Query syntax in GoogleSQL

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 Spanner.

SQL syntax notation rules

The following table lists and describes the syntax notation rules that GoogleSQL documentation commonly uses.

Notation	Example	Description
Square brackets	`[ ]`	Optional clauses
Parentheses	`( )`	Literal parentheses
Vertical bar	`\|`	Logical `XOR` (exclusive `OR`)
Curly braces	`{ }`	A set of options, such as `{ a \| b \| c }`. Select one option.
Ellipsis	`...`	The preceding item can repeat.
Comma	`,`	Literal comma
Comma followed by an ellipsis	`, ...`	The preceding item can repeat in a comma-separated list.
Item list	`item [, ...]`	One or more items
Item list	`[item, ...]`	Zero or more items
Double quotes	`""`	The enclosed syntax characters (for example, `"{"..."}"`) are literal and required.
Angle brackets	`<>`	Literal angle brackets

SQL syntax

query_statement:
  [ statement_hint_expr ]
  [ table_hint_expr ]
  [ group_hint_expr ]
  [ join_hint_expr ]
  query_expr

query_expr:
  [ WITH cte[, ...] ]
  { select | ( query_expr ) | set_operation }
  [ ORDER BY expression [{ ASC | DESC }] [, ...] ]
  [ LIMIT count [ OFFSET skip_rows ] ]
  [ FOR UPDATE ]

select:
  SELECT
    [ { ALL | DISTINCT } ]
    [ AS { typename | STRUCT | VALUE } ]
    select_list
  [ FROM from_clause[, ...] ]
  [ WHERE bool_expression ]
  [ GROUP [  group_hint_expr ] BY group_by_specification ]
  [ HAVING bool_expression ]

`SELECT` statement

SELECT
  [ { ALL | DISTINCT } ]
  [ AS { typename | 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's 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 doesn't 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 can't 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      |
 +-----------+----------*/

`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 doesn't 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      |
 +----------+-----------+----------*/

`SELECT DISTINCT`

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

PROTO
STRUCT
ARRAY
GRAPH_ELEMENT
GRAPH_PATH

`SELECT ALL`

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

Using STRUCTs with SELECT

Queries that return a STRUCT at the root of the return type aren't supported in Spanner APIs. For example, the following query is supported only as a subquery:
```
SELECT STRUCT(1, 2) FROM Users;
```

Returning an array of structs is supported. For example, the following queries are supported in Spanner APIs:

SELECT ARRAY(SELECT STRUCT(1 AS A, 2 AS B)) FROM Users;

SELECT ARRAY(SELECT AS STRUCT 1 AS a, 2 AS b) FROM Users;

However, query shapes that can return an ARRAY<STRUCT<...>> typed NULL value or an ARRAY<STRUCT<...>> typed value with an element that's NULL aren't supported in Spanner APIs, so the following query is supported only as a subquery:
```
SELECT ARRAY(SELECT IF(STARTS_WITH(Users.username, "a"), NULL, STRUCT(1, 2)))
FROM Users;
```

See Querying STRUCT elements in an ARRAY for more examples on how to query STRUCTs inside an ARRAY.

Also see notes about using STRUCTs in subqueries.

`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.

Anonymous columns are allowed.

Example:

SELECT AS STRUCT 1 x, 2, 3

The query above produces STRUCT values of type STRUCT<int64 x, int64, int64>. The first field has the name x while the second and third fields are anonymous.

The example above produces the same result as this SELECT AS VALUE query using a struct constructor:

SELECT AS VALUE STRUCT(1 AS x, 2, 3)

Duplicate columns are allowed.

Example:

SELECT AS STRUCT 1 x, 2 y, 3 x

The query above produces STRUCT values of type STRUCT<int64 x, int64 y, int64 x>. The first and third fields have the same name x while the second field has the name y.

The example above produces the same result as this SELECT AS VALUE query using a struct constructor:

SELECT AS VALUE STRUCT(1 AS x, 2 AS y, 3 AS x)

`SELECT AS typename`

SELECT AS typename
  expr [[AS] field]
  [, ...]

A SELECT AS typename statement produces a value table where the row type is a specific named type. Currently, protocol buffers are the only supported type that can be used with this syntax.

When selecting as a type that has fields, such as a proto message type, the SELECT list may produce multiple columns. Each produced column must have an explicit or implicit alias that matches a unique field of the named type.

When used with SELECT DISTINCT, or GROUP BY or ORDER BY using column ordinals, these operators are first applied on the columns in the SELECT list. The value construction happens last. This means that DISTINCT can be applied on the input columns to the value construction, including in cases where DISTINCT wouldn't be allowed after value construction because grouping isn't supported on the constructed type.

The following is an example of a SELECT AS typename query.

SELECT AS tests.TestProtocolBuffer mytable.key int64_val, mytable.name string_val
FROM mytable;

The query returns the output as a tests.TestProtocolBuffer protocol buffer. mytable.key int64_val means that values from the key column are stored in the int64_val field in the protocol buffer. Similarly, values from the mytable.name column are stored in the string_val protocol buffer field.

To learn more about protocol buffers, see Work with protocol buffers.

`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 1

The query above produces a table with row type INT64.

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>.

Example:

SELECT AS VALUE v FROM (SELECT AS STRUCT 1 a, true b) v WHERE v.b

Given a value table v as input, the query above filters out certain values in the WHERE clause, and then produces a value table using the exact same value that was in the input table. If the query above didn't use SELECT AS VALUE, then the output table schema would differ from the input table schema because the output table would be a regular table with a column named v containing the input value.

`FROM` clause

FROM from_clause[, ...]

from_clause:
  from_item
  [ tablesample_operator ]

from_item:
  {
    table_name [ table_hint_expr ] [ as_alias ]
    | { join_operation | ( join_operation ) }
    | ( query_expr ) [ table_hint_expr ] [ as_alias ]
    | field_path
    | unnest_operator
    | cte_name [ table_hint_expr ] [ as_alias ]
    | graph_table_operator [ 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.

`tablesample_operator`

See TABLESAMPLE operator.

`graph_table_operator`

See GRAPH_TABLE operator.

`table_name`

The name of an existing table.

SELECT * FROM Roster;

`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 or a repeated field. In addition, field paths can't contain arrays or repeated fields 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.

`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;

`UNNEST` operator

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

array:
  { array_expression | array_path }

as_alias:
  [AS] alias

The UNNEST operator takes an array and returns a table with one row for each element in the array. The output of UNNEST is one value table column. For these ARRAY element types, SELECT * against the value table column returns multiple columns:

STRUCT
PROTO

Input values:

array_expression: An expression that produces an array and that's not an array path.
array_path: The path to an ARRAY type.
- In an implicit UNNEST operation, the path must start with a range variable name.
- In an explicit UNNEST operation, the path can optionally start with a range variable name.
The UNNEST operation with any correlated array_path must be on the right side of a CROSS JOIN, LEFT JOIN, or INNER JOIN operation.
as_alias: If specified, defines the explicit name of the value table column containing the array element values. It can be used to refer to the column elsewhere in the query.
WITH OFFSET: UNNEST destroys the order of elements in the input array. Use this optional clause to return an additional column with the array element indexes, or offsets. Offset counting starts at zero for each row produced by the UNNEST operation. This column has an optional alias; If the optional alias isn't 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      |
 +---------+--------*/
```

You can also use UNNEST outside of the FROM clause with the IN operator.

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

To learn more about the ways you can use UNNEST explicitly and implicitly, see Explicit and implicit UNNEST.

`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 ARRAY<INT64>>>[
        (1, 'foo', [10, 11]),
        (3, 'bar', [20, 21])]);

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

`UNNEST` and protocol buffers

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

Example:

SELECT *
FROM UNNEST(
  ARRAY<googlesql.examples.music.Album>[
    NEW googlesql.examples.music.Album (
      'The Goldberg Variations' AS album_name,
      ['Aria', 'Variation 1', 'Variation 2'] AS song
    )
  ]
);

/*-------------------------+--------+----------------------------------+
 | album_name              | singer | song                             |
 +-------------------------+--------+----------------------------------+
 | The Goldberg Variations | NULL   | [Aria, Variation 1, Variation 2] |
 +-------------------------+--------+----------------------------------*/

As with structs, you can alias UNNEST to define a range variable. You can reference this alias in the SELECT list to return a value table where each row is a protocol buffer element from the array.

SELECT proto_value
FROM UNNEST(
  ARRAY<googlesql.examples.music.Album>[
    NEW googlesql.examples.music.Album (
      'The Goldberg Variations' AS album_name,
      ['Aria', 'Var. 1'] AS song
    )
  ]
) AS proto_value;

/*---------------------------------------------------------------------+
 | proto_value                                                         |
 +---------------------------------------------------------------------+
 | {album_name: "The Goldberg Variations" song: "Aria" song: "Var. 1"} |
 +---------------------------------------------------------------------*/

Explicit and implicit `UNNEST`

Array unnesting can be either explicit or implicit. To learn more, see the following sections.

Explicit unnesting

The UNNEST keyword is required in explicit unnesting. For example:

WITH Coordinates AS (SELECT [1,2] AS position)
SELECT results FROM Coordinates, UNNEST(Coordinates.position) AS results;

This example and the following examples use the array_path called Coordinates.position to illustrate unnesting.

Implicit unnesting

The UNNEST keyword isn't used in implicit unnesting.

For example:

WITH Coordinates AS (SELECT [1,2] AS position)
SELECT results FROM Coordinates, Coordinates.position AS results;

Tables and implicit unnesting

When you use array_path with implicit UNNEST, array_path must be prepended with the table. For example:

WITH Coordinates AS (SELECT [1,2] AS position)
SELECT results FROM Coordinates, Coordinates.position AS results;

`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.

`TABLESAMPLE` operator

tablesample_clause:
  TABLESAMPLE sample_method (sample_size percent_or_rows )

sample_method:
  { BERNOULLI | RESERVOIR }

sample_size:
  numeric_value_expression

percent_or_rows:
  { PERCENT | ROWS }

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.

sample_method: When using the TABLESAMPLE operator, you must specify the sampling algorithm to use:
- BERNOULLI: Each row is independently selected with the probability given in the percent clause. As a result, you get approximately N * percent/100 rows.
- RESERVOIR: Takes as parameter an actual sample size K (expressed as a number of rows). If the input is smaller than K, it outputs the entire input relation. If the input is larger than K, reservoir sampling outputs a sample of size exactly K, where any sample of size K is equally likely.
sample_size: The size of the sample.
percent_or_rows: The TABLESAMPLE operator requires that you choose either ROWS or PERCENT. If you choose PERCENT, the value must be between 0 and 100. If you choose ROWS, the value must be greater than or equal to 0.

Examples

The following examples illustrate the use of the TABLESAMPLE operator.

Select from a table using the RESERVOIR sampling method:

SELECT MessageId
FROM Messages TABLESAMPLE RESERVOIR (100 ROWS);

Select from a table using the BERNOULLI sampling method:

SELECT MessageId
FROM Messages TABLESAMPLE BERNOULLI (0.1 PERCENT);

Use TABLESAMPLE with a subquery:

SELECT Subject FROM
(SELECT MessageId, Subject FROM Messages WHERE ServerId="test")
TABLESAMPLE BERNOULLI(50 PERCENT)
WHERE MessageId > 3;

Use a TABLESAMPLE operation with a join to another table.

SELECT S.Subject
FROM
(SELECT MessageId, ThreadId FROM Messages WHERE ServerId="test") AS R
TABLESAMPLE RESERVOIR(5 ROWS),
Threads AS S
WHERE S.ServerId="test" AND R.ThreadId = S.ThreadId;

`GRAPH_TABLE` operator

To learn more about this operator, see GRAPH_TABLE operator in the Graph Query Language (GQL) reference guide.

Join operation

join_operation:
  { cross_join_operation | condition_join_operation }

cross_join_operation:
  from_item cross_join_operator [ join_hint_expr ] from_item

condition_join_operation:
  from_item condition_join_operator [ join_hint_expr ] from_item join_condition

cross_join_operator:
  { CROSS JOIN | , }

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

join_method:
  { HASH }

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 don't meet the join condition. Effectively means that it's 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      |
 +---------------------------*/

You can use a correlated INNER JOIN to flatten an array into a set of rows. To learn more, see Convert elements in an array to rows in a table.

`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, though the (equivalent) INNER JOIN is preferred over CROSS JOIN for this case. 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 can't 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 doesn't 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 doesn't 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 don't 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 doesn't 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 don't 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

ON bool_expression

Description

Given a row from each table, if the ON clause evaluates to TRUE, the query generates a consolidated row with the result of combining the given rows.

Definitions:

bool_expression: The boolean expression that specifies the condition for the join. This is frequently a comparison operation or logical combination of comparison operators.

Details:

Similarly to CROSS JOIN, ON produces a column once for each column in each input table.

A NULL join condition evaluation is equivalent to a FALSE evaluation.

If a column-order sensitive operation such as UNION or SELECT * is used with the ON join condition, the resulting table contains all of the columns from the left input in order, and then all of the columns from the right input in order.

Examples

The following examples show how to use the ON clause:

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT * FROM A INNER JOIN B ON A.x = B.x;

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT A.x, B.x FROM A INNER 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 |     +-------+
+---+     +---+
*/

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT * FROM A LEFT OUTER JOIN B ON A.x = B.x;

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT A.x, B.x FROM A LEFT OUTER JOIN B ON A.x = B.x;

/*
Table A    Table B   Result
+------+   +---+     +-------------+
| x    | * | x |  =  | x    | x    |
+------+   +---+     +-------------+
| 1    |   | 2 |     | 1    | NULL |
| 2    |   | 3 |     | 2    | 2    |
| 3    |   | 4 |     | 3    | 3    |
| NULL |   | 5 |     | NULL | NULL |
+------+   +---+     +-------------+
*/

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT * FROM A FULL OUTER JOIN B ON A.x = B.x;

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT A.x, B.x FROM A FULL OUTER JOIN B ON A.x = B.x;

/*
Table A    Table B   Result
+------+   +---+     +-------------+
| x    | * | x |  =  | x    | x    |
+------+   +---+     +-------------+
| 1    |   | 2 |     | 1    | NULL |
| 2    |   | 3 |     | 2    | 2    |
| 3    |   | 4 |     | 3    | 3    |
| NULL |   | 5 |     | NULL | NULL |
+------+   +---+     | NULL | 4    |
                     | NULL | 5    |
                     +-------------+
*/

`USING` clause

USING ( column_name_list )

column_name_list:
    column_name[, ...]

Description

When you are joining two tables, USING performs an equality comparison operation on the columns named in column_name_list. Each column name in column_name_list must appear in both input tables. For each pair of rows from the input tables, if the equality comparisons all evaluate to TRUE, one row is added to the resulting column.

Definitions:

column_name_list: A list of columns to include in the join condition.
column_name: The column that exists in both of the tables that you are joining.

Details:

A NULL join condition evaluation is equivalent to a FALSE evaluation.

If a column-order sensitive operation such as UNION or SELECT * is used with the USING join condition, the resulting table contains columns in this order:

The columns from column_name_list in the order they appear in the USING clause.
All other columns of the left input in the order they appear in the input.
All other columns of the right input in the order they appear in the input.

A column name in the USING clause must not be qualified by a table name.

If the join is an INNER JOIN or a LEFT OUTER JOIN, the output columns are populated from the values in the first table. If the join is a RIGHT OUTER JOIN, the output columns are populated from the values in the second table. If the join is a FULL OUTER JOIN, the output columns are populated by coalescing the values from the left and right tables in that order.

Examples

The following example shows how to use the USING clause with one column name in the column name list:

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 9 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 9 UNION ALL SELECT 9 UNION ALL SELECT 5)
SELECT * FROM A INNER JOIN B USING (x);

/*
Table A    Table B   Result
+------+   +---+     +---+
| x    | * | x |  =  | x |
+------+   +---+     +---+
| 1    |   | 2 |     | 2 |
| 2    |   | 9 |     | 9 |
| 9    |   | 9 |     | 9 |
| NULL |   | 5 |     +---+
+------+   +---+
*/

The following example shows how to use the USING clause with multiple column names in the column name list:

WITH
  A AS (
    SELECT 1 as x, 15 as y UNION ALL
    SELECT 2, 10 UNION ALL
    SELECT 9, 16 UNION ALL
    SELECT NULL, 12),
  B AS (
    SELECT 2 as x, 10 as y UNION ALL
    SELECT 9, 17 UNION ALL
    SELECT 9, 16 UNION ALL
    SELECT 5, 15)
SELECT * FROM A INNER JOIN B USING (x, y);

/*
Table A         Table B        Result
+-----------+   +---------+     +---------+
| x    | y  | * | x  | y  |  =  | x  | y  |
+-----------+   +---------+     +---------+
| 1    | 15 |   | 2  | 10 |     | 2  | 10 |
| 2    | 10 |   | 9  | 17 |     | 9  | 16 |
| 9    | 16 |   | 9  | 16 |     +---------+
| NULL | 12 |   | 5  | 15 |
+-----------+   +---------+
*/

The following examples show additional ways in which to use the USING clause with one column name in the column name list:

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 9 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 9 UNION ALL SELECT 9 UNION ALL SELECT 5)
SELECT x, A.x, B.x FROM A INNER JOIN B USING (x)

/*
Table A    Table B   Result
+------+   +---+     +--------------------+
| x    | * | x |  =  | x    | A.x  | B.x  |
+------+   +---+     +--------------------+
| 1    |   | 2 |     | 2    | 2    | 2    |
| 2    |   | 9 |     | 9    | 9    | 9    |
| 9    |   | 9 |     | 9    | 9    | 9    |
| NULL |   | 5 |     +--------------------+
+------+   +---+
*/

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 9 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 9 UNION ALL SELECT 9 UNION ALL SELECT 5)
SELECT x, A.x, B.x FROM A LEFT OUTER JOIN B USING (x)

/*
Table A    Table B   Result
+------+   +---+     +--------------------+
| x    | * | x |  =  | x    | A.x  | B.x  |
+------+   +---+     +--------------------+
| 1    |   | 2 |     | 1    | 1    | NULL |
| 2    |   | 9 |     | 2    | 2    | 2    |
| 9    |   | 9 |     | 9    | 9    | 9    |
| NULL |   | 5 |     | 9    | 9    | 9    |
+------+   +---+     | NULL | NULL | NULL |
                     +--------------------+
*/

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 2 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 9 UNION ALL SELECT 9 UNION ALL SELECT 5)
SELECT x, A.x, B.x FROM A RIGHT OUTER JOIN B USING (x)

/*
Table A    Table B   Result
+------+   +---+     +--------------------+
| x    | * | x |  =  | x    | A.x  | B.x  |
+------+   +---+     +--------------------+
| 1    |   | 2 |     | 2    | 2    | 2    |
| 2    |   | 9 |     | 2    | 2    | 2    |
| 2    |   | 9 |     | 9    | NULL | 9    |
| NULL |   | 5 |     | 9    | NULL | 9    |
+------+   +---+     | 5    | NULL | 5    |
                     +--------------------+
*/

WITH
  A AS ( SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 2 UNION ALL SELECT NULL),
  B AS ( SELECT 2 as x UNION ALL SELECT 9 UNION ALL SELECT 9 UNION ALL SELECT 5)
SELECT x, A.x, B.x FROM A FULL OUTER JOIN B USING (x);

/*
Table A    Table B   Result
+------+   +---+     +--------------------+
| x    | * | x |  =  | x    | A.x  | B.x  |
+------+   +---+     +--------------------+
| 1    |   | 2 |     | 1    | 1    | NULL |
| 2    |   | 9 |     | 2    | 2    | 2    |
| 2    |   | 9 |     | 2    | 2    | 2    |
| NULL |   | 5 |     | NULL | NULL | NULL |
+------+   +---+     | 9    | NULL | 9    |
                     | 9    | NULL | 9    |
                     | 5    | NULL | 5    |
                     +--------------------+
*/

The following example shows how to use the USING clause with only some column names in the column name list.

WITH
  A AS (
    SELECT 1 as x, 15 as y UNION ALL
    SELECT 2, 10 UNION ALL
    SELECT 9, 16 UNION ALL
    SELECT NULL, 12),
  B AS (
    SELECT 2 as x, 10 as y UNION ALL
    SELECT 9, 17 UNION ALL
    SELECT 9, 16 UNION ALL
    SELECT 5, 15)
SELECT * FROM A INNER JOIN B USING (x);

/*
Table A         Table B         Result
+-----------+   +---------+     +-----------------+
| x    | y  | * | x  | y  |  =  | x   | A.y | B.y |
+-----------+   +---------+     +-----------------+
| 1    | 15 |   | 2  | 10 |     | 2   | 10  | 10  |
| 2    | 10 |   | 9  | 17 |     | 9   | 16  | 17  |
| 9    | 16 |   | 9  | 16 |     | 9   | 16  | 16  |
| NULL | 12 |   | 5  | 15 |     +-----------------+
+-----------+   +---------+
*/

The following query performs an INNER JOIN on the Roster and TeamMascot table. The query 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 join conditions aren't equivalent, but they share some rules and sometimes they can produce similar results.

In the following examples, observe what is returned when all rows are produced for inner and outer joins. Also, look at how each join condition handles NULL values.

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT * FROM A INNER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT * FROM A INNER 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 |     +-------+     +---+
+---+     +---+
*/

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT * FROM A LEFT OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT * FROM A LEFT OUTER JOIN B USING (x);

/*
Table A    Table B   Result ON           Result USING
+------+   +---+     +-------------+     +------+
| x    | * | x |  =  | x    | x    |     | x    |
+------+   +---+     +-------------+     +------+
| 1    |   | 2 |     | 1    | NULL |     | 1    |
| 2    |   | 3 |     | 2    | 2    |     | 2    |
| 3    |   | 4 |     | 3    | 3    |     | 3    |
| NULL |   | 5 |     | NULL | NULL |     | NULL |
+------+   +---+     +-------------+     +------+
*/

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT * FROM A FULL OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4)
SELECT * FROM A FULL OUTER JOIN B USING (x);

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

Although ON and USING aren't equivalent, they can return the same results in some situations if you specify the columns you want to return.

In the following examples, observe what is returned when a specific row is produced for inner and outer joins. Also, look at how each join condition handles NULL values.

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT A.x FROM A INNER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT x FROM A INNER JOIN B USING (x);

/*
Table A    Table B   Result ON     Result USING
+------+   +---+     +---+         +---+
| x    | * | x |  =  | x |         | x |
+------+   +---+     +---+         +---+
| 1    |   | 2 |     | 2 |         | 2 |
| 2    |   | 3 |     | 3 |         | 3 |
| 3    |   | 4 |     +---+         +---+
| NULL |   | 5 |
+------+   +---+
*/

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT A.x FROM A LEFT OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT x FROM A LEFT OUTER JOIN B USING (x);

/*
Table A    Table B   Result ON    Result USING
+------+   +---+     +------+     +------+
| x    | * | x |  =  | x    |     | x    |
+------+   +---+     +------+     +------+
| 1    |   | 2 |     | 1    |     | 1    |
| 2    |   | 3 |     | 2    |     | 2    |
| 3    |   | 4 |     | 3    |     | 3    |
| NULL |   | 5 |     | NULL |     | NULL |
+------+   +---+     +------+     +------+
*/

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT A.x FROM A FULL OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT x FROM A FULL OUTER JOIN B USING (x);

/*
Table A    Table B   Result ON    Result USING
+------+   +---+     +------+     +------+
| x    | * | x |  =  | x    |     | x    |
+------+   +---+     +------+     +------+
| 1    |   | 2 |     | 1    |     | 1    |
| 2    |   | 3 |     | 2    |     | 2    |
| 3    |   | 4 |     | 3    |     | 3    |
| NULL |   | 5 |     | NULL |     | NULL |
+------+   +---+     | NULL |     | 4    |
                     | NULL |     | 5    |
                     +------+     +------+
*/

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT B.x FROM A FULL OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT x FROM A FULL OUTER JOIN B USING (x);

/*
Table A    Table B   Result ON    Result USING
+------+   +---+     +------+     +------+
| x    | * | x |  =  | x    |     | x    |
+------+   +---+     +------+     +------+
| 1    |   | 2 |     | 2    |     | 1    |
| 2    |   | 3 |     | 3    |     | 2    |
| 3    |   | 4 |     | NULL |     | 3    |
| NULL |   | 5 |     | NULL |     | NULL |
+------+   +---+     | 4    |     | 4    |
                     | 5    |     | 5    |
                     +------+     +------+
*/

In the following example, observe what is returned when COALESCE is used with the ON clause. It provides the same results as a query with the USING clause.

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT COALESCE(A.x, B.x) FROM A FULL OUTER JOIN B ON A.x = B.x;

WITH
  A AS (SELECT 1 as x UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT NULL),
  B AS (SELECT 2 as x UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5)
SELECT x FROM A FULL OUTER JOIN B USING (x);

/*
Table A    Table B   Result ON    Result USING
+------+   +---+     +------+     +------+
| x    | * | x |  =  | x    |     | x    |
+------+   +---+     +------+     +------+
| 1    |   | 2 |     | 1    |     | 1    |
| 2    |   | 3 |     | 2    |     | 2    |
| 3    |   | 4 |     | 3    |     | 3    |
| NULL |   | 5 |     | NULL |     | NULL |
+------+   +---+     | 4    |     | 4    |
                     | 5    |     | 5    |
                     +------+     +------+
*/

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

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 can't be a RIGHT JOIN or FULL JOIN after a comma cross join unless it's 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 can't be correlated because right from_item rows can't 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's 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's 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 INNER JOIN or 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
INNER 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
HAVING
DISTINCT
ORDER BY
LIMIT

Evaluation order doesn't always match syntax order.

The WHERE clause only references columns available via the FROM clause; it can't reference SELECT list aliases.

Examples

This query returns returns all rows from the Roster table where the SchoolID column has the value 52:

SELECT * FROM Roster
WHERE SchoolID = 52;

The bool_expression can contain multiple sub-conditions:

SELECT * FROM Roster
WHERE STARTS_WITH(LastName, "Mc") OR STARTS_WITH(LastName, "Mac");

Expressions in an INNER JOIN have an equivalent expression in the WHERE clause. For example, a query using INNER JOIN and ON has an equivalent expression using CROSS JOIN and WHERE. For example, the following two queries are equivalent:

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

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

`GROUP BY` clause

GROUP [ group_hint_expr ] BY groupable_items

Description

The GROUP BY clause groups together rows in a table that share common values for certain columns. For a group of rows in the source table with non-distinct values, the GROUP BY clause aggregates them into a single combined row. This clause is commonly used when aggregate functions are present in the SELECT list, or to eliminate redundancy in the output.

Definitions

group_hint_expr: Hints that you can apply to the GROUP BY clause. To learn more, see Group hints.
groupable_items: Group rows in a table that share common values for certain columns. To learn more, see Group rows by groupable items.

Group rows by groupable items

GROUP BY groupable_item[, ...]

groupable_item:
  {
    value
    | value_alias
    | column_ordinal
  }

Description

The GROUP BY clause can include groupable expressions and their ordinals.

Definitions

value: An expression that represents a non-distinct, groupable value. To learn more, see Group rows by values.
value_alias: An alias for value. To learn more, see Group rows by values.
column_ordinal: An INT64 value that represents the ordinal assigned to a groupable expression in the SELECT list. To learn more, see Group rows by column ordinals.

Group rows by values

The GROUP BY clause can group rows in a table with non-distinct values in the GROUP BY clause. For example:

WITH PlayerStats AS (
  SELECT 'Adams' as LastName, 'Noam' as FirstName, 3 as PointsScored UNION ALL
  SELECT 'Buchanan', 'Jie', 0 UNION ALL
  SELECT 'Coolidge', 'Kiran', 1 UNION ALL
  SELECT 'Adams', 'Noam', 4 UNION ALL
  SELECT 'Buchanan', 'Jie', 13)
SELECT SUM(PointsScored) AS total_points, LastName
FROM PlayerStats
GROUP BY LastName;

/*--------------+----------+
 | total_points | LastName |
 +--------------+----------+
 | 7            | Adams    |
 | 13           | Buchanan |
 | 1            | Coolidge |
 +--------------+----------*/

GROUP BY clauses may also refer to aliases. If a query contains aliases in the SELECT clause, those aliases override names in the corresponding FROM clause. For example:

WITH PlayerStats AS (
  SELECT 'Adams' as LastName, 'Noam' as FirstName, 3 as PointsScored UNION ALL
  SELECT 'Buchanan', 'Jie', 0 UNION ALL
  SELECT 'Coolidge', 'Kiran', 1 UNION ALL
  SELECT 'Adams', 'Noam', 4 UNION ALL
  SELECT 'Buchanan', 'Jie', 13)
SELECT SUM(PointsScored) AS total_points, LastName AS last_name
FROM PlayerStats
GROUP BY last_name;

/*--------------+-----------+
 | total_points | last_name |
 +--------------+-----------+
 | 7            | Adams     |
 | 13           | Buchanan  |
 | 1            | Coolidge  |
 +--------------+-----------*/

To learn more about the data types that are supported for values in the GROUP BY clause, see Groupable data types.

Group rows by column ordinals

The GROUP BY clause can refer to expression names in the SELECT list. The GROUP BY clause also allows ordinal references to expressions in the SELECT list, using integer values. 1 refers to the first value in the SELECT list, 2 the second, and so forth. The value list can combine ordinals and value names. The following queries are equivalent:

WITH PlayerStats AS (
  SELECT 'Adams' as LastName, 'Noam' as FirstName, 3 as PointsScored UNION ALL
  SELECT 'Buchanan', 'Jie', 0 UNION ALL
  SELECT 'Coolidge', 'Kiran', 1 UNION ALL
  SELECT 'Adams', 'Noam', 4 UNION ALL
  SELECT 'Buchanan', 'Jie', 13)
SELECT SUM(PointsScored) AS total_points, LastName, FirstName
FROM PlayerStats
GROUP BY LastName, FirstName;

/*--------------+----------+-----------+
 | total_points | LastName | FirstName |
 +--------------+----------+-----------+
 | 7            | Adams    | Noam      |
 | 13           | Buchanan | Jie       |
 | 1            | Coolidge | Kiran     |
 +--------------+----------+-----------*/

WITH PlayerStats AS (
  SELECT 'Adams' as LastName, 'Noam' as FirstName, 3 as PointsScored UNION ALL
  SELECT 'Buchanan', 'Jie', 0 UNION ALL
  SELECT 'Coolidge', 'Kiran', 1 UNION ALL
  SELECT 'Adams', 'Noam', 4 UNION ALL
  SELECT 'Buchanan', 'Jie', 13)
SELECT SUM(PointsScored) AS total_points, LastName, FirstName
FROM PlayerStats
GROUP BY 2, 3;

/*--------------+----------+-----------+
 | total_points | LastName | FirstName |
 +--------------+----------+-----------+
 | 7            | Adams    | Noam      |
 | 13           | Buchanan | Jie       |
 | 1            | Coolidge | Kiran     |
 +--------------+----------+-----------*/

`HAVING` clause

HAVING bool_expression

The HAVING clause filters the results produced by GROUP BY or aggregation. GROUP BY or aggregation must be present in the query. If aggregation is present, the HAVING clause is evaluated once for every aggregated row in the result set.

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 HAVING clause is typically completed in this order:

FROM
WHERE
GROUP BY and aggregation
HAVING
DISTINCT
ORDER BY
LIMIT

Evaluation order doesn't always match syntax order.

The HAVING clause references columns available via the FROM clause, as well as SELECT list aliases. Expressions referenced in the HAVING clause must either appear in the GROUP BY clause or they must be the result of an aggregate function:

SELECT LastName
FROM Roster
GROUP BY LastName
HAVING SUM(PointsScored) > 15;

If a query contains aliases in the SELECT clause, those aliases override names in a FROM clause.

SELECT LastName, SUM(PointsScored) AS ps
FROM Roster
GROUP BY LastName
HAVING ps > 0;

Mandatory aggregation

Aggregation doesn't have to be present in the HAVING clause itself, but aggregation must be present in at least one of the following forms:

Aggregation function in the `SELECT` list.

SELECT LastName, SUM(PointsScored) AS total
FROM PlayerStats
GROUP BY LastName
HAVING total > 15;

Aggregation function in the `HAVING` clause.

SELECT LastName
FROM PlayerStats
GROUP BY LastName
HAVING SUM(PointsScored) > 15;

Aggregation in both the `SELECT` list and `HAVING` clause.

When aggregation functions are present in both the SELECT list and HAVING clause, the aggregation functions and the columns they reference don't need to be the same. In the example below, the two aggregation functions, COUNT() and SUM(), are different and also use different columns.

SELECT LastName, COUNT(*)
FROM PlayerStats
GROUP BY LastName
HAVING SUM(PointsScored) > 15;

`ORDER BY` clause

ORDER BY expression
  [COLLATE collation_specification]
  [{ ASC | DESC }]
  [, ...]

collation_specification:
  language_tag[:collation_attribute]

The ORDER BY clause specifies a column or expression as the sort criterion for the result set. If an ORDER BY clause isn't present, the order of the results of a query isn't defined. Column aliases from a FROM clause or SELECT list are allowed. If a query contains aliases in the SELECT clause, those aliases override names in the corresponding FROM clause. The data type of expression must be orderable.

Optional Clauses

COLLATE: You can use the COLLATE clause to refine how data is ordered by an ORDER BY clause. Collation refers to a set of rules that determine how strings are compared according to the conventions and standards of a particular written language, region, or country. These rules might define the correct character sequence, with options for specifying case-insensitivity. You can use COLLATE only on columns of type STRING.

collation_specification represents the collation specification for the COLLATE clause. The collation specification can be a string literal or a query parameter. To learn more see collation specification details.
ASC | DESC: Sort the results in ascending or descending order of expression values. ASC is the default value.

Examples

Use the default sort order (ascending).

SELECT x, y
FROM (SELECT 1 AS x, true AS y UNION ALL
      SELECT 9, true)
ORDER BY x;

/*------+-------+
 | x    | y     |
 +------+-------+
 | 1    | true  |
 | 9    | true  |
 +------+-------*/

Use descending sort order.

SELECT x, y
FROM (SELECT 1 AS x, true AS y UNION ALL
      SELECT 9, true)
ORDER BY x DESC;

/*------+-------+
 | x    | y     |
 +------+-------+
 | 9    | true  |
 | 1    | true  |
 +------+-------*/

It's possible to order by multiple columns. In the example below, the result set is ordered first by SchoolID and then by LastName:

SELECT LastName, PointsScored, OpponentID
FROM PlayerStats
ORDER BY SchoolID, LastName;

When used in conjunction with set operators, the ORDER BY clause applies to the result set of the entire query; it doesn't apply only to the closest SELECT statement. For this reason, it can be helpful (though it isn't required) to use parentheses to show the scope of the ORDER BY.

This query without parentheses:

SELECT * FROM Roster
UNION ALL
SELECT * FROM TeamMascot
ORDER BY SchoolID;

is equivalent to this query with parentheses:

( SELECT * FROM Roster
  UNION ALL
  SELECT * FROM TeamMascot )
ORDER BY SchoolID;

but isn't equivalent to this query, where the ORDER BY clause applies only to the second SELECT statement:

SELECT * FROM Roster
UNION ALL
( SELECT * FROM TeamMascot
  ORDER BY SchoolID );

You can also use integer literals as column references in ORDER BY clauses. An integer literal becomes an ordinal (for example, counting starts at 1) into the SELECT list.

Example - the following two queries are equivalent:

SELECT SUM(PointsScored), LastName
FROM PlayerStats
GROUP BY LastName
ORDER BY LastName;

SELECT SUM(PointsScored), LastName
FROM PlayerStats
GROUP BY 2
ORDER BY 2;

Collate results using English - Canada:

SELECT Place
FROM Locations
ORDER BY Place COLLATE "en_CA"

Collate results using a parameter:

#@collate_param = "arg_EG"
SELECT Place
FROM Locations
ORDER BY Place COLLATE @collate_param

Using multiple COLLATE clauses in a statement:

SELECT APlace, BPlace, CPlace
FROM Locations
ORDER BY APlace COLLATE "en_US" ASC,
         BPlace COLLATE "ar_EG" DESC,
         CPlace COLLATE "en" DESC

Case insensitive collation:

SELECT Place
FROM Locations
ORDER BY Place COLLATE "en_US:ci"

Default Unicode case-insensitive collation:

SELECT Place
FROM Locations
ORDER BY Place COLLATE "und:ci"

Set operators

  query_expr
  
  {
    UNION { ALL | DISTINCT } |
    INTERSECT { ALL | DISTINCT } |
    EXCEPT { ALL | DISTINCT }
  }
  
  query_expr

Set operators combine or filter results from two or more input queries into a single result set.

Definitions

query_expr: One of two input queries whose results are combined or filtered into a single result set.
UNION: Returns the combined results of the left and right input queries. Values in columns that are matched by position are concatenated vertically.
INTERSECT: Returns rows that are found in the results of both the left and right input queries.
EXCEPT: Returns rows from the left input query that aren't present in the right input query.
ALL: Executes the set operation on all rows.
DISTINCT: Excludes duplicate rows in the set operation.

Positional column matching

Columns from input queries are matched by their position in the queries. That is, the first column in the first input query is paired with the first column in the second input query and so on.
The input queries on each side of the operator must return the same number of columns.

Other column-related rules

For set operations other than UNION ALL, all column types must support equality comparison.
The results of the set operation always use the column names from the first input query.
The results of the set operation always use the supertypes of input types in corresponding columns, so paired columns must also have either the same data type or a common supertype.

Parenthesized set operators

Parentheses must be used to separate different set operations. Set operations like UNION ALL and UNION DISTINCT are considered different.
Parentheses are also used to group set operations and control order of operations. In EXCEPT set operations, for example, query results can vary depending on the operation grouping.

The following examples illustrate the use of parentheses with set operations:

-- Same set operations, no parentheses.
query1
UNION ALL
query2
UNION ALL
query3;

-- Different set operations, parentheses needed.
query1
UNION ALL
(
  query2
  UNION DISTINCT
  query3
);

-- Invalid
query1
UNION ALL
query2
UNION DISTINCT
query3;

-- Same set operations, no parentheses.
query1
EXCEPT ALL
query2
EXCEPT ALL
query3;

-- Equivalent query with optional parentheses, returns same results.
(
  query1
  EXCEPT ALL
  query2
)
EXCEPT ALL
query3;

-- Different execution order with a subquery, parentheses needed.
query1
EXCEPT ALL
(
  query2
  EXCEPT ALL
  query3
);

Set operator behavior with duplicate rows

Consider a given row R that appears exactly m times in the first input query and n times in the second input query, where m >= 0 and n >= 0:

For UNION ALL, row R appears exactly m + n times in the result.
For INTERSECT ALL, row R appears exactly MIN(m, n) times in the result.
For EXCEPT ALL, row R appears exactly MAX(m - n, 0) times in the result.
For UNION DISTINCT, the DISTINCT is computed after the UNION is computed, so row R appears exactly one time.
For INTERSECT DISTINCT, row R appears once in the output if m > 0 and n > 0.
For EXCEPT DISTINCT, row R appears once in the output if m > 0 and n = 0.
If more than two input queries are used, the above operations generalize and the output is the same as if the input queries were combined incrementally from left to right.

`UNION`

The UNION operator returns the combined results of the left and right input queries. Columns are matched according to the rules described previously and rows are concatenated vertically.

Examples

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3]) AS number
UNION ALL
SELECT 1;

/*--------+
 | number |
 +--------+
 | 1      |
 | 2      |
 | 3      |
 | 1      |
 +--------*/

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3]) AS number
UNION DISTINCT
SELECT 1;

/*--------+
 | number |
 +--------+
 | 1      |
 | 2      |
 | 3      |
 +--------*/

The following example shows multiple chained operators:

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3]) AS number
UNION DISTINCT
SELECT 1
UNION DISTINCT
SELECT 2;

/*--------+
 | number |
 +--------+
 | 1      |
 | 2      |
 | 3      |
 +--------*/

`INTERSECT`

The INTERSECT operator returns rows that are found in the results of both the left and right input queries.

Examples

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
INTERSECT ALL
SELECT * FROM UNNEST(ARRAY<INT64>[2, 3, 3, 5]) AS number;

/*--------+
 | number |
 +--------+
 | 2      |
 | 3      |
 | 3      |
 +--------*/

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
INTERSECT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[2, 3, 3, 5]) AS number;

/*--------+
 | number |
 +--------+
 | 2      |
 | 3      |
 +--------*/

The following example shows multiple chained operations:

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
INTERSECT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[2, 3, 3, 5]) AS number
INTERSECT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[3, 3, 4, 5]) AS number;

/*--------+
 | number |
 +--------+
 | 3      |
 +--------*/

`EXCEPT`

The EXCEPT operator returns rows from the left input query that aren't present in the right input query.

Examples

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
EXCEPT ALL
SELECT * FROM UNNEST(ARRAY<INT64>[1, 2]) AS number;

/*--------+
 | number |
 +--------+
 | 3      |
 | 3      |
 | 4      |
 +--------*/

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
EXCEPT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[1, 2]) AS number;

/*--------+
 | number |
 +--------+
 | 3      |
 | 4      |
 +--------*/

The following example shows multiple chained operations:

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
EXCEPT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[1, 2]) AS number
EXCEPT DISTINCT
SELECT * FROM UNNEST(ARRAY<INT64>[1, 4]) AS number;

/*--------+
 | number |
 +--------+
 | 3      |
 +--------*/

The following example modifies the execution behavior of the set operations. The first input query is used against the result of the last two input queries instead of the values of the last two queries individually. In this example, the EXCEPT result of the last two input queries is 2. Therefore, the EXCEPT results of the entire query are any values other than 2 in the first input query.

SELECT * FROM UNNEST(ARRAY<INT64>[1, 2, 3, 3, 4]) AS number
EXCEPT DISTINCT
(
  SELECT * FROM UNNEST(ARRAY<INT64>[1, 2]) AS number
  EXCEPT DISTINCT
  SELECT * FROM UNNEST(ARRAY<INT64>[1, 4]) AS number
);

/*--------+
 | number |
 +--------+
 | 1      |
 | 3      |
 | 4      |
 +--------*/

`LIMIT` and `OFFSET` clause

LIMIT count [ OFFSET skip_rows ]

Limits the number of rows to return in a query. Optionally includes the ability to skip over rows.

Definitions

LIMIT: Limits the number of rows to produce.

count is an INT64 constant expression that represents the non-negative, non-NULL limit. No more than count rows are produced. LIMIT 0 returns 0 rows.

If there is a set operation, LIMIT is applied after the set operation is evaluated.
OFFSET: Skips a specific number of rows before applying LIMIT.

skip_rows is an INT64 constant expression that represents the non-negative, non-NULL number of rows to skip.

Details

The rows that are returned by LIMIT and OFFSET have undefined order unless these clauses are used after ORDER BY.

A constant expression can be represented by a general expression, literal, or parameter value.

Examples

SELECT *
FROM UNNEST(ARRAY<STRING>['a', 'b', 'c', 'd', 'e']) AS letter
ORDER BY letter ASC LIMIT 2;

/*---------+
 | letter  |
 +---------+
 | a       |
 | b       |
 +---------*/

SELECT *
FROM UNNEST(ARRAY<STRING>['a', 'b', 'c', 'd', 'e']) AS letter
ORDER BY letter ASC LIMIT 3 OFFSET 1;

/*---------+
 | letter  |
 +---------+
 | b       |
 | c       |
 | d       |
 +---------*/

`FOR UPDATE` clause

SELECT expression
FOR UPDATE;

UPDATE
expression;

In serializable isolation, when you use the SELECT query to scan a table, add a FOR UPDATE clause to enable exclusive locks at the row-and-column granularity level, otherwise known as cell-level. The lock remains in place for the lifetime of the read-write transaction. During this time, the FOR UPDATE clause prevents other transactions from modifying the locked cells until the current transaction completes. For more information, see Use SELECT FOR UPDATE in serializable isolation.

Unlike in serializable isolation, FOR UPDATE doesn't acquire locks under repeatable read isolation. For more information, see Use SELECT FOR UPDATE in repeatable read isolation.

Example:

SELECT MarketingBudget
FROM Albums
WHERE SingerId = 1 and AlbumId = 1
FOR UPDATE;

UPDATE Albums
SET MarketingBudget = 100000
WHERE SingerId = 1 and AlbumId = 1;

You can't use the FOR UPDATE clause in the following ways:

In combination with the LOCK_SCANNED_RANGES hint
In full-text search queries
In read-only transactions
Within DDL statements

`WITH` clause

WITH cte[, ...]

A WITH clause contains one or more common table expressions (CTEs). A CTE acts like a temporary table that you can reference within a single query expression. Each CTE binds the results of a subquery to a table name, which can be used elsewhere in the same query expression, but rules apply.

CTEs

cte:
  cte_name AS ( query_expr )

A common table expression (CTE) contains a subquery and a name associated with the CTE.

A CTE can't reference itself.
A CTE can be referenced by the query expression that contains the WITH clause, but rules apply.

Examples

In this example, a WITH clause defines two CTEs that are referenced in the related set operation, where one CTE is referenced by each of the set operation's input query expressions:

WITH subQ1 AS (SELECT SchoolID FROM Roster),
     subQ2 AS (SELECT OpponentID FROM PlayerStats)
SELECT * FROM subQ1
UNION ALL
SELECT * FROM subQ2

WITH isn't supported in a subquery. This returns an error:

SELECT account
FROM (
  WITH result AS (SELECT * FROM NPCs)
  SELECT *
  FROM result)

You can use a FOR UPDATE clause in a CTE subquery to lock the scanned range of the subquery.

The following query exclusively locks col1 and col2 in table t.

WITH t1 AS (SELECT col1, col2 FROM t FOR UPDATE)
SELECT * FROM t1;

However, a FOR UPDATE clause in the outer query won't propagate into the CTE. In the following example, an exclusive lock won't apply to any cells in table t.

WITH t2 AS (SELECT col1, col2 FROM t)
SELECT * FROM t2 FOR UPDATE;

WITH clause isn't supported in DML statements.

Temporary tables defined by the WITH clause are stored in memory. Spanner dynamically allocates memory for all temporary tables created by a query. If the available resources aren't sufficient then the query will fail.

CTE rules and constraints

Common table expressions (CTEs) can be referenced inside the query expression that contains the WITH clause.

Here are some general rules and constraints to consider when working with CTEs:

Each CTE in the same WITH clause must have a unique name.
A CTE defined in a WITH clause is only visible to other CTEs in the same WITH clause that were defined after it.
A local CTE overrides an outer CTE or table with the same name.
A CTE on a subquery may not reference correlated columns from the outer query.
UNION DISTINCT isn't allowed inside a WITH RECURSIVE clause.

CTE visibility

References between common table expressions (CTEs) in the WITH clause can go backward but not forward.

This is what happens when you have two CTEs that reference themselves or each other in a WITH clause. Assume that A is the first CTE and B is the second CTE in the clause:

A references A = Invalid
A references B = Invalid
B references A = Valid
A references B references A = Invalid (cycles aren't allowed)

This produces an error. A can't reference itself because self-references aren't supported:

WITH
  A AS (SELECT 1 AS n UNION ALL (SELECT n + 1 FROM A WHERE n < 3))
SELECT * FROM A

-- Error

This produces an error. A can't reference B because references between CTEs can go backwards but not forwards:

WITH
  A AS (SELECT * FROM B),
  B AS (SELECT 1 AS n)
SELECT * FROM B

-- Error

B can reference A because references between CTEs can go backwards:

WITH
  A AS (SELECT 1 AS n),
  B AS (SELECT * FROM A)
SELECT * FROM B

/*---+
 | n |
 +---+
 | 1 |
 +---*/

This produces an error. A and B reference each other, which creates a cycle:

WITH
  A AS (SELECT * FROM B),
  B AS (SELECT * FROM A)
SELECT * FROM B

-- Error

Using aliases

An alias is a temporary name given to a table, column, or expression present in a query. You can introduce explicit aliases in the SELECT list or FROM clause, or GoogleSQL infers an implicit alias for some expressions. Expressions with neither an explicit nor implicit alias are anonymous and the query can't reference them by name.

Explicit aliases

You can introduce explicit aliases in either the FROM clause or the SELECT list.

In a FROM clause, you can introduce explicit aliases for any item, including tables, arrays, subqueries, and UNNEST clauses, using [AS] alias. The AS keyword is optional.

Example:

SELECT s.FirstName, s2.SongName
FROM Singers AS s, (SELECT * FROM Songs) AS s2;

You can introduce explicit aliases for any expression in the SELECT list using [AS] alias. The AS keyword is optional.

Example:

SELECT s.FirstName AS name, LOWER(s.FirstName) AS lname
FROM Singers s;

Implicit aliases

In the SELECT list, if there is an expression that doesn't have an explicit alias, GoogleSQL assigns an implicit alias according to the following rules. There can be multiple columns with the same alias in the SELECT list.

For identifiers, the alias is the identifier. For example, SELECT abc implies AS abc.
For path expressions, the alias is the last identifier in the path. For example, SELECT abc.def.ghi implies AS ghi.
For field access using the "dot" member field access operator, the alias is the field name. For example, SELECT (struct_function()).fname implies AS fname.

In all other cases, there is no implicit alias, so the column is anonymous and can't be referenced by name. The data from that column will still be returned and the displayed query results may have a generated label for that column, but the label can't be used like an alias.

In a FROM clause, from_items aren't required to have an alias. The following rules apply:

If there is an expression that doesn't have an explicit alias, GoogleSQL assigns an implicit alias in these cases:
- For identifiers, the alias is the identifier. For example, FROM abc implies AS abc.
- For path expressions, the alias is the last identifier in the path. For example, FROM abc.def.ghi implies AS ghi
- The column produced using WITH OFFSET has the implicit alias offset.
Table subqueries don't have implicit aliases.
FROM UNNEST(x) doesn't have an implicit alias.

Alias visibility

After you introduce an explicit alias in a query, there are restrictions on where else in the query you can reference that alias. These restrictions on alias visibility are the result of GoogleSQL name scoping rules.

Visibility in the `FROM` clause

GoogleSQL processes aliases in a FROM clause from left to right, and aliases are visible only to subsequent path expressions in a FROM clause.

Example:

Assume the Singers table had a Concerts column of ARRAY type.

SELECT FirstName
FROM Singers AS s, s.Concerts;

Invalid:

SELECT FirstName
FROM s.Concerts, Singers AS s;  // INVALID.

FROM clause aliases are not visible to subqueries in the same FROM clause. Subqueries in a FROM clause can't contain correlated references to other tables in the same FROM clause.

Invalid:

SELECT FirstName
FROM Singers AS s, (SELECT (2020 - ReleaseDate) FROM s)  // INVALID.

You can use any column name from a table in the FROM as an alias anywhere in the query, with or without qualification with the table name.

Example:

SELECT FirstName, s.ReleaseDate
FROM Singers s WHERE ReleaseDate = 1975;

If the FROM clause contains an explicit alias, you must use the explicit alias instead of the implicit alias for the remainder of the query (see Implicit Aliases). A table alias is useful for brevity or to eliminate ambiguity in cases such as self-joins, where the same table is scanned multiple times during query processing.

Example:

SELECT * FROM Singers as s, Songs as s2
ORDER BY s.LastName

Invalid — ORDER BY doesn't use the table alias:

SELECT * FROM Singers as s, Songs as s2
ORDER BY Singers.LastName;  // INVALID.

Visibility in the `SELECT` list

Aliases in the SELECT list are visible only to the following clauses:

GROUP BY clause
ORDER BY clause
HAVING clause

Example:

SELECT LastName AS last, SingerID
FROM Singers
ORDER BY last;

Visibility in the `GROUP BY`, `ORDER BY`, and `HAVING` clauses

These three clauses, GROUP BY, ORDER BY, and HAVING, can refer to only the following values:

Tables in the FROM clause and any of their columns.
Aliases from the SELECT list.

GROUP BY and ORDER BY can also refer to a third group:

Integer literals, which refer to items in the SELECT list. The integer 1 refers to the first item in the SELECT list, 2 refers to the second item, etc.

Example:

SELECT SingerID AS sid, COUNT(Songid) AS s2id
FROM Songs
GROUP BY 1
ORDER BY 2 DESC;

The previous query is equivalent to:

SELECT SingerID AS sid, COUNT(Songid) AS s2id
FROM Songs
GROUP BY sid
ORDER BY s2id DESC;

Duplicate aliases

A SELECT list or subquery containing multiple explicit or implicit aliases of the same name is allowed, as long as the alias name isn't referenced elsewhere in the query, since the reference would be ambiguous.

Example:

SELECT 1 AS a, 2 AS a;

/*---+---+
 | a | a |
 +---+---+
 | 1 | 2 |
 +---+---*/

Ambiguous aliases

GoogleSQL provides an error if accessing a name is ambiguous, meaning it can resolve to more than one unique object in the query or in a table schema, including the schema of a destination table.

The following query contains column names that conflict between tables, since both Singers and Songs have a column named SingerID:

SELECT SingerID
FROM Singers, Songs;

The following query contains aliases that are ambiguous in the GROUP BY clause because they are duplicated in the SELECT list:

SELECT FirstName AS name, LastName AS name,
FROM Singers
GROUP BY name;

The following query contains aliases that are ambiguous in the SELECT list and FROM clause because they share a column and field with same name.

Assume the Person table has three columns: FirstName, LastName, and PrimaryContact.
Assume the PrimaryContact column represents a struct with these fields: FirstName and LastName.

The alias P is ambiguous and will produce an error because P.FirstName in the GROUP BY clause could refer to either Person.FirstName or Person.PrimaryContact.FirstName.

SELECT FirstName, LastName, PrimaryContact AS P
FROM Person AS P
GROUP BY P.FirstName;

A name is not ambiguous in GROUP BY, ORDER BY or HAVING if it's both a column name and a SELECT list alias, as long as the name resolves to the same underlying object. In the following example, the alias BirthYear isn't ambiguous because it resolves to the same underlying column, Singers.BirthYear.

SELECT LastName, BirthYear AS BirthYear
FROM Singers
GROUP BY BirthYear;

Range variables

In GoogleSQL, a range variable is a table expression alias in the FROM clause. Sometimes a range variable is known as a table alias. A range variable lets you reference rows being scanned from a table expression. A table expression represents an item in the FROM clause that returns a table. Common items that this expression can represent include tables, value tables, subqueries, joins, and parenthesized joins.

In general, a range variable provides a reference to the rows of a table expression. A range variable can be used to qualify a column reference and unambiguously identify the related table, for example range_variable.column_1.

When referencing a range variable on its own without a specified column suffix, the result of a table expression is the row type of the related table. Value tables have explicit row types, so for range variables related to value tables, the result type is the value table's row type. Other tables don't have explicit row types, and for those tables, the range variable type is a dynamically defined struct that includes all of the columns in the table.

Examples

In these examples, the WITH clause is used to emulate a temporary table called Grid. This table has columns x and y. A range variable called Coordinate refers to the current row as the table is scanned. Coordinate can be used to access the entire row or columns in the row.

The following example selects column x from range variable Coordinate, which in effect selects column x from table Grid.

WITH Grid AS (SELECT 1 x, 2 y)
SELECT Coordinate.x FROM Grid AS Coordinate;

/*---+
 | x |
 +---+
 | 1 |
 +---*/

The following example selects all columns from range variable Coordinate, which in effect selects all columns from table Grid.

WITH Grid AS (SELECT 1 x, 2 y)
SELECT Coordinate.* FROM Grid AS Coordinate;

/*---+---+
 | x | y |
 +---+---+
 | 1 | 2 |
 +---+---*/

The following example selects the range variable Coordinate, which is a reference to rows in table Grid. Since Grid isn't a value table, the result type of Coordinate is a struct that contains all the columns from Grid.

WITH Grid AS (SELECT 1 x, 2 y)
SELECT Coordinate FROM Grid AS Coordinate;

/*--------------+
 | Coordinate   |
 +--------------+
 | {x: 1, y: 2} |
 +--------------*/

Hints

@{hint_key=hint_value[, ...]}

GoogleSQL supports hints, which make the query optimizer use a specific operator in the execution plan. If performance is an issue for you, a hint might be able to help by suggesting a different query execution plan shape.

The syntax for specifying hints differs between the GoogleSQL and PostgreSQL dialects:

GoogleSQL dialect: Hints use the format @{ hint_key = value, ... }.
PostgreSQL dialect: Hints use a comment-like format /*@ hint_key = value, ... */.

While the syntax varies, the available hints and their intended effects remain largely consistent. For details on PostgreSQL hint syntax and usage, see Hints in PostgreSQL.

Definitions

hint_key: The name of the hint key.
hint_value: The value for hint_key.

Examples

@{KEY_ONE=TRUE}

@{KEY_TWO=10, KEY_THREE=FALSE}

Statement hints

The following query statement hints are supported: Hint key Possible values Description USE_ADDITIONAL_PARALLELISM TRUE
| FALSE (default) If TRUE, the execution engine favors using more parallelism when possible. Because this can reduce resources available to other operations, you may want to avoid this hint if you run latency-sensitive operations on the same instance. OPTIMIZER_VERSION 1 to N
| latest_version
| default_version

Executes the query using the specified optimizer version. Possible values are 1 to N (the latest optimizer version), default_version, or latest_version. If the hint isn't set, the optimizer executes against the package that's set in database options or specified through the client API. If neither of those are set, the optimizer uses the default version.

In terms of version setting precedence, the value set by the client API takes precedence over the value in the database options and the value set by this hint takes precedence over everything else.

For more information, see Query optimizer.

OPTIMIZER_STATISTICS_PACKAGE package_name
| latest

Executes the query using the specified optimizer statistics package. Possible values for package_name can be found by running the following query:

SELECT * FROM INFORMATION_SCHEMA.SPANNER_STATISTICS

If the hint isn't set, the optimizer executes against the package that's set in the database option or specified through the client API. If neither of those are set, the optimizer defaults to the latest package.

The value set by the client API takes precedence over the value in the database options and the value set by this hint takes precedence over everything else.

The specified package needs to be pinned by the database option or have allow_gc=false to prevent garbage collection.

For more information, see Query optimizer statistics packages.

ALLOW_DISTRIBUTED_MERGE TRUE (default)
| FALSE

If TRUE (default), the engine favors using a distributed merge sort algorithm for certain ORDER BY queries. When applicable, global sorts are changed to local sorts. This gives the advantage of parallel sorting close to where the data is stored. The locally sorted data is then merged to provide globally sorted data. This allows for removal of full global sorts and potentially improved latency.

This feature can increase parallelism of certain ORDER BY queries. This hint has been provided so that users can experiment with turning off the distributed merge algorithm if desired.

LOCK_SCANNED_RANGES exclusive
| shared (default)

Use this hint to request an exclusive lock on a set of ranges scanned by a transaction. Acquiring an exclusive lock helps in scenarios when you observe high write contention, that is, you notice that multiple transactions are concurrently trying to read and write to the same data, resulting in a large number of aborts.

Without the hint, it's possible that multiple simultaneous transactions will acquire shared locks, and then try to upgrade to exclusive locks. This will cause a deadlock, because each transaction's shared lock is preventing the other transaction(s) from upgrading to exclusive. Spanner aborts all but one of the transactions.

When requesting an exclusive lock using this hint, one transaction acquires the lock and proceeds to execute, while other transactions wait their turn for the lock. Throughput is still limited because the conflicting transactions can only be performed one at a time, but in this case Spanner is always making progress on one transaction, saving time that would otherwise be spent aborting and retrying transactions.

This hint is supported on all statement types, both query and DML.

Spanner always enforces serializability Lock mode hints can affect which transactions wait or abort in contended workloads, but don't change the isolation level.

Because this is just a hint, it shouldn't be considered equivalent to a mutex. In other words, you shouldn't use Spanner exclusive locks as a mutual exclusion mechanism for the execution of code outside of Spanner. For more information, see Locking.

You can't use both the FOR UPDATE clause and the LOCK_SCANNED_RANGES hint in the same query. An error is returned. For more information, see . SCAN_METHOD AUTO (default)
| BATCH
| ROW
| COLUMNAR
| NO_COLUMNAR Use this hint to enforce the query scan method.

The default Spanner scan method is AUTO (automatic). The AUTO setting specifies that batch-oriented query processing and columnar formatted data, if available, could be used to improve query performance. The ROW scan method forces the scan to read row formatted data and use row-oriented execution, whereas the BATCH scan method uses row formatted data and batch-oriented execution. The COLUMNAR scan method will read columnar formatted data, if available, and utilize batch-oriented execution. The NO_COLUMNAR scan method disables automatic selection of columnar formatted data but allows automatic selection of batch-oriented query processing, similar to the AUTO scan method. You can't manually set the scan method to AUTO, but if you remove the hint, then uses the AUTO scan method. For more information, see Optimize scans.

EXECUTION_METHOD DEFAULT
| BATCH
| ROW Use this hint to enforce the query execution method.

The default Spanner query execution method is DEFAULT. The DEFAULT setting specifies that batch-oriented execution might be used to improve query performance, depending on the heuristics of the query. If you want to change the default execution method, you can use a statement hint to enforce the BATCH-oriented or ROW-oriented execution method. You can't manually set the query execution method to DEFAULT; to do so, remove the statement hint, and Spanner will set it to the default method. For more information, see Optimize query execution.

USE_UNENFORCED_FOREIGN_KEY TRUE (default)
| FALSE Use this hint to enforce the query scan method.

If TRUE (default), the query optimizer relies on informational (NOT ENFORCED) foreign key relationships to improve query performance. For example, if set to TRUE, the optimizer can remove redundant scans, and push some LIMIT operators through the join operators. USE_UNENFORCED_FOREIGN_KEY overrides the value of the use_unenforced_foreign_key_for_query_optimization database option for the applied statement. This hint might introduce incorrect results if the data is inconsistent with the foreign key relationships.

For more information, see informational foreign keys.

ALLOW_TIMESTAMP_PREDICATE_PUSHDOWN TRUE
| FALSE (default) If set to TRUE, the query execution engine uses the timestamp predicate pushdown optimization technique. This technique improves the efficiency of queries that use timestamps and data with an age-based tiered storage policy. For more information, see Optimize queries with timestamp predicate pushdown.

Table hints

The following table hints are supported:

Hint key	Possible values	Description
`FORCE_INDEX`	`STRING`	If set to the name of an index, use that index instead of the base table. If the index can't provide all needed columns, perform a back join with the base table. If set to the string `_BASE_TABLE`, use the base table for the index strategy instead of an index. Note that this is the only valid value when `FORCE_INDEX` is used in a statement hint expression. Note: `FORCE_INDEX` is actually a directive, not a hint, which means an error is raised if the index doesn't exist.
`GROUPBY_SCAN_OPTIMIZATION`	`TRUE` \| `FALSE`	The group by scan optimization can make queries faster if they use `GROUP BY` or `SELECT DISTINCT`. It can be applied if the grouping keys can form a prefix of the underlying table or index key, and if the query requires only the first row from each group. The optimization is applied if the optimizer estimates that it will make the query more efficient. The hint overrides that decision. If the hint is set to `FALSE`, the optimization isn't considered. If the hint is set to `TRUE`, the optimization will be applied as long as it's legal to do so.
`SCAN_METHOD`	`AUTO` (default) \| `BATCH` \| `ROW` \| `COLUMNAR` \| `NO_COLUMNAR`	Use this hint to enforce the query scan method. The default Spanner scan method is `AUTO` (automatic). The `AUTO` setting specifies that batch-oriented query processing and columnar formatted data, if available, could be used to improve query performance. The `ROW` scan method forces the scan to read row formatted data and use row-oriented execution, whereas the `BATCH` scan method uses row formatted data and batch-oriented execution. The `COLUMNAR` scan method will read columnar formatted data, if available, and utilize batch-oriented execution. The `NO_COLUMNAR` scan method disables automatic selection of columnar formatted data but allows automatic selection of batch-oriented query processing, similar to the `AUTO` scan method. You can't manually set the scan method to `AUTO`, but if you remove the hint, then uses the `AUTO` scan method. For more information, see Optimize scans.
`INDEX_STRATEGY`	`FORCE_INDEX_UNION`	Use the `INDEX_STRATEGY=FORCE_INDEX_UNION` hint to access data, using the Index Union pattern (reading from two or more indexes and unioning the results). This hint is useful when the condition in the `WHERE` clause is a disjunction. If an index union isn't possible, an error is raised.
`SEEKABLE_KEY_SIZE`	`0` to `16`	Forces the seekable key size to be equal to the specified value. The seekable key size is the length of the key (primary key or index key) that's used in a seekable condition, while the rest of the key is used in a residual condition. This hint requires the `FORCE_INDEX` hint to also be specified.

The following example shows how to use a secondary index when reading from a table, by appending an index directive of the form @{FORCE_INDEX=index_name} to the table name:

SELECT s.SingerId, s.FirstName, s.LastName, s.SingerInfo
FROM Singers@{FORCE_INDEX=SingersByFirstLastName} AS s
WHERE s.FirstName = "Catalina" AND s.LastName > "M";

You can include multiple indexes in a query, though only a single index is supported for each distinct table reference. Example:

SELECT s.SingerId, s.FirstName, s.LastName, s.SingerInfo, c.ConcertDate
FROM Singers@{FORCE_INDEX=SingersByFirstLastName} AS s JOIN
     Concerts@{FORCE_INDEX=ConcertsBySingerId} AS c ON s.SingerId = c.SingerId
WHERE s.FirstName = "Catalina" AND s.LastName > "M";

Read more about index directives in Secondary Indexes.

Join hints

The following join hints are supported:

Hint key	Possible values	Description
`FORCE_JOIN_ORDER`	`TRUE` `FALSE` (default)	If set to `TRUE`, use the join order that's specified in the query.
`JOIN_METHOD`	`HASH_JOIN` `APPLY_JOIN` `MERGE_JOIN` `PUSH_BROADCAST_HASH_JOIN`	When implementing a logical join, choose a specific alternative to use for the underlying join method. Learn more in Join methods. To use a HASH join, either use `HASH JOIN` or `JOIN@{JOIN_METHOD=HASH_JOIN}`, but not both.
`HASH_JOIN_BUILD_SIDE`	`BUILD_LEFT` `BUILD_RIGHT`	Specifies which side of the hash join is used as the build side. Can only be used with `JOIN_METHOD=HASH_JOIN`
`BATCH_MODE`	`TRUE` (default) `FALSE`	Used to disable batched apply join in favor of row-at-a-time apply join. Can only be used with `JOIN_METHOD=APPLY_JOIN`.
`FACTORIZED_MODE`	`FACTORIZE_LEFT` `FACTORIZE_RIGHT` `FACTORIZE_BOTH`	Specifies which sides of the join should be factorized (deduplicated) prior to the join. Learn more in Factorized Mode.
`HASH_JOIN_EXECUTION`	`MULTI_PASS` (default) `ONE_PASS`	For a hash join, specifies what should be done when the hash table size reaches its memory limit. Can only be used when `JOIN_METHOD=HASH_JOIN`. See Hash Join Execution for more details.

Join methods

Join methods are specific implementations of the various logical join types. Some join methods are available only for certain join types. The choice of which join method to use depends on the specifics of your query and of the data being queried. The best way to figure out if a particular join method helps with the performance of your query is to try the method and view the resulting query execution plan. See Query Execution Operators for more details.

Join Method	Description	Operands
`HASH_JOIN`	The hash join operator builds a hash table out of one side (the build side), and probes in the hash table for all the elements in the other side (the probe side).	Different variants are used for various join types. View the query execution plan for your query to see which variant is used. Read more about the Hash join operator.
`APPLY_JOIN`	The apply join operator gets each item from one side (the input side), and evaluates the subquery on other side (the map side) using the values of the item from the input side.	Different variants are used for various join types. Cross apply is used for inner join, and outer apply is used for left joins. Read more about the Cross apply operator and Outer apply operator
`MERGE_JOIN`	The merge join operator joins two streams of sorted data. The optimizer adds Sort operators to the plan if the data isn't already providing the required sort property for the given join condition. The engine provides a distributed merge sort by default, which when coupled with merge join may allow for larger joins, potentially avoiding disk spilling and improving scale and latency.	Different variants are used for various join types. View the query execution plan for your query to see which variant is used. Read more about the Merge join operator.
`PUSH_BROADCAST_HASH_JOIN`	The push broadcast hash join operator builds a batch of data from the build side of the join. The batch is then sent in parallel to all the local splits of the probe side of the join. On each of the local servers, a hash join is executed between the batch and the local data. This join is most likely to be beneficial when the input can fit within one batch, but isn't strict. Another potential area of benefit is when operations can be distributed to the local servers, such as an aggregation that occurs after a join. A push broadcast hash join can distribute some aggregation where a traditional hash join can't.	Different variants are used for various join types. View the query execution plan for your query to see which variant is used. Read more about the Push broadcast hash join operator.

Factorized mode

When you execute a join, multiple rows on one side can match a single row or multiple rows on the other side. This can lead to overhead from repeated join-key comparisons or hashtable probes during execution.

Factorization is a join execution technique that reduces redundant work by deduplicating join-key values on one or both sides before joining them. Deduplication decreases the number of processed rows and eliminates repeated computations without altering the join result. Use the hint values FACTORIZE_LEFT, FACTORIZE_RIGHT, or FACTORIZE_BOTH to explicitly control which join sides are factorized. Factorized mode is supported only for INNER JOINs with equality conditions.

Explicit deduplication can introduce its own overhead, potentially outweighing the benefits of factorized join execution. The performance impact depends on several factors, including the join-key's duplication factor, the chosen join method (hash, apply, or merge), the join cardinality (for example, one-to-many, many-to-one, or many-to-many), and the overall join topology. However, the most benefit is expected for queries with a large number of many-to-many joins, which is typical in graph traversal scenarios. For more guidance on when to apply the factorized execution mode, see Best practices for tuning Spanner Graph queries.

Hash Join Execution

To execute a hash join between two tables, Spanner first scans rows from the build side and loads them into a hash table. Then it scans rows from the probe side, while comparing them against the hash table. If the hash table reaches its memory limit, depending on the value of the HASH_JOIN_EXECUTION query hint, the hash join has one of the following behaviors:

HASH_JOIN_EXECUTION=MULTI_PASS (default): The query engine splits the build side table into partitions in a way that the size of a hash table corresponding to each partition is less than the memory size limit. For every partition of the build side table, the probe side is scanned once.
HASH_JOIN_EXECUTION=ONE_PASS: The query engine writes both the build side table and the probe side table to disk in partitions in a way that the hash table of the build side table in each partition is less than the memory limit. The probe side is only scanned once.

Group hints

The following group hints are supported:

Group hint key	Group hint values	Description
`GROUP_METHOD`	`HASH_GROUP` `STREAM_GROUP`	Specifies an alternative to choose when implementing a `GROUP BY` operator.

Graph hints

Hints are supported for graphs. For more information, see Graph hints.

Value tables

In addition to standard SQL tables, GoogleSQL supports value tables. In a value table, rather than having rows made up of a list of columns, each row is a single value of a specific type, and there are no column names.

In the following example, a value table for a STRUCT is produced with the SELECT AS VALUE statement:

SELECT * FROM (SELECT AS VALUE STRUCT(123 AS a, FALSE AS b))

/*-----+-------+
 | a   | b     |
 +-----+-------+
 | 123 | FALSE |
 +-----+-------*/

Value tables are often but not exclusively used with compound data types. A value table can consist of any supported GoogleSQL data type, although value tables consisting of scalar types occur less frequently than structs.

Return query results as a value table

Spanner doesn't support value tables as base tables in database schemas and doesn't support returning value tables in query results. As a consequence, value table producing queries aren't supported as top-level queries.

Value tables can also occur as the output of the UNNEST operator or a subquery. The WITH clause introduces a value table if the subquery used produces a value table.

In contexts where a query with exactly one column is expected, a value table query can be used instead. For example, scalar and array subqueries normally require a single-column query, but in GoogleSQL, they also allow using a value table query.

Use a set operation on a value table

In SET operations like UNION ALL you can combine tables with value tables, provided that the table consists of a single column with a type that matches the value table's type. The result of these operations is always a value table.

Appendix A: examples with sample data

These examples include statements which perform queries on the Roster and TeamMascot, and PlayerStats tables.

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

`GROUP BY` clause

Example:

SELECT LastName, SUM(PointsScored)
FROM PlayerStats
GROUP BY LastName;

LastName	SUM
Adams	7
Buchanan	13
Coolidge	1

`UNION`

The UNION operator combines the result sets of two or more SELECT statements by pairing columns from the result set of each SELECT statement and vertically concatenating them.

Example:

SELECT Mascot AS X, SchoolID AS Y
FROM TeamMascot
UNION ALL
SELECT LastName, PointsScored
FROM PlayerStats;

Results:

X	Y
Jaguars	50
Knights	51
Lakers	52
Mustangs	53
Adams	3
Buchanan	0
Coolidge	1
Adams	4
Buchanan	13

`INTERSECT`

This query returns the last names that are present in both Roster and PlayerStats.

SELECT LastName
FROM Roster
INTERSECT ALL
SELECT LastName
FROM PlayerStats;

Results:

LastName
Adams
Coolidge
Buchanan

`EXCEPT`

The query below returns last names in Roster that are not present in PlayerStats.

SELECT LastName
FROM Roster
EXCEPT DISTINCT
SELECT LastName
FROM PlayerStats;

Results:

LastName
Eisenhower
Davis

Reversing the order of the SELECT statements will return last names in PlayerStats that are not present in Roster:

SELECT LastName
FROM PlayerStats
EXCEPT DISTINCT
SELECT LastName
FROM Roster;

Results:

(empty)

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Last updated 2026-06-09 UTC.

Query syntax in GoogleSQL Stay organized with collections Save and categorize content based on your preferences.

SQL syntax notation rules

SQL syntax

SELECT statement

SELECT *

SELECT expression

SELECT expression.*

SELECT * EXCEPT

SELECT * REPLACE

SELECT DISTINCT

SELECT ALL

Using STRUCTs with SELECT

SELECT AS STRUCT

SELECT AS typename

SELECT AS VALUE

FROM clause

tablesample_operator

graph_table_operator

table_name

join_operation

query_expr

field_path

unnest_operator

cte_name

UNNEST operator

UNNEST and structs

UNNEST and protocol buffers

Explicit and implicit UNNEST

Explicit unnesting

Implicit unnesting

Tables and implicit unnesting

UNNEST and NULL values

TABLESAMPLE operator

GRAPH_TABLE operator

Join operation

[INNER] JOIN

CROSS JOIN

Comma cross join (,)

FULL [OUTER] JOIN

LEFT [OUTER] JOIN

RIGHT [OUTER] JOIN

Join conditions

ON clause

USING clause

ON and USING equivalency

Join operations in a sequence

Correlated join operation

WHERE clause

GROUP BY clause

Group rows by groupable items

Group rows by values

Group rows by column ordinals

HAVING clause

Mandatory aggregation

Aggregation function in the SELECT list.

Aggregation function in the HAVING clause.

Aggregation in both the SELECT list and HAVING clause.

ORDER BY clause

Set operators

UNION

INTERSECT

EXCEPT

LIMIT and OFFSET clause

FOR UPDATE clause

WITH clause

CTEs

Examples

CTE rules and constraints

CTE visibility

Using aliases

Explicit aliases

Implicit aliases

Alias visibility

Visibility in the FROM clause

Visibility in the SELECT list

Visibility in the GROUP BY, ORDER BY, and HAVING clauses

Duplicate aliases

Ambiguous aliases

Range variables

Hints

Query syntax in GoogleSQL

`SELECT` statement

`SELECT *`

`SELECT expression`

`SELECT expression.*`

`SELECT * EXCEPT`

`SELECT * REPLACE`

`SELECT DISTINCT`

`SELECT ALL`

`SELECT AS STRUCT`

`SELECT AS typename`

`SELECT AS VALUE`

`FROM` clause

`tablesample_operator`

`graph_table_operator`

`table_name`

`join_operation`

`query_expr`

`field_path`

`unnest_operator`

`cte_name`

`UNNEST` operator

`UNNEST` and structs

`UNNEST` and protocol buffers

Explicit and implicit `UNNEST`

`UNNEST` and `NULL` values

`TABLESAMPLE` operator

`GRAPH_TABLE` operator

`[INNER] JOIN`

`CROSS JOIN`

`FULL [OUTER] JOIN`

`LEFT [OUTER] JOIN`

`RIGHT [OUTER] JOIN`

`ON` clause

`USING` clause

`ON` and `USING` equivalency

`WHERE` clause

`GROUP BY` clause

`HAVING` clause

Aggregation function in the `SELECT` list.

Aggregation function in the `HAVING` clause.

Aggregation in both the `SELECT` list and `HAVING` clause.

`ORDER BY` clause

`UNION`

`INTERSECT`

`EXCEPT`

`LIMIT` and `OFFSET` clause

`FOR UPDATE` clause

`WITH` clause

Visibility in the `FROM` clause

Visibility in the `SELECT` list

Visibility in the `GROUP BY`, `ORDER BY`, and `HAVING` clauses

`GROUP BY` clause

`UNION`

`INTERSECT`

`EXCEPT`