't'
INSERT INTO t VALUES (1);

-- Check the updated status of the stream 's' after the insertion
SELECT * FROM STREAM_STATUS('s');

-- The result should now be 'true' indicating the presence of change data capture records
┌──────────┐
│ has_data │
├──────────┤
│ true     │
└──────────┘

-- Example with database name specified
SELECT * FROM STREAM_STATUS('mydb.s');

23.8 - TASK_HISTORY

Displays task running history given variables.

SQL Syntax

TASK_HISTORY(
      [ SCHEDULED_TIME_RANGE_START => <constant_expr> ]
      [, SCHEDULED_TIME_RANGE_END => <constant_expr> ]
      [, RESULT_LIMIT => <integer> ]
      [, TASK_NAME => '<string>' ]
      [, ERROR_ONLY => { TRUE | FALSE } ]
      [, ROOT_TASK_ID => '<string>'] )

Arguments

All the arguments are optional.

SCHEDULED_TIME_RANGE_START => <constant_expr>, SCHEDULED_TIME_RANGE_END => <constant_expr>

Time range (in TIMESTAMP_LTZ format), within the last 7 days, in which the task execution was scheduled. If the time range does not fall within the last 7 days, an error is returned.

  • If SCHEDULED_TIME_RANGE_END is not specified, the function returns those tasks that have already completed, are currently running, or are scheduled in the future.
  • If SCHEDULED_TIME_RANGE_END is CURRENT_TIMESTAMP, the function returns those tasks that have already completed or are currently running. Note that a task that is executed immediately before the current time might still be identified as scheduled.
  • To query only those tasks that have already completed or are currently running, include WHERE query_id IS NOT NULL as a filter. The QUERY_ID column in the TASK_HISTORY output is populated only when a task has started running.

If no start or end time is specified, the most recent tasks are returned, up to the specified RESULT_LIMIT value.

RESULT_LIMIT => <integer>

A number specifying the maximum number of rows returned by the function.

If the number of matching rows is greater than this limit, the task executions with the most recent timestamp are returned, up to the specified limit.

Range: 1 to 10000

Default: 100.

TASK_NAME => <string>

A case-insensitive string specifying a task. Only non-qualified task names are supported. Only executions of the specified task are returned. Note that if multiple tasks have the same name, the function returns the history for each of these tasks.

ERROR_ONLY => { TRUE | FALSE }

When set to TRUE, this function returns only task runs that failed or were cancelled.

ROOT_TASK_ID => <string>

Unique identifier for the root task in a task graph. This ID matches the ID column value in the SHOW TASKS output for the same task. Specify the ROOT_TASK_ID to show the history of the root task and any child tasks that are part of the task graph.

Usage Notes

  • This function returns a maximum of 10,000 rows, set in the RESULT_LIMIT argument value. The default value is 100.
  • This function returns results only for the ACCOUNTADMIN role.

SQL Examples

SELECT
  *
FROM TASK_HISTORY() order by scheduled_time;

The above SQL query retrieves all task history records from the TASK_HISTORY function, ordered by the scheduled_time column.(maximum 10,000)

SELECT *
  FROM TASK_HISTORY(
    SCHEDULED_TIME_RANGE_START=>TO_TIMESTAMP('2022-01-02T01:12:00-07:00'),
    SCHEDULED_TIME_RANGE_END=>TO_TIMESTAMP('2022-01-02T01:12:30-07:00'))

The above SQL query retrieves all task history records from the TASK_HISTORY function where the scheduled time range starts at '2022-01-02T01:12:00-07:00' and ends at '2022-01-02T01:12:30-07:00'. This means it will return the tasks that were scheduled to run within this specific 30-second time window. The result will include details of the tasks that match this criteria.

24 - Test Functions

This section provides reference information for test functions in PlaidCloud Lakehouse.

24.1 - SLEEP

Sleeps seconds seconds on each data block.

!!! warning Only used for testing where sleep is required.

SQL Syntax

SLEEP(seconds)

Arguments

ArgumentsDescription
secondsMust be a constant column of any nonnegative number or float.｜

Return Type

UInt8

SQL Examples

SELECT sleep(2);
+----------+
| sleep(2) |
+----------+
|        0 |
+----------+

25 - UUID Functions

This section provides reference information for the UUID-related functions in PlaidCloud Lakehouse.

25.1 - GEN_RANDOM_UUID

Generates a random UUID based on v4.

Analyze Syntax

func.gen_random_uuid()

SQL Examples

func.gen_random_uuid()

┌───────────────────────────────────────┐
│           func.gen_random_uuid()      │
├───────────────────────────────────────|
│ f88e7efe-1bc2-494b-806b-3ffe90db8f47  │
└───────────────────────────────────────┘

SQL Syntax

GEN_RANDOM_UUID()

Aliases

SQL Examples

SELECT GEN_RANDOM_UUID(), UUID();

┌─────────────────────────────────────────────────────────────────────────────┐
│           gen_random_uuid()          │                uuid()                │
├──────────────────────────────────────┼──────────────────────────────────────┤
│ f88e7efe-1bc2-494b-806b-3ffe90db8f47 │ f88e7efe-1bc2-494b-806b-3ffe90db8f47 │
└─────────────────────────────────────────────────────────────────────────────┘

25.2 - UUID

Alias for GEN_RANDOM_UUID.

26 - Window Functions

Overview

A window function operates on a group ("window") of related rows.

For each input row, a window function returns one output row that depends on the specific row passed to the function and the values of the other rows in the window.

There are two main types of order-sensitive window functions:

  • Rank-related functions: Rank-related functions list information based on the "rank" of a row. For example, ranking stores in descending order by profit per year, the store with the most profit will be ranked 1, and the second-most profitable store will be ranked 2, and so on.

  • Window frame functions: Window frame functions enable you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.

List of Functions that Support Windows

The list below shows all the window functions.

Function NameCategoryWindowWindow FrameNotes
ARRAY_AGGGeneral✔
AVGGeneral✔✔
AVG_IFGeneral✔✔
COUNTGeneral✔✔
COUNT_IFGeneral✔✔
COVAR_POPGeneral✔
COVAR_SAMPGeneral✔
MAXGeneral✔✔
MAX_IFGeneral✔✔
MINGeneral✔✔
MIN_IFGeneral✔✔
STDDEV_POPGeneral✔✔
STDDEV_SAMPGeneral✔✔
MEDIANGeneral✔✔
QUANTILE_CONTGeneral✔✔
QUANTILE_DISCGeneral✔✔
KURTOSISGeneral✔✔
SKEWNESSGeneral✔✔
SUMGeneral✔✔
SUM_IFGeneral✔✔
CUME_DISTRank-related✔
PERCENT_RANKRank-related✔✔
DENSE_RANKRank-related✔✔
RANKRank-related✔✔
ROW_NUMBERRank-related✔
NTILERank-related✔
FIRST_VALUERank-related✔✔
FIRSTRank-related✔✔
LAST_VALUERank-related✔✔
LASTRank-related✔✔
NTH_VALUERank-related✔✔
LEADRank-related✔
LAGRank-related✔

Window Syntax

<function> ( [ <arguments> ] ) OVER ( { named window | inline window } )

named window ::=
    { window_name | ( window_name ) }

inline window ::=
    [ PARTITION BY <expression_list> ]
    [ ORDER BY <expression_list> ]
    [ window frame ]

The named window is a window that is defined in the WINDOW clause of the SELECT statement, eg: SELECT a, SUM(a) OVER w FROM t WINDOW w AS ( inline window ).

The <function> is one of (aggregate function, rank function, value function).

The OVER clause specifies that the function is being used as a window function.

The PARTITION BY sub-clause allows rows to be grouped into sub-groups, for example by city, by year, etc. The PARTITION BY clause is optional. You can analyze an entire group of rows without breaking it into sub-groups.

The ORDER BY clause orders rows within the window.

The window frame clause specifies the window frame type and the window frame extent. The window frame clause is optional. If you omit the window frame clause, the default window frame type is RANGE and the default window frame extent is UNBOUNDED PRECEDING AND CURRENT ROW.

Window Frame Syntax

window frame can be one of the following types:

cumulativeFrame ::=
    {
       { ROWS | RANGE } BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
     | { ROWS | RANGE } BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
    }

slidingFrame ::=
    {
       ROWS BETWEEN <N> { PRECEDING | FOLLOWING } AND <N> { PRECEDING | FOLLOWING }
     | ROWS BETWEEN UNBOUNDED PRECEDING AND <N> { PRECEDING | FOLLOWING }
     | ROWS BETWEEN <N> { PRECEDING | FOLLOWING } AND UNBOUNDED FOLLOWING
    }

SQL Examples

Create the table

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR,
  last_name VARCHAR,
  department VARCHAR,
  salary INT
);

Insert data

INSERT INTO employees (employee_id, first_name, last_name, department, salary) VALUES
  (1, 'John', 'Doe', 'IT', 75000),
  (2, 'Jane', 'Smith', 'HR', 85000),
  (3, 'Mike', 'Johnson', 'IT', 90000),
  (4, 'Sara', 'Williams', 'Sales', 60000),
  (5, 'Tom', 'Brown', 'HR', 82000),
  (6, 'Ava', 'Davis', 'Sales', 62000),
  (7, 'Olivia', 'Taylor', 'IT', 72000),
  (8, 'Emily', 'Anderson', 'HR', 77000),
  (9, 'Sophia', 'Lee', 'Sales', 58000),
  (10, 'Ella', 'Thomas', 'IT', 67000);

Example 1: Ranking employees by salary

In this example, we use the RANK() function to rank employees based on their salaries in descending order. The highest salary will get a rank of 1, and the lowest salary will get the highest rank number.

SELECT employee_id, first_name, last_name, department, salary, RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;

Result:

employee_idfirst_namelast_namedepartmentsalaryrank
3MikeJohnsonIT900001
2JaneSmithHR850002
5TomBrownHR820003
8EmilyAndersonHR770004
1JohnDoeIT750005
7OliviaTaylorIT720006
10EllaThomasIT670007
6AvaDavisSales620008
4SaraWilliamsSales600009
9SophiaLeeSales5800010

Example 2: Calculating the total salary per department

In this example, we use the SUM() function with PARTITION BY to calculate the total salary paid per department. Each row will show the department and the total salary for that department.

SELECT department, SUM(salary) OVER (PARTITION BY department) AS total_salary
FROM employees;

Result:

departmenttotal_salary
HR244000
HR244000
HR244000
IT304000
IT304000
IT304000
IT304000
Sales180000
Sales180000
Sales180000

Example 3: Calculating a running total of salaries per department

In this example, we use the SUM() function with a cumulative window frame to calculate a running total of salaries within each department. The running total is calculated based on the employee's salary ordered by their employee_id.

SELECT employee_id, first_name, last_name, department, salary, 
       SUM(salary) OVER (PARTITION BY department ORDER BY employee_id
                         ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total
FROM employees;

Result:

employee_idfirst_namelast_namedepartmentsalaryrunning_total
2JaneSmithHR8500085000
5TomBrownHR82000167000
8EmilyAndersonHR77000244000
1JohnDoeIT7500075000
3MikeJohnsonIT90000165000
7OliviaTaylorIT72000237000
10EllaThomasIT67000304000
4SaraWilliamsSales6000060000
6AvaDavisSales62000122000
9SophiaLeeSales58000180000

26.1 - CUME_DIST

Returns the cumulative distribution of a given value in a set of values. It calculates the proportion of rows that have values less than or equal to the specified value, divided by the total number of rows. Please note that the resulting value falls between 0 and 1, inclusive.

See also: PERCENT_RANK

Analyze Syntax

func.cume_dist().over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.name, table.score, table.grade, func.cume_dist().over(partition_by=[table.grade], order_by=table.score).alias('cume_dist_val')

name    |score|grade|cume_dist_val|
--------+-----+-----+-------------+
Smith   |   81|A    |         0.25|
Davies  |   84|A    |          0.5|
Evans   |   87|A    |         0.75|
Johnson |  100|A    |          1.0|
Taylor  |   62|B    |          0.5|
Brown   |   62|B    |          0.5|
Wilson  |   72|B    |          1.0|
Thomas  |   72|B    |          1.0|
Jones   |   55|C    |          1.0|
Williams|   55|C    |          1.0|

SQL Syntax

CUME_DIST() OVER (
	PARTITION BY expr, ...
	ORDER BY expr [ASC | DESC], ...
)

SQL Examples

This example retrieves the students' names, scores, grades, and the cumulative distribution values (cume_dist_val) within each grade using the CUME_DIST() window function.

CREATE TABLE students (
    name VARCHAR(20),
    score INT NOT NULL,
    grade CHAR(1) NOT NULL
);

INSERT INTO students (name, score, grade)
VALUES
    ('Smith', 81, 'A'),
    ('Jones', 55, 'C'),
    ('Williams', 55, 'C'),
    ('Taylor', 62, 'B'),
    ('Brown', 62, 'B'),
    ('Davies', 84, 'A'),
    ('Evans', 87, 'A'),
    ('Wilson', 72, 'B'),
    ('Thomas', 72, 'B'),
    ('Johnson', 100, 'A');

SELECT
    name,
    score,
    grade,
    CUME_DIST() OVER (PARTITION BY grade ORDER BY score) AS cume_dist_val
FROM
    students;

name    |score|grade|cume_dist_val|
--------+-----+-----+-------------+
Smith   |   81|A    |         0.25|
Davies  |   84|A    |          0.5|
Evans   |   87|A    |         0.75|
Johnson |  100|A    |          1.0|
Taylor  |   62|B    |          0.5|
Brown   |   62|B    |          0.5|
Wilson  |   72|B    |          1.0|
Thomas  |   72|B    |          1.0|
Jones   |   55|C    |          1.0|
Williams|   55|C    |          1.0|

26.2 - DENSE_RANK

Returns the rank of a value within a group of values, without gaps in the ranks.

The rank value starts at 1 and continues up sequentially.

If two values are the same, they have the same rank.

Analyze Syntax

func.dense_rank().over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.department, func.sum(salary), func.dense_rank().over(order_by=func.sum(table.salary).desc()).alias('dense_rank')

| department | total_salary | dense_rank |
|------------|--------------|------------|
| IT         | 172000       | 1          |
| HR         | 160000       | 2          |
| Sales      | 77000        | 3          |

SQL Syntax

DENSE_RANK() OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ ASC | DESC ] [ <window_frame> ] )

SQL Examples

Create the table

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR,
  last_name VARCHAR,
  department VARCHAR,
  salary INT
);

Insert data

INSERT INTO employees (employee_id, first_name, last_name, department, salary) VALUES
  (1, 'John', 'Doe', 'IT', 90000),
  (2, 'Jane', 'Smith', 'HR', 85000),
  (3, 'Mike', 'Johnson', 'IT', 82000),
  (4, 'Sara', 'Williams', 'Sales', 77000),
  (5, 'Tom', 'Brown', 'HR', 75000);

Calculating the total salary per department using DENSE_RANK

SELECT
    department,
    SUM(salary) AS total_salary,
    DENSE_RANK() OVER (ORDER BY SUM(salary) DESC) AS dense_rank
FROM
    employees
GROUP BY
    department;

Result:

departmenttotal_salarydense_rank
IT1720001
HR1600002
Sales770003

26.3 - FIRST

Alias for FIRST_VALUE.

26.4 - FIRST_VALUE

Returns the first value from an ordered group of values.

See also:

Analyze Syntax

func.first_value(<expr>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.employee_id, table.first_name, table.last_name, table.salary, func.first_value(table.first_name).over(order_by=table.salary.desc()).alias('highest_salary_first_name')

employee_id | first_name | last_name | salary  | highest_salary_first_name
------------+------------+-----------+---------+--------------------------
4           | Mary       | Williams  | 7000.00 | Mary
2           | Jane       | Smith     | 6000.00 | Mary
3           | David      | Johnson   | 5500.00 | Mary
1           | John       | Doe       | 5000.00 | Mary
5           | Michael    | Brown     | 4500.00 | Mary

SQL Syntax

FIRST_VALUE(expression) OVER ([PARTITION BY partition_expression] ORDER BY order_expression [window_frame])

For the syntax of window frame, see Window Frame Syntax.

SQL Examples

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  salary DECIMAL(10,2)
);

INSERT INTO employees (employee_id, first_name, last_name, salary)
VALUES
  (1, 'John', 'Doe', 5000.00),
  (2, 'Jane', 'Smith', 6000.00),
  (3, 'David', 'Johnson', 5500.00),
  (4, 'Mary', 'Williams', 7000.00),
  (5, 'Michael', 'Brown', 4500.00);

-- Use FIRST_VALUE to retrieve the first name of the employee with the highest salary
SELECT employee_id, first_name, last_name, salary,
       FIRST_VALUE(first_name) OVER (ORDER BY salary DESC) AS highest_salary_first_name
FROM employees;


employee_id | first_name | last_name | salary  | highest_salary_first_name
------------+------------+-----------+---------+--------------------------
4           | Mary       | Williams  | 7000.00 | Mary
2           | Jane       | Smith     | 6000.00 | Mary
3           | David      | Johnson   | 5500.00 | Mary
1           | John       | Doe       | 5000.00 | Mary
5           | Michael    | Brown     | 4500.00 | Mary

26.5 - LAG

LAG allows you to access the value of a column from a preceding row within the same result set. It is typically used to retrieve the value of a column in the previous row, based on a specified ordering.

See also: LEAD

Analyze Syntax

func.lag(<expr>, <offset>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.product_name, table.sale_amount, func.lag(table.sale_amount, 1).over(partition_by=table.product_name, order_by=table.sale_id).alias('previous_sale_amount')

product_name | sale_amount | previous_sale_amount
-----------------------------------------------
Product A    | 1000.00     | NULL
Product A    | 1500.00     | 1000.00
Product A    | 2000.00     | 1500.00
Product B    | 500.00      | NULL
Product B    | 800.00      | 500.00
Product B    | 1200.00     | 800.00

SQL Syntax

LAG(expression [, offset [, default]]) OVER (PARTITION BY partition_expression ORDER BY sort_expression)
  • offset: Specifies the number of rows ahead (LEAD) or behind (LAG) the current row within the partition to retrieve the value from. Defaults to 1.

Note that setting a negative offset has the same effect as using the LEAD function.

  • default: Specifies a value to be returned if the LEAD or LAG function encounters a situation where there is no value available due to the offset exceeding the partition's boundaries. Defaults to NULL.

SQL Examples

CREATE TABLE sales (
  sale_id INT,
  product_name VARCHAR(50),
  sale_amount DECIMAL(10, 2)
);

INSERT INTO sales (sale_id, product_name, sale_amount)
VALUES (1, 'Product A', 1000.00),
       (2, 'Product A', 1500.00),
       (3, 'Product A', 2000.00),
       (4, 'Product B', 500.00),
       (5, 'Product B', 800.00),
       (6, 'Product B', 1200.00);

SELECT product_name, sale_amount, LAG(sale_amount) OVER (PARTITION BY product_name ORDER BY sale_id) AS previous_sale_amount
FROM sales;

product_name | sale_amount | previous_sale_amount
-----------------------------------------------
Product A    | 1000.00     | NULL
Product A    | 1500.00     | 1000.00
Product A    | 2000.00     | 1500.00
Product B    | 500.00      | NULL
Product B    | 800.00      | 500.00
Product B    | 1200.00     | 800.00

-- The following statements return the same result.
SELECT product_name, sale_amount, LAG(sale_amount, -1) OVER (PARTITION BY product_name ORDER BY sale_id) AS next_sale_amount
FROM sales;

SELECT product_name, sale_amount, LEAD(sale_amount) OVER (PARTITION BY product_name ORDER BY sale_id) AS next_sale_amount
FROM sales;

product_name|sale_amount|next_sale_amount|
------------+-----------+----------------+
Product A   |    1000.00|         1500.00|
Product A   |    1500.00|         2000.00|
Product A   |    2000.00|                |
Product B   |     500.00|          800.00|
Product B   |     800.00|         1200.00|
Product B   |    1200.00|                |

26.6 - LAST

Alias for LAST_VALUE.

26.7 - LAST_VALUE

Returns the last value from an ordered group of values.

See also:

Analyze Syntax

func.last_value(<expr>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.employee_id, table.first_name, table.last_name, table.salary, func.last_value(table.first_name).over(order_by=table.salary.desc()).alias('lowest_salary_first_name')

employee_id | first_name | last_name | salary  | lowest_salary_first_name
------------+------------+-----------+---------+------------------------
4           | Mary       | Williams  | 7000.00 | Michael
2           | Jane       | Smith     | 6000.00 | Michael
3           | David      | Johnson   | 5500.00 | Michael
1           | John       | Doe       | 5000.00 | Michael
5           | Michael    | Brown     | 4500.00 | Michael

SQL Syntax

LAST_VALUE(expression) OVER ([PARTITION BY partition_expression] ORDER BY order_expression [window_frame])

For the syntax of window frame, see Window Frame Syntax.

SQL Examples

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  salary DECIMAL(10,2)
);

INSERT INTO employees (employee_id, first_name, last_name, salary)
VALUES
  (1, 'John', 'Doe', 5000.00),
  (2, 'Jane', 'Smith', 6000.00),
  (3, 'David', 'Johnson', 5500.00),
  (4, 'Mary', 'Williams', 7000.00),
  (5, 'Michael', 'Brown', 4500.00);

-- Use LAST_VALUE to retrieve the first name of the employee with the lowest salary
SELECT employee_id, first_name, last_name, salary,
       LAST_VALUE(first_name) OVER (ORDER BY salary DESC) AS lowest_salary_first_name
FROM employees;

employee_id | first_name | last_name | salary  | lowest_salary_first_name
------------+------------+-----------+---------+------------------------
4           | Mary       | Williams  | 7000.00 | Michael
2           | Jane       | Smith     | 6000.00 | Michael
3           | David      | Johnson   | 5500.00 | Michael
1           | John       | Doe       | 5000.00 | Michael
5           | Michael    | Brown     | 4500.00 | Michael

26.8 - LEAD

LEAD allows you to access the value of a column from a subsequent row within the same result set. It is typically used to retrieve the value of a column in the next row, based on a specified ordering.

See also: LAG

Analyze Syntax

func.lead(<expr>, <offset>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.product_name, table.sale_amount, func.lead(table.sale_amount, 1).over(partition_by=table.product_name, order_by=table.sale_id).alias('next_sale_amount')

product_name | sale_amount | next_sale_amount
----------------------------------------------
Product A    | 1000.00     | 1500.00
Product A    | 1500.00     | 2000.00
Product A    | 2000.00     | NULL
Product B    | 500.00      | 800.00
Product B    | 800.00      | 1200.00
Product B    | 1200.00     | NULL

SQL Syntax

LEAD(expression [, offset [, default]]) OVER (PARTITION BY partition_expression ORDER BY sort_expression)
  • offset: Specifies the number of rows ahead (LEAD) or behind (LAG) the current row within the partition to retrieve the value from. Defaults to 1.

Note that setting a negative offset has the same effect as using the LAG function.

  • default: Specifies a value to be returned if the LEAD or LAG function encounters a situation where there is no value available due to the offset exceeding the partition's boundaries. Defaults to NULL.

SQL Examples

CREATE TABLE sales (
  sale_id INT,
  product_name VARCHAR(50),
  sale_amount DECIMAL(10, 2)
);

INSERT INTO sales (sale_id, product_name, sale_amount)
VALUES (1, 'Product A', 1000.00),
       (2, 'Product A', 1500.00),
       (3, 'Product A', 2000.00),
       (4, 'Product B', 500.00),
       (5, 'Product B', 800.00),
       (6, 'Product B', 1200.00);

SELECT product_name, sale_amount, LEAD(sale_amount) OVER (PARTITION BY product_name ORDER BY sale_id) AS next_sale_amount
FROM sales;

product_name | sale_amount | next_sale_amount
----------------------------------------------
Product A    | 1000.00     | 1500.00
Product A    | 1500.00     | 2000.00
Product A    | 2000.00     | NULL
Product B    | 500.00      | 800.00
Product B    | 800.00      | 1200.00
Product B    | 1200.00     | NULL

-- The following statements return the same result.
SELECT product_name, sale_amount, LEAD(sale_amount, -1) OVER (PARTITION BY product_name ORDER BY sale_id) AS previous_sale_amount
FROM sales;

SELECT product_name, sale_amount, LAG(sale_amount) OVER (PARTITION BY product_name ORDER BY sale_id) AS previous_sale_amount
FROM sales;

product_name|sale_amount|previous_sale_amount|
------------+-----------+--------------------+
Product A   |    1000.00|                    |
Product A   |    1500.00|             1000.00|
Product A   |    2000.00|             1500.00|
Product B   |     500.00|                    |
Product B   |     800.00|              500.00|
Product B   |    1200.00|              800.00|

26.9 - NTH_VALUE

Returns the Nth value from an ordered group of values.

See also:

Analyze Syntax

func.nth_value(<expr>, <n>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.employee_id, table.first_name, table.last_name, table.salary, func.nth_value(table.first_name, 2).over(order_by=table.salary.desc()).alias('second_highest_salary_first_name')

employee_id | first_name | last_name | salary  | second_highest_salary_first_name
------------+------------+-----------+---------+----------------------------------
4           | Mary       | Williams  | 7000.00 | Jane
2           | Jane       | Smith     | 6000.00 | Jane
3           | David      | Johnson   | 5500.00 | Jane
1           | John       | Doe       | 5000.00 | Jane
5           | Michael    | Brown     | 4500.00 | Jane

SQL Syntax

NTH_VALUE(expression, n) OVER ([PARTITION BY partition_expression] ORDER BY order_expression [window_frame])

For the syntax of window frame, see Window Frame Syntax.

SQL Examples

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  salary DECIMAL(10,2)
);

INSERT INTO employees (employee_id, first_name, last_name, salary)
VALUES
  (1, 'John', 'Doe', 5000.00),
  (2, 'Jane', 'Smith', 6000.00),
  (3, 'David', 'Johnson', 5500.00),
  (4, 'Mary', 'Williams', 7000.00),
  (5, 'Michael', 'Brown', 4500.00);

-- Use NTH_VALUE to retrieve the first name of the employee with the second highest salary
SELECT employee_id, first_name, last_name, salary,
       NTH_VALUE(first_name, 2) OVER (ORDER BY salary DESC) AS second_highest_salary_first_name
FROM employees;

employee_id | first_name | last_name | salary  | second_highest_salary_first_name
------------+------------+-----------+---------+----------------------------------
4           | Mary       | Williams  | 7000.00 | Jane
2           | Jane       | Smith     | 6000.00 | Jane
3           | David      | Johnson   | 5500.00 | Jane
1           | John       | Doe       | 5000.00 | Jane
5           | Michael    | Brown     | 4500.00 | Jane

26.10 - NTILE

Divides the sorted result set into a specified number of buckets or groups. It evenly distributes the sorted rows into these buckets and assigns a bucket number to each row. The NTILE function is typically used with the ORDER BY clause to sort the results.

Please note that the NTILE function evenly distributes the rows into buckets based on the sorting order of the rows and ensures that the number of rows in each bucket is as equal as possible. If the number of rows cannot be evenly distributed into the buckets, some buckets may have one extra row compared to the others.

Analyze Syntax

func.ntile(<n>).over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.name, table.score, table.grade, func.ntile(3).over(partition_by=[table.grade], order_by=table.score).alias('bucket')

name    |score|grade|bucket|
--------+-----+-----+------+
Johnson |  100|A    |     1|
Evans   |   87|A    |     1|
Davies  |   84|A    |     2|
Smith   |   81|A    |     3|
Wilson  |   72|B    |     1|
Thomas  |   72|B    |     1|
Taylor  |   62|B    |     2|
Brown   |   62|B    |     3|
Jones   |   55|C    |     1|
Williams|   55|C    |     2|

SQL Syntax

NTILE(n) OVER (
	PARTITION BY expr, ...
	ORDER BY expr [ASC | DESC], ...
)

SQL Examples

This example retrieves the students' names, scores, grades, and assigns them to buckets based on their scores within each grade using the NTILE() window function.

CREATE TABLE students (
    name VARCHAR(20),
    score INT NOT NULL,
    grade CHAR(1) NOT NULL
);

INSERT INTO students (name, score, grade)
VALUES
    ('Smith', 81, 'A'),
    ('Jones', 55, 'C'),
    ('Williams', 55, 'C'),
    ('Taylor', 62, 'B'),
    ('Brown', 62, 'B'),
    ('Davies', 84, 'A'),
    ('Evans', 87, 'A'),
    ('Wilson', 72, 'B'),
    ('Thomas', 72, 'B'),
    ('Johnson', 100, 'A');

SELECT
    name,
    score,
    grade,
    ntile(3) OVER (PARTITION BY grade ORDER BY score DESC) AS bucket
FROM
    students;

name    |score|grade|bucket|
--------+-----+-----+------+
Johnson |  100|A    |     1|
Evans   |   87|A    |     1|
Davies  |   84|A    |     2|
Smith   |   81|A    |     3|
Wilson  |   72|B    |     1|
Thomas  |   72|B    |     1|
Taylor  |   62|B    |     2|
Brown   |   62|B    |     3|
Jones   |   55|C    |     1|
Williams|   55|C    |     2|

26.11 - PERCENT_RANK

Returns the relative rank of a given value within a set of values. The resulting value falls between 0 and 1, inclusive. Please note that the first row in any set has a PERCENT_RANK of 0.

See also: CUME_DIST

Analyze Syntax

func.percent_rank().over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.name, table.score, table.grade, func.percent_rank().over(partition_by=[table.grade], order_by=table.score).alias('percent_rank')

name    |score|grade|percent_rank      |
--------+-----+-----+------------------+
Smith   |   81|A    |               0.0|
Davies  |   84|A    |0.3333333333333333|
Evans   |   87|A    |0.6666666666666666|
Johnson |  100|A    |               1.0|
Taylor  |   62|B    |               0.0|
Brown   |   62|B    |               0.0|
Wilson  |   72|B    |0.6666666666666666|
Thomas  |   72|B    |0.6666666666666666|
Jones   |   55|C    |               0.0|
Williams|   55|C    |               0.0|

SQL Syntax

PERCENT_RANK() OVER (
	PARTITION BY expr, ...
	ORDER BY expr [ASC | DESC], ...
)

SQL Examples

This example retrieves the students' names, scores, grades, and the percentile ranks (percent_rank) within each grade using the PERCENT_RANK() window function.

CREATE TABLE students (
    name VARCHAR(20),
    score INT NOT NULL,
    grade CHAR(1) NOT NULL
);

INSERT INTO students (name, score, grade)
VALUES
    ('Smith', 81, 'A'),
    ('Jones', 55, 'C'),
    ('Williams', 55, 'C'),
    ('Taylor', 62, 'B'),
    ('Brown', 62, 'B'),
    ('Davies', 84, 'A'),
    ('Evans', 87, 'A'),
    ('Wilson', 72, 'B'),
    ('Thomas', 72, 'B'),
    ('Johnson', 100, 'A');

SELECT
    name,
    score,
    grade,
    PERCENT_RANK() OVER (PARTITION BY grade ORDER BY score) AS percent_rank
FROM
    students;

name    |score|grade|percent_rank      |
--------+-----+-----+------------------+
Smith   |   81|A    |               0.0|
Davies  |   84|A    |0.3333333333333333|
Evans   |   87|A    |0.6666666666666666|
Johnson |  100|A    |               1.0|
Taylor  |   62|B    |               0.0|
Brown   |   62|B    |               0.0|
Wilson  |   72|B    |0.6666666666666666|
Thomas  |   72|B    |0.6666666666666666|
Jones   |   55|C    |               0.0|
Williams|   55|C    |               0.0|

26.12 - RANK

The RANK() function assigns a unique rank to each value within an ordered group of values.

The rank value starts at 1 and continues up sequentially. If two values are the same, they have the same rank.

Analyze Syntax

func.rank().over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.employee_id, table.first_name, table.last_name, table.department, table.salary, func.rank().over(order_by=table.salary).alias('rank')

| employee_id | first_name | last_name | department | salary | rank |
|-------------|------------|-----------|------------|--------|------|
| 1           | John       | Doe       | IT         | 90000  | 1    |
| 2           | Jane       | Smith     | HR         | 85000  | 2    |
| 3           | Mike       | Johnson   | IT         | 82000  | 3    |
| 4           | Sara       | Williams  | Sales      | 77000  | 4    |
| 5           | Tom        | Brown     | HR         | 75000  | 5    |

SQL Syntax

RANK() OVER (
  [ PARTITION BY <expr1> ]
  ORDER BY <expr2> [ { ASC | DESC } ]
  [ <window_frame> ]
)

SQL Examples

Create the table

CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR,
  last_name VARCHAR,
  department VARCHAR,
  salary INT
);

Insert data

INSERT INTO employees (employee_id, first_name, last_name, department, salary) VALUES
  (1, 'John', 'Doe', 'IT', 90000),
  (2, 'Jane', 'Smith', 'HR', 85000),
  (3, 'Mike', 'Johnson', 'IT', 82000),
  (4, 'Sara', 'Williams', 'Sales', 77000),
  (5, 'Tom', 'Brown', 'HR', 75000);

Ranking employees by salary

SELECT
  employee_id,
  first_name,
  last_name,
  department,
  salary,
  RANK() OVER (ORDER BY salary DESC) AS rank
FROM
  employees;

Result:

employee_idfirst_namelast_namedepartmentsalaryrank
1JohnDoeIT900001
2JaneSmithHR850002
3MikeJohnsonIT820003
4SaraWilliamsSales770004
5TomBrownHR750005

26.13 - ROW_NUMBER

Assigns a temporary sequential number to each row within a partition of a result set, starting at 1 for the first row in each partition.

Analyze Syntax

func.row_number().over(partition_by=[<columns>], order_by=[<columns>])

Analyze Examples

table.employee_id, table.first_name, table.last_name, table.department, table.salary, func.row_number().over(partition=table.department, order_by=table.salary).alias('row_num')

┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   employee_id   │    first_name    │     last_name    │    department    │      salary     │ row_num │
├─────────────────┼──────────────────┼──────────────────┼──────────────────┼─────────────────┼─────────┤
│               2 │ Jane             │ Smith            │ HR               │           85000 │       1 │
│               5 │ Tom              │ Brown            │ HR               │           75000 │       2 │
│               1 │ John             │ Doe              │ IT               │           90000 │       1 │
│               3 │ Mike             │ Johnson          │ IT               │           82000 │       2 │
│               4 │ Sara             │ Williams         │ Sales            │           77000 │       1 │
└──────────────────────────────────────────────────────────────────────────────────────────────────────┘

SQL Syntax

ROW_NUMBER() 
  OVER ( [ PARTITION BY <expr1> [, <expr2> ... ] ]
  ORDER BY <expr3> [ , <expr4> ... ] [ { ASC | DESC } ] )
ParameterRequired?Description
ORDER BYYesSpecifies the order of rows within each partition.
ASC / DESCNoSpecifies the sorting order within each partition. ASC (ascending) is the default.
QUALIFYNoFilters rows based on conditions.

SQL Examples

This example demonstrates the use of ROW_NUMBER() to assign sequential numbers to employees within their departments, ordered by descending salary.

-- Prepare the data
CREATE TABLE employees (
  employee_id INT,
  first_name VARCHAR,
  last_name VARCHAR,
  department VARCHAR,
  salary INT
);

INSERT INTO employees (employee_id, first_name, last_name, department, salary) VALUES
  (1, 'John', 'Doe', 'IT', 90000),
  (2, 'Jane', 'Smith', 'HR', 85000),
  (3, 'Mike', 'Johnson', 'IT', 82000),
  (4, 'Sara', 'Williams', 'Sales', 77000),
  (5, 'Tom', 'Brown', 'HR', 75000);

-- Select employee details along with the row number partitioned by department and ordered by salary in descending order.
SELECT
    employee_id,
    first_name,
    last_name,
    department,
    salary,
    ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num
FROM
    employees;

┌──────────────────────────────────────────────────────────────────────────────────────────────────────┐
│   employee_id   │    first_name    │     last_name    │    department    │      salary     │ row_num │
├─────────────────┼──────────────────┼──────────────────┼──────────────────┼─────────────────┼─────────┤
│               2 │ Jane             │ Smith            │ HR               │           85000 │       1 │
│               5 │ Tom              │ Brown            │ HR               │           75000 │       2 │
│               1 │ John             │ Doe              │ IT               │           90000 │       1 │
│               3 │ Mike             │ Johnson          │ IT               │           82000 │       2 │
│               4 │ Sara             │ Williams         │ Sales            │           77000 │       1 │
└──────────────────────────────────────────────────────────────────────────────────────────────────────┘