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Function calls | BigQuery | Google Cloud

When you call a function, specific rules may apply. You can also add the SAFE. prefix, which prevents functions from generating some types of errors. To learn more, see the next sections.

The following rules apply to all built-in GoogleSQL functions unless explicitly indicated otherwise in the function description:

• If an operand is NULL, the function result is NULL.
• For functions that are time zone sensitive, the default time zone, UTC, is used when a time zone isn't specified.

named_argument => value

You can provide parameter arguments by name when calling some functions and procedures. These arguments are called named arguments. An argument that isn't named is called a positional argument.

• Named arguments are optional, unless specified as required in the function signature.
• Named arguments don't need to be in order.
• You can specify positional arguments before named arguments.
• You can't specify positional arguments after named arguments.
• An optional positional argument that isn't used doesn't need to be added before a named argument.

Examples

These examples reference a function called CountTokensInText, which counts the number of tokens in a paragraph. The function signature looks like this:

CountTokensInText(paragraph STRING, tokens ARRAY<STRING>, delimiters STRING)

CountTokensInText contains three arguments: paragraph, tokens, and delimiters. paragraph represents a body of text to analyze, tokens represents the tokens to search for in the paragraph, and delimiters represents the characters that specify a boundary between tokens in the paragraph.

This is a query that includes CountTokensInText without named arguments:

SELECT token, count
FROM CountTokensInText(
  'Would you prefer softball, baseball, or tennis? There is also swimming.',
  ['baseball', 'football', 'tennis'],
  ' .,!?()')

This is the query with named arguments:

SELECT token, count
FROM CountTokensInText(
  paragraph => 'Would you prefer softball, baseball, or tennis? There is also swimming.',
  tokens => ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()')

If named arguments are used, the order of the arguments doesn't matter. This works:

SELECT token, count
FROM CountTokensInText(
  tokens => ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()',
  paragraph => 'Would you prefer softball, baseball, or tennis? There is also swimming.')

You can mix positional arguments and named arguments, as long as the positional arguments in the function signature come first:

SELECT token, count
FROM CountTokensInText(
  'Would you prefer softball, baseball, or tennis? There is also swimming.',
  tokens => ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()')

This doesn't work because a positional argument appears after a named argument:

SELECT token, count
FROM CountTokensInText(
  paragraph => 'Would you prefer softball, baseball, or tennis? There is also swimming.',
  ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()')

If you want to use tokens as a positional argument, any arguments that appear before it in the function signature must also be positional arguments. If you try to use a named argument for paragraph and a positional argument for tokens, this will not work.

-- This doesn't work.
SELECT token, count
FROM CountTokensInText(
  ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()',
  paragraph => 'Would you prefer softball, baseball, or tennis? There is also swimming.')

-- This works.
SELECT token, count
FROM CountTokensInText(
  'Would you prefer softball, baseball, or tennis? There is also swimming.',
  ['baseball', 'football', 'tennis'],
  delimiters => ' .,!?()')

Writing nested expressions in GoogleSQL is common, particularly when you're cleaning or transforming data. Deeply nested expressions can be hard to read and maintain.

Here's an example of an expression with deep nesting. The nesting makes it difficult to read:

SELECT
  REPLACE(
    REPLACE(
      REPLACE(
        REPLACE(
          REPLACE('one two three four five', 'one', '1'),
          'two', '2'),
        'three', '3'),
      'four', '4'),
    'five', '5');

Here is the same example rewritten using chained function syntax:

SELECT
  ('one two three four five')
  .REPLACE('one', '1')
  .REPLACE('two', '2')
  .REPLACE('three', '3')
  .REPLACE('four', '4')
  .REPLACE('five', '5');

Chained function calls provide a syntax for simplifying nested function calls. Chained function calls have the following properties:

• Chained function calls consist of functions connected together with a . character.
• Each function in the chain must meet certain requirements.
• Each function in the chain uses the output from the previous function as its first argument.
• If the chain starts from a column name (or other identifier), that initial argument must be surrounded by () characters, for example: (x).UPPER(). Parentheses aren't required on the input for other cases.
• For functions with multi-part names, like SAFE.SQRT, the function name must be parenthesized. For example, (x).(SAFE.SQRT)().

Chained function calls are generally easier to read, understand, and maintain than deeply nested function calls because they're applied in the order in which they're written.

You can write function calls in chained call syntax if the functions meet these requirements:

• The function must use standard function call syntax, using comma-separated arguments. Function-like syntaxes that include special-case keywords, such as CAST(valus AS type), aren't included.
• The function must have at least one argument. The first argument becomes the chained input, and must meet the following requirements:
  • It must be an expression. It can't be a table, connection, model, descriptor, or other non-expression argument type.
  • It must be a positional argument. It can't be a named argument (name => value).
  • It can't have an AS alias (as in value AS alias).

There are a few additional special cases. Chained function calls are allowed for these functions:

• Aggregate functions with standard modifiers like DISTINCT, ORDER BY, etc. You write the modifiers inside the parentheses with their usual syntax. For example, you write COUNT(DISTINCT x) as (x).COUNT(DISTINCT).
• FLATTEN (and other functions that do implicit flattening)

Chained function calls aren't allowed for these functions:

• GROUPING

The following examples show the chained function call equivalent of some standard syntax calls:

UPPER(x)
(x).UPPER()  # Chained function call equivalent; the x must be within ()

SUBSTR(x, 1, 4)
(x).SUBSTR(1, 4)  # Chained function call equivalent

STRPOS(x, 'pattern string')
(x).STRPOS('pattern string')  # Chained function call equivalent

FUNC(x, y, named_argument=>z)  # Some function that meets the chained function call requirements
(x).FUNC(y, named_argument=>z)  # Chained function call equivalent

ARRAY_CONCAT(array1, array2)
(array1).ARRAY_CONCAT(array2)  # Chained function call equivalent

SELECT SAFE.LEFT(x, count) AS result;      # Multi-part function name
SELECT (x).(SAFE.LEFT)(count) AS result;   # Chained function call equivalent

Here are chained function call examples with multiple function calls:

SELECT "Two birds and one mouse"
  .REPLACE("bird", "dog")
  .REPLACE("mouse", "cat") AS result;

/*----------------------*
 |      result          |
 +----------------------+
 | Two dogs and one cat |
 *----------------------*/

The following examples result in errors because the function being called doesn't meet the necessary requirements.

FUNC(named_argument=>x).  # Some function
(x).FUNC()  # Error: The first argument can't be a named argument.

CAST(x AS INT64)
(x).CAST(AS INT64)  # Error: CAST syntax isn't supported in chained function calls.

GROUPING(x)
(x).GROUPING()  # Error: The argument isn't an expression.

Syntax:

SAFE.function_name()

Description

If you begin a function with the SAFE. prefix, it will return NULL instead of an error. The SAFE. prefix only prevents errors from the prefixed function itself: it doesn't prevent errors that occur while evaluating argument expressions. The SAFE. prefix only prevents errors that occur because of the value of the function inputs, such as "value out of range" errors; other errors, such as internal or system errors, may still occur. If the function doesn't return an error, SAFE. has no effect on the output.

Exclusions

• Operators, such as + and =, don't support the SAFE. prefix. To prevent errors from a division operation, use SAFE_DIVIDE.
• Some operators, such as IN, ARRAY, and UNNEST, resemble functions but don't support the SAFE. prefix.
• The CAST and EXTRACT functions don't support the SAFE. prefix. To prevent errors from casting, use SAFE_CAST.

Example

In the following example, the first use of the SUBSTR function would normally return an error, because the function doesn't support length arguments with negative values. However, the SAFE. prefix causes the function to return NULL instead. The second use of the SUBSTR function provides the expected output: the SAFE. prefix has no effect.

SELECT SAFE.SUBSTR('foo', 0, -2) AS safe_output UNION ALL
SELECT SAFE.SUBSTR('bar', 0, 2) AS safe_output;

/*-------------*
 | safe_output |
 +-------------+
 | NULL        |
 | ba          |
 *-------------*/

Supported functions

BigQuery supports the use of the SAFE. prefix with most scalar functions that can raise errors, including STRING functions, math functions, DATE functions, DATETIME functions, TIMESTAMP functions, and JSON functions. BigQuery does not support the use of the SAFE. prefix with aggregate, window, or user-defined functions.

After creating a persistent UDF, you can call it as you would any other function, prepended with the name of the dataset in which it is defined as a prefix.

Syntax

[`project_name`].dataset_name.function_name([parameter_value[, ...]])

To call a UDF in a project other than the project that you are using to run the query, project_name is required.

Examples

The following example creates a UDF named multiply_by_three and calls it from the same project.

CREATE FUNCTION my_dataset.multiply_by_three(x INT64) AS (x * 3);

SELECT my_dataset.multiply_by_three(5) AS result; -- returns 15

The following example calls a persistent UDF from a different project.

CREATE `other_project`.other_dataset.other_function(x INT64, y INT64)
  AS (x * y * 2);

SELECT `other_project`.other_dataset.other_function(3, 4); --returns 24

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