Query syntax

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Query statements scan one or more tables or expressions and return the computed result rows. This topic describes the syntax for SQL queries in GoogleSQL for BigQuery.

SQL syntax

query_statement:
    query_expr

query_expr:
    [ WITH [ RECURSIVE ] { non_recursive_cte | recursive_cte }[, ...] ]
    { select | ( query_expr ) | set_operation }
    [ ORDER BY expression [{ ASC | DESC }] [, ...] ]
    [ LIMIT count [ OFFSET skip_rows ] ]

select:
    SELECT
        [ { ALL | DISTINCT } ]
        [ AS { STRUCT | VALUE } ]
        select_list
    [ FROM from_clause[, ...] ]
    [ WHERE bool_expression ]
    [ GROUP BY { expression [, ...] | ROLLUP ( expression [, ...] ) } ]
    [ HAVING bool_expression ]
    [ QUALIFY bool_expression ]
    [ WINDOW window_clause ]

Notation rules

• Square brackets [ ] indicate optional clauses.
• Parentheses ( ) indicate literal parentheses.
• The vertical bar | indicates a logical OR.
• Curly braces { } enclose a set of options.
• A comma followed by an ellipsis within square brackets [, ... ] indicates that the preceding item can repeat in a comma-separated list.

Sample tables

The following tables are used to illustrate the behavior of different query clauses in this reference.

Roster table

The Roster table includes a list of player names (LastName) and the unique ID assigned to their school (SchoolID). It looks like this:

+-----------------------+
| LastName   | SchoolID |
+-----------------------+
| Adams      | 50       |
| Buchanan   | 52       |
| Coolidge   | 52       |
| Davis      | 51       |
| Eisenhower | 77       |
+-----------------------+

You can use this WITH clause to emulate a temporary table name for the examples in this reference:

WITH Roster AS
 (SELECT 'Adams' as LastName, 50 as SchoolID UNION ALL
  SELECT 'Buchanan', 52 UNION ALL
  SELECT 'Coolidge', 52 UNION ALL
  SELECT 'Davis', 51 UNION ALL
  SELECT 'Eisenhower', 77)
SELECT * FROM Roster

PlayerStats table

The PlayerStats table includes a list of player names (LastName) and the unique ID assigned to the opponent they played in a given game (OpponentID) and the number of points scored by the athlete in that game (PointsScored).

+----------------------------------------+
| LastName   | OpponentID | PointsScored |
+----------------------------------------+
| Adams      | 51         | 3            |
| Buchanan   | 77         | 0            |
| Coolidge   | 77         | 1            |
| Adams      | 52         | 4            |
| Buchanan   | 50         | 13           |
+----------------------------------------+

You can use this WITH clause to emulate a temporary table name for the examples in this reference:

WITH PlayerStats AS
 (SELECT 'Adams' as LastName, 51 as OpponentID, 3 as PointsScored UNION ALL
  SELECT 'Buchanan', 77, 0 UNION ALL
  SELECT 'Coolidge', 77, 1 UNION ALL
  SELECT 'Adams', 52, 4 UNION ALL
  SELECT 'Buchanan', 50, 13)
SELECT * FROM PlayerStats

TeamMascot table

The TeamMascot table includes a list of unique school IDs (SchoolID) and the mascot for that school (Mascot).

+---------------------+
| SchoolID | Mascot   |
+---------------------+
| 50       | Jaguars  |
| 51       | Knights  |
| 52       | Lakers   |
| 53       | Mustangs |
+---------------------+

You can use this WITH clause to emulate a temporary table name for the examples in this reference:

WITH TeamMascot AS
 (SELECT 50 as SchoolID, 'Jaguars' as Mascot UNION ALL
  SELECT 51, 'Knights' UNION ALL
  SELECT 52, 'Lakers' UNION ALL
  SELECT 53, 'Mustangs')
SELECT * FROM TeamMascot

SELECT statement

SELECT
    [ { ALL | DISTINCT } ]
    [ AS { STRUCT | VALUE } ]
   select_list

select_list:
    { select_all | select_expression } [, ...]

select_all:
    [ expression. ]*
    [ EXCEPT ( column_name [, ...] ) ]
    [ REPLACE ( expression [ AS ] column_name [, ...] ) ]

select_expression:
    expression [ [ AS ] alias ]

The SELECT list defines the columns that the query will return. Expressions in the SELECT list can refer to columns in any of the from_items in its corresponding FROM clause.

Each item in the SELECT list is one of:

• *
• expression
• expression.*

SELECT *

SELECT *, often referred to as select star, produces one output column for each column that is visible after executing the full query.

SELECT * FROM (SELECT "apple" AS fruit, "carrot" AS vegetable);

+-------+-----------+
| fruit | vegetable |
+-------+-----------+
| apple | carrot    |
+-------+-----------+

SELECT expression

Items in a SELECT list can be expressions. These expressions evaluate to a single value and produce one output column, with an optional explicit alias.

If the expression does not have an explicit alias, it receives an implicit alias according to the rules for implicit aliases, if possible. Otherwise, the column is anonymous and you cannot refer to it by name elsewhere in the query.

SELECT expression.*

An item in a SELECT list can also take the form of expression.*. This produces one output column for each column or top-level field of expression. The expression must either be a table alias or evaluate to a single value of a data type with fields, such as a STRUCT.

The following query produces one output column for each column in the table groceries, aliased as g.

WITH groceries AS
  (SELECT "milk" AS dairy,
   "eggs" AS protein,
   "bread" AS grain)
SELECT g.*
FROM groceries AS g;

+-------+---------+-------+
| dairy | protein | grain |
+-------+---------+-------+
| milk  | eggs    | bread |
+-------+---------+-------+

More examples:

WITH locations AS
  (SELECT STRUCT("Seattle" AS city, "Washington" AS state) AS location
  UNION ALL
  SELECT STRUCT("Phoenix" AS city, "Arizona" AS state) AS location)
SELECT l.location.*
FROM locations l;

+---------+------------+
| city    | state      |
+---------+------------+
| Seattle | Washington |
| Phoenix | Arizona    |
+---------+------------+

WITH locations AS
  (SELECT ARRAY<STRUCT<city STRING, state STRING>>[("Seattle", "Washington"),
    ("Phoenix", "Arizona")] AS location)
SELECT l.LOCATION[offset(0)].*
FROM locations l;

+---------+------------+
| city    | state      |
+---------+------------+
| Seattle | Washington |
+---------+------------+

SELECT * EXCEPT

A SELECT * EXCEPT statement specifies the names of one or more columns to exclude from the result. All matching column names are omitted from the output.

WITH orders AS
  (SELECT 5 as order_id,
  "sprocket" as item_name,
  200 as quantity)
SELECT * EXCEPT (order_id)
FROM orders;

+-----------+----------+
| item_name | quantity |
+-----------+----------+
| sprocket  | 200      |
+-----------+----------+

Note: SELECT * EXCEPT does not exclude columns that do not have names.

SELECT * REPLACE

A SELECT * REPLACE statement specifies one or more expression AS identifier clauses. Each identifier must match a column name from the SELECT * statement. In the output column list, the column that matches the identifier in a REPLACE clause is replaced by the expression in that REPLACE clause.

A SELECT * REPLACE statement does not change the names or order of columns. However, it can change the value and the value type.

WITH orders AS
  (SELECT 5 as order_id,
  "sprocket" as item_name,
  200 as quantity)
SELECT * REPLACE ("widget" AS item_name)
FROM orders;

+----------+-----------+----------+
| order_id | item_name | quantity |
+----------+-----------+----------+
| 5        | widget    | 200      |
+----------+-----------+----------+

WITH orders AS
  (SELECT 5 as order_id,
  "sprocket" as item_name,
  200 as quantity)
SELECT * REPLACE (quantity/2 AS quantity)
FROM orders;

+----------+-----------+----------+
| order_id | item_name | quantity |
+----------+-----------+----------+
| 5        | sprocket  | 100      |
+----------+-----------+----------+

Note: SELECT * REPLACE does not replace columns that do not have names.

SELECT DISTINCT

A SELECT DISTINCT statement discards duplicate rows and returns only the remaining rows. SELECT DISTINCT cannot return columns of the following types:

• STRUCT
• ARRAY

SELECT ALL

A SELECT ALL statement returns all rows, including duplicate rows. SELECT ALL is the default behavior of SELECT.

SELECT AS STRUCT

SELECT AS STRUCT expr [[AS] struct_field_name1] [,...]

This produces a value table with a STRUCT row type, where the STRUCT field names and types match the column names and types produced in the SELECT list.

Example:

SELECT ARRAY(SELECT AS STRUCT 1 a, 2 b)

SELECT AS STRUCT can be used in a scalar or array subquery to produce a single STRUCT type grouping multiple values together. Scalar and array subqueries (see Subqueries) are normally not allowed to return multiple columns, but can return a single column with STRUCT type.

SELECT AS VALUE

SELECT AS VALUE produces a value table from any SELECT list that produces exactly one column. Instead of producing an output table with one column, possibly with a name, the output will be a value table where the row type is just the value type that was produced in the one SELECT column. Any alias the column had will be discarded in the value table.

Example:

SELECT AS VALUE STRUCT(1 AS a, 2 AS b) xyz

The query above produces a table with row type STRUCT<a int64, b int64>.

FROM clause

FROM from_clause[, ...]

from_clause:
    from_item
    [ { pivot_operator | unpivot_operator } ]
    [ tablesample_operator ]

from_item:
    {
      table_name [ as_alias ] [ FOR SYSTEM_TIME AS OF timestamp_expression ] 
      | { join_operation | ( join_operation ) }
      | ( query_expr ) [ as_alias ]
      | field_path
      | unnest_operator
      | cte_name [ as_alias ]
    }

as_alias:
    [ AS ] alias

The FROM clause indicates the table or tables from which to retrieve rows, and specifies how to join those rows together to produce a single stream of rows for processing in the rest of the query.

pivot_operator

See PIVOT operator.

unpivot_operator

See UNPIVOT operator.

tablesample_operator

See TABLESAMPLE operator.

table_name

The name (optionally qualified) of an existing table.

SELECT * FROM Roster;
SELECT * FROM dataset.Roster;
SELECT * FROM project.dataset.Roster;

FOR SYSTEM_TIME AS OF

FOR SYSTEM_TIME AS OF references the historical versions of the table definition and rows that were current at timestamp_expression.

Limitations:

The source table in the FROM clause containing FOR SYSTEM_TIME AS OF must not be any of the following:

• An array scan, including a flattened array or the output of the UNNEST operator.
• A common table expression defined by a WITH clause.

timestamp_expression must be a constant expression. It cannot contain the following:

• Subqueries.
• Correlated references (references to columns of a table that appear at a higher level of the query statement, such as in the SELECT list).
• User-defined functions (UDFs).

The value of timestamp_expression cannot fall into the following ranges:

• After the current timestamp (in the future).
• More than seven (7) days before the current timestamp.

A single query statement cannot reference a single table at more than one point in time, including the current time. That is, a query can reference a table multiple times at the same timestamp, but not the current version and a historical version, or two different historical versions.

Note: DML statements always operate on the current version of the destination table, so if the destination table is used multiple times in the query, all of them must use the current version.

Examples:

The following query returns a historical version of the table from one hour ago.

SELECT *
FROM t
  FOR SYSTEM_TIME AS OF TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 HOUR);

The following query returns a historical version of the table at an absolute point in time.

SELECT *
FROM t
  FOR SYSTEM_TIME AS OF '2017-01-01 10:00:00-07:00';

The following query returns an error because the timestamp_expression contains a correlated reference to a column in the containing query.

SELECT *
FROM t1
WHERE t1.a IN (SELECT t2.a
               FROM t2 FOR SYSTEM_TIME AS OF t1.timestamp_column);

The following operations show accessing a historical version of the table before table is replaced.

DECLARE before_replace_timestamp TIMESTAMP;

-- Create table books.
CREATE TABLE books AS
SELECT 'Hamlet' title, 'William Shakespeare' author;

-- Get current timestamp before table replacement.
SET before_replace_timestamp = CURRENT_TIMESTAMP();

-- Replace table with different schema(title and release_date).
CREATE OR REPLACE TABLE books AS
SELECT 'Hamlet' title, DATE '1603-01-01' release_date;

-- This query returns Hamlet, William Shakespeare as result.
SELECT * FROM books FOR SYSTEM_TIME AS OF before_replace_timestamp;

The following operations show accessing a historical version of the table before a DML job.

DECLARE JOB_START_TIMESTAMP TIMESTAMP;

-- Create table books.
CREATE OR REPLACE TABLE books AS
SELECT 'Hamlet' title, 'William Shakespeare' author;

-- Insert two rows into the books.
INSERT books (title, author)
VALUES('The Great Gatsby', 'F. Scott Fizgerald'),
      ('War and Peace', 'Leo Tolstoy');

SELECT * FROM books;

SET JOB_START_TIMESTAMP = (
  SELECT start_time
  FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_USER
  WHERE job_type="QUERY"
    AND statement_type="INSERT"
  ORDER BY start_time DESC
  LIMIT 1
 );

-- This query only returns Hamlet, William Shakespeare as result.
SELECT * FROM books FOR SYSTEM_TIME AS OF JOB_START_TIMESTAMP;

The following query returns an error because the DML operates on the current version of the table, and a historical version of the table from one day ago.

INSERT INTO t1
SELECT * FROM t1
  FOR SYSTEM_TIME AS OF TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY);

join_operation

See Join operation.

query_expr

( query_expr ) [ [ AS ] alias ] is a table subquery.

field_path

In the FROM clause, field_path is any path that resolves to a field within a data type. field_path can go arbitrarily deep into a nested data structure.

Some examples of valid field_path values include:

SELECT * FROM T1 t1, t1.array_column;

SELECT * FROM T1 t1, t1.struct_column.array_field;

SELECT (SELECT ARRAY_AGG(c) FROM t1.array_column c) FROM T1 t1;

SELECT a.struct_field1 FROM T1 t1, t1.array_of_structs a;

SELECT (SELECT STRING_AGG(a.struct_field1) FROM t1.array_of_structs a) FROM T1 t1;

Field paths in the FROM clause must end in an array field. In addition, field paths cannot contain arrays before the end of the path. For example, the path array_column.some_array.some_array_field is invalid because it contains an array before the end of the path.

Note: If a path has only one name, it is interpreted as a table. To work around this, wrap the path using UNNEST, or use the fully-qualified path.

Note: If a path has more than one name, and it matches a field name, it is interpreted as a field name. To force the path to be interpreted as a table name, wrap the path using `.

unnest_operator

See UNNEST operator.

cte_name

Common table expressions (CTEs) in a WITH Clause act like temporary tables that you can reference anywhere in the FROM clause. In the example below, subQ1 and subQ2 are CTEs.

Example:

WITH
  subQ1 AS (SELECT * FROM Roster WHERE SchoolID = 52),
  subQ2 AS (SELECT SchoolID FROM subQ1)
SELECT DISTINCT * FROM subQ2;

The WITH clause hides any permanent tables with the same name for the duration of the query, unless you qualify the table name, for example:

dataset.Roster or project.dataset.Roster.

UNNEST operator

unnest_operator:
    {
      UNNEST( array_expression )
      | UNNEST( array_path )
      | array_path
    }
    [ as_alias ]
    [ WITH OFFSET [ as_alias ] ]

as_alias:
    [AS] alias

The UNNEST operator takes an array and returns a table, with one row for each element in the array. You can also use UNNEST outside of the FROM clause with the IN operator.

For input arrays of most element types, the output of UNNEST generally has one column. This single column has an optional alias, which you can use to refer to the column elsewhere in the query. ARRAYS with these element types return multiple columns:

• STRUCT

UNNEST destroys the order of elements in the input array. Use the optional WITH OFFSET clause to return a second column with the array element indexes.

For several ways to use UNNEST, including construction, flattening, and filtering, see Work with arrays.

UNNEST and structs

For an input array of structs, UNNEST returns a row for each struct, with a separate column for each field in the struct. The alias for each column is the name of the corresponding struct field.

Example:

SELECT *
FROM UNNEST(
  ARRAY<
    STRUCT<
      x INT64,
      y STRING,
      z STRUCT<a INT64, b INT64>>>[
        (1, 'foo', (10, 11)),
        (3, 'bar', (20, 21))]);

+---+-----+----------+
| x | y   | z        |
+---+-----+----------+
| 1 | foo | {10, 11} |
| 3 | bar | {20, 21} |
+---+-----+----------+

Because the UNNEST operator returns a value table, you can alias UNNEST to define a range variable that you can reference elsewhere in the query. If you reference the range variable in the SELECT list, the query returns a struct containing all of the fields of the original struct in the input table.

Example:

SELECT *, struct_value
FROM UNNEST(
  ARRAY<
    STRUCT<
    x INT64,
    y STRING>>[
      (1, 'foo'),
      (3, 'bar')]) AS struct_value;

+---+-----+--------------+
| x | y   | struct_value |
+---+-----+--------------+
| 3 | bar | {3, bar}     |
| 1 | foo | {1, foo}     |
+---+-----+--------------+

Explicit and implicit UNNEST

Array unnesting can be either explicit or implicit. In explicit unnesting, array_expression must return an array value but does not need to resolve to an array, and the UNNEST keyword is required.

Example:

SELECT * FROM UNNEST ([1, 2, 3]);

In implicit unnesting, array_path must resolve to an array and the UNNEST keyword is optional.

Example:

SELECT x
FROM mytable AS t,
  t.struct_typed_column.array_typed_field1 AS x;

In this scenario, array_path can go arbitrarily deep into a data structure, but the last field must be array-typed. No previous field in the expression can be array-typed because it is not possible to extract a named field from an array.

UNNEST and NULL values

UNNEST treats NULL values as follows:

• NULL and empty arrays produce zero rows.
• An array containing NULL values produces rows containing NULL values.

UNNEST and WITH OFFSET

The optional WITH OFFSET clause returns a separate column containing the offset value, in which counting starts at zero for each row produced by the UNNEST operation. This column has an optional alias; If the optional alias is not used, the default column name is offset.

Example:

SELECT * FROM UNNEST ([10,20,30]) as numbers WITH OFFSET;

+---------+--------+
| numbers | offset |
+---------+--------+
| 10      | 0      |
| 20      | 1      |
| 30      | 2      |
+---------+--------+

PIVOT operator

FROM from_item[, ...] pivot_operator

pivot_operator:
    PIVOT(
        aggregate_function_call [as_alias][, ...]
        FOR input_column
        IN ( pivot_column [as_alias][, ...] )
    ) [AS alias]

as_alias:
    [AS] alias

The PIVOT operator rotates rows into columns, using aggregation. PIVOT is part of the FROM clause.

• PIVOT can be used to modify any table expression.
• Combining PIVOT with FOR SYSTEM_TIME AS OF is not allowed, although users may use PIVOT against a subquery input which itself uses FOR SYSTEM_TIME AS OF.
• A WITH OFFSET clause immediately preceding the PIVOT operator is not allowed.

Conceptual example:

-- Before PIVOT is used to rotate sales and quarter into Q1, Q2, Q3, Q4 columns:
+---------+-------+---------+------+
| product | sales | quarter | year |
+---------+-------+---------+------|
| Kale    | 51    | Q1      | 2020 |
| Kale    | 23    | Q2      | 2020 |
| Kale    | 45    | Q3      | 2020 |
| Kale    | 3     | Q4      | 2020 |
| Kale    | 70    | Q1      | 2021 |
| Kale    | 85    | Q2      | 2021 |
| Apple   | 77    | Q1      | 2020 |
| Apple   | 0     | Q2      | 2020 |
| Apple   | 1     | Q1      | 2021 |
+---------+-------+---------+------+

-- After PIVOT is used to rotate sales and quarter into Q1, Q2, Q3, Q4 columns:
+---------+------+----+------+------+------+
| product | year | Q1 | Q2   | Q3   | Q4   |
+---------+------+----+------+------+------+
| Apple   | 2020 | 77 | 0    | NULL | NULL |
| Apple   | 2021 | 1  | NULL | NULL | NULL |
| Kale    | 2020 | 51 | 23   | 45   | 3    |
| Kale    | 2021 | 70 | 85   | NULL | NULL |
+---------+------+----+------+------+------+

Definitions

Top-level definitions:

• from_item: The table or subquery on which to perform a pivot operation. The from_item must follow these rules.
• pivot_operator: The pivot operation to perform on a from_item.
• alias: An alias to use for an item in the query.

pivot_operator definitions:

• aggregate_function_call: An aggregate function call that aggregates all input rows such that input_column matches a particular value in pivot_column. Each aggregation corresponding to a different pivot_column value produces a different column in the output. Follow these rules when creating an aggregate function call.
• input_column: Takes a column and retrieves the row values for the column, following these rules.
• pivot_column: A pivot column to create for each aggregate function call. If an alias is not provided, a default alias is created. A pivot column value type must match the value type in input_column so that the values can be compared. It is possible to have a value in pivot_column that does not match a value in input_column. Must be a constant and follow these rules.

Rules

Rules for a from_item passed to PIVOT:

• The from_item may consist of any table or subquery result.
• The from_item may not produce a value table.
• The from_item may not be a subquery using SELECT AS STRUCT.

Rules for aggregate_function_call:

• Must be an aggregate function. For example, SUM.
• You may reference columns in a table passed to PIVOT, as well as correlated columns, but may not access columns defined by the PIVOT clause itself.
• A table passed to PIVOT may be accessed through its alias if one is provided.
• You can only use an aggregate function that takes one argument.
• Except for COUNT, you can only use aggregate functions that ignore NULL inputs.
• If you are using COUNT, you can use * as an argument.

Rules for input_column:

• May access columns from the input table, as well as correlated columns, not columns defined by the PIVOT clause, itself.
• Evaluated against each row in the input table; aggregate and window function calls are prohibited.
• Non-determinism is okay.
• The type must be groupable.
• The input table may be accessed through its alias if one is provided.

Rules for pivot_column:

• A pivot_column must be a constant.
• Named constants, such as variables, are not supported.
• Query parameters are not supported.
• If a name is desired for a named constant or query parameter, specify it explicitly with an alias.
• Corner cases exist where a distinct pivot_columns can end up with the same default column names. For example, an input column might contain both a NULL value and the string literal "NULL". When this happens, multiple pivot columns are created with the same name. To avoid this situation, use aliases for pivot column names.
• If a pivot_column does not specify an alias, a column name is constructed as follows:

From	To	Example
NULL	NULL	Input: NULL Output: "NULL"
INT64 NUMERIC BIGNUMERIC	The number in string format with the following rules: Positive numbers are preceded with _. Negative numbers are preceded with minus_. A decimal point is replaced with _point_.	Input: 1 Output: _1 Input: -1 Output: minus_1 Input: 1.0 Output: _1_point_0
BOOL	TRUE or FALSE.	Input: TRUE Output: TRUE Input: FALSE Output: FALSE
STRING	The string value.	Input: "PlayerName" Output: PlayerName
DATE	The date in _YYYY_MM_DD format.	Input: DATE '2013-11-25' Output: _2013_11_25
ENUM	The name of the enumeration constant.	Input: COLOR.RED Output: RED
STRUCT	A string formed by computing the pivot_column name for each field and joining the results together with an underscore. The following rules apply: If the field is named: <field_name>_<pivot_column_name_for_field_name>. If the field is unnamed: <pivot_column_name_for_field_name>. <pivot_column_name_for_field_name> is determined by applying the rules in this table, recursively. If no rule is available for any STRUCT field, the entire pivot column is unnamed. Due to implicit type coercion from the IN list values to the type of <value-expression>, field names must be present in input_column to have an effect on the names of the pivot columns.	Input: STRUCT("one", "two") Output: one_two Input: STRUCT("one" AS a, "two" AS b) Output: one_a_two_b
All other data types	Not supported. You must provide an alias.

Examples

The following examples reference a table called Produce that looks like this:

WITH Produce AS (
  SELECT 'Kale' as product, 51 as sales, 'Q1' as quarter, 2020 as year UNION ALL
  SELECT 'Kale', 23, 'Q2', 2020 UNION ALL
  SELECT 'Kale', 45, 'Q3', 2020 UNION ALL
  SELECT 'Kale', 3, 'Q4', 2020 UNION ALL
  SELECT 'Kale', 70, 'Q1', 2021 UNION ALL
  SELECT 'Kale', 85, 'Q2', 2021 UNION ALL
  SELECT 'Apple', 77, 'Q1', 2020 UNION ALL
  SELECT 'Apple', 0, 'Q2', 2020 UNION ALL
  SELECT 'Apple', 1, 'Q1', 2021)
SELECT * FROM Produce

+---------+-------+---------+------+
| product | sales | quarter | year |
+---------+-------+---------+------|
| Kale    | 51    | Q1      | 2020 |
| Kale    | 23    | Q2      | 2020 |
| Kale    | 45    | Q3      | 2020 |
| Kale    | 3     | Q4      | 2020 |
| Kale    | 70    | Q1      | 2021 |
| Kale    | 85    | Q2      | 2021 |
| Apple   | 77    | Q1      | 2020 |
| Apple   | 0     | Q2      | 2020 |
| Apple   | 1     | Q1      | 2021 |
+---------+-------+---------+------+

With the PIVOT operator, the rows in the quarter column are rotated into these new columns: Q1, Q2, Q3, Q4. The aggregate function SUM is implicitly grouped by all unaggregated columns other than the pivot_column: product and year.

SELECT * FROM
  Produce
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4'))

+---------+------+----+------+------+------+
| product | year | Q1 | Q2   | Q3   | Q4   |
+---------+------+----+------+------+------+
| Apple   | 2020 | 77 | 0    | NULL | NULL |
| Apple   | 2021 | 1  | NULL | NULL | NULL |
| Kale    | 2020 | 51 | 23   | 45   | 3    |
| Kale    | 2021 | 70 | 85   | NULL | NULL |
+---------+------+----+------+------+------+

If you do not include year, then SUM is grouped only by product.

SELECT * FROM
  (SELECT product, sales, quarter FROM Produce)
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4'))

+---------+-----+-----+------+------+
| product | Q1  | Q2  | Q3   | Q4   |
+---------+-----+-----+------+------+
| Apple   | 78  | 0   | NULL | NULL |
| Kale    | 121 | 108 | 45   | 3    |
+---------+-----+-----+------+------+

You can select a subset of values in the pivot_column:

SELECT * FROM
  (SELECT product, sales, quarter FROM Produce)
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3'))
+---------+-----+-----+------+
| product | Q1  | Q2  | Q3   |
+---------+-----+-----+------+
| Apple   | 78  | 0   | NULL |
| Kale    | 121 | 108 | 45   |
+---------+-----+-----+------+

SELECT * FROM
  (SELECT sales, quarter FROM Produce)
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3'))
+-----+-----+----+
| Q1  | Q2  | Q3 |
+-----+-----+----+
| 199 | 108 | 45 |
+-----+-----+----+

You can include multiple aggregation functions in the PIVOT. In this case, you must specify an alias for each aggregation. These aliases are used to construct the column names in the resulting table.

SELECT * FROM
  (SELECT product, sales, quarter FROM Produce)
  PIVOT(SUM(sales) total_sales, COUNT(*) num_records FOR quarter IN ('Q1', 'Q2'))

+--------+----------------+----------------+----------------+----------------+
|product | total_sales_Q1 | num_records_Q1 | total_sales_Q2 | num_records_Q2 |
+--------+----------------+----------------+----------------+----------------+
| Kale   | 121            | 2              | 108            | 2              |
| Apple  | 78             | 2              | 0              | 1              |
+--------+----------------+----------------+----------------+----------------+

UNPIVOT operator

FROM from_item[, ...] unpivot_operator

unpivot_operator:
    UNPIVOT [ { INCLUDE NULLS | EXCLUDE NULLS } ] (
        { single_column_unpivot | multi_column_unpivot }
    ) [unpivot_alias]

single_column_unpivot:
    values_column
    FOR name_column
    IN (columns_to_unpivot)

multi_column_unpivot:
    values_column_set
    FOR name_column
    IN (column_sets_to_unpivot)

values_column_set:
    (values_column[, ...])

columns_to_unpivot:
    unpivot_column [row_value_alias][, ...]

column_sets_to_unpivot:
    (unpivot_column [row_value_alias][, ...])

unpivot_alias and row_value_alias:
    [AS] alias

The UNPIVOT operator rotates columns into rows. UNPIVOT is part of the FROM clause.

• UNPIVOT can be used to modify any table expression.
• Combining UNPIVOT with FOR SYSTEM_TIME AS OF is not allowed, although users may use UNPIVOT against a subquery input which itself uses FOR SYSTEM_TIME AS OF.
• A WITH OFFSET clause immediately preceding the UNPIVOT operator is not allowed.
• PIVOT aggregations cannot be reversed with UNPIVOT.

Conceptual example:

-- Before UNPIVOT is used to rotate Q1, Q2, Q3, Q4 into sales and quarter columns:
+---------+----+----+----+----+
| product | Q1 | Q2 | Q3 | Q4 |
+---------+----+----+----+----+
| Kale    | 51 | 23 | 45 | 3  |
| Apple   | 77 | 0  | 25 | 2  |
+---------+----+----+----+----+

-- After UNPIVOT is used to rotate Q1, Q2, Q3, Q4 into sales and quarter columns:
+---------+-------+---------+
| product | sales | quarter |
+---------+-------+---------+
| Kale    | 51    | Q1      |
| Kale    | 23    | Q2      |
| Kale    | 45    | Q3      |
| Kale    | 3     | Q4      |
| Apple   | 77    | Q1      |
| Apple   | 0     | Q2      |
| Apple   | 25    | Q3      |
| Apple   | 2     | Q4      |
+---------+-------+---------+

Definitions

Top-level definitions:

• from_item: The table or subquery on which to perform a pivot operation. The from_item must follow these rules.
• unpivot_operator: The pivot operation to perform on a from_item.

unpivot_operator definitions:

• INCLUDE NULLS: Add rows with NULL values to the result.
• EXCLUDE NULLS: Do not add rows with NULL values to the result. By default, UNPIVOT excludes rows with NULL values.
• single_column_unpivot: Rotates columns into one values_column and one name_column.
• multi_column_unpivot: Rotates columns into multiple values_columns and one name_column.
• unpivot_alias: An alias for the results of the UNPIVOT operation. This alias can be referenced elsewhere in the query.

single_column_unpivot definitions:

• values_column: A column to contain the row values from columns_to_unpivot. Follow these rules when creating a values column.
• name_column: A column to contain the column names from columns_to_unpivot. Follow these rules when creating a name column.
• columns_to_unpivot: The columns from the from_item to populate values_column and name_column. Follow these rules when creating an unpivot column.
  • row_value_alias: An optional alias for a column that is displayed for the column in name_column. If not specified, the string value of the column name is used. Follow these rules when creating a row value alias.

multi_column_unpivot definitions:

• values_column_set: A set of columns to contain the row values from columns_to_unpivot. Follow these rules when creating a values column.
• name_column: A set of columns to contain the column names from columns_to_unpivot. Follow these rules when creating a name column.
• column_sets_to_unpivot: The columns from the from_item to unpivot. Follow these rules when creating an unpivot column.
  • row_value_alias: An optional alias for a column set that is displayed for the column set in name_column. If not specified, a string value for the column set is used and each column in the string is separated with an underscore (_). For example, (col1, col2) outputs col1_col2. Follow these rules when creating a row value alias.

Rules

Rules for a from_item passed to UNPIVOT:

• The from_item may consist of any table or subquery result.
• The from_item may not produce a value table.
• Duplicate columns in a from_item cannot be referenced in the UNPIVOT clause.

Rules for unpivot_operator:

• Expressions are not permitted.
• Qualified names are not permitted. For example, mytable.mycolumn is not allowed.
• In the case where the UNPIVOT result has duplicate column names:
  • SELECT * is allowed.
  • SELECT values_column causes ambiguity.

Rules for values_column:

• It cannot be a name used for a name_column or an unpivot_column.
• It can be the same name as a column from the from_item.

Rules for name_column:

• It cannot be a name used for a values_column or an unpivot_column.
• It can be the same name as a column from the from_item.

Rules for unpivot_column:

• Must be a column name from the from_item.
• It cannot reference duplicate from_item column names.
• All columns in a column set must have equivalent data types.
  • Data types cannot be coerced to a common supertype.
  • If the data types are exact matches (for example, a struct with different field names), the data type of the first input is the data type of the output.
• You cannot have the same name in the same column set. For example, (emp1, emp1) results in an error.
• You can have a the same name in different column sets. For example, (emp1, emp2), (emp1, emp3) is valid.

Rules for row_value_alias:

• This can be a string or an INT64 literal.
• The data type for all row_value_alias clauses must be the same.
• If the value is an INT64, the row_value_alias for each unpivot_column must be specified.

Examples

The following examples reference a table called Produce that looks like this:

WITH Produce AS (
  SELECT 'Kale' as product, 51 as Q1, 23 as Q2, 45 as Q3, 3 as Q4 UNION ALL
  SELECT 'Apple', 77, 0, 25, 2)
SELECT * FROM Produce

+---------+----+----+----+----+
| product | Q1 | Q2 | Q3 | Q4 |
+---------+----+----+----+----+
| Kale    | 51 | 23 | 45 | 3  |
| Apple   | 77 | 0  | 25 | 2  |
+---------+----+----+----+----+

With the UNPIVOT operator, the columns Q1, Q2, Q3, and Q4 are rotated. The values of these columns now populate a new column called Sales and the names of these columns now populate a new column called Quarter. This is a single-column unpivot operation.

SELECT * FROM Produce
UNPIVOT(sales FOR quarter IN (Q1, Q2, Q3, Q4))

+---------+-------+---------+
| product | sales | quarter |
+---------+-------+---------+
| Kale    | 51    | Q1      |
| Kale    | 23    | Q2      |
| Kale    | 45    | Q3      |
| Kale    | 3     | Q4      |
| Apple   | 77    | Q1      |
| Apple   | 0     | Q2      |
| Apple   | 25    | Q3      |
| Apple   | 2     | Q4      |
+---------+-------+---------+

In this example, we UNPIVOT four quarters into two semesters. This is a multi-column unpivot operation.

SELECT * FROM Produce
UNPIVOT(
  (first_half_sales, second_half_sales)
  FOR semesters
  IN ((Q1, Q2) AS 'semester_1', (Q3, Q4) AS 'semester_2'))

+---------+------------------+-------------------+------------+
| product | first_half_sales | second_half_sales | semesters  |
+---------+------------------+-------------------+------------+
| Kale    | 51               | 23                | semester_1 |
| Kale    | 45               | 3                 | semester_2 |
| Apple   | 77               | 0                 | semester_1 |
| Apple   | 25               | 2                 | semester_2 |
+---------+------------------+-------------------+------------+

TABLESAMPLE operator

TABLESAMPLE SYSTEM ( percent PERCENT )

Description

You can use the TABLESAMPLE operator to select a random sample of a dataset. This operator is useful when you're working with tables that have large amounts of data and you don't need precise answers.

Sampling returns a variety of records while avoiding the costs associated with scanning and processing an entire table. Each execution of the query might return different results because each execution processes an independently computed sample. GoogleSQL does not cache the results of queries that include a TABLESAMPLE clause.

Replace percent with the percentage of the dataset that you want to include in the results. The value must be between 0 and 100. The value can be a literal value or a query parameter. It cannot be a variable.

For more information, see Table sampling.

Example

The following query selects approximately 10% of a table's data:

SELECT * FROM dataset.my_table TABLESAMPLE SYSTEM (10 PERCENT)

Join operation

join_operation:
    { cross_join_operation | condition_join_operation }

cross_join_operation:
    from_item cross_join_operator from_item

condition_join_operation:
    from_item condition_join_operator from_item join_condition

cross_join_operator:
    { CROSS JOIN | , }

condition_join_operator:
    {
      [INNER] JOIN
      | FULL [OUTER] JOIN
      | LEFT [OUTER] JOIN
      | RIGHT [OUTER] JOIN
    }

join_condition:
    { on_clause | using_clause }

on_clause:
    ON bool_expression

using_clause:
    USING ( column_list )

The JOIN operation merges two from_items so that the SELECT clause can query them as one source. The join operator and join condition specify how to combine and discard rows from the two from_items to form a single source.

[INNER] JOIN

An INNER JOIN, or simply JOIN, effectively calculates the Cartesian product of the two from_items and discards all rows that do not meet the join condition. Effectively means that it is possible to implement an INNER JOIN without actually calculating the Cartesian product.

FROM A INNER JOIN B ON A.w = B.y

Table A       Table B       Result
+-------+     +-------+     +---------------+
| w | x |  *  | y | z |  =  | w | x | y | z |
+-------+     +-------+     +---------------+
| 1 | a |     | 2 | k |     | 2 | b | 2 | k |
| 2 | b |     | 3 | m |     | 3 | c | 3 | m |
| 3 | c |     | 3 | n |     | 3 | c | 3 | n |
| 3 | d |     | 4 | p |     | 3 | d | 3 | m |
+-------+     +-------+     | 3 | d | 3 | n |
                            +---------------+

FROM A INNER JOIN B USING (x)

Table A       Table B       Result
+-------+     +-------+     +-----------+
| x | y |  *  | x | z |  =  | x | y | z |
+-------+     +-------+     +-----------+
| 1 | a |     | 2 | k |     | 2 | b | k |
| 2 | b |     | 3 | m |     | 3 | c | m |
| 3 | c |     | 3 | n |     | 3 | c | n |
| 3 | d |     | 4 | p |     | 3 | d | m |
+-------+     +-------+     | 3 | d | n |
                            +-----------+

Example

This query performs an INNER JOIN on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster JOIN TeamMascot ON Roster.SchoolID = TeamMascot.SchoolID;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Buchanan   | Lakers       |
| Coolidge   | Lakers       |
| Davis      | Knights      |
+---------------------------+

CROSS JOIN

CROSS JOIN returns the Cartesian product of the two from_items. In other words, it combines each row from the first from_item with each row from the second from_item.

If the rows of the two from_items are independent, then the result has M * N rows, given M rows in one from_item and N in the other. Note that this still holds for the case when either from_item has zero rows.

In a FROM clause, a CROSS JOIN can be written like this:

FROM A CROSS JOIN B

Table A       Table B       Result
+-------+     +-------+     +---------------+
| w | x |  *  | y | z |  =  | w | x | y | z |
+-------+     +-------+     +---------------+
| 1 | a |     | 2 | c |     | 1 | a | 2 | c |
| 2 | b |     | 3 | d |     | 1 | a | 3 | d |
+-------+     +-------+     | 2 | b | 2 | c |
                            | 2 | b | 3 | d |
                            +---------------+

You can use a correlated cross join to convert or flatten an array into a set of rows. To learn more, see Convert elements in an array to rows in a table.

Examples

This query performs an CROSS JOIN on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster CROSS JOIN TeamMascot;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Adams      | Knights      |
| Adams      | Lakers       |
| Adams      | Mustangs     |
| Buchanan   | Jaguars      |
| Buchanan   | Knights      |
| Buchanan   | Lakers       |
| Buchanan   | Mustangs     |
| ...                       |
+---------------------------+

Comma cross join (,)

CROSS JOINs can be written implicitly with a comma. This is called a comma cross join.

A comma cross join looks like this in a FROM clause:

FROM A, B

Table A       Table B       Result
+-------+     +-------+     +---------------+
| w | x |  *  | y | z |  =  | w | x | y | z |
+-------+     +-------+     +---------------+
| 1 | a |     | 2 | c |     | 1 | a | 2 | c |
| 2 | b |     | 3 | d |     | 1 | a | 3 | d |
+-------+     +-------+     | 2 | b | 2 | c |
                            | 2 | b | 3 | d |
                            +---------------+

You cannot write comma cross joins inside parentheses. To learn more, see Join operations in a sequence.

FROM (A, B)  // INVALID

You can use a correlated comma cross join to convert or flatten an array into a set of rows. To learn more, see Convert elements in an array to rows in a table.

Examples

This query performs a comma cross join on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster, TeamMascot;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Adams      | Knights      |
| Adams      | Lakers       |
| Adams      | Mustangs     |
| Buchanan   | Jaguars      |
| Buchanan   | Knights      |
| Buchanan   | Lakers       |
| Buchanan   | Mustangs     |
| ...                       |
+---------------------------+

FULL [OUTER] JOIN

A FULL OUTER JOIN (or simply FULL JOIN) returns all fields for all matching rows in both from_items that meet the join condition. If a given row from one from_item does not join to any row in the other from_item, the row returns with NULL values for all columns from the other from_item.

FROM A FULL OUTER JOIN B ON A.w = B.y

Table A       Table B       Result
+-------+     +-------+     +---------------------------+
| w | x |  *  | y | z |  =  | w    | x    | y    | z    |
+-------+     +-------+     +---------------------------+
| 1 | a |     | 2 | k |     | 1    | a    | NULL | NULL |
| 2 | b |     | 3 | m |     | 2    | b    | 2    | k    |
| 3 | c |     | 3 | n |     | 3    | c    | 3    | m    |
| 3 | d |     | 4 | p |     | 3    | c    | 3    | n    |
+-------+     +-------+     | 3    | d    | 3    | m    |
                            | 3    | d    | 3    | n    |
                            | NULL | NULL | 4    | p    |
                            +---------------------------+

FROM A FULL OUTER JOIN B USING (x)

Table A       Table B       Result
+-------+     +-------+     +--------------------+
| x | y |  *  | x | z |  =  | x    | y    | z    |
+-------+     +-------+     +--------------------+
| 1 | a |     | 2 | k |     | 1    | a    | NULL |
| 2 | b |     | 3 | m |     | 2    | b    | k    |
| 3 | c |     | 3 | n |     | 3    | c    | m    |
| 3 | d |     | 4 | p |     | 3    | c    | n    |
+-------+     +-------+     | 3    | d    | m    |
                            | 3    | d    | n    |
                            | 4    | NULL | p    |
                            +--------------------+

Example

This query performs a FULL JOIN on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster FULL JOIN TeamMascot ON Roster.SchoolID = TeamMascot.SchoolID;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Buchanan   | Lakers       |
| Coolidge   | Lakers       |
| Davis      | Knights      |
| Eisenhower | NULL         |
| NULL       | Mustangs     |
+---------------------------+

LEFT [OUTER] JOIN

The result of a LEFT OUTER JOIN (or simply LEFT JOIN) for two from_items always retains all rows of the left from_item in the JOIN operation, even if no rows in the right from_item satisfy the join predicate.

All rows from the left from_item are retained; if a given row from the left from_item does not join to any row in the right from_item, the row will return with NULL values for all columns exclusively from the right from_item. Rows from the right from_item that do not join to any row in the left from_item are discarded.

FROM A LEFT OUTER JOIN B ON A.w = B.y

Table A       Table B       Result
+-------+     +-------+     +---------------------------+
| w | x |  *  | y | z |  =  | w    | x    | y    | z    |
+-------+     +-------+     +---------------------------+
| 1 | a |     | 2 | k |     | 1    | a    | NULL | NULL |
| 2 | b |     | 3 | m |     | 2    | b    | 2    | k    |
| 3 | c |     | 3 | n |     | 3    | c    | 3    | m    |
| 3 | d |     | 4 | p |     | 3    | c    | 3    | n    |
+-------+     +-------+     | 3    | d    | 3    | m    |
                            | 3    | d    | 3    | n    |
                            +---------------------------+

FROM A LEFT OUTER JOIN B USING (x)

Table A       Table B       Result
+-------+     +-------+     +--------------------+
| x | y |  *  | x | z |  =  | x    | y    | z    |
+-------+     +-------+     +--------------------+
| 1 | a |     | 2 | k |     | 1    | a    | NULL |
| 2 | b |     | 3 | m |     | 2    | b    | k    |
| 3 | c |     | 3 | n |     | 3    | c    | m    |
| 3 | d |     | 4 | p |     | 3    | c    | n    |
+-------+     +-------+     | 3    | d    | m    |
                            | 3    | d    | n    |
                            +--------------------+

Example

This query performs a LEFT JOIN on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster LEFT JOIN TeamMascot ON Roster.SchoolID = TeamMascot.SchoolID;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Buchanan   | Lakers       |
| Coolidge   | Lakers       |
| Davis      | Knights      |
| Eisenhower | NULL         |
+---------------------------+

RIGHT [OUTER] JOIN

The result of a RIGHT OUTER JOIN (or simply RIGHT JOIN) for two from_items always retains all rows of the right from_item in the JOIN operation, even if no rows in the left from_item satisfy the join predicate.

All rows from the right from_item are returned; if a given row from the right from_item does not join to any row in the left from_item, the row will return with NULL values for all columns exclusively from the left from_item. Rows from the left from_item that do not join to any row in the right from_item are discarded.

FROM A RIGHT OUTER JOIN B ON A.w = B.y

Table A       Table B       Result
+-------+     +-------+     +---------------------------+
| w | x |  *  | y | z |  =  | w    | x    | y    | z    |
+-------+     +-------+     +---------------------------+
| 1 | a |     | 2 | k |     | 2    | b    | 2    | k    |
| 2 | b |     | 3 | m |     | 3    | c    | 3    | m    |
| 3 | c |     | 3 | n |     | 3    | c    | 3    | n    |
| 3 | d |     | 4 | p |     | 3    | d    | 3    | m    |
+-------+     +-------+     | 3    | d    | 3    | n    |
                            | NULL | NULL | 4    | p    |
                            +---------------------------+

FROM A RIGHT OUTER JOIN B USING (x)

Table A       Table B       Result
+-------+     +-------+     +--------------------+
| x | y |  *  | x | z |  =  | x    | y    | z    |
+-------+     +-------+     +--------------------+
| 1 | a |     | 2 | k |     | 2    | b    | k    |
| 2 | b |     | 3 | m |     | 3    | c    | m    |
| 3 | c |     | 3 | n |     | 3    | c    | n    |
| 3 | d |     | 4 | p |     | 3    | d    | m    |
+-------+     +-------+     | 3    | d    | n    |
                            | 4    | NULL | p    |
                            +--------------------+

Example

This query performs a RIGHT JOIN on the Roster and TeamMascot tables.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster RIGHT JOIN TeamMascot ON Roster.SchoolID = TeamMascot.SchoolID;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Buchanan   | Lakers       |
| Coolidge   | Lakers       |
| Davis      | Knights      |
| NULL       | Mustangs     |
+---------------------------+

Join conditions

In a join operation, a join condition helps specify how to combine rows in two from_items to form a single source.

The two types of join conditions are the ON clause and USING clause. You must use a join condition when you perform a conditional join operation. You can't use a join condition when you perform a cross join operation.

ON clause

A combined row (the result of joining two rows) meets the ON join condition if join condition returns TRUE.

FROM A JOIN B ON A.x = B.x

Table A   Table B   Result (A.x, B.x)
+---+     +---+     +-------+
| x |  *  | x |  =  | x | x |
+---+     +---+     +-------+
| 1 |     | 2 |     | 2 | 2 |
| 2 |     | 3 |     | 3 | 3 |
| 3 |     | 4 |     +-------+
+---+     +---+

Example

This query performs an INNER JOIN on the Roster and TeamMascot table.

SELECT Roster.LastName, TeamMascot.Mascot
FROM Roster JOIN TeamMascot ON Roster.SchoolID = TeamMascot.SchoolID;

+---------------------------+
| LastName   | Mascot       |
+---------------------------+
| Adams      | Jaguars      |
| Buchanan   | Lakers       |
| Coolidge   | Lakers       |
| Davis      | Knights      |
+---------------------------+

USING clause

The USING clause requires a column list of one or more columns which occur in both input tables. It performs an equality comparison on that column, and the rows meet the join condition if the equality comparison returns TRUE.

FROM A JOIN B USING (x)

Table A   Table B   Result
+---+     +---+     +---+
| x |  *  | x |  =  | x |
+---+     +---+     +---+
| 1 |     | 2 |     | 2 |
| 2 |     | 3 |     | 3 |
| 3 |     | 4 |     +---+
+---+     +---+

Example

This query performs an INNER JOIN on the Roster and TeamMascot table.

This statement returns the rows from Roster and TeamMascot where Roster.SchoolID is the same as TeamMascot.SchoolID. The results include a single SchoolID column.

SELECT * FROM Roster INNER JOIN TeamMascot USING (SchoolID);

+----------------------------------------+
| SchoolID   | LastName   | Mascot       |
+----------------------------------------+
| 50         | Adams      | Jaguars      |
| 52         | Buchanan   | Lakers       |
| 52         | Coolidge   | Lakers       |
| 51         | Davis      | Knights      |
+----------------------------------------+

ON and USING equivalency

The ON and USING keywords are not equivalent, but they are similar. ON returns multiple columns, and USING returns one.

FROM A JOIN B ON A.x = B.x
FROM A JOIN B USING (x)

Table A   Table B   Result ON     Result USING
+---+     +---+     +-------+     +---+
| x |  *  | x |  =  | x | x |     | x |
+---+     +---+     +-------+     +---+
| 1 |     | 2 |     | 2 | 2 |     | 2 |
| 2 |     | 3 |     | 3 | 3 |     | 3 |
| 3 |     | 4 |     +-------+     +---+
+---+     +---+

Although ON and USING are not equivalent, they can return the same results if you specify the columns you want to return.

SELECT x FROM A JOIN B USING (x);
SELECT A.x FROM A JOIN B ON A.x = B.x;

Table A   Table B   Result
+---+     +---+     +---+
| x |  *  | x |  =  | x |
+---+     +---+     +---+
| 1 |     | 2 |     | 2 |
| 2 |     | 3 |     | 3 |
| 3 |     | 4 |     +---+
+---+     +---+

Join operations in a sequence

The FROM clause can contain multiple JOIN operations in a sequence. JOINs are bound from left to right. For example:

FROM A JOIN B USING (x) JOIN C USING (x)

-- A JOIN B USING (x)        = result_1
-- result_1 JOIN C USING (x) = result_2
-- result_2                  = return value

You can also insert parentheses to group JOINs:

FROM ( (A JOIN B USING (x)) JOIN C USING (x) )

-- A JOIN B USING (x)        = result_1
-- result_1 JOIN C USING (x) = result_2
-- result_2                  = return value

With parentheses, you can group JOINs so that they are bound in a different order:

FROM ( A JOIN (B JOIN C USING (x)) USING (x) )

-- B JOIN C USING (x)       = result_1
-- A JOIN result_1          = result_2
-- result_2                 = return value

A FROM clause can have multiple joins. Provided there are no comma cross joins in the FROM clause, joins do not require parenthesis, though parenthesis can help readability:

FROM A JOIN B JOIN C JOIN D USING (w) ON B.x = C.y ON A.z = B.x

If your clause contains comma cross joins, you must use parentheses:

FROM A, B JOIN C JOIN D ON C.x = D.y ON B.z = C.x    // INVALID

FROM A, B JOIN (C JOIN D ON C.x = D.y) ON B.z = C.x  // VALID

When comma cross joins are present in a query with a sequence of JOINs, they group from left to right like other JOIN types:

FROM A JOIN B USING (x) JOIN C USING (x), D

-- A JOIN B USING (x)        = result_1
-- result_1 JOIN C USING (x) = result_2
-- result_2 CROSS JOIN D     = return value

There cannot be a RIGHT JOIN or FULL JOIN after a comma cross join unless it is parenthesized:

FROM A, B RIGHT JOIN C ON TRUE // INVALID

FROM A, B FULL JOIN C ON TRUE  // INVALID

FROM A, B JOIN C ON TRUE       // VALID

FROM A, (B RIGHT JOIN C ON TRUE) // VALID

FROM A, (B FULL JOIN C ON TRUE)  // VALID

Correlated join operation

A join operation is correlated when the right from_item contains a reference to at least one range variable or column name introduced by the left from_item.

In a correlated join operation, rows from the right from_item are determined by a row from the left from_item. Consequently, RIGHT OUTER and FULL OUTER joins cannot be correlated because right from_item rows cannot be determined in the case when there is no row from the left from_item.

All correlated join operations must reference an array in the right from_item.

This is a conceptual example of a correlated join operation that includes a correlated subquery:

FROM A JOIN UNNEST(ARRAY(SELECT AS STRUCT * FROM B WHERE A.ID = B.ID)) AS C

• Left from_item: A
• Right from_item: UNNEST(...) AS C
• A correlated subquery: (SELECT AS STRUCT * FROM B WHERE A.ID = B.ID)

This is another conceptual example of a correlated join operation. array_of_IDs is part of the left from_item but is referenced in the right from_item.

FROM A JOIN UNNEST(A.array_of_IDs) AS C

The UNNEST operator can be explicit or implicit. These are both allowed:

FROM A JOIN UNNEST(A.array_of_IDs) AS IDs

FROM A JOIN A.array_of_IDs AS IDs

In a correlated join operation, the right from_item is re-evaluated against each distinct row from the left from_item. In the following conceptual example, the correlated join operation first evaluates A and B, then A and C:

FROM
  A
  JOIN
  UNNEST(ARRAY(SELECT AS STRUCT * FROM B WHERE A.ID = B.ID)) AS C
  ON A.Name = C.Name

Caveats

• In a correlated LEFT JOIN, when the input table on the right side is empty for some row from the left side, it is as if no rows from the right side satisfied the join condition in a regular LEFT JOIN. When there are no joining rows, a row with NULL values for all columns on the right side is generated to join with the row from the left side.
• In a correlated CROSS JOIN, when the input table on the right side is empty for some row from the left side, it is as if no rows from the right side satisfied the join condition in a regular correlated INNER JOIN. This means that the row is dropped from the results.

Examples

This is an example of a correlated join, using the Roster and PlayerStats tables:

SELECT *
FROM
  Roster
JOIN
  UNNEST(
    ARRAY(
      SELECT AS STRUCT *
      FROM PlayerStats
      WHERE PlayerStats.OpponentID = Roster.SchoolID
    )) AS PlayerMatches
  ON PlayerMatches.LastName = 'Buchanan'

+------------+----------+----------+------------+--------------+
| LastName   | SchoolID | LastName | OpponentID | PointsScored |
+------------+----------+----------+------------+--------------+
| Adams      | 50       | Buchanan | 50         | 13           |
| Eisenhower | 77       | Buchanan | 77         | 0            |
+------------+----------+----------+------------+--------------+

A common pattern for a correlated LEFT JOIN is to have an UNNEST operation on the right side that references an array from some column introduced by input on the left side. For rows where that array is empty or NULL, the UNNEST operation produces no rows on the right input. In that case, a row with a NULL entry in each column of the right input is created to join with the row from the left input. For example:

SELECT A.name, item, ARRAY_LENGTH(A.items) item_count_for_name
FROM
  UNNEST(
    [
      STRUCT(
        'first' AS name,
        [1, 2, 3, 4] AS items),
      STRUCT(
        'second' AS name,
        [] AS items)]) AS A
LEFT JOIN
  A.items AS item;

+--------+------+---------------------+
| name   | item | item_count_for_name |
+--------+------+---------------------+
| first  | 1    | 4                   |
| first  | 2    | 4                   |
| first  | 3    | 4                   |
| first  | 4    | 4                   |
| second | NULL | 0                   |
+--------+------+---------------------+

In the case of a correlated CROSS JOIN, when the input on the right side is empty for some row from the left side, the final row is dropped from the results. For example:

SELECT A.name, item
FROM
  UNNEST(
    [
      STRUCT(
        'first' AS name,
        [1, 2, 3, 4] AS items),
      STRUCT(
        'second' AS name,
        [] AS items)]) AS A
CROSS JOIN
  A.items AS item;

+-------+------+
| name  | item |
+-------+------+
| first | 1    |
| first | 2    |
| first | 3    |
| first | 4    |
+-------+------+

WHERE clause

WHERE bool_expression

The WHERE clause filters the results of the FROM clause.

Only rows whose bool_expression evaluates to TRUE are included. Rows whose bool_expression evaluates to NULL or FALSE are discarded.

The evaluation of a query with a WHERE clause is typically completed in this order:

• FROM
• WHERE
• GROUP BY and aggregation