7.2. Table Expressions

A table expression computes a table. The table expression contains a FROM clause that is optionally followed by WHERE, GROUP BY, and HAVING clauses. Trivial table expressions simply refer to a table on disk, a so-called base table, but more complex expressions can be used to modify or combine base tables in various ways.

The optional WHERE, GROUP BY, and HAVING clauses in the table expression specify a pipeline of successive transformations performed on the table derived in the FROM clause. All these transformations produce a virtual table that provides the rows that are passed to the select list to compute the output rows of the query.

7.2.1. The FROM Clause

The FROM clause derives a table from one or more other tables given in a comma-separated table reference list.

FROM table_reference [, table_reference [, ...]]

A table reference can be a table name (possibly schema-qualified), or a derived table such as a subquery, a JOIN construct, or complex combinations of these. If more than one table reference is listed in the FROM clause, the tables are cross-joined (that is, the Cartesian product of their rows is formed; see below). The result of the FROM list is an intermediate virtual table that can then be subject to transformations by the WHERE, GROUP BY, and HAVING clauses and is finally the result of the overall table expression.

When a table reference names a table that is the parent of a table inheritance hierarchy, the table reference produces rows of not only that table but all of its descendant tables, unless the key word ONLY precedes the table name. However, the reference produces only the columns that appear in the named table — any columns added in subtables are ignored.

Instead of writing ONLY before the table name, you can write * after the table name to explicitly specify that descendant tables are included. There is no real reason to use this syntax any more, because searching descendant tables is now always the default behavior. However, it is supported for compatibility with older releases. Joined Tables

A joined table is a table derived from two other (real or derived) tables according to the rules of the particular join type. Inner, outer, and cross-joins are available. The general syntax of a joined table is

T1 join_type T2 [ join_condition ]

Joins of all types can be chained together, or nested: either or both T1 and T2 can be joined tables. Parentheses can be used around JOIN clauses to control the join order. In the absence of parentheses, JOIN clauses nest left-to-right.

Join Types

Cross join

For every possible combination of rows from T1 and T2 (i.e., a Cartesian product), the joined table will contain a row consisting of all columns in T1 followed by all columns in T2. If the tables have N and M rows respectively, the joined table will have N * M rows.

FROM T1 CROSS JOIN T2 is equivalent to FROM T1 INNER JOIN T2 ON TRUE (see below). It is also equivalent to FROM T1, T2.


This latter equivalence does not hold exactly when more than two tables appear, because JOIN binds more tightly than comma. For example FROM T1 CROSS JOIN T2 INNER JOIN T3 ON condition is not the same as FROM T1, T2 INNER JOIN T3 ON condition because the condition can reference T1 in the first case but not the second.

Qualified joins
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 ON boolean_expression
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 USING ( join column list )

The words INNER and OUTER are optional in all forms. INNER is the default; LEFT, RIGHT, and FULL imply an outer join.

The join condition is specified in the ON or USING clause, or implicitly by the word NATURAL. The join condition determines which rows from the two source tables are considered to match, as explained in detail below.

The possible types of qualified join are:


For each row R1 of T1, the joined table has a row for each row in T2 that satisfies the join condition with R1.


First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Thus, the joined table always has at least one row for each row in T1.


First, an inner join is performed. Then, for each row in T2 that does not satisfy the join condition with any row in T1, a joined row is added with null values in columns of T1. This is the converse of a left join: the result table will always have a row for each row in T2.


First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Also, for each row of T2 that does not satisfy the join condition with any row in T1, a joined row with null values in the columns of T1 is added.

The ON clause is the most general kind of join condition: it takes a Boolean value expression of the same kind as is used in a WHERE clause. A pair of rows from T1 and T2 match if the ON expression evaluates to true.

The USING clause is a shorthand that allows you to take advantage of the specific situation where both sides of the join use the same name for the joining column(s). It takes a comma-separated list of the shared column names and forms a join condition that includes an equality comparison for each one. For example, joining T1 and T2 with USING (a, b) produces the join condition ON T1.a = T2.a AND T1.b = T2.b.

Furthermore, the output of JOIN USING suppresses redundant columns: there is no need to print both of the matched columns, since they must have equal values. While JOIN ON produces all columns from T1 followed by all columns from T2, JOIN USING produces one output column for each of the listed column pairs (in the listed order), followed by any remaining columns from T1, followed by any remaining columns from T2.

Finally, NATURAL is a shorthand form of USING: it forms a USING list consisting of all column names that appear in both input tables. As with USING, these columns appear only once in the output table. If there are no common column names, NATURAL JOIN behaves like JOIN ... ON TRUE, producing a cross-product join.


USING is reasonably safe from column changes in the joined relations since only the listed columns are combined. NATURAL is considerably more risky since any schema changes to either relation that cause a new matching column name to be present will cause the join to combine that new column as well.

To put this together, assume we have tables t1:

 num | name
   1 | a
   2 | b
   3 | c

and t2:

 num | value
   1 | xxx
   3 | yyy
   5 | zzz

then we get the following results for the various joins:

 num | name | num | value
   1 | a    |   1 | xxx
   1 | a    |   3 | yyy
   1 | a    |   5 | zzz
   2 | b    |   1 | xxx
   2 | b    |   3 | yyy
   2 | b    |   5 | zzz
   3 | c    |   1 | xxx
   3 | c    |   3 | yyy
   3 | c    |   5 | zzz
(9 rows)

=> SELECT * FROM t1 INNER JOIN t2 ON t1.num = t2.num;
 num | name | num | value
   1 | a    |   1 | xxx
   3 | c    |   3 | yyy
(2 rows)

 num | name | value
   1 | a    | xxx
   3 | c    | yyy
(2 rows)

 num | name | value
   1 | a    | xxx
   3 | c    | yyy
(2 rows)

=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num;
 num | name | num | value
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |   3 | yyy
(3 rows)

 num | name | value
   1 | a    | xxx
   2 | b    |
   3 | c    | yyy
(3 rows)

=> SELECT * FROM t1 RIGHT JOIN t2 ON t1.num = t2.num;
 num | name | num | value
   1 | a    |   1 | xxx
   3 | c    |   3 | yyy
     |      |   5 | zzz
(3 rows)

=> SELECT * FROM t1 FULL JOIN t2 ON t1.num = t2.num;
 num | name | num | value
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |   3 | yyy
     |      |   5 | zzz
(4 rows)

The join condition specified with ON can also contain conditions that do not relate directly to the join. This can prove useful for some queries but needs to be thought out carefully. For example:

=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num AND t2.value = 'xxx';
 num | name | num | value
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |     |
(3 rows)

Notice that placing the restriction in the WHERE clause produces a different result:

=> SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num WHERE t2.value = 'xxx';
 num | name | num | value
   1 | a    |   1 | xxx
(1 row)

This is because a restriction placed in the ON clause is processed before the join, while a restriction placed in the WHERE clause is processed after the join. That does not matter with inner joins, but it matters a lot with outer joins. Table and Column Aliases

A temporary name can be given to tables and complex table references to be used for references to the derived table in the rest of the query. This is called a table alias.

To create a table alias, write

FROM table_reference AS alias


FROM table_reference alias

The AS key word is optional noise. alias can be any identifier.

A typical application of table aliases is to assign short identifiers to long table names to keep the join clauses readable. For example:

SELECT * FROM some_very_long_table_name s JOIN another_fairly_long_name a ON s.id = a.num;

The alias becomes the new name of the table reference so far as the current query is concerned — it is not allowed to refer to the table by the original name elsewhere in the query. Thus, this is not valid:

SELECT * FROM my_table AS m WHERE my_table.a > 5;    -- wrong

Table aliases are mainly for notational convenience, but it is necessary to use them when joining a table to itself, e.g.:

SELECT * FROM people AS mother JOIN people AS child ON mother.id = child.mother_id;

Additionally, an alias is required if the table reference is a subquery (see Section

Parentheses are used to resolve ambiguities. In the following example, the first statement assigns the alias b to the second instance of my_table, but the second statement assigns the alias to the result of the join:

SELECT * FROM my_table AS a CROSS JOIN my_table AS b ...
SELECT * FROM (my_table AS a CROSS JOIN my_table) AS b ...

Another form of table aliasing gives temporary names to the columns of the table, as well as the table itself:

FROM table_reference [AS] alias ( column1 [, column2 [, ...]] )

If fewer column aliases are specified than the actual table has columns, the remaining columns are not renamed. This syntax is especially useful for self-joins or subqueries.

When an alias is applied to the output of a JOIN clause, the alias hides the original name(s) within the JOIN. For example:

SELECT a.* FROM my_table AS a JOIN your_table AS b ON ...

is valid SQL, but:

SELECT a.* FROM (my_table AS a JOIN your_table AS b ON ...) AS c

is not valid; the table alias a is not visible outside the alias c. Subqueries

Subqueries specifying a derived table must be enclosed in parentheses and must be assigned a table alias name (as in Section For example:

FROM (SELECT * FROM table1) AS alias_name

This example is equivalent to FROM table1 AS alias_name. More interesting cases, which cannot be reduced to a plain join, arise when the subquery involves grouping or aggregation.

A subquery can also be a VALUES list:

FROM (VALUES ('anne', 'smith'), ('bob', 'jones'), ('joe', 'blow'))
     AS names(first, last)

Again, a table alias is required. Assigning alias names to the columns of the VALUES list is optional, but is good practice. For more information see Section 7.7. Table Functions

Table functions are functions that produce a set of rows, made up of either base data types (scalar types) or composite data types (table rows). They are used like a table, view, or subquery in the FROM clause of a query. Columns returned by table functions can be included in SELECT, JOIN, or WHERE clauses in the same manner as columns of a table, view, or subquery.

Table functions may also be combined using the ROWS FROM syntax, with the results returned in parallel columns; the number of result rows in this case is that of the largest function result, with smaller results padded with null values to match.

function_call [WITH ORDINALITY] [[AS] table_alias [(column_alias [, ... ])]]
ROWS FROM( function_call [, ... ] ) [WITH ORDINALITY] [[AS] table_alias [(column_alias [, ... ])]]

If the WITH ORDINALITY clause is specified, an additional column of type bigint will be added to the function result columns. This column numbers the rows of the function result set, starting from 1. (This is a generalization of the SQL-standard syntax for UNNEST ... WITH ORDINALITY.) By default, the ordinal column is called ordinality, but a different column name can be assigned to it using an AS clause.

The special table function UNNEST may be called with any number of array parameters, and it returns a corresponding number of columns, as if UNNEST (Section 9.19) had been called on each parameter separately and combined using the ROWS FROM construct.

UNNEST( array_expression [, ... ] ) [WITH ORDINALITY] [[AS] table_alias [(column_alias [, ... ])]]

If no table_alias is specified, the function name is used as the table name; in the case of a ROWS FROM() construct, the first function's name is used.

If column aliases are not supplied, then for a function returning a base data type, the column name is also the same as the function name. For a function returning a composite type, the result columns get the names of the individual attributes of the type.

Some examples:

CREATE TABLE foo (fooid int, foosubid int, fooname text);

    SELECT * FROM foo WHERE fooid = $1;

SELECT * FROM getfoo(1) AS t1;

    WHERE foosubid IN (
                        SELECT foosubid
                        FROM getfoo(foo.fooid) z
                        WHERE z.fooid = foo.fooid

CREATE VIEW vw_getfoo AS SELECT * FROM getfoo(1);

SELECT * FROM vw_getfoo;

In some cases it is useful to define table functions that can return different column sets depending on how they are invoked. To support this, the table function can be declared as returning the pseudo-type record with no OUT parameters. When such a function is used in a query, the expected row structure must be specified in the query itself, so that the system can know how to parse and plan the query. This syntax looks like:

function_call [AS] alias (column_definition [, ... ])
function_call AS [alias] (column_definition [, ... ])
ROWS FROM( ... function_call AS (column_definition [, ... ]) [, ... ] )

When not using the ROWS FROM() syntax, the column_definition list replaces the column alias list that could otherwise be attached to the FROM item; the names in the column definitions serve as column aliases. When using the ROWS FROM() syntax, a column_definition list can be attached to each member function separately; or if there is only one member function and no WITH ORDINALITY clause, a column_definition list can be written in place of a column alias list following ROWS FROM().

Consider this example:

    FROM dblink('dbname=mydb', 'SELECT proname, prosrc FROM pg_proc')
      AS t1(proname name, prosrc text)
    WHERE proname LIKE 'bytea%';

The dblink function (part of the dblink module) executes a remote query. It is declared to return record since it might be used for any kind of query. The actual column set must be specified in the calling query so that the parser knows, for example, what * should expand to.

This example uses ROWS FROM:

            AS (a INTEGER, b TEXT),
        generate_series(1, 3)
    ) AS x (p, q, s)

  p  |  q  | s
  40 | foo | 1
 100 | bar | 2
     |     | 3

It joins two functions into a single FROM target. json_to_recordset() is instructed to return two columns, the first integer and the second text. The result of generate_series() is used directly. The ORDER BY clause sorts the column values as integers. LATERAL Subqueries

Subqueries appearing in FROM can be preceded by the key word LATERAL. This allows them to reference columns provided by preceding FROM items. (Without LATERAL, each subquery is evaluated independently and so cannot cross-reference any other FROM item.)

Table functions appearing in FROM can also be preceded by the key word LATERAL, but for functions the key word is optional; the function's arguments can contain references to columns provided by preceding FROM items in any case.

A LATERAL item can appear at the top level in the FROM list, or within a JOIN tree. In the latter case it can also refer to any items that are on the left-hand side of a JOIN that it is on the right-hand side of.

When a FROM item contains LATERAL cross-references, evaluation proceeds as follows: for each row of the FROM item providing the cross-referenced column(s), or set of rows of multiple FROM items providing the columns, the LATERAL item is evaluated using that row or row set's values of the columns. The resulting row(s) are joined as usual with the rows they were computed from. This is repeated for each row or set of rows from the column source table(s).

A trivial example of LATERAL is

SELECT * FROM foo, LATERAL (SELECT * FROM bar WHERE bar.id = foo.bar_id) ss;

This is not especially useful since it has exactly the same result as the more conventional

SELECT * FROM foo, bar WHERE bar.id = foo.bar_id;

LATERAL is primarily useful when the cross-referenced column is necessary for computing the row(s) to be joined. A common application is providing an argument value for a set-returning function. For example, supposing that vertices(polygon) returns the set of vertices of a polygon, we could identify close-together vertices of polygons stored in a table with:

SELECT p1.id, p2.id, v1, v2
FROM polygons p1, polygons p2,
     LATERAL vertices(p1.poly) v1,
     LATERAL vertices(p2.poly) v2
WHERE (v1 <-> v2) < 10 AND p1.id != p2.id;

This query could also be written

SELECT p1.id, p2.id, v1, v2
FROM polygons p1 CROSS JOIN LATERAL vertices(p1.poly) v1,
     polygons p2 CROSS JOIN LATERAL vertices(p2.poly) v2
WHERE (v1 <-> v2) < 10 AND p1.id != p2.id;

or in several other equivalent formulations. (As already mentioned, the LATERAL key word is unnecessary in this example, but we use it for clarity.)

It is often particularly handy to LEFT JOIN to a LATERAL subquery, so that source rows will appear in the result even if the LATERAL subquery produces no rows for them. For example, if get_product_names() returns the names of products made by a manufacturer, but some manufacturers in our table currently produce no products, we could find out which ones those are like this:

SELECT m.name
FROM manufacturers m LEFT JOIN LATERAL get_product_names(m.id) pname ON true

7.2.2. The WHERE Clause

The syntax of the WHERE clause is

WHERE search_condition

where search_condition is any value expression (see Section 4.2) that returns a value of type boolean.

After the processing of the FROM clause is done, each row of the derived virtual table is checked against the search condition. If the result of the condition is true, the row is kept in the output table, otherwise (i.e., if the result is false or null) it is discarded. The search condition typically references at least one column of the table generated in the FROM clause; this is not required, but otherwise the WHERE clause will be fairly useless.


The join condition of an inner join can be written either in the WHERE clause or in the JOIN clause. For example, these table expressions are equivalent:

FROM a, b WHERE a.id = b.id AND b.val > 5


FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val > 5

or perhaps even:


Which one of these you use is mainly a matter of style. The JOIN syntax in the FROM clause is probably not as portable to other SQL database management systems, even though it is in the SQL standard. For outer joins there is no choice: they must be done in the FROM clause. The ON or USING clause of an outer join is not equivalent to a WHERE condition, because it results in the addition of rows (for unmatched input rows) as well as the removal of rows in the final result.

Here are some examples of WHERE clauses:

SELECT ... FROM fdt WHERE c1 > 5

SELECT ... FROM fdt WHERE c1 IN (1, 2, 3)


SELECT ... FROM fdt WHERE c1 IN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10)

SELECT ... FROM fdt WHERE c1 BETWEEN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10) AND 100


fdt is the table derived in the FROM clause. Rows that do not meet the search condition of the WHERE clause are eliminated from fdt. Notice the use of scalar subqueries as value expressions. Just like any other query, the subqueries can employ complex table expressions. Notice also how fdt is referenced in the subqueries. Qualifying c1 as fdt.c1 is only necessary if c1 is also the name of a column in the derived input table of the subquery. But qualifying the column name adds clarity even when it is not needed. This example shows how the column naming scope of an outer query extends into its inner queries.

7.2.3. The GROUP BY and HAVING Clauses

After passing the WHERE filter, the derived input table might be subject to grouping, using the GROUP BY clause, and elimination of group rows using the HAVING clause.

SELECT select_list
    FROM ...
    [WHERE ...]
    GROUP BY grouping_column_reference [, grouping_column_reference]...

The GROUP BY clause is used to group together those rows in a table that have the same values in all the columns listed. The order in which the columns are listed does not matter. The effect is to combine each set of rows having common values into one group row that represents all rows in the group. This is done to eliminate redundancy in the output and/or compute aggregates that apply to these groups. For instance:

=> SELECT * FROM test1;
 x | y
 a | 3
 c | 2
 b | 5
 a | 1
(4 rows)

=> SELECT x FROM test1 GROUP BY x;
(3 rows)

In the second query, we could not have written SELECT * FROM test1 GROUP BY x, because there is no single value for the column y that could be associated with each group. The grouped-by columns can be referenced in the select list since they have a single value in each group.

In general, if a table is grouped, columns that are not listed in GROUP BY cannot be referenced except in aggregate expressions. An example with aggregate expressions is:

=> SELECT x, sum(y) FROM test1 GROUP BY x;
 x | sum
 a |   4
 b |   5
 c |   2
(3 rows)

Here sum is an aggregate function that computes a single value over the entire group. More information about the available aggregate functions can be found in Section 9.21.


Grouping without aggregate expressions effectively calculates the set of distinct values in a column. This can also be achieved using the DISTINCT clause (see Section 7.3.3).

Here is another example: it calculates the total sales for each product (rather than the total sales of all products):

SELECT product_id, p.name, (sum(s.units) * p.price) AS sales
    FROM products p LEFT JOIN sales s USING (product_id)
    GROUP BY product_id, p.name, p.price;

In this example, the columns product_id, p.name, and p.price must be in the GROUP BY clause since they are referenced in the query select list (but see below). The column s.units does not have to be in the GROUP BY list since it is only used in an aggregate expression (sum(...)), which represents the sales of a product. For each product, the query returns a summary row about all sales of the product.

If the products table is set up so that, say, product_id is the primary key, then it would be enough to group by product_id in the above example, since name and price would be functionally dependent on the product ID, and so there would be no ambiguity about which name and price value to return for each product ID group.

In strict SQL, GROUP BY can only group by columns of the source table but Postgres Pro extends this to also allow GROUP BY to group by columns in the select list. Grouping by value expressions instead of simple column names is also allowed.

If a table has been grouped using GROUP BY, but only certain groups are of interest, the HAVING clause can be used, much like a WHERE clause, to eliminate groups from the result. The syntax is:

SELECT select_list FROM ... [WHERE ...] GROUP BY ... HAVING boolean_expression

Expressions in the HAVING clause can refer both to grouped expressions and to ungrouped expressions (which necessarily involve an aggregate function).


=> SELECT x, sum(y) FROM test1 GROUP BY x HAVING sum(y) > 3;
 x | sum
 a |   4
 b |   5
(2 rows)

=> SELECT x, sum(y) FROM test1 GROUP BY x HAVING x < 'c';
 x | sum
 a |   4
 b |   5
(2 rows)

Again, a more realistic example:

SELECT product_id, p.name, (sum(s.units) * (p.price - p.cost)) AS profit
    FROM products p LEFT JOIN sales s USING (product_id)
    WHERE s.date > CURRENT_DATE - INTERVAL '4 weeks'
    GROUP BY product_id, p.name, p.price, p.cost
    HAVING sum(p.price * s.units) > 5000;

In the example above, the WHERE clause is selecting rows by a column that is not grouped (the expression is only true for sales during the last four weeks), while the HAVING clause restricts the output to groups with total gross sales over 5000. Note that the aggregate expressions do not necessarily need to be the same in all parts of the query.

If a query contains aggregate function calls, but no GROUP BY clause, grouping still occurs: the result is a single group row (or perhaps no rows at all, if the single row is then eliminated by HAVING). The same is true if it contains a HAVING clause, even without any aggregate function calls or GROUP BY clause.


More complex grouping operations than those described above are possible using the concept of grouping sets. The data selected by the FROM and WHERE clauses is grouped separately by each specified grouping set, aggregates computed for each group just as for simple GROUP BY clauses, and then the results returned. For example:

=> SELECT * FROM items_sold;
 brand | size | sales
 Foo   | L    |  10
 Foo   | M    |  20
 Bar   | M    |  15
 Bar   | L    |  5
(4 rows)

=> SELECT brand, size, sum(sales) FROM items_sold GROUP BY GROUPING SETS ((brand), (size), ());
 brand | size | sum
 Foo   |      |  30
 Bar   |      |  20
       | L    |  15
       | M    |  35
       |      |  50
(5 rows)

Each sublist of GROUPING SETS may specify zero or more columns or expressions and is interpreted the same way as though it were directly in the GROUP BY clause. An empty grouping set means that all rows are aggregated down to a single group (which is output even if no input rows were present), as described above for the case of aggregate functions with no GROUP BY clause.

References to the grouping columns or expressions are replaced by null values in result rows for grouping sets in which those columns do not appear. To distinguish which grouping a particular output row resulted from, see Table 9.61.

A shorthand notation is provided for specifying two common types of grouping set. A clause of the form

ROLLUP ( e1, e2, e3, ... )

represents the given list of expressions and all prefixes of the list including the empty list; thus it is equivalent to

    ( e1, e2, e3, ... ),
    ( e1, e2 ),
    ( e1 ),
    ( )

This is commonly used for analysis over hierarchical data; e.g., total salary by department, division, and company-wide total.

A clause of the form

CUBE ( e1, e2, ... )

represents the given list and all of its possible subsets (i.e., the power set). Thus

CUBE ( a, b, c )

is equivalent to

    ( a, b, c ),
    ( a, b    ),
    ( a,    c ),
    ( a       ),
    (    b, c ),
    (    b    ),
    (       c ),
    (         )

The individual elements of a CUBE or ROLLUP clause may be either individual expressions, or sublists of elements in parentheses. In the latter case, the sublists are treated as single units for the purposes of generating the individual grouping sets. For example:

CUBE ( (a, b), (c, d) )

is equivalent to

    ( a, b, c, d ),
    ( a, b       ),
    (       c, d ),
    (            )


ROLLUP ( a, (b, c), d )

is equivalent to

    ( a, b, c, d ),
    ( a, b, c    ),
    ( a          ),
    (            )

The CUBE and ROLLUP constructs can be used either directly in the GROUP BY clause, or nested inside a GROUPING SETS clause. If one GROUPING SETS clause is nested inside another, the effect is the same as if all the elements of the inner clause had been written directly in the outer clause.

If multiple grouping items are specified in a single GROUP BY clause, then the final list of grouping sets is the cross product of the individual items. For example:

GROUP BY a, CUBE (b, c), GROUPING SETS ((d), (e))

is equivalent to

    (a, b, c, d), (a, b, c, e),
    (a, b, d),    (a, b, e),
    (a, c, d),    (a, c, e),
    (a, d),       (a, e)

When specifying multiple grouping items together, the final set of grouping sets might contain duplicates. For example:


is equivalent to

    (a, b, c),
    (a, b),
    (a, b),
    (a, c),
    (a, c),

If these duplicates are undesirable, they can be removed using the DISTINCT clause directly on the GROUP BY. Therefore:


is equivalent to

    (a, b, c),
    (a, b),
    (a, c),

This is not the same as using SELECT DISTINCT because the output rows may still contain duplicates. If any of the ungrouped columns contains NULL, it will be indistinguishable from the NULL used when that same column is grouped.


The construct (a, b) is normally recognized in expressions as a row constructor. Within the GROUP BY clause, this does not apply at the top levels of expressions, and (a, b) is parsed as a list of expressions as described above. If for some reason you need a row constructor in a grouping expression, use ROW(a, b).

7.2.5. Window Function Processing

If the query contains any window functions (see Section 3.5, Section 9.22 and Section 4.2.8), these functions are evaluated after any grouping, aggregation, and HAVING filtering is performed. That is, if the query uses any aggregates, GROUP BY, or HAVING, then the rows seen by the window functions are the group rows instead of the original table rows from FROM/WHERE.

When multiple window functions are used, all the window functions having syntactically equivalent PARTITION BY and ORDER BY clauses in their window definitions are guaranteed to be evaluated in a single pass over the data. Therefore they will see the same sort ordering, even if the ORDER BY does not uniquely determine an ordering. However, no guarantees are made about the evaluation of functions having different PARTITION BY or ORDER BY specifications. (In such cases a sort step is typically required between the passes of window function evaluations, and the sort is not guaranteed to preserve ordering of rows that its ORDER BY sees as equivalent.)

Currently, window functions always require presorted data, and so the query output will be ordered according to one or another of the window functions' PARTITION BY/ORDER BY clauses. It is not recommended to rely on this, however. Use an explicit top-level ORDER BY clause if you want to be sure the results are sorted in a particular way.