Is the process of comparing a value to the endpoints of numerical ranges to find a category in which the value belongs?

This topic is a compilation of functions, operators, and conditional expressions.

To learn more about how to call functions, function call rules, the SAFE prefix, and special types of arguments, see Function calls.

OPERATORS AND CONDITIONALS

Operators

Operators are represented by special characters or keywords; they do not use function call syntax. An operator manipulates any number of data inputs, also called operands, and returns a result.

Common conventions:

• Unless otherwise specified, all operators return NULL when one of the operands is NULL.
• All operators will throw an error if the computation result overflows.
• For all floating point operations, +/-inf and NaN may only be returned if one of the operands is +/-inf or NaN. In other cases, an error is returned.

Operator precedence

The following table lists all BigQuery operators from highest to lowest precedence, i.e. the order in which they will be evaluated within a statement.

Operators with the same precedence are left associative. This means that those operators are grouped together starting from the left and moving right. For example, the expression:

x AND y AND z

is interpreted as

( ( x AND y ) AND z )

The expression:

x * y / z

is interpreted as:

( ( x * y ) / z )

All comparison operators have the same priority, but comparison operators are not associative. Therefore, parentheses are required in order to resolve ambiguity. For example:

(x < y) IS FALSE

Field access operator

expression.fieldname[. ...]

Description

Gets the value of a field. Alternatively known as the dot operator. Can be used to access nested fields. For example, expression.fieldname1.fieldname2.

Input types

• STRUCT
• JSON

Return type

• For STRUCT: SQL data type of fieldname. If a field is not found in the struct, an error is thrown.
• For JSON: JSON. If a field is not found in a JSON value, a SQL NULL is returned.

Example

In the following example, the expression is t.customer and the field access operations are .address and .country. An operation is an application of an operator (.) to specific operands (in this case, address and country, or more specifically, t.customer and address, for the first operation, and t.customer.address and country for the second operation).

WITH orders AS (
  SELECT STRUCT(STRUCT('Yonge Street' AS street, 'Canada' AS country) AS address) AS customer
)
SELECT t.customer.address.country FROM orders AS t;

+---------+
| country |
+---------+
| Canada  |
+---------+

Array subscript operator

array_expression[array_subscript_specifier]

array_subscript_specifier:
  position_keyword(index)

position_keyword:
  { OFFSET | SAFE_OFFSET | ORDINAL | SAFE_ORDINAL }

Description

Gets a value from an array at a specific location.

Input types

• array_expression: The input array.
• position_keyword: Where the index for the array should start and how out-of-range indexes are handled. Your choices are:
  • OFFSET: The index starts at zero. Produces an error if the index is out of range.
  • SAFE_OFFSET: The index starts at zero. Returns NULL if the index is out of range.
  • ORDINAL: The index starts at one. Produces an error if the index is out of range.
  • SAFE_ORDINAL: The index starts at one. Returns NULL if the index is out of range.
• index: An integer that represents a specific position in the array.

Return type

T where array_expression is ARRAY<T>.

Examples

In this example, the array subscript operator is used to return values at specific locations in item_array. This example also shows what happens when you reference an index (6) in an array that is out of range. If the SAFE prefix is included, NULL is returned, otherwise an error is produced.

WITH Items AS (SELECT ["coffee", "tea", "milk"] AS item_array)
SELECT
  item_array,
  item_array[OFFSET(1)] AS item_offset,
  item_array[ORDINAL(1)] AS item_ordinal,
  item_array[SAFE_OFFSET(6)] AS item_safe_offset,
FROM Items

+----------------------------------+--------------+--------------+------------------+
| item_array                       | item_offset  | item_ordinal | item_safe_offset |
+----------------------------------+--------------+--------------+------------------+
| [coffee, tea, milk]              | tea          | coffee       | NULL             |
+----------------------------------+--------------+--------------+------------------+

In the following example, when you reference an index in an array that is out of range and the SAFE prefix is not included, an error is produced.

WITH Items AS (SELECT ["coffee", "tea", "milk"] AS item_array)
SELECT
  item_array[OFFSET(6)] AS item_offset
FROM Items

-- Error. OFFSET(6) is out of range.

JSON subscript operator

json_expression[array_element_id]

json_expression[field_name]

Description

Gets a value of an array element or field in a JSON expression. Can be used to access nested data.

Input types

• JSON expression: The JSON expression that contains an array element or field to return.
• [array_element_id]: An INT64 expression that represents a zero-based index in the array. If a negative value is entered, or the value is greater than or equal to the size of the array, or the JSON expression doesn't represent a JSON array, a SQL NULL is returned.
• [field_name]: A STRING expression that represents the name of a field in JSON. If the field name is not found, or the JSON expression is not a JSON object, a SQL NULL is returned.

Return type

JSON

Example

In the following example:

• json_value is a JSON expression.
• .class is a JSON field access.
• .students is a JSON field access.
• [0] is a JSON subscript expression with an element offset that accesses the zeroth element of an array in the JSON value.
• ['name'] is a JSON subscript expression with a field name that accesses a field.

SELECT json_value.class.students[0]['name'] AS first_student
FROM
  UNNEST(
    [
      JSON '{"class" : {"students" : [{"name" : "Jane"}]}}',
      JSON '{"class" : {"students" : []}}',
      JSON '{"class" : {"students" : [{"name" : "John"}, {"name": "Jamie"}]}}'])
    AS json_value;

+-----------------+
| first_student   |
+-----------------+
| "Jane"          |
| NULL            |
| "John"          |
+-----------------+

Arithmetic operators

All arithmetic operators accept input of numeric type T, and the result type has type T unless otherwise indicated in the description below:

NOTE: Divide by zero operations return an error. To return a different result, consider the IEEE_DIVIDE or SAFE_DIVIDE functions.

Result types for Addition, Subtraction and Multiplication:

Result types for Division:

Result types for Unary Plus:

Result types for Unary Minus:

Date arithmetics operators

Operators '+' and '-' can be used for arithmetic operations on dates.

date_expression + int64_expression
int64_expression + date_expression
date_expression - int64_expression

Description

Adds or subtracts int64_expression days to or from date_expression. This is equivalent to DATE_ADD or DATE_SUB functions, when interval is expressed in days.

Return Data Type

DATE

Example

SELECT DATE "2020-09-22" + 1 AS day_later, DATE "2020-09-22" - 7 AS week_ago

+------------+------------+
| day_later  | week_ago   |
+------------+------------+
| 2020-09-23 | 2020-09-15 |
+------------+------------+

Datetime subtraction

date_expression - date_expression
timestamp_expression - timestamp_expression
datetime_expression - datetime_expression

Description

Computes the difference between two datetime values as an interval.

Return Data Type

INTERVAL

Example

SELECT
  DATE "2021-05-20" - DATE "2020-04-19" AS date_diff,
  TIMESTAMP "2021-06-01 12:34:56.789" - TIMESTAMP "2021-05-31 00:00:00" AS time_diff

+-------------------+------------------------+
| date_diff         | time_diff              |
+-------------------+------------------------+
| 0-0 396 0:0:0     | 0-0 0 36:34:56.789     |
+-------------------+------------------------+

Interval arithmetic operators

Addition and subtraction

date_expression + interval_expression = DATETIME
date_expression - interval_expression = DATETIME
timestamp_expression + interval_expression = TIMESTAMP
timestamp_expression - interval_expression = TIMESTAMP
datetime_expression + interval_expression = DATETIME
datetime_expression - interval_expression = DATETIME

Description

Adds an interval to a datetime value or subtracts an interval from a datetime value. Example

SELECT
  DATE "2021-04-20" + INTERVAL 25 HOUR AS date_plus,
  TIMESTAMP "2021-05-02 00:01:02.345" - INTERVAL 10 SECOND AS time_minus;

+-------------------------+--------------------------------+
| date_plus               | time_minus                     |
+-------------------------+--------------------------------+
| 2021-04-21 01:00:00     | 2021-05-02 00:00:52.345+00     |
+-------------------------+--------------------------------+

Multiplication and division

interval_expression * integer_expression = INTERVAL
interval_expression / integer_expression = INTERVAL

Description

Multiplies or divides an interval value by an integer.

Example

SELECT
  INTERVAL '1:2:3' HOUR TO SECOND * 10 AS mul1,
  INTERVAL 35 SECOND * 4 AS mul2,
  INTERVAL 10 YEAR / 3 AS div1,
  INTERVAL 1 MONTH / 12 AS div2

+----------------+--------------+-------------+--------------+
| mul1           | mul2         | div1        | div2         |
+----------------+--------------+-------------+--------------+
| 0-0 0 10:20:30 | 0-0 0 0:2:20 | 3-4 0 0:0:0 | 0-0 2 12:0:0 |
+----------------+--------------+-------------+--------------+

Bitwise operators

All bitwise operators return the same type and the same length as the first operand.

Logical operators

BigQuery supports the AND, OR, and NOT logical operators. Logical operators allow only BOOL or NULL input and use three-valued logic to produce a result. The result can be TRUE, FALSE, or NULL:

Examples

The examples in this section reference a table called entry_table:

+-------+
| entry |
+-------+
| a     |
| b     |
| c     |
| NULL  |
+-------+

SELECT 'a' FROM entry_table WHERE entry = 'a'

-- a => 'a' = 'a' => TRUE
-- b => 'b' = 'a' => FALSE
-- NULL => NULL = 'a' => NULL

+-------+
| entry |
+-------+
| a     |
+-------+

SELECT entry FROM entry_table WHERE NOT (entry = 'a')

-- a => NOT('a' = 'a') => NOT(TRUE) => FALSE
-- b => NOT('b' = 'a') => NOT(FALSE) => TRUE
-- NULL => NOT(NULL = 'a') => NOT(NULL) => NULL

+-------+
| entry |
+-------+
| b     |
| c     |
+-------+

SELECT entry FROM entry_table WHERE entry IS NULL

-- a => 'a' IS NULL => FALSE
-- b => 'b' IS NULL => FALSE
-- NULL => NULL IS NULL => TRUE

+-------+
| entry |
+-------+
| NULL  |
+-------+

Comparison operators

Comparisons always return BOOL. Comparisons generally require both operands to be of the same type. If operands are of different types, and if BigQuery can convert the values of those types to a common type without loss of precision, BigQuery will generally coerce them to that common type for the comparison; BigQuery will generally coerce literals to the type of non-literals, where present. Comparable data types are defined in Data Types.

STRUCTs support only 4 comparison operators: equal (=), not equal (!= and <>), and IN.

The following rules apply when comparing these data types:

• FLOAT64: All comparisons with NaN return FALSE, except for != and <>, which return TRUE.
• BOOL: FALSE is less than TRUE.
• STRING: Strings are compared codepoint-by-codepoint, which means that canonically equivalent strings are only guaranteed to compare as equal if they have been normalized first.
• NULL: The convention holds here: any operation with a NULL input returns NULL.

When testing values that have a STRUCT data type for equality, it's possible that one or more fields are NULL. In such cases:

• If all non-NULL field values are equal, the comparison returns NULL.
• If any non-NULL field values are not equal, the comparison returns false.

The following table demonstrates how STRUCT data types are compared when they have fields that are NULL valued.

EXISTS operator

EXISTS ( subquery )

Description

Returns TRUE if the subquery produces one or more rows. Returns FALSE if the subquery produces zero rows. Never returns NULL. To learn more about how you can use a subquery with EXISTS, see EXISTS subqueries.

Examples

In this example, the EXISTS operator returns FALSE because there are no rows in Words where the direction is south:

WITH Words AS (
  SELECT 'Intend' as value, 'east' as direction UNION ALL
  SELECT 'Secure', 'north' UNION ALL
  SELECT 'Clarity', 'west'
 )
SELECT EXISTS ( SELECT value FROM Words WHERE direction = 'south' ) as result;

+--------+
| result |
+--------+
| FALSE  |
+--------+

IN operator

The IN operator supports the following syntax:

search_value [NOT] IN value_set

value_set:
  {
    (expression[, ...])
    | (subquery)
    | UNNEST(array_expression)
  }

Description

Checks for an equal value in a set of values. Semantic rules apply, but in general, IN returns TRUE if an equal value is found, FALSE if an equal value is excluded, otherwise NULL. NOT IN returns FALSE if an equal value is found, TRUE if an equal value is excluded, otherwise NULL.

• search_value: The expression that is compared to a set of values.

• value_set: One or more values to compare to a search value.

  • (expression[, ...]): A list of expressions.
  • (subquery): A subquery that returns a single column. The values in that column are the set of values. If no rows are produced, the set of values is empty.

  • UNNEST(array_expression): An UNNEST operator that returns a column of values from an array expression. This is equivalent to:

    IN (SELECT element FROM UNNEST(array_expression) AS element)
    

Semantic rules

When using the IN operator, the following semantics apply in this order:

• Returns FALSE if value_set is empty.
• Returns NULL if search_value is NULL.
• Returns TRUE if value_set contains a value equal to search_value.
• Returns NULL if value_set contains a NULL.
• Returns FALSE.

When using the NOT IN operator, the following semantics apply in this order:

• Returns TRUE if value_set is empty.
• Returns NULL if search_value is NULL.
• Returns FALSE if value_set contains a value equal to search_value.
• Returns NULL if value_set contains a NULL.
• Returns TRUE.

This operator generally supports collation, however, x [NOT] IN UNNEST is not supported.

The semantics of:

x IN (y, z, ...)

are defined as equivalent to:

(x = y) OR (x = z) OR ...

and the subquery and array forms are defined similarly.

x NOT IN ...

is equivalent to:

NOT(x IN ...)

The UNNEST form treats an array scan like UNNEST in the FROM clause:

x [NOT] IN UNNEST(<array expression>)

This form is often used with ARRAY parameters. For example:

x IN UNNEST(@array_parameter)

See the Arrays topic for more information on how to use this syntax.

IN can be used with multi-part keys by using the struct constructor syntax. For example:

(Key1, Key2) IN ( (12,34), (56,78) )
(Key1, Key2) IN ( SELECT (table.a, table.b) FROM table )

See the Struct Type for more information.

Return Data Type

BOOL

Examples

You can use these WITH clauses to emulate temporary tables for Words and Items in the following examples:

WITH Words AS (
  SELECT 'Intend' as value UNION ALL
  SELECT 'Secure' UNION ALL
  SELECT 'Clarity' UNION ALL
  SELECT 'Peace' UNION ALL
  SELECT 'Intend'
 )
SELECT * FROM Words;

+----------+
| value    |
+----------+
| Intend   |
| Secure   |
| Clarity  |
| Peace    |
| Intend   |
+----------+

WITH
  Items AS (
    SELECT STRUCT('blue' AS color, 'round' AS shape) AS info UNION ALL
    SELECT STRUCT('blue', 'square') UNION ALL
    SELECT STRUCT('red', 'round')
  )
SELECT * FROM Items;

+----------------------------+
| info                       |
+----------------------------+
| {blue color, round shape}  |
| {blue color, square shape} |
| {red color, round shape}   |
+----------------------------+

Example with IN and an expression:

SELECT * FROM Words WHERE value IN ('Intend', 'Secure');

+----------+
| value    |
+----------+
| Intend   |
| Secure   |
| Intend   |
+----------+

Example with NOT IN and an expression:

SELECT * FROM Words WHERE value NOT IN ('Intend');

+----------+
| value    |
+----------+
| Secure   |
| Clarity  |
| Peace    |
+----------+

Example with IN, a scalar subquery, and an expression:

SELECT * FROM Words WHERE value IN ((SELECT 'Intend'), 'Clarity');

+----------+
| value    |
+----------+
| Intend   |
| Clarity  |
| Intend   |
+----------+

Example with IN and an UNNEST operation:

SELECT * FROM Words WHERE value IN UNNEST(['Secure', 'Clarity']);

+----------+
| value    |
+----------+
| Secure   |
| Clarity  |
+----------+

Example with IN and a STRUCT:

SELECT
  (SELECT AS STRUCT Items.info) as item
FROM
  Items
WHERE (info.shape, info.color) IN (('round', 'blue'));

+------------------------------------+
| item                               |
+------------------------------------+
| { {blue color, round shape} info } |
+------------------------------------+

IS operators

IS operators return TRUE or FALSE for the condition they are testing. They never return NULL, even for NULL inputs, unlike the IS_INF and IS_NAN functions defined in Mathematical Functions. If NOT is present, the output BOOL value is inverted.

X IS TRUE

X IS NOT TRUE

X IS FALSE

X IS NOT FALSE

X IS NULL

X IS NOT NULL

X IS UNKNOWN

X IS NOT UNKNOWN

IS DISTINCT FROM operator

expression_1 IS [NOT] DISTINCT FROM expression_2

Description

IS DISTINCT FROM returns TRUE if the input values are considered to be distinct from each other by the DISTINCT and GROUP BY clauses. Otherwise, returns FALSE.

a IS DISTINCT FROM b being TRUE is equivalent to:

• SELECT COUNT(DISTINCT x) FROM UNNEST([a,b]) x returning 2.
• SELECT * FROM UNNEST([a,b]) x GROUP BY x returning 2 rows.

a IS DISTINCT FROM b is equivalent to NOT (a = b), except for the following cases:

• This operator never returns NULL so NULL values are considered to be distinct from non-NULL values, not other NULL values.
• NaN values are considered to be distinct from non-NaN values, but not other NaN values.

You can use this operation with fields in a complex data type, but not on the complex data types themselves. These complex data types cannot be compared directly:

• STRUCT
• ARRAY

Input types

• expression_1: The first value to compare. This can be a groupable data type, NULL or NaN.
• expression_2: The second value to compare. This can be a groupable data type, NULL or NaN.
• NOT: If present, the output BOOL value is inverted.

Return type

BOOL

Examples

These return TRUE:

SELECT 1 IS DISTINCT FROM 2

SELECT 1 IS DISTINCT FROM NULL

SELECT 1 IS NOT DISTINCT FROM 1

SELECT NULL IS NOT DISTINCT FROM NULL

These return FALSE:

SELECT NULL IS DISTINCT FROM NULL

SELECT 1 IS DISTINCT FROM 1

SELECT 1 IS NOT DISTINCT FROM 2

SELECT 1 IS NOT DISTINCT FROM NULL

Concatenation operator

The concatenation operator combines multiple values into one.

STRING || STRING [ || ... ]

BYTES || BYTES [ || ... ]

ARRAY<T> || ARRAY<T> [ || ... ]

Conditional expressions

Conditional expressions impose constraints on the evaluation order of their inputs. In essence, they are evaluated left to right, with short-circuiting, and only evaluate the output value that was chosen. In contrast, all inputs to regular functions are evaluated before calling the function. Short-circuiting in conditional expressions can be exploited for error handling or performance tuning.

CASE expr

CASE expr
  WHEN expr_to_match THEN result
  [ ... ]
  [ ELSE else_result ]
  END

Description

Compares expr to expr_to_match of each successive WHEN clause and returns the first result where this comparison returns true. The remaining WHEN clauses and else_result are not evaluated. If the expr = expr_to_match comparison returns false or NULL for all WHEN clauses, returns else_result if present; if not present, returns NULL.

expr and expr_to_match can be any type. They must be implicitly coercible to a common supertype; equality comparisons are done on coerced values. There may be multiple result types. result and else_result expressions must be coercible to a common supertype.

This expression supports specifying collation.

Return Data Type

Supertype of result[, ...] and else_result.

Example

WITH Numbers AS (
  SELECT 90 as A, 2 as B UNION ALL
  SELECT 50, 8 UNION ALL
  SELECT 60, 6 UNION ALL
  SELECT 50, 10
)
SELECT
  A,
  B,
  CASE A
    WHEN 90 THEN 'red'
    WHEN 50 THEN 'blue'
    ELSE 'green'
    END
    AS result
FROM Numbers

+------------------+
| A  | B  | result |
+------------------+
| 90 | 2  | red    |
| 50 | 8  | blue   |
| 60 | 6  | green  |
| 50 | 10 | blue   |
+------------------+

CASE

CASE
  WHEN condition THEN result
  [ ... ]
  [ ELSE else_result ]
  END

Description

Evaluates the condition of each successive WHEN clause and returns the first result where the condition is true; any remaining WHEN clauses and else_result are not evaluated. If all conditions are false or NULL, returns else_result if present; if not present, returns NULL.

condition must be a boolean expression. There may be multiple result types. result and else_result expressions must be implicitly coercible to a common supertype.

This expression supports specifying collation.

Return Data Type

Supertype of result[, ...] and else_result.

Example

WITH Numbers AS (
  SELECT 90 as A, 2 as B UNION ALL
  SELECT 50, 6 UNION ALL
  SELECT 20, 10
)
SELECT
  A,
  B,
  CASE
    WHEN A > 60 THEN 'red'
    WHEN A > 30 THEN 'blue'
    ELSE 'green'
    END
    AS result
FROM Numbers

+------------------+
| A  | B  | result |
+------------------+
| 90 | 2  | red    |
| 50 | 6  | blue   |
| 20 | 10 | green  |
+------------------+

COALESCE

COALESCE(expr[, ...])

Description

Returns the value of the first non-null expression. The remaining expressions are not evaluated. An input expression can be any type. There may be multiple input expression types. All input expressions must be implicitly coercible to a common supertype.

Return Data Type

Supertype of expr[, ...].

Examples

SELECT COALESCE('A', 'B', 'C') as result

+--------+
| result |
+--------+
| A      |
+--------+

SELECT COALESCE(NULL, 'B', 'C') as result

+--------+
| result |
+--------+
| B      |
+--------+

IF

IF(expr, true_result, else_result)

Description

If expr is true, returns true_result, else returns else_result. else_result is not evaluated if expr is true. true_result is not evaluated if expr is false or NULL.

expr must be a boolean expression. true_result and else_result must be coercible to a common supertype.

Return Data Type

Supertype of true_result and else_result.

Example

WITH Numbers AS (
  SELECT 10 as A, 20 as B UNION ALL
  SELECT 50, 30 UNION ALL
  SELECT 60, 60
)
SELECT
  A,
  B,
  IF(A < B, 'true', 'false') AS result
FROM Numbers

+------------------+
| A  | B  | result |
+------------------+
| 10 | 20 | true   |
| 50 | 30 | false  |
| 60 | 60 | false  |
+------------------+

IFNULL

IFNULL(expr, null_result)

Description

If expr is NULL, return null_result. Otherwise, return expr. If expr is not NULL, null_result is not evaluated.

expr and null_result can be any type and must be implicitly coercible to a common supertype. Synonym for COALESCE(expr, null_result).

Return Data Type

Supertype of expr or null_result.

Examples

SELECT IFNULL(NULL, 0) as result

+--------+
| result |
+--------+
| 0      |
+--------+

SELECT IFNULL(10, 0) as result

+--------+
| result |
+--------+
| 10     |
+--------+

NULLIF

NULLIF(expr, expr_to_match)

Description

Returns NULL if expr = expr_to_match is true, otherwise returns expr.

expr and expr_to_match must be implicitly coercible to a common supertype, and must be comparable.

This expression supports specifying collation.

Return Data Type

Supertype of expr and expr_to_match.

Example

SELECT NULLIF(0, 0) as result

+--------+
| result |
+--------+
| NULL   |
+--------+

SELECT NULLIF(10, 0) as result

+--------+
| result |
+--------+
| 10     |
+--------+

FUNCTIONS

Aggregate functions

The following general aggregate functions are available in Google Standard SQL. To learn about the syntax for aggregate function calls, see Aggregate function calls.

ANY_VALUE

ANY_VALUE(
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns expression for some row chosen from the group. Which row is chosen is nondeterministic, not random. Returns NULL when the input produces no rows. Returns NULL when expression is NULL for all rows in the group.

ANY_VALUE behaves as if RESPECT NULLS is specified; rows for which expression is NULL are considered and may be selected.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

Any

Returned Data Types

Matches the input data type.

Examples

SELECT ANY_VALUE(fruit) as any_value
FROM UNNEST(["apple", "banana", "pear"]) as fruit;

+-----------+
| any_value |
+-----------+
| apple     |
+-----------+

SELECT
  fruit,
  ANY_VALUE(fruit) OVER (ORDER BY LENGTH(fruit) ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS any_value
FROM UNNEST(["apple", "banana", "pear"]) as fruit;

+--------+-----------+
| fruit  | any_value |
+--------+-----------+
| pear   | pear      |
| apple  | pear      |
| banana | apple     |
+--------+-----------+

ARRAY_AGG

ARRAY_AGG(
  [ DISTINCT ]
  expression
  [ { IGNORE | RESPECT } NULLS ]
  [ ORDER BY key [ { ASC | DESC } ] [, ... ] ]
  [ LIMIT n ]
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns an ARRAY of expression values.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

An error is raised if an array in the final query result contains a NULL element.

Supported Argument Types

All data types except ARRAY.

Returned Data Types

ARRAY

If there are zero input rows, this function returns NULL.

Examples

SELECT ARRAY_AGG(x) AS array_agg FROM UNNEST([2, 1,-2, 3, -2, 1, 2]) AS x;

+-------------------------+
| array_agg               |
+-------------------------+
| [2, 1, -2, 3, -2, 1, 2] |
+-------------------------+

SELECT ARRAY_AGG(DISTINCT x) AS array_agg
FROM UNNEST([2, 1, -2, 3, -2, 1, 2]) AS x;

+---------------+
| array_agg     |
+---------------+
| [2, 1, -2, 3] |
+---------------+

SELECT ARRAY_AGG(x IGNORE NULLS) AS array_agg
FROM UNNEST([NULL, 1, -2, 3, -2, 1, NULL]) AS x;

+-------------------+
| array_agg         |
+-------------------+
| [1, -2, 3, -2, 1] |
+-------------------+

SELECT ARRAY_AGG(x ORDER BY ABS(x)) AS array_agg
FROM UNNEST([2, 1, -2, 3, -2, 1, 2]) AS x;

+-------------------------+
| array_agg               |
+-------------------------+
| [1, 1, 2, -2, -2, 2, 3] |
+-------------------------+

SELECT ARRAY_AGG(x LIMIT 5) AS array_agg
FROM UNNEST([2, 1, -2, 3, -2, 1, 2]) AS x;

+-------------------+
| array_agg         |
+-------------------+
| [2, 1, -2, 3, -2] |
+-------------------+

WITH vals AS
  (
    SELECT 1 x UNION ALL
    SELECT -2 x UNION ALL
    SELECT 3 x UNION ALL
    SELECT -2 x UNION ALL
    SELECT 1 x
  )
SELECT ARRAY_AGG(DISTINCT x ORDER BY x) as array_agg
FROM vals;

+------------+
| array_agg  |
+------------+
| [-2, 1, 3] |
+------------+

WITH vals AS
  (
    SELECT 1 x, 'a' y UNION ALL
    SELECT 1 x, 'b' y UNION ALL
    SELECT 2 x, 'a' y UNION ALL
    SELECT 2 x, 'c' y
  )
SELECT x, ARRAY_AGG(y) as array_agg
FROM vals
GROUP BY x;

+---------------+
| x | array_agg |
+---------------+
| 1 | [a, b]    |
| 2 | [a, c]    |
+---------------+

SELECT
  x,
  ARRAY_AGG(x) OVER (ORDER BY ABS(x)) AS array_agg
FROM UNNEST([2, 1, -2, 3, -2, 1, 2]) AS x;

+----+-------------------------+
| x  | array_agg               |
+----+-------------------------+
| 1  | [1, 1]                  |
| 1  | [1, 1]                  |
| 2  | [1, 1, 2, -2, -2, 2]    |
| -2 | [1, 1, 2, -2, -2, 2]    |
| -2 | [1, 1, 2, -2, -2, 2]    |
| 2  | [1, 1, 2, -2, -2, 2]    |
| 3  | [1, 1, 2, -2, -2, 2, 3] |
+----+-------------------------+

ARRAY_CONCAT_AGG

ARRAY_CONCAT_AGG(
  expression
  [ ORDER BY key [ { ASC | DESC } ] [, ... ] ]
  [ LIMIT n ]
)

Description

Concatenates elements from expression of type ARRAY, returning a single array as a result.

This function ignores NULL input arrays, but respects the NULL elements in non-NULL input arrays. An error is raised, however, if an array in the final query result contains a NULL element. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

ARRAY

Returned Data Types

ARRAY

Examples

SELECT FORMAT("%T", ARRAY_CONCAT_AGG(x)) AS array_concat_agg FROM (
  SELECT [NULL, 1, 2, 3, 4] AS x
  UNION ALL SELECT NULL
  UNION ALL SELECT [5, 6]
  UNION ALL SELECT [7, 8, 9]
);

+-----------------------------------+
| array_concat_agg                  |
+-----------------------------------+
| [NULL, 1, 2, 3, 4, 5, 6, 7, 8, 9] |
+-----------------------------------+

SELECT FORMAT("%T", ARRAY_CONCAT_AGG(x ORDER BY ARRAY_LENGTH(x))) AS array_concat_agg FROM (
  SELECT [1, 2, 3, 4] AS x
  UNION ALL SELECT [5, 6]
  UNION ALL SELECT [7, 8, 9]
);

+-----------------------------------+
| array_concat_agg                  |
+-----------------------------------+
| [5, 6, 7, 8, 9, 1, 2, 3, 4]       |
+-----------------------------------+

SELECT FORMAT("%T", ARRAY_CONCAT_AGG(x LIMIT 2)) AS array_concat_agg FROM (
  SELECT [1, 2, 3, 4] AS x
  UNION ALL SELECT [5, 6]
  UNION ALL SELECT [7, 8, 9]
);

+--------------------------+
| array_concat_agg         |
+--------------------------+
| [1, 2, 3, 4, 5, 6]       |
+--------------------------+

SELECT FORMAT("%T", ARRAY_CONCAT_AGG(x ORDER BY ARRAY_LENGTH(x) LIMIT 2)) AS array_concat_agg FROM (
  SELECT [1, 2, 3, 4] AS x
  UNION ALL SELECT [5, 6]
  UNION ALL SELECT [7, 8, 9]
);

+------------------+
| array_concat_agg |
+------------------+
| [5, 6, 7, 8, 9]  |
+------------------+

AVG

AVG(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the average of non-NULL input values, or NaN if the input contains a NaN.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

Any numeric input type, such as INT64. Note that, for floating point input types, the return result is non-deterministic, which means you might receive a different result each time you use this function.

Returned Data Types

Examples

SELECT AVG(x) as avg
FROM UNNEST([0, 2, 4, 4, 5]) as x;

+-----+
| avg |
+-----+
| 3   |
+-----+

SELECT AVG(DISTINCT x) AS avg
FROM UNNEST([0, 2, 4, 4, 5]) AS x;

+------+
| avg  |
+------+
| 2.75 |
+------+

SELECT
  x,
  AVG(x) OVER (ORDER BY x ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS avg
FROM UNNEST([0, 2, NULL, 4, 4, 5]) AS x;

+------+------+
| x    | avg  |
+------+------+
| NULL | NULL |
| 0    | 0    |
| 2    | 1    |
| 4    | 3    |
| 4    | 4    |
| 5    | 4.5  |
+------+------+

BIT_AND

BIT_AND(
  expression
)

Description

Performs a bitwise AND operation on expression and returns the result.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• INT64

Returned Data Types

INT64

Examples

SELECT BIT_AND(x) as bit_and FROM UNNEST([0xF001, 0x00A1]) as x;

+---------+
| bit_and |
+---------+
| 1       |
+---------+

BIT_OR

BIT_OR(
  expression
)

Description

Performs a bitwise OR operation on expression and returns the result.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• INT64

Returned Data Types

INT64

Examples

SELECT BIT_OR(x) as bit_or FROM UNNEST([0xF001, 0x00A1]) as x;

+--------+
| bit_or |
+--------+
| 61601  |
+--------+

BIT_XOR

BIT_XOR(
  [ DISTINCT ]
  expression
)

Description

Performs a bitwise XOR operation on expression and returns the result.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• INT64

Returned Data Types

INT64

Examples

SELECT BIT_XOR(x) AS bit_xor FROM UNNEST([5678, 1234]) AS x;

+---------+
| bit_xor |
+---------+
| 4860    |
+---------+

SELECT BIT_XOR(x) AS bit_xor FROM UNNEST([1234, 5678, 1234]) AS x;

+---------+
| bit_xor |
+---------+
| 5678    |
+---------+

SELECT BIT_XOR(DISTINCT x) AS bit_xor FROM UNNEST([1234, 5678, 1234]) AS x;

+---------+
| bit_xor |
+---------+
| 4860    |
+---------+

COUNT

1.

COUNT(*)
[OVER over_clause]

2.

COUNT(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

1. Returns the number of rows in the input.
2. Returns the number of rows with expression evaluated to any value other than NULL.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

This function with DISTINCT supports specifying collation.

Supported Argument Types

expression can be any data type. If DISTINCT is present, expression can only be a data type that is groupable.

Return Data Types

INT64

Examples

You can use the COUNT function to return the number of rows in a table or the number of distinct values of an expression. For example:

SELECT
  COUNT(*) AS count_star,
  COUNT(DISTINCT x) AS count_dist_x
FROM UNNEST([1, 4, 4, 5]) AS x;

+------------+--------------+
| count_star | count_dist_x |
+------------+--------------+
| 4          | 3            |
+------------+--------------+

SELECT
  x,
  COUNT(*) OVER (PARTITION BY MOD(x, 3)) AS count_star,
  COUNT(DISTINCT x) OVER (PARTITION BY MOD(x, 3)) AS count_dist_x
FROM UNNEST([1, 4, 4, 5]) AS x;

+------+------------+--------------+
| x    | count_star | count_dist_x |
+------+------------+--------------+
| 1    | 3          | 2            |
| 4    | 3          | 2            |
| 4    | 3          | 2            |
| 5    | 1          | 1            |
+------+------------+--------------+

SELECT
  x,
  COUNT(*) OVER (PARTITION BY MOD(x, 3)) AS count_star,
  COUNT(x) OVER (PARTITION BY MOD(x, 3)) AS count_x
FROM UNNEST([1, 4, NULL, 4, 5]) AS x;

+------+------------+---------+
| x    | count_star | count_x |
+------+------------+---------+
| NULL | 1          | 0       |
| 1    | 3          | 3       |
| 4    | 3          | 3       |
| 4    | 3          | 3       |
| 5    | 1          | 1       |
+------+------------+---------+

If you want to count the number of distinct values of an expression for which a certain condition is satisfied, this is one recipe that you can use:

COUNT(DISTINCT IF(condition, expression, NULL))

Here, IF will return the value of expression if condition is TRUE, or NULL otherwise. The surrounding COUNT(DISTINCT ...) will ignore the NULL values, so it will count only the distinct values of expression for which condition is TRUE.

For example, to count the number of distinct positive values of x:

SELECT COUNT(DISTINCT IF(x > 0, x, NULL)) AS distinct_positive
FROM UNNEST([1, -2, 4, 1, -5, 4, 1, 3, -6, 1]) AS x;

+-------------------+
| distinct_positive |
+-------------------+
| 3                 |
+-------------------+

Or to count the number of distinct dates on which a certain kind of event occurred:

WITH Events AS (
  SELECT DATE '2021-01-01' AS event_date, 'SUCCESS' AS event_type
  UNION ALL
  SELECT DATE '2021-01-02' AS event_date, 'SUCCESS' AS event_type
  UNION ALL
  SELECT DATE '2021-01-02' AS event_date, 'FAILURE' AS event_type
  UNION ALL
  SELECT DATE '2021-01-03' AS event_date, 'SUCCESS' AS event_type
  UNION ALL
  SELECT DATE '2021-01-04' AS event_date, 'FAILURE' AS event_type
  UNION ALL
  SELECT DATE '2021-01-04' AS event_date, 'FAILURE' AS event_type
)
SELECT
  COUNT(DISTINCT IF(event_type = 'FAILURE', event_date, NULL))
    AS distinct_dates_with_failures
FROM Events;

+------------------------------+
| distinct_dates_with_failures |
+------------------------------+
| 2                            |
+------------------------------+

COUNTIF

COUNTIF(
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the count of TRUE values for expression. Returns 0 if there are zero input rows, or if expression evaluates to FALSE or NULL for all rows.

Since expression must be a BOOL, the form COUNTIF(DISTINCT ...) is not supported. This would not be useful: there is only one distinct value of TRUE. Usually when someone wants to combine COUNTIF and DISTINCT, they want to count the number of distinct values of an expression for which a certain condition is satisfied. One recipe to achieve this is the following:

COUNT(DISTINCT IF(condition, expression, NULL))

Note that this uses COUNT, not COUNTIF; the IF part has been moved inside. To learn more, see the examples for COUNT.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

BOOL

Return Data Types

INT64

Examples

SELECT COUNTIF(x<0) AS num_negative, COUNTIF(x>0) AS num_positive
FROM UNNEST([5, -2, 3, 6, -10, -7, 4, 0]) AS x;

+--------------+--------------+
| num_negative | num_positive |
+--------------+--------------+
| 3            | 4            |
+--------------+--------------+

SELECT
  x,
  COUNTIF(x<0) OVER (ORDER BY ABS(x) ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS num_negative
FROM UNNEST([5, -2, 3, 6, -10, NULL, -7, 4, 0]) AS x;

+------+--------------+
| x    | num_negative |
+------+--------------+
| NULL | 0            |
| 0    | 1            |
| -2   | 1            |
| 3    | 1            |
| 4    | 0            |
| 5    | 0            |
| 6    | 1            |
| -7   | 2            |
| -10  | 2            |
+------+--------------+

LOGICAL_AND

LOGICAL_AND(
  expression
)

Description

Returns the logical AND of all non-NULL expressions. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

BOOL

Return Data Types

BOOL

Examples

SELECT LOGICAL_AND(x) AS logical_and FROM UNNEST([true, false, true]) AS x;

+-------------+
| logical_and |
+-------------+
| false       |
+-------------+

LOGICAL_OR

LOGICAL_OR(
  expression
)

Description

Returns the logical OR of all non-NULL expressions. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

BOOL

Return Data Types

BOOL

Examples

SELECT LOGICAL_OR(x) AS logical_or FROM UNNEST([true, false, true]) AS x;

+------------+
| logical_or |
+------------+
| true       |
+------------+

MAX

MAX(
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the maximum value of non-NULL expressions. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows. Returns NaN if the input contains a NaN.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

This function supports specifying collation.

Supported Argument Types

Any orderable data type.

Return Data Types

Same as the data type used as the input values.

Examples

SELECT MAX(x) AS max
FROM UNNEST([8, 37, 4, 55]) AS x;

+-----+
| max |
+-----+
| 55  |
+-----+

SELECT x, MAX(x) OVER (PARTITION BY MOD(x, 2)) AS max
FROM UNNEST([8, NULL, 37, 4, NULL, 55]) AS x;

+------+------+
| x    | max  |
+------+------+
| NULL | NULL |
| NULL | NULL |
| 8    | 8    |
| 4    | 8    |
| 37   | 55   |
| 55   | 55   |
+------+------+

MIN

MIN(
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the minimum value of non-NULL expressions. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows. Returns NaN if the input contains a NaN.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

This function supports specifying collation.

Supported Argument Types

Any orderable data type.

Return Data Types

Same as the data type used as the input values.

Examples

SELECT MIN(x) AS min
FROM UNNEST([8, 37, 4, 55]) AS x;

+-----+
| min |
+-----+
| 4   |
+-----+

SELECT x, MIN(x) OVER (PARTITION BY MOD(x, 2)) AS min
FROM UNNEST([8, NULL, 37, 4, NULL, 55]) AS x;

+------+------+
| x    | min  |
+------+------+
| NULL | NULL |
| NULL | NULL |
| 8    | 4    |
| 4    | 4    |
| 37   | 37   |
| 55   | 37   |
+------+------+

STRING_AGG

STRING_AGG(
  [ DISTINCT ]
  expression [, delimiter]
  [ ORDER BY key [ { ASC | DESC } ] [, ... ] ]
  [ LIMIT n ]
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns a value (either STRING or BYTES) obtained by concatenating non-NULL values. Returns NULL if there are zero input rows or expression evaluates to NULL for all rows.

If a delimiter is specified, concatenated values are separated by that delimiter; otherwise, a comma is used as a delimiter.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

Either STRING or BYTES.

Return Data Types

Either STRING or BYTES.

Examples

SELECT STRING_AGG(fruit) AS string_agg
FROM UNNEST(["apple", NULL, "pear", "banana", "pear"]) AS fruit;

+------------------------+
| string_agg             |
+------------------------+
| apple,pear,banana,pear |
+------------------------+

SELECT STRING_AGG(fruit, " & ") AS string_agg
FROM UNNEST(["apple", "pear", "banana", "pear"]) AS fruit;

+------------------------------+
| string_agg                   |
+------------------------------+
| apple & pear & banana & pear |
+------------------------------+

SELECT STRING_AGG(DISTINCT fruit, " & ") AS string_agg
FROM UNNEST(["apple", "pear", "banana", "pear"]) AS fruit;

+-----------------------+
| string_agg            |
+-----------------------+
| apple & pear & banana |
+-----------------------+

SELECT STRING_AGG(fruit, " & " ORDER BY LENGTH(fruit)) AS string_agg
FROM UNNEST(["apple", "pear", "banana", "pear"]) AS fruit;

+------------------------------+
| string_agg                   |
+------------------------------+
| pear & pear & apple & banana |
+------------------------------+

SELECT STRING_AGG(fruit, " & " LIMIT 2) AS string_agg
FROM UNNEST(["apple", "pear", "banana", "pear"]) AS fruit;

+--------------+
| string_agg   |
+--------------+
| apple & pear |
+--------------+

SELECT STRING_AGG(DISTINCT fruit, " & " ORDER BY fruit DESC LIMIT 2) AS string_agg
FROM UNNEST(["apple", "pear", "banana", "pear"]) AS fruit;

+---------------+
| string_agg    |
+---------------+
| pear & banana |
+---------------+

SELECT
  fruit,
  STRING_AGG(fruit, " & ") OVER (ORDER BY LENGTH(fruit)) AS string_agg
FROM UNNEST(["apple", NULL, "pear", "banana", "pear"]) AS fruit;

+--------+------------------------------+
| fruit  | string_agg                   |
+--------+------------------------------+
| NULL   | NULL                         |
| pear   | pear & pear                  |
| pear   | pear & pear                  |
| apple  | pear & pear & apple          |
| banana | pear & pear & apple & banana |
+--------+------------------------------+

SUM

SUM(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the sum of non-null values.

If the expression is a floating point value, the sum is non-deterministic, which means you might receive a different result each time you use this function.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

Any supported numeric data types and INTERVAL.

Return Data Types

Special cases:

Returns NULL if the input contains only NULLs.

Returns NULL if the input contains no rows.

Returns Inf if the input contains Inf.

Returns -Inf if the input contains -Inf.

Returns NaN if the input contains a NaN.

Returns NaN if the input contains a combination of Inf and -Inf.

Examples

SELECT SUM(x) AS sum
FROM UNNEST([1, 2, 3, 4, 5, 4, 3, 2, 1]) AS x;

+-----+
| sum |
+-----+
| 25  |
+-----+

SELECT SUM(DISTINCT x) AS sum
FROM UNNEST([1, 2, 3, 4, 5, 4, 3, 2, 1]) AS x;

+-----+
| sum |
+-----+
| 15  |
+-----+

SELECT
  x,
  SUM(x) OVER (PARTITION BY MOD(x, 3)) AS sum
FROM UNNEST([1, 2, 3, 4, 5, 4, 3, 2, 1]) AS x;

+---+-----+
| x | sum |
+---+-----+
| 3 | 6   |
| 3 | 6   |
| 1 | 10  |
| 4 | 10  |
| 4 | 10  |
| 1 | 10  |
| 2 | 9   |
| 5 | 9   |
| 2 | 9   |
+---+-----+

SELECT
  x,
  SUM(DISTINCT x) OVER (PARTITION BY MOD(x, 3)) AS sum
FROM UNNEST([1, 2, 3, 4, 5, 4, 3, 2, 1]) AS x;

+---+-----+
| x | sum |
+---+-----+
| 3 | 3   |
| 3 | 3   |
| 1 | 5   |
| 4 | 5   |
| 4 | 5   |
| 1 | 5   |
| 2 | 7   |
| 5 | 7   |
| 2 | 7   |
+---+-----+

SELECT SUM(x) AS sum
FROM UNNEST([]) AS x;

+------+
| sum  |
+------+
| NULL |
+------+

Statistical aggregate functions

The following statistical aggregate functions are available in Google Standard SQL. To learn about the syntax for aggregate function calls, see Aggregate function calls.

CORR

CORR(
  X1, X2
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the Pearson coefficient of correlation of a set of number pairs. For each number pair, the first number is the dependent variable and the second number is the independent variable. The return result is between -1 and 1. A result of 0 indicates no correlation.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any input pairs that contain one or more NULL values. If there are fewer than two input pairs without NULL values, this function returns NULL.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

COVAR_POP

COVAR_POP(
  X1, X2
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the population covariance of a set of number pairs. The first number is the dependent variable; the second number is the independent variable. The return result is between -Inf and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any input pairs that contain one or more NULL values. If there is no input pair without NULL values, this function returns NULL. If there is exactly one input pair without NULL values, this function returns 0.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

COVAR_SAMP

COVAR_SAMP(
  X1, X2
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the sample covariance of a set of number pairs. The first number is the dependent variable; the second number is the independent variable. The return result is between -Inf and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any input pairs that contain one or more NULL values. If there are fewer than two input pairs without NULL values, this function returns NULL.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

STDDEV_POP

STDDEV_POP(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the population (biased) standard deviation of the values. The return result is between 0 and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any NULL inputs. If all inputs are ignored, this function returns NULL.

If this function receives a single non-NULL input, it returns 0.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

STDDEV_SAMP

STDDEV_SAMP(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the sample (unbiased) standard deviation of the values. The return result is between 0 and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any NULL inputs. If there are fewer than two non-NULL inputs, this function returns NULL.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

STDDEV

STDDEV(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

An alias of STDDEV_SAMP.

VAR_POP

VAR_POP(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the population (biased) variance of the values. The return result is between 0 and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any NULL inputs. If all inputs are ignored, this function returns NULL.

If this function receives a single non-NULL input, it returns 0.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

VAR_SAMP

VAR_SAMP(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

Returns the sample (unbiased) variance of the values. The return result is between 0 and +Inf.

All numeric types are supported. If the input is NUMERIC or BIGNUMERIC then the internal aggregation is stable with the final output converted to a FLOAT64. Otherwise the input is converted to a FLOAT64 before aggregation, resulting in a potentially unstable result.

This function ignores any NULL inputs. If there are fewer than two non-NULL inputs, this function returns NULL.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Data Type

FLOAT64

VARIANCE

VARIANCE(
  [ DISTINCT ]
  expression
)
[ OVER over_clause ]

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]

Description

An alias of VAR_SAMP.

Approximate aggregate functions

The following approximate aggregate functions are available in Google Standard SQL. To learn about the syntax for aggregate function calls, see Aggregate function calls.

Approximate aggregate functions are scalable in terms of memory usage and time, but produce approximate results instead of exact results. These functions typically require less memory than exact aggregation functions like COUNT(DISTINCT ...), but also introduce statistical uncertainty. This makes approximate aggregation appropriate for large data streams for which linear memory usage is impractical, as well as for data that is already approximate.

The approximate aggregate functions in this section work directly on the input data, rather than an intermediate estimation of the data. These functions do not allow users to specify the precision for the estimation with sketches. If you would like specify precision with sketches, see:

• HyperLogLog++ functions to estimate cardinality.

APPROX_COUNT_DISTINCT

APPROX_COUNT_DISTINCT(
  expression
)

Description

Returns the approximate result for COUNT(DISTINCT expression). The value returned is a statistical estimate—not necessarily the actual value.

This function is less accurate than COUNT(DISTINCT expression), but performs better on huge input.

Supported Argument Types

Any data type except: ARRAY STRUCT

Returned Data Types

INT64

Examples

SELECT APPROX_COUNT_DISTINCT(x) as approx_distinct
FROM UNNEST([0, 1, 1, 2, 3, 5]) as x;

+-----------------+
| approx_distinct |
+-----------------+
| 5               |
+-----------------+

APPROX_QUANTILES

APPROX_QUANTILES(
  [ DISTINCT ]
  expression, number
  [ { IGNORE | RESPECT } NULLS ]
)

Description

Returns the approximate boundaries for a group of expression values, where number represents the number of quantiles to create. This function returns an array of number + 1 elements, where the first element is the approximate minimum and the last element is the approximate maximum.

Returns NULL if there are zero input rows or expression evaluates to NULL for all rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• expression: Any supported data type except:

  • ARRAY
  • STRUCT

• number: INT64 literal or query parameter.

Returned Data Types

ARRAY<T> where T is the type specified by expression.

Examples

SELECT APPROX_QUANTILES(x, 2) AS approx_quantiles
FROM UNNEST([1, 1, 1, 4, 5, 6, 7, 8, 9, 10]) AS x;

+------------------+
| approx_quantiles |
+------------------+
| [1, 5, 10]       |
+------------------+

SELECT APPROX_QUANTILES(x, 100)[OFFSET(90)] AS percentile_90
FROM UNNEST([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) AS x;

+---------------+
| percentile_90 |
+---------------+
| 9             |
+---------------+

SELECT APPROX_QUANTILES(DISTINCT x, 2) AS approx_quantiles
FROM UNNEST([1, 1, 1, 4, 5, 6, 7, 8, 9, 10]) AS x;

+------------------+
| approx_quantiles |
+------------------+
| [1, 6, 10]       |
+------------------+

SELECT FORMAT("%T", APPROX_QUANTILES(x, 2 RESPECT NULLS)) AS approx_quantiles
FROM UNNEST([NULL, NULL, 1, 1, 1, 4, 5, 6, 7, 8, 9, 10]) AS x;

+------------------+
| approx_quantiles |
+------------------+
| [NULL, 4, 10]    |
+------------------+

SELECT FORMAT("%T", APPROX_QUANTILES(DISTINCT x, 2 RESPECT NULLS)) AS approx_quantiles
FROM UNNEST([NULL, NULL, 1, 1, 1, 4, 5, 6, 7, 8, 9, 10]) AS x;

+------------------+
| approx_quantiles |
+------------------+
| [NULL, 6, 10]    |
+------------------+

APPROX_TOP_COUNT

APPROX_TOP_COUNT(
  expression, number
)

Description

Returns the approximate top elements of expression as an array of STRUCTs. The number parameter specifies the number of elements returned.

Each STRUCT contains two fields. The first field (named value) contains an input value. The second field (named count) contains an INT64 specifying the number of times the value was returned.

Returns NULL if there are zero input rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• expression: Any data type that the GROUP BY clause supports.
• number: INT64 literal or query parameter.

Returned Data Types

ARRAY<STRUCT>

Examples

SELECT APPROX_TOP_COUNT(x, 2) as approx_top_count
FROM UNNEST(["apple", "apple", "pear", "pear", "pear", "banana"]) as x;

+-------------------------+
| approx_top_count        |
+-------------------------+
| [{pear, 3}, {apple, 2}] |
+-------------------------+

NULL handling

APPROX_TOP_COUNT does not ignore NULLs in the input. For example:

SELECT APPROX_TOP_COUNT(x, 2) as approx_top_count
FROM UNNEST([NULL, "pear", "pear", "pear", "apple", NULL]) as x;

+------------------------+
| approx_top_count       |
+------------------------+
| [{pear, 3}, {NULL, 2}] |
+------------------------+

APPROX_TOP_SUM

APPROX_TOP_SUM(
  expression, weight, number
)

Description

Returns the approximate top elements of expression, based on the sum of an assigned weight. The number parameter specifies the number of elements returned.

If the weight input is negative or NaN, this function returns an error.

The elements are returned as an array of STRUCTs. Each STRUCT contains two fields: value and sum. The value field contains the value of the input expression. The sum field is the same type as weight, and is the approximate sum of the input weight associated with the value field.

Returns NULL if there are zero input rows.

To learn more about the optional arguments in this function and how to use them, see Aggregate function calls.

Supported Argument Types

• expression: Any data type that the GROUP BY clause supports.

• weight: One of the following:

  • INT64
  • NUMERIC
  • BIGNUMERIC
  • FLOAT64

• number: INT64 literal or query parameter.

Returned Data Types

ARRAY<STRUCT>

Examples

SELECT APPROX_TOP_SUM(x, weight, 2) AS approx_top_sum FROM
UNNEST([
  STRUCT("apple" AS x, 3 AS weight),
  ("pear", 2),
  ("apple", 0),
  ("banana", 5),
  ("pear", 4)
]);

+--------------------------+
| approx_top_sum           |
+--------------------------+
| [{pear, 6}, {banana, 5}] |
+--------------------------+

NULL handling

APPROX_TOP_SUM does not ignore NULL values for the expression and weight parameters.

SELECT APPROX_TOP_SUM(x, weight, 2) AS approx_top_sum FROM
UNNEST([STRUCT("apple" AS x, NULL AS weight), ("pear", 0), ("pear", NULL)]);

+----------------------------+
| approx_top_sum             |
+----------------------------+
| [{pear, 0}, {apple, NULL}] |
+----------------------------+

SELECT APPROX_TOP_SUM(x, weight, 2) AS approx_top_sum FROM
UNNEST([STRUCT("apple" AS x, 0 AS weight), (NULL, 2)]);

+-------------------------+
| approx_top_sum          |
+-------------------------+
| [{NULL, 2}, {apple, 0}] |
+-------------------------+

SELECT APPROX_TOP_SUM(x, weight, 2) AS approx_top_sum FROM
UNNEST([STRUCT("apple" AS x, 0 AS weight), (NULL, NULL)]);

+----------------------------+
| approx_top_sum             |
+----------------------------+
| [{apple, 0}, {NULL, NULL}] |
+----------------------------+

HyperLogLog++ functions

The HyperLogLog++ algorithm (HLL++) estimates cardinality from sketches.

HLL++ functions are approximate aggregate functions. Approximate aggregation typically requires less memory than exact aggregation functions, like COUNT(DISTINCT), but also introduces statistical error. This makes HLL++ functions appropriate for large data streams for which linear memory usage is impractical, as well as for data that is already approximate.

If you do not need materialized sketches, you can alternatively use an approximate aggregate function with system-defined precision, such as APPROX_COUNT_DISTINCT. However, APPROX_COUNT_DISTINCT does not allow partial aggregations, re-aggregations, and custom precision.

BigQuery supports the following HLL++ functions:

HLL_COUNT.INIT

HLL_COUNT.INIT(input [, precision])

Description

An aggregate function that takes one or more input values and aggregates them into a HLL++ sketch. Each sketch is represented using the BYTES data type. You can then merge sketches using HLL_COUNT.MERGE or HLL_COUNT.MERGE_PARTIAL. If no merging is needed, you can extract the final count of distinct values from the sketch using HLL_COUNT.EXTRACT.

This function supports an optional parameter, precision. This parameter defines the accuracy of the estimate at the cost of additional memory required to process the sketches or store them on disk. The range for this value is 10 to 24. The default value is 15. For more information about precision, see Precision for sketches.

If the input is NULL, this function returns NULL.

For more information, see HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm.

Supported input types

• INT64
• NUMERIC
• BIGNUMERIC
• STRING
• BYTES

Return type

BYTES

Example

The following query creates HLL++ sketches that count the number of distinct users with at least one invoice per country.

SELECT
  country,
  HLL_COUNT.INIT(customer_id, 10)
    AS hll_sketch
FROM
  UNNEST(
    ARRAY<STRUCT<country STRING, customer_id STRING, invoice_id STRING>>[
      ('UA', 'customer_id_1', 'invoice_id_11'),
      ('CZ', 'customer_id_2', 'invoice_id_22'),
      ('CZ', 'customer_id_2', 'invoice_id_23'),
      ('BR', 'customer_id_3', 'invoice_id_31'),
      ('UA', 'customer_id_2', 'invoice_id_24')])
GROUP BY country;

+---------+------------------------------------------------------------------------------------+
| country | hll_sketch                                                                         |
+---------+------------------------------------------------------------------------------------+
| UA      | "\010p\020\002\030\002 \013\202\007\r\020\002\030\n \0172\005\371\344\001\315\010" |
| CZ      | "\010p\020\002\030\002 \013\202\007\013\020\001\030\n \0172\003\371\344\001"       |
| BR      | "\010p\020\001\030\002 \013\202\007\013\020\001\030\n \0172\003\202\341\001"       |
+---------+------------------------------------------------------------------------------------+

HLL_COUNT.MERGE

HLL_COUNT.MERGE(sketch)

Description

An aggregate function that returns the cardinality of several HLL++ set sketches by computing their union.

Each sketch must be initialized on the same type. Attempts to merge sketches for different types results in an error. For example, you cannot merge a sketch initialized from INT64 data with one initialized from STRING data.

If the merged sketches were initialized with different precisions, the precision will be downgraded to the lowest precision involved in the merge.

This function ignores NULL values when merging sketches. If the merge happens over zero rows or only over NULL values, the function returns 0.

Supported input types

BYTES

Return type

INT64

Example

The following query counts the number of distinct users across all countries who have at least one invoice.

SELECT HLL_COUNT.MERGE(hll_sketch) AS distinct_customers_with_open_invoice
FROM
  (
    SELECT
      country,
      HLL_COUNT.INIT(customer_id) AS hll_sketch
    FROM
      UNNEST(
        ARRAY<STRUCT<country STRING, customer_id STRING, invoice_id STRING, invoice_status STRING>>[
          ('UA', 'customer_id_1', 'invoice_id_11'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('CZ', 'customer_id_2', 'invoice_id_22'),
          ('CZ', 'customer_id_2', 'invoice_id_23'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('UA', 'customer_id_2', 'invoice_id_24')])
    GROUP BY country
  );

+--------------------------------------+
| distinct_customers_with_open_invoice |
+--------------------------------------+
|                                    3 |
+--------------------------------------+

HLL_COUNT.MERGE_PARTIAL

HLL_COUNT.MERGE_PARTIAL(sketch)

Description

An aggregate function that takes one or more HLL++ sketch inputs and merges them into a new sketch.

Each sketch must be initialized on the same type. Attempts to merge sketches for different types results in an error. For example, you cannot merge a sketch initialized from INT64 data with one initialized from STRING data.

If the merged sketches were initialized with different precisions, the precision will be downgraded to the lowest precision involved in the merge. For example, if MERGE_PARTIAL encounters sketches of precision 14 and 15, the returned new sketch will have precision 14.

This function returns NULL if there is no input or all inputs are NULL.

Supported input types

BYTES

Return type

BYTES

Example

The following query returns an HLL++ sketch that counts the number of distinct users who have at least one invoice across all countries.

SELECT HLL_COUNT.MERGE_PARTIAL(HLL_sketch) AS distinct_customers_with_open_invoice
FROM
  (
    SELECT
      country,
      HLL_COUNT.INIT(customer_id) AS hll_sketch
    FROM
      UNNEST(
        ARRAY<STRUCT<country STRING, customer_id STRING, invoice_id STRING, invoice_status STRING>>[
          ('UA', 'customer_id_1', 'invoice_id_11'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('CZ', 'customer_id_2', 'invoice_id_22'),
          ('CZ', 'customer_id_2', 'invoice_id_23'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('UA', 'customer_id_2', 'invoice_id_24')])
    GROUP BY country
  );

+----------------------------------------------------------------------------------------------+
| distinct_customers_with_open_invoice                                                         |
+----------------------------------------------------------------------------------------------+
| "\010p\020\006\030\002 \013\202\007\020\020\003\030\017 \0242\010\320\2408\352}\244\223\002" |
+----------------------------------------------------------------------------------------------+

HLL_COUNT.EXTRACT(sketch)

Description

A scalar function that extracts a cardinality estimate of a single HLL++ sketch.

If sketch is NULL, this function returns a cardinality estimate of 0.

Supported input types

BYTES

Return type

INT64

Example

The following query returns the number of distinct users for each country who have at least one invoice.

SELECT
  country,
  HLL_COUNT.EXTRACT(HLL_sketch) AS distinct_customers_with_open_invoice
FROM
  (
    SELECT
      country,
      HLL_COUNT.INIT(customer_id) AS hll_sketch
    FROM
      UNNEST(
        ARRAY<STRUCT<country STRING, customer_id STRING, invoice_id STRING>>[
          ('UA', 'customer_id_1', 'invoice_id_11'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('CZ', 'customer_id_2', 'invoice_id_22'),
          ('CZ', 'customer_id_2', 'invoice_id_23'),
          ('BR', 'customer_id_3', 'invoice_id_31'),
          ('UA', 'customer_id_2', 'invoice_id_24')])
    GROUP BY country
  );

+---------+--------------------------------------+
| country | distinct_customers_with_open_invoice |
+---------+--------------------------------------+
| UA      |                                    2 |
| BR      |                                    1 |
| CZ      |                                    1 |
+---------+--------------------------------------+

Numbering functions

The following sections describe the numbering functions that BigQuery supports. Numbering functions are a subset of window functions. To create a window function call and learn about the syntax for window functions, see Window function_calls.

Numbering functions assign integer values to each row based on their position within the specified window. The OVER clause syntax varies across numbering functions.

RANK

RANK()
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Returns the ordinal (1-based) rank of each row within the ordered partition. All peer rows receive the same rank value. The next row or set of peer rows receives a rank value which increments by the number of peers with the previous rank value, instead of DENSE_RANK, which always increments by 1.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

INT64

Examples

WITH Numbers AS
 (SELECT 1 as x
  UNION ALL SELECT 2
  UNION ALL SELECT 2
  UNION ALL SELECT 5
  UNION ALL SELECT 8
  UNION ALL SELECT 10
  UNION ALL SELECT 10
)
SELECT x,
  RANK() OVER (ORDER BY x ASC) AS rank
FROM Numbers

+-------------------------+
| x          | rank       |
+-------------------------+
| 1          | 1          |
| 2          | 2          |
| 2          | 2          |
| 5          | 4          |
| 8          | 5          |
| 10         | 6          |
| 10         | 6          |
+-------------------------+

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  RANK() OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+-------------+
| name            | finish_time            | division | finish_rank |
+-----------------+------------------------+----------+-------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 1           |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 4           |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 1           |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 2           |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 3           |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 4           |
+-----------------+------------------------+----------+-------------+

DENSE_RANK

DENSE_RANK()
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Returns the ordinal (1-based) rank of each row within the window partition. All peer rows receive the same rank value, and the subsequent rank value is incremented by one.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

INT64

Examples

WITH Numbers AS
 (SELECT 1 as x
  UNION ALL SELECT 2
  UNION ALL SELECT 2
  UNION ALL SELECT 5
  UNION ALL SELECT 8
  UNION ALL SELECT 10
  UNION ALL SELECT 10
)
SELECT x,
  DENSE_RANK() OVER (ORDER BY x ASC) AS dense_rank
FROM Numbers

+-------------------------+
| x          | dense_rank |
+-------------------------+
| 1          | 1          |
| 2          | 2          |
| 2          | 2          |
| 5          | 3          |
| 8          | 4          |
| 10         | 5          |
| 10         | 5          |
+-------------------------+

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  DENSE_RANK() OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+-------------+
| name            | finish_time            | division | finish_rank |
+-----------------+------------------------+----------+-------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 1           |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 3           |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 1           |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 2           |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 3           |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 4           |
+-----------------+------------------------+----------+-------------+

PERCENT_RANK

PERCENT_RANK()
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Return the percentile rank of a row defined as (RK-1)/(NR-1), where RK is the RANK of the row and NR is the number of rows in the partition. Returns 0 if NR=1.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

FLOAT64

Example

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  PERCENT_RANK() OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+---------------------+
| name            | finish_time            | division | finish_rank         |
+-----------------+------------------------+----------+---------------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 0                   |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 0.33333333333333331 |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 0.33333333333333331 |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 1                   |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 0                   |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 0.33333333333333331 |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 0.66666666666666663 |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 1                   |
+-----------------+------------------------+----------+---------------------+

CUME_DIST

CUME_DIST()
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Return the relative rank of a row defined as NP/NR. NP is defined to be the number of rows that either precede or are peers with the current row. NR is the number of rows in the partition.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

FLOAT64

Example

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  CUME_DIST() OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+-------------+
| name            | finish_time            | division | finish_rank |
+-----------------+------------------------+----------+-------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 0.25        |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 0.75        |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 0.75        |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 1           |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 0.25        |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 0.5         |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 0.75        |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 1           |
+-----------------+------------------------+----------+-------------+

NTILE

NTILE(constant_integer_expression)
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

This function divides the rows into constant_integer_expression buckets based on row ordering and returns the 1-based bucket number that is assigned to each row. The number of rows in the buckets can differ by at most 1. The remainder values (the remainder of number of rows divided by buckets) are distributed one for each bucket, starting with bucket 1. If constant_integer_expression evaluates to NULL, 0 or negative, an error is provided.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

INT64

Example

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  NTILE(3) OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+-------------+
| name            | finish_time            | division | finish_rank |
+-----------------+------------------------+----------+-------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 1           |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 1           |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 3           |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 1           |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 1           |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 2           |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 3           |
+-----------------+------------------------+----------+-------------+

ROW_NUMBER

ROW_NUMBER()
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]

Description

Does not require the ORDER BY clause. Returns the sequential row ordinal (1-based) of each row for each ordered partition. If the ORDER BY clause is unspecified then the result is non-deterministic.

To learn more about the OVER clause and how to use it, see Window function calls.

Return Type

INT64

Examples

WITH Numbers AS
 (SELECT 1 as x
  UNION ALL SELECT 2
  UNION ALL SELECT 2
  UNION ALL SELECT 5
  UNION ALL SELECT 8
  UNION ALL SELECT 10
  UNION ALL SELECT 10
)
SELECT x,
  ROW_NUMBER() OVER (ORDER BY x) AS row_num
FROM Numbers

+-------------------------+
| x          | row_num    |
+-------------------------+
| 1          | 1          |
| 2          | 2          |
| 2          | 3          |
| 5          | 4          |
| 8          | 5          |
| 10         | 6          |
| 10         | 7          |
+-------------------------+

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 2:59:01', 'F30-34')
SELECT name,
  finish_time,
  division,
  ROW_NUMBER() OVER (PARTITION BY division ORDER BY finish_time ASC) AS finish_rank
FROM finishers;

+-----------------+------------------------+----------+-------------+
| name            | finish_time            | division | finish_rank |
+-----------------+------------------------+----------+-------------+
| Sophia Liu      | 2016-10-18 09:51:45+00 | F30-34   | 1           |
| Meghan Lederer  | 2016-10-18 09:59:01+00 | F30-34   | 2           |
| Nikki Leith     | 2016-10-18 09:59:01+00 | F30-34   | 3           |
| Jen Edwards     | 2016-10-18 10:06:36+00 | F30-34   | 4           |
| Lisa Stelzner   | 2016-10-18 09:54:11+00 | F35-39   | 1           |
| Lauren Matthews | 2016-10-18 10:01:17+00 | F35-39   | 2           |
| Desiree Berry   | 2016-10-18 10:05:42+00 | F35-39   | 3           |
| Suzy Slane      | 2016-10-18 10:06:24+00 | F35-39   | 4           |
+-----------------+------------------------+----------+-------------+

Bit functions

BigQuery supports the following bit functions.

BIT_COUNT

BIT_COUNT(expression)

Description

The input, expression, must be an integer or BYTES.

Returns the number of bits that are set in the input expression. For signed integers, this is the number of bits in two's complement form.

Return Data Type

INT64

Example

SELECT a, BIT_COUNT(a) AS a_bits, FORMAT("%T", b) as b, BIT_COUNT(b) AS b_bits
FROM UNNEST([
  STRUCT(0 AS a, b'' AS b), (0, b'\x00'), (5, b'\x05'), (8, b'\x00\x08'),
  (0xFFFF, b'\xFF\xFF'), (-2, b'\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFE'),
  (-1, b'\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF'),
  (NULL, b'\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF')
]) AS x;

+-------+--------+---------------------------------------------+--------+
| a     | a_bits | b                                           | b_bits |
+-------+--------+---------------------------------------------+--------+
| 0     | 0      | b""                                         | 0      |
| 0     | 0      | b"\x00"                                     | 0      |
| 5     | 2      | b"\x05"                                     | 2      |
| 8     | 1      | b"\x00\x08"                                 | 1      |
| 65535 | 16     | b"\xff\xff"                                 | 16     |
| -2    | 63     | b"\xff\xff\xff\xff\xff\xff\xff\xfe"         | 63     |
| -1    | 64     | b"\xff\xff\xff\xff\xff\xff\xff\xff"         | 64     |
| NULL  | NULL   | b"\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff" | 80     |
+-------+--------+---------------------------------------------+--------+

Conversion functions

BigQuery supports the following conversion functions. These data type conversions are explicit, but some conversions can happen implicitly. You can learn more about implicit and explicit conversion here.

CAST overview

CAST(expression AS typename [format_clause])

Description

Cast syntax is used in a query to indicate that the result type of an expression should be converted to some other type.

When using CAST, a query can fail if BigQuery is unable to perform the cast. If you want to protect your queries from these types of errors, you can use SAFE_CAST.

Casts between supported types that do not successfully map from the original value to the target domain produce runtime errors. For example, casting BYTES to STRING where the byte sequence is not valid UTF-8 results in a runtime error.

Some casts can include a format clause, which provides instructions for how to conduct the cast. For example, you could instruct a cast to convert a sequence of bytes to a BASE64-encoded string instead of a UTF-8-encoded string.

The structure of the format clause is unique to each type of cast and more information is available in the section for that cast.

Examples

The following query results in "true" if x is 1, "false" for any other non-NULL value, and NULL if x is NULL.

CAST(x=1 AS STRING)

CAST AS ARRAY

CAST(expression AS ARRAY<element_type>)

Description

BigQuery supports casting to ARRAY. The expression parameter can represent an expression for these data types:

• ARRAY

Conversion rules

CAST AS BIGNUMERIC

CAST(expression AS BIGNUMERIC)

Description

BigQuery supports casting to BIGNUMERIC. The expression parameter can represent an expression for these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• STRING

Conversion rules

CAST AS BOOL

CAST(expression AS BOOL)

Description

BigQuery supports casting to BOOL. The expression parameter can represent an expression for these data types:

• INT64
• BOOL
• STRING

Conversion rules

CAST AS BYTES

CAST(expression AS BYTES [format_clause])

Description

BigQuery supports casting to BYTES. The expression parameter can represent an expression for these data types:

• BYTES
• STRING

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is a STRING.

• Format string as bytes

Conversion rules

CAST AS DATE

CAST(expression AS DATE [format_clause])

Description

BigQuery supports casting to DATE. The expression parameter can represent an expression for these data types:

• STRING
• TIME
• DATETIME
• TIMESTAMP

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is a STRING.

• Format string as date and time

Conversion rules

CAST AS DATETIME

CAST(expression AS DATETIME [format_clause])

Description

BigQuery supports casting to DATETIME. The expression parameter can represent an expression for these data types:

• STRING
• TIME
• DATETIME
• TIMESTAMP

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is a STRING.

• Format string as date and time

Conversion rules

CAST AS FLOAT64

CAST(expression AS FLOAT64)

Description

BigQuery supports casting to floating point types. The expression parameter can represent an expression for these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• STRING

Conversion rules

CAST AS INT64

CAST(expression AS INT64)

Description

BigQuery supports casting to integer types. The expression parameter can represent an expression for these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• BOOL
• STRING

Conversion rules

Examples

If you are working with hex strings (0x123), you can cast those strings as integers:

SELECT '0x123' as hex_value, CAST('0x123' as INT64) as hex_to_int;

+-----------+------------+
| hex_value | hex_to_int |
+-----------+------------+
| 0x123     | 291        |
+-----------+------------+

SELECT '-0x123' as hex_value, CAST('-0x123' as INT64) as hex_to_int;

+-----------+------------+
| hex_value | hex_to_int |
+-----------+------------+
| -0x123    | -291       |
+-----------+------------+

CAST AS INTERVAL

CAST(expression AS INTERVAL)

Description

BigQuery supports casting to INTERVAL. The expression parameter can represent an expression for these data types:

• STRING

Conversion rules

Examples

SELECT input, CAST(input AS INTERVAL) AS output
FROM UNNEST([
  '1-2 3 10:20:30.456',
  '1-2',
  '10:20:30',
  'P1Y2M3D',
  'PT10H20M30,456S'
]) input

+--------------------+--------------------+
| input              | output             |
+--------------------+--------------------+
| 1-2 3 10:20:30.456 | 1-2 3 10:20:30.456 |
| 1-2                | 1-2 0 0:0:0        |
| 10:20:30           | 0-0 0 10:20:30     |
| P1Y2M3D            | 1-2 3 0:0:0        |
| PT10H20M30,456S    | 0-0 0 10:20:30.456 |
+--------------------+--------------------+

CAST AS NUMERIC

CAST(expression AS NUMERIC)

Description

BigQuery supports casting to NUMERIC. The expression parameter can represent an expression for these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• STRING

Conversion rules

CAST AS STRING

CAST(expression AS STRING [format_clause [AT TIME ZONE timezone_expr]])

Description

BigQuery supports casting to STRING. The expression parameter can represent an expression for these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• BOOL
• BYTES
• TIME
• DATE
• DATETIME
• TIMESTAMP
• INTERVAL
• STRING

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is one of these data types:

• INT64
• FLOAT64
• NUMERIC
• BIGNUMERIC
• BYTES
• TIME
• DATE
• DATETIME
• TIMESTAMP

The format clause for STRING has an additional optional clause called AT TIME ZONE timezone_expr, which you can use to specify a specific time zone to use during formatting of a TIMESTAMP. If this optional clause is not included when formatting a TIMESTAMP, your current time zone is used.

For more information, see the following topics:

• Format bytes as string
• Format date and time as string
• Format numeric type as string

Conversion rules

Examples

SELECT CAST(CURRENT_DATE() AS STRING) AS current_date

+---------------+
| current_date  |
+---------------+
| 2021-03-09    |
+---------------+

SELECT CAST(CURRENT_DATE() AS STRING FORMAT 'DAY') AS current_day

+-------------+
| current_day |
+-------------+
| MONDAY      |
+-------------+

SELECT CAST(
  TIMESTAMP '2008-12-25 00:00:00+00:00'
  AS STRING FORMAT 'YYYY-MM-DD HH24:MI:SS TZH:TZM') AS date_time_to_string

-- Results depend upon where this query was executed.
+------------------------------+
| date_time_to_string          |
+------------------------------+
| 2008-12-24 16:00:00 -08:00   |
+------------------------------+

SELECT CAST(
  TIMESTAMP '2008-12-25 00:00:00+00:00'
  AS STRING FORMAT 'YYYY-MM-DD HH24:MI:SS TZH:TZM'
  AT TIME ZONE 'Asia/Kolkata') AS date_time_to_string

-- Because the time zone is specified, the result is always the same.
+------------------------------+
| date_time_to_string          |
+------------------------------+
| 2008-12-25 05:30:00 +05:30   |
+------------------------------+

SELECT CAST(INTERVAL 3 DAY AS STRING) AS interval_to_string

+--------------------+
| interval_to_string |
+--------------------+
| 0-0 3 0:0:0        |
+--------------------+

SELECT CAST(
  INTERVAL "1-2 3 4:5:6.789" YEAR TO SECOND
  AS STRING) AS interval_to_string

+--------------------+
| interval_to_string |
+--------------------+
| 1-2 3 4:5:6.789    |
+--------------------+

CAST AS STRUCT

CAST(expression AS STRUCT)

Description

BigQuery supports casting to STRUCT. The expression parameter can represent an expression for these data types:

• STRUCT

Conversion rules

CAST AS TIME

CAST(expression AS TIME [format_clause])

Description

BigQuery supports casting to TIME. The expression parameter can represent an expression for these data types:

• STRING
• TIME
• DATETIME
• TIMESTAMP

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is a STRING.

• Format string as date and time

Conversion rules

CAST AS TIMESTAMP

CAST(expression AS TIMESTAMP [format_clause [AT TIME ZONE timezone_expr]])

Description

BigQuery supports casting to TIMESTAMP. The expression parameter can represent an expression for these data types:

• STRING
• TIME
• DATETIME
• TIMESTAMP

Format clause

When an expression of one type is cast to another type, you can use the format clause to provide instructions for how to conduct the cast. You can use the format clause in this section if expression is a STRING.

• Format string as date and time

The format clause for TIMESTAMP has an additional optional clause called AT TIME ZONE timezone_expr, which you can use to specify a specific time zone to use during formatting. If this optional clause is not included, your current time zone is used.

Conversion rules

Examples

The following example casts a string-formatted timestamp as a timestamp:

SELECT CAST("2020-06-02 17:00:53.110+00:00" AS TIMESTAMP) AS as_timestamp

-- Results depend upon where this query was executed.
+-----------------------------+
| as_timestamp                |
+-----------------------------+
| 2020-06-03 00:00:53.110 UTC |
+-----------------------------+

The following examples cast a string-formatted date and time as a timestamp. These examples return the same output as the previous example.

SELECT CAST("06/02/2020 17:00:53.110" AS TIMESTAMP FORMAT 'MM/DD/YYYY HH:MI:SS' AT TIME ZONE 'America/Los_Angeles') AS as_timestamp

SELECT CAST("06/02/2020 17:00:53.110" AS TIMESTAMP FORMAT 'MM/DD/YYYY HH:MI:SS' AT TIME ZONE '00') AS as_timestamp

SELECT CAST('06/02/2020 17:00:53.110 +00' AS TIMESTAMP FORMAT 'YYYY-MM-DD HH:MI:SS TZH') AS as_timestamp

PARSE_BIGNUMERIC

PARSE_BIGNUMERIC(string_expression)

Description

Converts a string to a BIGNUMERIC value.

The numeric literal contained in the string must not exceed the maximum precision or range of the BIGNUMERIC type, or an error occurs. If the number of digits after the decimal point exceeds 38, then the resulting BIGNUMERIC value rounds half away from zero to have 38 digits after the decimal point.

-- This example shows how a string with a decimal point is parsed.
SELECT PARSE_BIGNUMERIC("123.45") AS parsed

+--------+
| parsed |
+--------+
| 123.45 |
+--------+

-- This example shows how a string with an exponent is parsed.
SELECT PARSE_BIGNUMERIC("123.456E37") AS parsed

+-----------------------------------------+
| parsed                                  |
+-----------------------------------------+
| 123400000000000000000000000000000000000 |
+-----------------------------------------+

-- This example shows the rounding when digits after the decimal point exceeds 38.
SELECT PARSE_BIGNUMERIC("1.123456789012345678901234567890123456789") as parsed

+------------------------------------------+
| parsed                                   |
+------------------------------------------+
| 1.12345678901234567890123456789012345679 |
+------------------------------------------+

This funcion is similar to using the CAST AS BIGNUMERIC function except that the PARSE_BIGNUMERIC function only accepts string inputs and allows the following in the string:

• Spaces between the sign (+/-) and the number
• Signs (+/-) after the number

Rules for valid input strings:

Return Data Type

BIGNUMERIC

Examples

This example shows an input with spaces before, after, and between the sign and the number:

SELECT PARSE_BIGNUMERIC("  -  12.34 ") as parsed;

+--------+
| parsed |
+--------+
| -12.34 |
+--------+

This example shows an input with an exponent as well as the sign after the number:

SELECT PARSE_BIGNUMERIC("12.34e-1-") as parsed;

+--------+
| parsed |
+--------+
| -1.234 |
+--------+

This example shows an input with multiple commas in the integer part of the number:

SELECT PARSE_BIGNUMERIC("  1,2,,3,.45 + ") as parsed;

+--------+
| parsed |
+--------+
| 123.45 |
+--------+

This example shows an input with a decimal point and no digits in the whole number part:

SELECT PARSE_BIGNUMERIC(".1234  ") as parsed;

+--------+
| parsed |
+--------+
| 0.1234 |
+--------+

Examples of invalid inputs

This example is invalid because the whole number part contains no digits:

SELECT PARSE_BIGNUMERIC(",,,.1234  ") as parsed;

This example is invalid because there are whitespaces between digits:

SELECT PARSE_BIGNUMERIC("1  23.4 5  ") as parsed;

This example is invalid because the number is empty except for an exponent:

SELECT PARSE_BIGNUMERIC("  e1 ") as parsed;

This example is invalid because the string contains multiple signs:

SELECT PARSE_BIGNUMERIC("  - 12.3 - ") as parsed;

This example is invalid because the value of the number falls outside the range of BIGNUMERIC:

SELECT PARSE_BIGNUMERIC("12.34E100 ") as parsed;

This example is invalid because the string contains invalid characters:

SELECT PARSE_BIGNUMERIC("$12.34") as parsed;

PARSE_NUMERIC

PARSE_NUMERIC(string_expression)

Description

Converts a string to a NUMERIC value.

The numeric literal contained in the string must not exceed the maximum precision or range of the NUMERIC type, or an error occurs. If the number of digits after the decimal point exceeds nine, then the resulting NUMERIC value rounds half away from zero to have nine digits after the decimal point.

-- This example shows how a string with a decimal point is parsed.
SELECT PARSE_NUMERIC("123.45") AS parsed

+--------+
| parsed |
+--------+
| 123.45 |
+--------+

-- This example shows how a string with an exponent is parsed.
SELECT PARSE_NUMERIC("12.34E27") as parsed

+-------------------------------+
| parsed                        |
+-------------------------------+
| 12340000000000000000000000000 |
+-------------------------------+

-- This example shows the rounding when digits after the decimal point exceeds 9.
SELECT PARSE_NUMERIC("1.0123456789") as parsed

+-------------+
| parsed      |
+-------------+
| 1.012345679 |
+-------------+

This function is similar to using the CAST AS NUMERIC function except that the PARSE_NUMERIC function only accepts string inputs and allows the following in the string:

• Spaces between the sign (+/-) and the number
• Signs (+/-) after the number

Rules for valid input strings:

Return Data Type

NUMERIC

Examples

This example shows an input with spaces before, after, and between the sign and the number:

SELECT PARSE_NUMERIC("  -  12.34 ") as parsed;

+--------+
| parsed |
+--------+
| -12.34 |
+--------+

This example shows an input with an exponent as well as the sign after the number:

SELECT PARSE_NUMERIC("12.34e-1-") as parsed;

+--------+
| parsed |
+--------+
| -1.234 |
+--------+

This example shows an input with multiple commas in the integer part of the number:

SELECT PARSE_NUMERIC("  1,2,,3,.45 + ") as parsed;

+--------+
| parsed |
+--------+
| 123.45 |
+--------+

This example shows an input with a decimal point and no digits in the whole number part:

SELECT PARSE_NUMERIC(".1234  ") as parsed;

+--------+
| parsed |
+--------+
| 0.1234 |
+--------+

Examples of invalid inputs

This example is invalid because the whole number part contains no digits:

SELECT PARSE_NUMERIC(",,,.1234  ") as parsed;

This example is invalid because there are whitespaces between digits:

SELECT PARSE_NUMERIC("1  23.4 5  ") as parsed;

This example is invalid because the number is empty except for an exponent:

SELECT PARSE_NUMERIC("  e1 ") as parsed;

This example is invalid because the string contains multiple signs:

SELECT PARSE_NUMERIC("  - 12.3 - ") as parsed;

This example is invalid because the value of the number falls outside the range of BIGNUMERIC:

SELECT PARSE_NUMERIC("12.34E100 ") as parsed;

This example is invalid because the string contains invalid characters:

SELECT PARSE_NUMERIC("$12.34") as parsed;

SAFE_CAST

SAFE_CAST(expression AS typename [format_clause])

Description

When using CAST, a query can fail if BigQuery is unable to perform the cast. For example, the following query generates an error:

SELECT CAST("apple" AS INT64) AS not_a_number;

If you want to protect your queries from these types of errors, you can use SAFE_CAST. SAFE_CAST is identical to CAST, except it returns NULL instead of raising an error.

SELECT SAFE_CAST("apple" AS INT64) AS not_a_number;

+--------------+
| not_a_number |
+--------------+
| NULL         |
+--------------+

If you are casting from bytes to strings, you can also use the function, SAFE_CONVERT_BYTES_TO_STRING. Any invalid UTF-8 characters are replaced with the unicode replacement character, U+FFFD. See SAFE_CONVERT_BYTES_TO_STRING for more information.

Other conversion functions

You can learn more about these conversion functions elsewhere in the documentation:

Format clause for CAST

format_clause:
  FORMAT format_model

format_model:
  format_string_expression

The format clause can be used in some CAST functions. You use a format clause to provide instructions for how to conduct a cast. For example, you could instruct a cast to convert a sequence of bytes to a BASE64-encoded string instead of a UTF-8-encoded string.

The format clause includes a format model. The format model can contain format elements combined together as a format string.

Format bytes as string

CAST(bytes_expression AS STRING FORMAT format_string_expression)

You can cast a sequence of bytes to a string with a format element in the format string. If the bytes cannot be formatted with a format element, an error is returned. If the sequence of bytes is NULL, the result is NULL. Format elements are case-insensitive.

Return type

STRING

Example

SELECT CAST(b'\x48\x65\x6c\x6c\x6f' AS STRING FORMAT 'ASCII') AS bytes_to_string;

+-----------------+
| bytes_to_string |
+-----------------+
| Hello           |
+-----------------+

Format string as bytes

CAST(string_expression AS BYTES FORMAT format_string_expression)

You can cast a string to bytes with a format element in the format string. If the string cannot be formatted with the format element, an error is returned. Format elements are case-insensitive.

In the string expression, whitespace characters, such as \n, are ignored if the BASE64 or BASE64M format element is used.

Return type

BYTES

Example

SELECT CAST('Hello' AS BYTES FORMAT 'ASCII') AS string_to_bytes

+-------------------------+
| string_to_bytes         |
+-------------------------+
| b'\x48\x65\x6c\x6c\x6f' |
+-------------------------+

Format date and time as string

You can format these date and time parts as a string:

• Format year part as string
• Format month part as string
• Format day part as string
• Format hour part as string
• Format minute part as string
• Format second part as string
• Format meridian indicator as string
• Format time zone as string
• Format literal as string

Case matching is supported when you format some date or time parts as a string and the output contains letters. To learn more, see Case matching.

Case matching

When the output of some format element contains letters, the letter cases of the output is matched with the letter cases of the format element, meaning the words in the output are capitalized according to how the format element is capitalized. This is called case matching. The rules are:

• If the first two letters of the element are both upper case, the words in the output are capitalized. For example DAY = THURSDAY.
• If the first letter of the element is upper case, and the second letter is lowercase, the first letter of each word in the output is capitalized and other letters are lowercase. For example Day = Thursday.
• If the first letter of the element is lowercase, then all letters in the output are lowercase. For example, day = thursday.

Format year part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the year part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the year that you need to format.
• format_string_expression: A string which contains format elements, including the year format element.

These data types include a year part:

• DATE
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Example

SELECT CAST(DATE '2018-01-30' AS STRING FORMAT 'YYYY') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 2018                |
+---------------------+

Format month part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the month part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the month that you need to format.
• format_string_expression: A string which contains format elements, including the month format element.

These data types include a month part:

• DATE
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Example

SELECT CAST(DATE '2018-01-30' AS STRING FORMAT 'MONTH') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| JANUARY             |
+---------------------+

Format day part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the day part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the day that you need to format.
• format_string_expression: A string which contains format elements, including the day format element.

These data types include a day part:

• DATE
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Example

SELECT CAST(DATE '2018-02-15' AS STRING FORMAT 'DD') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 15                  |
+---------------------+

Format hour part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the hour part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the hour that you need to format.
• format_string_expression: A string which contains format elements, including the hour format element.

These data types include a hour part:

• TIME
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Examples

SELECT CAST(TIME '21:30:00' AS STRING FORMAT 'HH24') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 21                  |
+---------------------+

SELECT CAST(TIME '21:30:00' AS STRING FORMAT 'HH12') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 09                  |
+---------------------+

Format minute part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the minute part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the minute that you need to format.
• format_string_expression: A string which contains format elements, including the minute format element.

These data types include a minute part:

• TIME
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Example

SELECT CAST(TIME '21:30:00' AS STRING FORMAT 'MI') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 30                  |
+---------------------+

Format second part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the second part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the second that you need to format.
• format_string_expression: A string which contains format elements, including the second format element.

These data types include a second part:

• TIME
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Examples

SELECT CAST(TIME '21:30:25.16' AS STRING FORMAT 'SS') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 25                  |
+---------------------+

SELECT CAST(TIME '21:30:25.16' AS STRING FORMAT 'FF2') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| 16                  |
+---------------------+

Format meridian indicator part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the meridian indicator part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the meridian indicator that you need to format.
• format_string_expression: A string which contains format elements, including the meridian indicator format element.

These data types include a meridian indicator part:

• TIME
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Examples

SELECT CAST(TIME '21:30:00' AS STRING FORMAT 'AM') AS date_time_to_string;
SELECT CAST(TIME '21:30:00' AS STRING FORMAT 'PM') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| PM                  |
+---------------------+

SELECT CAST(TIME '01:30:00' AS STRING FORMAT 'AM') AS date_time_to_string;
SELECT CAST(TIME '01:30:00' AS STRING FORMAT 'PM') AS date_time_to_string;

+---------------------+
| date_time_to_string |
+---------------------+
| AM                  |
+---------------------+

Format time zone part as string

CAST(expression AS STRING FORMAT format_string_expression)

Casts a data type that contains the time zone part to a string. Includes format elements, which provide instructions for how to conduct the cast.

• expression: This expression contains the data type with the time zone that you need to format.
• format_string_expression: A string which contains format elements, including the time zone format element.

These data types include a time zone part:

• DATE
• TIME
• DATETIME
• TIMESTAMP

If expression or format_string_expression is NULL the return value is NULL. If format_string_expression is an empty string, the output is an empty string. An error is generated if a value that is not a supported format element appears in format_string_expression or expression does not contain a value specified by a format element.

Return type

STRING

Examples

SELECT CAST(TIMESTAMP '2008-12-25 00:00:00+00:00' AS STRING FORMAT 'TZH') AS date_time_to_string;

-- Results depend upon where this query was executed.
+---------------------+
| date_time_to_string |
+---------------------+
| -08                 |
+---------------------+

SELECT CAST(TIMESTAMP '2008-12-25 00:00:00+00:00' AS STRING FORMAT 'TZH' AT TIME ZONE 'Asia/Kolkata')
AS date_time_to_string;

-- Because the time zone is specified, the result is always the same.
+---------------------+
| date_time_to_string |
+---------------------+
| +05                 |
+---------------------+

SELECT CAST(TIMESTAMP '2008-12-25 00:00:00+00:00' AS STRING FORMAT 'TZM') AS date_time_to_string;

-- Results depend upon where this query was executed.
+---------------------+
| date_time_to_string |
+---------------------+
| 00                  |
+---------------------+

SELECT CAST(TIMESTAMP '2008-12-25 00:00:00+00:00' AS STRING FORMAT 'TZM' AT TIME ZONE 'Asia/Kolkata')
AS date_time_to_string;

-- Because the time zone is specified, the result is always the same.
+---------------------+
| date_time_to_string |
+---------------------+
| 30                  |
+---------------------+

Format literal as string

CAST(expression AS STRING FORMAT format_string_expression)

Format string as date and time

You can format a string with these date and time parts:

• Format string as year part
• Format string as month part
• Format string as day part
• Format string as hour part
• Format string as minute part
• Format string as second part
• Format string as meridian indicator part
• Format string as time zone part
• Format string as literal part

When formatting a string with date and time parts, you must follow the format model rules.

Format model rules

When casting a string to date and time parts, you must ensure the format model is valid. The format model represents the elements passed into CAST(string_expression AS type FORMAT format_string_expression) as the format_string_expression and is validated according to the following rules:

• It contains at most one of each of the following parts: meridian indicator, year, month, day, hour.
• A non-literal, non-whitespace format element cannot appear more than once.
• If it contains the day of year format element, DDD, then it cannot contain the month.
• If it contains the 24-hour format element, HH24, then it cannot contain the 12-hour format element or a meridian indicator.
• If it contains the 12-hour format element, HH12 or HH, then it must also contain a meridian indicator.
• If it contains a meridian indicator, then it must also contain a 12-hour format element.
• If it contains the second of the day format element, SSSSS, then it cannot contain any of the following: hour, minute, second, or meridian indicator.
• It cannot contain a format element such that the value it sets does not exist in the target type. For example, an hour format element such as HH24 cannot appear in a string you are casting as a DATE.

Format string as year part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted year to a data type that contains the year part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the year that you need to format.
• type: The data type to which you are casting. Must include the year part.
• format_string_expression: A string which contains format elements, including the year format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a year part:

• DATE
• DATETIME
• TIMESTAMP

If the YEAR part is missing from string_expression and the return type includes this part, YEAR is set to the current year.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• DATE
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('18-12-03' AS DATE FORMAT 'YY-MM-DD') AS string_to_date

+----------------+
| string_to_date |
+----------------+
| 2018-02-03     |
+----------------+

Format string as month part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted month to a data type that contains the month part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the month that you need to format.
• type: The data type to which you are casting. Must include the month part.
• format_string_expression: A string which contains format elements, including the month format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a month part:

• DATE
• DATETIME
• TIMESTAMP

If the MONTH part is missing from string_expression and the return type includes this part, MONTH is set to the current month.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• DATE
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('DEC 03, 2018' AS DATE FORMAT 'MON DD, YYYY') AS string_to_date

+----------------+
| string_to_date |
+----------------+
| 2018-12-03     |
+----------------+

Format string as day part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted day to a data type that contains the day part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the day that you need to format.
• type: The data type to which you are casting. Must include the day part.
• format_string_expression: A string which contains format elements, including the day format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a day part:

• DATE
• DATETIME
• TIMESTAMP

If the DAY part is missing from string_expression and the return type includes this part, DAY is set to 1.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• DATE
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('DECEMBER 03, 2018' AS DATE FORMAT 'MONTH DD, YYYY') AS string_to_date

+----------------+
| string_to_date |
+----------------+
| 2018-12-03     |
+----------------+

Format string as hour part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted hour to a data type that contains the hour part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the hour that you need to format.
• type: The data type to which you are casting. Must include the hour part.
• format_string_expression: A string which contains format elements, including the hour format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a hour part:

• TIME
• DATETIME
• TIMESTAMP

If the HOUR part is missing from string_expression and the return type includes this part, HOUR is set to 0.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• TIME
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('15:30' AS TIME FORMAT 'HH24:MI') AS string_to_date_time

+---------------------+
| string_to_date_time |
+---------------------+
| 15:30:00            |
+---------------------+

Format string as minute part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted minute to a data type that contains the minute part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the minute that you need to format.
• type: The data type to which you are casting. Must include the minute part.
• format_string_expression: A string which contains format elements, including the minute format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a minute part:

• TIME
• DATETIME
• TIMESTAMP

If the MINUTE part is missing from string_expression and the return type includes this part, MINUTE is set to 0.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• TIME
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('03:30 P.M.' AS TIME FORMAT 'HH:MI P.M.') AS string_to_date_time

+---------------------+
| string_to_date_time |
+---------------------+
| 15:30:00            |
+---------------------+

Format string as second part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted second to a data type that contains the second part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the second that you need to format.
• type: The data type to which you are casting. Must include the second part.
• format_string_expression: A string which contains format elements, including the second format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a second part:

• TIME
• DATETIME
• TIMESTAMP

If the SECOND part is missing from string_expression and the return type includes this part, SECOND is set to 0.

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• TIME
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('01:05:07.16' AS TIME FORMAT 'HH24:MI:SS.FF1') AS string_to_date_time

+---------------------+
| string_to_date_time |
+---------------------+
| 01:05:07.2          |
+---------------------+

Format string as meridian indicator part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted meridian indicator to a data type that contains the meridian indicator part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the meridian indicator that you need to format.
• type: The data type to which you are casting. Must include the meridian indicator part.
• format_string_expression: A string which contains format elements, including the meridian indicator format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a meridian indicator part:

• TIME
• DATETIME
• TIMESTAMP

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• TIME
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('03:30 P.M.' AS TIME FORMAT 'HH:MI A.M.') AS string_to_date_time

+---------------------+
| string_to_date_time |
+---------------------+
| 15:30:00            |
+---------------------+

Format string as time zone part

CAST(string_expression AS type FORMAT format_string_expression)

Casts a string-formatted time zone to a data type that contains the time zone part. Includes format elements, which provide instructions for how to conduct the cast.

• string_expression: This expression contains the string with the time zone that you need to format.
• type: The data type to which you are casting. Must include the time zone part.
• format_string_expression: A string which contains format elements, including the time zone format element. The formats elements in this string are defined collectively as the format model, which must follow these rules.

These data types include a time zone part:

• DATE
• TIME
• DATETIME
• TIMESTAMP

An error is generated if a value that is not a supported format element appears in format_string_expression or string_expression does not contain a value specified by a format element.

Return type

The data type to which the string was cast. This can be:

• DATE
• TIME
• DATETIME
• TIMESTAMP

Examples

SELECT CAST('2020.06.03 00:00:53+00' AS TIMESTAMP FORMAT 'YYYY.MM.DD HH:MI:SSTZH') AS string_to_date_time

+-----------------------------+
| as_timestamp                |
+-----------------------------+
| 2020-06-03 00:00:53.110 UTC |
+-----------------------------+

Format string as literal

CAST(string_expression AS data_type FORMAT format_string_expression)

Format numeric type as string

CAST(numeric_expression AS STRING FORMAT format_string_expression)

You can cast a numeric type to a string by combining the following format elements:

• Digits
• Decimal point
• Sign
• Currency symbol
• Group separator
• Other format elements

Except for the exponent format element (EEEE), all of the format elements generate a fixed number of characters in the output, and the output is aligned by the decimal point. The first character outputs a - for negative numbers; otherwise a space. To suppress blank characters and trailing zeroes, use the FM flag.

Return type

STRING

Example

SELECT input, CAST(input AS STRING FORMAT '$999,999.999') AS output
FROM UNNEST([1.2, 12.3, 123.456, 1234.56, -12345.678, 1234567.89]) AS input

+------------+---------------+
|   input    |    output     |
+------------+---------------+
|        1.2 |        $1.200 |
|       12.3 |       $12.300 |
|    123.456 |      $123.456 |
|    1234.56 |    $1,234.560 |
| -12345.678 |  -$12,345.678 |
| 1234567.89 |  $###,###.### |
+------------+---------------+

Format digits as string

The following format elements output digits. If there aren't enough digit format elements to represent the input, all digit format elements are replaced with # in the output.

Return type

STRING

Example

SELECT
  CAST(12 AS STRING FORMAT '999') as a,
  CAST(-12 AS STRING FORMAT '999') as b;

+------+------+
|  a   |  b   |
+------+------+
|   12 |  -12 |
+------+------+

Format decimal point as string

The following format elements output a decimal point. These format elements are mutually exclusive. At most one can appear in the format string.

Return type

STRING

Example

SELECT CAST(12.5 AS STRING FORMAT '99.99') as a;

+--------+
|   a    |
+--------+
|  12.50 |
+--------+

Format sign as string

The following format elements output the sign (+/-). These format elements are mutually exclusive. At most one can appear in the format string.

If there are no sign format elements, one extra space is reserved for the sign. For example, if the input is 12 and the format string is '99', then the output is ' 12', with a length of three characters.

The sign appears before the number. If the format model includes a currency symbol element, then the sign appears before the currency symbol.

Return type

STRING

Example

SELECT
  CAST(12 AS STRING FORMAT 'S99') as a,
  CAST(-12 AS STRING FORMAT 'S99') as b;

+-----+-----+
|  a  |  b  |
+-----+-----+
| +12 | -12 |
+-----+-----+

Format currency symbol as string

The following format elements output a currency symbol. These format elements are mutually exclusive. At most one can appear in the format string. In the output, the currency symbol appears before the first digit or decimal point.

Return type

STRING

Example

SELECT
  CAST(12 AS STRING FORMAT '$99') as a,
  CAST(-12 AS STRING FORMAT '$99') as b;

+------+------+
|  a   |  b   |
+------+------+
|  $12 | -$12 |
+------+------+

Format group separator as string

The following format elements output a group separator.

Return type

STRING

Example

SELECT CAST(1234 AS STRING FORMAT '999,999') as a;

+----------+
|    a     |
+----------+
|    1,234 |
+----------+

Other numeric format elements

Return type

STRING

Example

SELECT CAST(-123456 AS STRING FORMAT '9.999EEEE') as a;"

+------------+
|     a      |
+------------+
| -1.235E+05 |
+------------+

About BASE encoding

BASE encoding translates binary data in string format into a radix-X representation.

If X is 2, 8, or 16, Arabic numerals 0–9 and the Latin letters a–z are used in the encoded string. So for example, BASE16/Hexadecimal encoding results contain 0~9 and a~f.

If X is 32 or 64, the default character tables are defined in rfc 4648. When you decode a BASE string where X is 2, 8, or 16, the Latin letters in the input string are case-insensitive. For example, both "3a" and "3A" are valid input strings for BASE16/Hexadecimal decoding, and will output the same result.

Mathematical functions

All mathematical functions have the following behaviors:

• They return NULL if any of the input parameters is NULL.
• They return NaN if any of the arguments is NaN.

ABS

ABS(X)

Description

Computes absolute value. Returns an error if the argument is an integer and the output value cannot be represented as the same type; this happens only for the largest negative input value, which has no positive representation.

Return Data Type

SIGN

SIGN(X)

Description

Returns -1, 0, or +1 for negative, zero and positive arguments respectively. For floating point arguments, this function does not distinguish between positive and negative zero.

Return Data Type

IS_INF

IS_INF(X)

Description

Returns TRUE if the value is positive or negative infinity.

IS_NAN

IS_NAN(X)

Description

Returns TRUE if the value is a NaN value.

IEEE_DIVIDE

IEEE_DIVIDE(X, Y)

Description

Divides X by Y; this function never fails. Returns FLOAT64. Unlike the division operator (/), this function does not generate errors for division by zero or overflow.

RAND

RAND()

Description

Generates a pseudo-random value of type FLOAT64 in the range of [0, 1), inclusive of 0 and exclusive of 1.

SQRT

SQRT(X)

Description

Computes the square root of X. Generates an error if X is less than 0.

Return Data Type

POW

POW(X, Y)

Description

Returns the value of X raised to the power of Y. If the result underflows and is not representable, then the function returns a value of zero.

Return Data Type

The return data type is determined by the argument types with the following table.

POWER

POWER(X, Y)

Description

Synonym of POW(X, Y).

EXP

EXP(X)

Description

Computes e to the power of X, also called the natural exponential function. If the result underflows, this function returns a zero. Generates an error if the result overflows.

Return Data Type

LN

LN(X)

Description

Computes the natural logarithm of X. Generates an error if X is less than or equal to zero.

Return Data Type

LOG

LOG(X [, Y])

Description

If only X is present, LOG is a synonym of LN. If Y is also present, LOG computes the logarithm of X to base Y.

Return Data Type

LOG10

LOG10(X)

Description

Similar to LOG, but computes logarithm to base 10.

Return Data Type

GREATEST

GREATEST(X1,...,XN)

Description

Returns the greatest value among X1,...,XN. If any argument is NULL, returns NULL. Otherwise, in the case of floating-point arguments, if any argument is NaN, returns NaN. In all other cases, returns the value among X1,...,XN that has the greatest value according to the ordering used by the ORDER BY clause. The arguments X1, ..., XN must be coercible to a common supertype, and the supertype must support ordering.

This function supports specifying collation.

Return Data Types

Data type of the input values.

LEAST

LEAST(X1,...,XN)

Description

Returns the least value among X1,...,XN. If any argument is NULL, returns NULL. Otherwise, in the case of floating-point arguments, if any argument is NaN, returns NaN. In all other cases, returns the value among X1,...,XN that has the least value according to the ordering used by the ORDER BY clause. The arguments X1, ..., XN must be coercible to a common supertype, and the supertype must support ordering.

This function supports specifying collation.

Return Data Types

Data type of the input values.

DIV

DIV(X, Y)

Description

Returns the result of integer division of X by Y. Division by zero returns an error. Division by -1 may overflow.

Return Data Type

The return data type is determined by the argument types with the following table.

SAFE_DIVIDE

SAFE_DIVIDE(X, Y)

Description

Equivalent to the division operator (X / Y), but returns NULL if an error occurs, such as a division by zero error.

Return Data Type

SAFE_MULTIPLY

SAFE_MULTIPLY(X, Y)

Description

Equivalent to the multiplication operator (*), but returns NULL if overflow occurs.

Return Data Type

SAFE_NEGATE

SAFE_NEGATE(X)

Description

Equivalent to the unary minus operator (-), but returns NULL if overflow occurs.

Return Data Type

SAFE_ADD

SAFE_ADD(X, Y)

Description

Equivalent to the addition operator (+), but returns NULL if overflow occurs.

Return Data Type

SAFE_SUBTRACT

SAFE_SUBTRACT(X, Y)

Description

Returns the result of Y subtracted from X. Equivalent to the subtraction operator (-), but returns NULL if overflow occurs.

Return Data Type

MOD

MOD(X, Y)

Description

Modulo function: returns the remainder of the division of X by Y. Returned value has the same sign as X. An error is generated if Y is 0.

Return Data Type

The return data type is determined by the argument types with the following table.

ROUND

ROUND(X [, N] [, rounding_mode])

Description

If only X is present, rounds X to the nearest integer. If N is present, rounds X to N decimal places after the decimal point. If N is negative, rounds off digits to the left of the decimal point. Rounds halfway cases away from zero. Generates an error if overflow occurs.

If X is a NUMERIC or BIGNUMERIC type, then you can explicitly set rounding_mode to one of the following:

• "ROUND_HALF_AWAY_FROM_ZERO": (Default) Rounds halfway cases away from zero.
• "ROUND_HALF_EVEN": Rounds halfway cases towards the nearest even digit.

If you set the rounding_mode and X is not a NUMERIC or BIGNUMERIC type, then the function generates an error.

Return Data Type

TRUNC

TRUNC(X [, N])

Description

If only X is present, TRUNC rounds X to the nearest integer whose absolute value is not greater than the absolute value of X. If N is also present, TRUNC behaves like ROUND(X, N), but always rounds towards zero and never overflows.

Return Data Type

CEIL

CEIL(X)

Description

Returns the smallest integral value that is not less than X.

Return Data Type

CEILING

CEILING(X)

Description

Synonym of CEIL(X)

FLOOR

FLOOR(X)

Description

Returns the largest integral value that is not greater than X.

Return Data Type

COS

COS(X)

Description

Computes the cosine of X where X is specified in radians. Never fails.

COSH

COSH(X)

Description

Computes the hyperbolic cosine of X where X is specified in radians. Generates an error if overflow occurs.

ACOS

ACOS(X)

Description

Computes the principal value of the inverse cosine of X. The return value is in the range [0,π]. Generates an error if X is a value outside of the range [-1, 1].

ACOSH

ACOSH(X)

Description

Computes the inverse hyperbolic cosine of X. Generates an error if X is a value less than 1.

COT

COT(X)

Description

Computes the cotangent for the angle of X, where X is specified in radians. X can be any data type that coerces to FLOAT64. Supports the SAFE. prefix.

Return Data Type

FLOAT64

Example

SELECT COT(1) AS a, SAFE.COT(0) AS b;

+---------------------+------+
| a                   | b    |
+---------------------+------+
| 0.64209261593433065 | NULL |
+---------------------+------+

COTH

COTH(X)

Description

Computes the hyperbolic cotangent for the angle of X, where X is specified in radians. X can be any data type that coerces to FLOAT64. Supports the SAFE. prefix.

Return Data Type

FLOAT64

Example

SELECT COTH(1) AS a, SAFE.COTH(0) AS b;

+----------------+------+
| a              | b    |
+----------------+------+
| 1.313035285499 | NULL |
+----------------+------+

CSC

CSC(X)

Description

Computes the cosecant of the input angle, which is in radians. X can be any data type that coerces to FLOAT64. Supports the SAFE. prefix.

Return Data Type

FLOAT64

Example

SELECT CSC(100) AS a, CSC(-1) AS b, SAFE.CSC(0) AS c;

+----------------+-----------------+------+
| a              | b               | c    |
+----------------+-----------------+------+
| -1.97485753142 | -1.188395105778 | NULL |
+----------------+-----------------+------+

CSCH

CSCH(X)

Description

Computes the hyperbolic cosecant of the input angle, which is in radians. X can be any data type that coerces to FLOAT64. Supports the SAFE. prefix.

Return Data Type

FLOAT64

Example

SELECT CSCH(0.5) AS a, CSCH(-2) AS b, SAFE.CSCH(0) AS c;

+----------------+----------------+------+
| a              | b              | c    |
+----------------+----------------+------+
| 1.919034751334 | -0.27572056477 | NULL |
+----------------+----------------+------+

SEC

SEC(X)

Description

Computes the secant for the angle of X, where X is specified in radians. X can be any data type that coerces to FLOAT64.

Return Data Type

FLOAT64

Example

SELECT SEC(100) AS a, SEC(-1) AS b;

+----------------+---------------+
| a              | b             |
+----------------+---------------+
| 1.159663822905 | 1.85081571768 |
+----------------+---------------+

SECH

SECH(X)

Description

Computes the hyperbolic secant for the angle of X, where X is specified in radians. X can be any data type that coerces to FLOAT64. Never produces an error.

Return Data Type

FLOAT64

Example

SELECT SECH(0.5) AS a, SECH(-2) AS b, SECH(100) AS c;

+----------------+----------------+---------------------+
| a              | b              | c                   |
+----------------+----------------+---------------------+
| 0.88681888397  | 0.265802228834 | 7.4401519520417E-44 |
+----------------+----------------+---------------------+

SIN

SIN(X)

Description

Computes the sine of X where X is specified in radians. Never fails.

SINH

SINH(X)

Description

Computes the hyperbolic sine of X where X is specified in radians. Generates an error if overflow occurs.

ASIN

ASIN(X)

Description

Computes the principal value of the inverse sine of X. The return value is in the range [-π/2,π/2]. Generates an error if X is outside of the range [-1, 1].

ASINH

ASINH(X)

Description

Computes the inverse hyperbolic sine of X. Does not fail.

TAN

TAN(X)

Description

Computes the tangent of X where X is specified in radians. Generates an error if overflow occurs.

TANH

TANH(X)

Description

Computes the hyperbolic tangent of X where X is specified in radians. Does not fail.

ATAN

ATAN(X)

Description

Computes the principal value of the inverse tangent of X. The return value is in the range [-π/2,π/2]. Does not fail.

ATANH

ATANH(X)

Description

Computes the inverse hyperbolic tangent of X. Generates an error if X is outside of the range [-1, 1].

ATAN2

ATAN2(X, Y)

Description

Calculates the principal value of the inverse tangent of X/Y using the signs of the two arguments to determine the quadrant. The return value is in the range [-π,π].

CBRT

CBRT(X)

Description

Computes the cube root of X. X can be any data type that coerces to FLOAT64. Supports the SAFE. prefix.

Return Data Type

FLOAT64

Example

SELECT CBRT(27) AS cube_root;

+--------------------+
| cube_root          |
+--------------------+
| 3.0000000000000004 |
+--------------------+

RANGE_BUCKET

RANGE_BUCKET(point, boundaries_array)

Description

RANGE_BUCKET scans through a sorted array and returns the 0-based position of the point's upper bound. This can be useful if you need to group your data to build partitions, histograms, business-defined rules, and more.

RANGE_BUCKET follows these rules:

• If the point exists in the array, returns the index of the next larger value.

  RANGE_BUCKET(20, [0, 10, 20, 30, 40]) -- 3 is return value
  RANGE_BUCKET(20, [0, 10, 20, 20, 40, 40]) -- 4 is return value
  

• If the point does not exist in the array, but it falls between two values, returns the index of the larger value.

  RANGE_BUCKET(25, [0, 10, 20, 30, 40]) -- 3 is return value
  

• If the point is smaller than the first value in the array, returns 0.

  RANGE_BUCKET(-10, [5, 10, 20, 30, 40]) -- 0 is return value
  

• If the point is greater than or equal to the last value in the array, returns the length of the array.

  RANGE_BUCKET(80, [0, 10, 20, 30, 40]) -- 5 is return value
  

• If the array is empty, returns 0.

  RANGE_BUCKET(80, []) -- 0 is return value
  

• If the point is NULL or NaN, returns NULL.

  RANGE_BUCKET(NULL, [0, 10, 20, 30, 40]) -- NULL is return value
  

• The data type for the point and array must be compatible.

  RANGE_BUCKET('a', ['a', 'b', 'c', 'd']) -- 1 is return value
  RANGE_BUCKET(1.2, [1, 1.2, 1.4, 1.6]) -- 2 is return value
  RANGE_BUCKET(1.2, [1, 2, 4, 6]) -- execution failure
  

Execution failure occurs when:

• The array has a NaN or NULL value in it.

  RANGE_BUCKET(80, [NULL, 10, 20, 30, 40]) -- execution failure
  

• The array is not sorted in ascending order.

  RANGE_BUCKET(30, [10, 30, 20, 40, 50]) -- execution failure
  

Parameters

• point: A generic value.
• boundaries_array: A generic array of values.

Return Value

INT64

Examples

In a table called students, check to see how many records would exist in each age_group bucket, based on a student's age:

• age_group 0 (age < 10)
• age_group 1 (age >= 10, age < 20)
• age_group 2 (age >= 20, age < 30)
• age_group 3 (age >= 30)

WITH students AS
(
  SELECT 9 AS age UNION ALL
  SELECT 20 AS age UNION ALL
  SELECT 25 AS age UNION ALL
  SELECT 31 AS age UNION ALL
  SELECT 32 AS age UNION ALL
  SELECT 33 AS age
)
SELECT RANGE_BUCKET(age, [10, 20, 30]) AS age_group, COUNT(*) AS count
FROM students
GROUP BY 1

+--------------+-------+
| age_group    | count |
+--------------+-------+
| 0            | 1     |
| 2            | 2     |
| 3            | 3     |
+--------------+-------+

Navigation functions

The following sections describe the navigation functions that BigQuery supports. Navigation functions are a subset window functions. To create a window function call and learn about the syntax for window functions, see Window function_calls.

Navigation functions generally compute some value_expression over a different row in the window frame from the current row. The OVER clause syntax varies across navigation functions.

For all navigation functions, the result data type is the same type as value_expression.

FIRST_VALUE

FIRST_VALUE (value_expression [{RESPECT | IGNORE} NULLS])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]
  [ window_frame_clause ]

Description

Returns the value of the value_expression for the first row in the current window frame.

This function includes NULL values in the calculation unless IGNORE NULLS is present. If IGNORE NULLS is present, the function excludes NULL values from the calculation.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

value_expression can be any data type that an expression can return.

Return Data Type

Same type as value_expression.

Examples

The following example computes the fastest time for each division.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  FORMAT_TIMESTAMP('%X', finish_time) AS finish_time,
  division,
  FORMAT_TIMESTAMP('%X', fastest_time) AS fastest_time,
  TIMESTAMP_DIFF(finish_time, fastest_time, SECOND) AS delta_in_seconds
FROM (
  SELECT name,
  finish_time,
  division,
  FIRST_VALUE(finish_time)
    OVER (PARTITION BY division ORDER BY finish_time ASC
    ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS fastest_time
  FROM finishers);

+-----------------+-------------+----------+--------------+------------------+
| name            | finish_time | division | fastest_time | delta_in_seconds |
+-----------------+-------------+----------+--------------+------------------+
| Carly Forte     | 03:08:58    | F25-29   | 03:08:58     | 0                |
| Sophia Liu      | 02:51:45    | F30-34   | 02:51:45     | 0                |
| Nikki Leith     | 02:59:01    | F30-34   | 02:51:45     | 436              |
| Jen Edwards     | 03:06:36    | F30-34   | 02:51:45     | 891              |
| Meghan Lederer  | 03:07:41    | F30-34   | 02:51:45     | 956              |
| Lauren Reasoner | 03:10:14    | F30-34   | 02:51:45     | 1109             |
| Lisa Stelzner   | 02:54:11    | F35-39   | 02:54:11     | 0                |
| Lauren Matthews | 03:01:17    | F35-39   | 02:54:11     | 426              |
| Desiree Berry   | 03:05:42    | F35-39   | 02:54:11     | 691              |
| Suzy Slane      | 03:06:24    | F35-39   | 02:54:11     | 733              |
+-----------------+-------------+----------+--------------+------------------+

LAST_VALUE

LAST_VALUE (value_expression [{RESPECT | IGNORE} NULLS])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]
  [ window_frame_clause ]

Description

Returns the value of the value_expression for the last row in the current window frame.

This function includes NULL values in the calculation unless IGNORE NULLS is present. If IGNORE NULLS is present, the function excludes NULL values from the calculation.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

value_expression can be any data type that an expression can return.

Return Data Type

Same type as value_expression.

Examples

The following example computes the slowest time for each division.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  FORMAT_TIMESTAMP('%X', finish_time) AS finish_time,
  division,
  FORMAT_TIMESTAMP('%X', slowest_time) AS slowest_time,
  TIMESTAMP_DIFF(slowest_time, finish_time, SECOND) AS delta_in_seconds
FROM (
  SELECT name,
  finish_time,
  division,
  LAST_VALUE(finish_time)
    OVER (PARTITION BY division ORDER BY finish_time ASC
    ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS slowest_time
  FROM finishers);

+-----------------+-------------+----------+--------------+------------------+
| name            | finish_time | division | slowest_time | delta_in_seconds |
+-----------------+-------------+----------+--------------+------------------+
| Carly Forte     | 03:08:58    | F25-29   | 03:08:58     | 0                |
| Sophia Liu      | 02:51:45    | F30-34   | 03:10:14     | 1109             |
| Nikki Leith     | 02:59:01    | F30-34   | 03:10:14     | 673              |
| Jen Edwards     | 03:06:36    | F30-34   | 03:10:14     | 218              |
| Meghan Lederer  | 03:07:41    | F30-34   | 03:10:14     | 153              |
| Lauren Reasoner | 03:10:14    | F30-34   | 03:10:14     | 0                |
| Lisa Stelzner   | 02:54:11    | F35-39   | 03:06:24     | 733              |
| Lauren Matthews | 03:01:17    | F35-39   | 03:06:24     | 307              |
| Desiree Berry   | 03:05:42    | F35-39   | 03:06:24     | 42               |
| Suzy Slane      | 03:06:24    | F35-39   | 03:06:24     | 0                |
+-----------------+-------------+----------+--------------+------------------+

NTH_VALUE

NTH_VALUE (value_expression, constant_integer_expression [{RESPECT | IGNORE} NULLS])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]
  [ window_frame_clause ]

Description

Returns the value of value_expression at the Nth row of the current window frame, where Nth is defined by constant_integer_expression. Returns NULL if there is no such row.

This function includes NULL values in the calculation unless IGNORE NULLS is present. If IGNORE NULLS is present, the function excludes NULL values from the calculation.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

• value_expression can be any data type that can be returned from an expression.
• constant_integer_expression can be any constant expression that returns an integer.

Return Data Type

Same type as value_expression.

Examples

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  FORMAT_TIMESTAMP('%X', finish_time) AS finish_time,
  division,
  FORMAT_TIMESTAMP('%X', fastest_time) AS fastest_time,
  FORMAT_TIMESTAMP('%X', second_fastest) AS second_fastest
FROM (
  SELECT name,
  finish_time,
  division,finishers,
  FIRST_VALUE(finish_time)
    OVER w1 AS fastest_time,
  NTH_VALUE(finish_time, 2)
    OVER w1 as second_fastest
  FROM finishers
  WINDOW w1 AS (
    PARTITION BY division ORDER BY finish_time ASC
    ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING));

+-----------------+-------------+----------+--------------+----------------+
| name            | finish_time | division | fastest_time | second_fastest |
+-----------------+-------------+----------+--------------+----------------+
| Carly Forte     | 03:08:58    | F25-29   | 03:08:58     | NULL           |
| Sophia Liu      | 02:51:45    | F30-34   | 02:51:45     | 02:59:01       |
| Nikki Leith     | 02:59:01    | F30-34   | 02:51:45     | 02:59:01       |
| Jen Edwards     | 03:06:36    | F30-34   | 02:51:45     | 02:59:01       |
| Meghan Lederer  | 03:07:41    | F30-34   | 02:51:45     | 02:59:01       |
| Lauren Reasoner | 03:10:14    | F30-34   | 02:51:45     | 02:59:01       |
| Lisa Stelzner   | 02:54:11    | F35-39   | 02:54:11     | 03:01:17       |
| Lauren Matthews | 03:01:17    | F35-39   | 02:54:11     | 03:01:17       |
| Desiree Berry   | 03:05:42    | F35-39   | 02:54:11     | 03:01:17       |
| Suzy Slane      | 03:06:24    | F35-39   | 02:54:11     | 03:01:17       |
+-----------------+-------------+----------+--------------+----------------+

LEAD

LEAD (value_expression[, offset [, default_expression]])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Returns the value of the value_expression on a subsequent row. Changing the offset value changes which subsequent row is returned; the default value is 1, indicating the next row in the window frame. An error occurs if offset is NULL or a negative value.

The optional default_expression is used if there isn't a row in the window frame at the specified offset. This expression must be a constant expression and its type must be implicitly coercible to the type of value_expression. If left unspecified, default_expression defaults to NULL.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

• value_expression can be any data type that can be returned from an expression.
• offset must be a non-negative integer literal or parameter.
• default_expression must be compatible with the value expression type.

Return Data Type

Same type as value_expression.

Examples

The following example illustrates a basic use of the LEAD function.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LEAD(name)
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS followed_by
FROM finishers;

+-----------------+-------------+----------+-----------------+
| name            | finish_time | division | followed_by     |
+-----------------+-------------+----------+-----------------+
| Carly Forte     | 03:08:58    | F25-29   | NULL            |
| Sophia Liu      | 02:51:45    | F30-34   | Nikki Leith     |
| Nikki Leith     | 02:59:01    | F30-34   | Jen Edwards     |
| Jen Edwards     | 03:06:36    | F30-34   | Meghan Lederer  |
| Meghan Lederer  | 03:07:41    | F30-34   | Lauren Reasoner |
| Lauren Reasoner | 03:10:14    | F30-34   | NULL            |
| Lisa Stelzner   | 02:54:11    | F35-39   | Lauren Matthews |
| Lauren Matthews | 03:01:17    | F35-39   | Desiree Berry   |
| Desiree Berry   | 03:05:42    | F35-39   | Suzy Slane      |
| Suzy Slane      | 03:06:24    | F35-39   | NULL            |
+-----------------+-------------+----------+-----------------+

This next example uses the optional offset parameter.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LEAD(name, 2)
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS two_runners_back
FROM finishers;

+-----------------+-------------+----------+------------------+
| name            | finish_time | division | two_runners_back |
+-----------------+-------------+----------+------------------+
| Carly Forte     | 03:08:58    | F25-29   | NULL             |
| Sophia Liu      | 02:51:45    | F30-34   | Jen Edwards      |
| Nikki Leith     | 02:59:01    | F30-34   | Meghan Lederer   |
| Jen Edwards     | 03:06:36    | F30-34   | Lauren Reasoner  |
| Meghan Lederer  | 03:07:41    | F30-34   | NULL             |
| Lauren Reasoner | 03:10:14    | F30-34   | NULL             |
| Lisa Stelzner   | 02:54:11    | F35-39   | Desiree Berry    |
| Lauren Matthews | 03:01:17    | F35-39   | Suzy Slane       |
| Desiree Berry   | 03:05:42    | F35-39   | NULL             |
| Suzy Slane      | 03:06:24    | F35-39   | NULL             |
+-----------------+-------------+----------+------------------+

The following example replaces NULL values with a default value.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LEAD(name, 2, 'Nobody')
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS two_runners_back
FROM finishers;

+-----------------+-------------+----------+------------------+
| name            | finish_time | division | two_runners_back |
+-----------------+-------------+----------+------------------+
| Carly Forte     | 03:08:58    | F25-29   | Nobody           |
| Sophia Liu      | 02:51:45    | F30-34   | Jen Edwards      |
| Nikki Leith     | 02:59:01    | F30-34   | Meghan Lederer   |
| Jen Edwards     | 03:06:36    | F30-34   | Lauren Reasoner  |
| Meghan Lederer  | 03:07:41    | F30-34   | Nobody           |
| Lauren Reasoner | 03:10:14    | F30-34   | Nobody           |
| Lisa Stelzner   | 02:54:11    | F35-39   | Desiree Berry    |
| Lauren Matthews | 03:01:17    | F35-39   | Suzy Slane       |
| Desiree Berry   | 03:05:42    | F35-39   | Nobody           |
| Suzy Slane      | 03:06:24    | F35-39   | Nobody           |
+-----------------+-------------+----------+------------------+

LAG

LAG (value_expression[, offset [, default_expression]])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  ORDER BY expression [ { ASC | DESC }  ] [, ...]

Description

Returns the value of the value_expression on a preceding row. Changing the offset value changes which preceding row is returned; the default value is 1, indicating the previous row in the window frame. An error occurs if offset is NULL or a negative value.

The optional default_expression is used if there isn't a row in the window frame at the specified offset. This expression must be a constant expression and its type must be implicitly coercible to the type of value_expression. If left unspecified, default_expression defaults to NULL.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

• value_expression can be any data type that can be returned from an expression.
• offset must be a non-negative integer literal or parameter.
• default_expression must be compatible with the value expression type.

Return Data Type

Same type as value_expression.

Examples

The following example illustrates a basic use of the LAG function.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LAG(name)
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS preceding_runner
FROM finishers;

+-----------------+-------------+----------+------------------+
| name            | finish_time | division | preceding_runner |
+-----------------+-------------+----------+------------------+
| Carly Forte     | 03:08:58    | F25-29   | NULL             |
| Sophia Liu      | 02:51:45    | F30-34   | NULL             |
| Nikki Leith     | 02:59:01    | F30-34   | Sophia Liu       |
| Jen Edwards     | 03:06:36    | F30-34   | Nikki Leith      |
| Meghan Lederer  | 03:07:41    | F30-34   | Jen Edwards      |
| Lauren Reasoner | 03:10:14    | F30-34   | Meghan Lederer   |
| Lisa Stelzner   | 02:54:11    | F35-39   | NULL             |
| Lauren Matthews | 03:01:17    | F35-39   | Lisa Stelzner    |
| Desiree Berry   | 03:05:42    | F35-39   | Lauren Matthews  |
| Suzy Slane      | 03:06:24    | F35-39   | Desiree Berry    |
+-----------------+-------------+----------+------------------+

This next example uses the optional offset parameter.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LAG(name, 2)
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS two_runners_ahead
FROM finishers;

+-----------------+-------------+----------+-------------------+
| name            | finish_time | division | two_runners_ahead |
+-----------------+-------------+----------+-------------------+
| Carly Forte     | 03:08:58    | F25-29   | NULL              |
| Sophia Liu      | 02:51:45    | F30-34   | NULL              |
| Nikki Leith     | 02:59:01    | F30-34   | NULL              |
| Jen Edwards     | 03:06:36    | F30-34   | Sophia Liu        |
| Meghan Lederer  | 03:07:41    | F30-34   | Nikki Leith       |
| Lauren Reasoner | 03:10:14    | F30-34   | Jen Edwards       |
| Lisa Stelzner   | 02:54:11    | F35-39   | NULL              |
| Lauren Matthews | 03:01:17    | F35-39   | NULL              |
| Desiree Berry   | 03:05:42    | F35-39   | Lisa Stelzner     |
| Suzy Slane      | 03:06:24    | F35-39   | Lauren Matthews   |
+-----------------+-------------+----------+-------------------+

The following example replaces NULL values with a default value.

WITH finishers AS
 (SELECT 'Sophia Liu' as name,
  TIMESTAMP '2016-10-18 2:51:45' as finish_time,
  'F30-34' as division
  UNION ALL SELECT 'Lisa Stelzner', TIMESTAMP '2016-10-18 2:54:11', 'F35-39'
  UNION ALL SELECT 'Nikki Leith', TIMESTAMP '2016-10-18 2:59:01', 'F30-34'
  UNION ALL SELECT 'Lauren Matthews', TIMESTAMP '2016-10-18 3:01:17', 'F35-39'
  UNION ALL SELECT 'Desiree Berry', TIMESTAMP '2016-10-18 3:05:42', 'F35-39'
  UNION ALL SELECT 'Suzy Slane', TIMESTAMP '2016-10-18 3:06:24', 'F35-39'
  UNION ALL SELECT 'Jen Edwards', TIMESTAMP '2016-10-18 3:06:36', 'F30-34'
  UNION ALL SELECT 'Meghan Lederer', TIMESTAMP '2016-10-18 3:07:41', 'F30-34'
  UNION ALL SELECT 'Carly Forte', TIMESTAMP '2016-10-18 3:08:58', 'F25-29'
  UNION ALL SELECT 'Lauren Reasoner', TIMESTAMP '2016-10-18 3:10:14', 'F30-34')
SELECT name,
  finish_time,
  division,
  LAG(name, 2, 'Nobody')
    OVER (PARTITION BY division ORDER BY finish_time ASC) AS two_runners_ahead
FROM finishers;

+-----------------+-------------+----------+-------------------+
| name            | finish_time | division | two_runners_ahead |
+-----------------+-------------+----------+-------------------+
| Carly Forte     | 03:08:58    | F25-29   | Nobody            |
| Sophia Liu      | 02:51:45    | F30-34   | Nobody            |
| Nikki Leith     | 02:59:01    | F30-34   | Nobody            |
| Jen Edwards     | 03:06:36    | F30-34   | Sophia Liu        |
| Meghan Lederer  | 03:07:41    | F30-34   | Nikki Leith       |
| Lauren Reasoner | 03:10:14    | F30-34   | Jen Edwards       |
| Lisa Stelzner   | 02:54:11    | F35-39   | Nobody            |
| Lauren Matthews | 03:01:17    | F35-39   | Nobody            |
| Desiree Berry   | 03:05:42    | F35-39   | Lisa Stelzner     |
| Suzy Slane      | 03:06:24    | F35-39   | Lauren Matthews   |
+-----------------+-------------+----------+-------------------+

PERCENTILE_CONT

PERCENTILE_CONT (value_expression, percentile [{RESPECT | IGNORE} NULLS])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]

Description

Computes the specified percentile value for the value_expression, with linear interpolation.

This function ignores NULL values if RESPECT NULLS is absent. If RESPECT NULLS is present:

• Interpolation between two NULL values returns NULL.
• Interpolation between a NULL value and a non-NULL value returns the non-NULL value.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

• value_expression and percentile must have one of the following types:
  • NUMERIC
  • BIGNUMERIC
  • FLOAT64
• percentile must be a literal in the range [0, 1].

Return Data Type

The return data type is determined by the argument types with the following table.

Examples

The following example computes the value for some percentiles from a column of values while ignoring nulls.

SELECT
  PERCENTILE_CONT(x, 0) OVER() AS min,
  PERCENTILE_CONT(x, 0.01) OVER() AS percentile1,
  PERCENTILE_CONT(x, 0.5) OVER() AS median,
  PERCENTILE_CONT(x, 0.9) OVER() AS percentile90,
  PERCENTILE_CONT(x, 1) OVER() AS max
FROM UNNEST([0, 3, NULL, 1, 2]) AS x LIMIT 1;

+-----+-------------+--------+--------------+-----+
| min | percentile1 | median | percentile90 | max |
+-----+-------------+--------+--------------+-----+
| 0   | 0.03        | 1.5    | 2.7          | 3   |
+-----+-------------+--------+--------------+-----+

The following example computes the value for some percentiles from a column of values while respecting nulls.

SELECT
  PERCENTILE_CONT(x, 0 RESPECT NULLS) OVER() AS min,
  PERCENTILE_CONT(x, 0.01 RESPECT NULLS) OVER() AS percentile1,
  PERCENTILE_CONT(x, 0.5 RESPECT NULLS) OVER() AS median,
  PERCENTILE_CONT(x, 0.9 RESPECT NULLS) OVER() AS percentile90,
  PERCENTILE_CONT(x, 1 RESPECT NULLS) OVER() AS max
FROM UNNEST([0, 3, NULL, 1, 2]) AS x LIMIT 1;

+------+-------------+--------+--------------+-----+
| min  | percentile1 | median | percentile90 | max |
+------+-------------+--------+--------------+-----+
| NULL | 0           | 1      | 2.6          | 3   |
+------+-------------+--------+--------------+-----+

PERCENTILE_DISC

PERCENTILE_DISC (value_expression, percentile [{RESPECT | IGNORE} NULLS])
OVER over_clause

over_clause:
  { named_window | ( [ window_specification ] ) }

window_specification:
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]

Description

Computes the specified percentile value for a discrete value_expression. The returned value is the first sorted value of value_expression with cumulative distribution greater than or equal to the given percentile value.

This function ignores NULL values unless RESPECT NULLS is present.

To learn more about the OVER clause and how to use it, see Window function calls.

Supported Argument Types

• value_expression can be any orderable type.
• percentile must be a literal in the range [0, 1], with one of the following types:
  • NUMERIC
  • BIGNUMERIC
  • FLOAT64

Return Data Type

Same type as value_expression.

Examples

The following example computes the value for some percentiles from a column of values while ignoring nulls.

SELECT
  x,
  PERCENTILE_DISC(x, 0) OVER() AS min,
  PERCENTILE_DISC(x, 0.5) OVER() AS median,
  PERCENTILE_DISC(x, 1) OVER() AS max
FROM UNNEST(['c', NULL, 'b', 'a']) AS x;

+------+-----+--------+-----+
| x    | min | median | max |
+------+-----+--------+-----+
| c    | a   | b      | c   |
| NULL | a   | b      | c   |
| b    | a   | b      | c   |
| a    | a   | b      | c   |
+------+-----+--------+-----+

The following example computes the value for some percentiles from a column of values while respecting nulls.

SELECT
  x,
  PERCENTILE_DISC(x, 0 RESPECT NULLS) OVER() AS min,
  PERCENTILE_DISC(x, 0.5 RESPECT NULLS) OVER() AS median,
  PERCENTILE_DISC(x, 1 RESPECT NULLS) OVER() AS max
FROM UNNEST(['c', NULL, 'b', 'a']) AS x;

+------+------+--------+-----+
| x    | min  | median | max |
+------+------+--------+-----+
| c    | NULL | a      | c   |
| NULL | NULL | a      | c   |
| b    | NULL | a      | c   |
| a    | NULL | a      | c   |
+------+------+--------+-----+

Hash functions

FARM_FINGERPRINT

FARM_FINGERPRINT(value)

Description

Computes the fingerprint of the STRING or BYTES input using the Fingerprint64 function from the open-source FarmHash library. The output of this function for a particular input will never change.

Return type

INT64

Examples