| PostgreSQL 8.2.23 Documentation | ||||
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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.
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 may be a table name (possibly schema-qualified), or a derived table such as a subquery, a table join, or complex combinations of these. If more than one table reference is listed in the FROM clause they are cross-joined (see below) to form the intermediate virtual table that may 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.
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.
Join Types
T1 CROSS JOIN T2
For each combination of rows from T1 and T2, the derived 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, T2. It is also equivalent to FROM T1 INNER JOIN T2 ON TRUE (see below).
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 ON boolean_expression
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 USING ( join column list )
T1 NATURAL { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2
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 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 for them.
USING is a shorthand notation: it takes a comma-separated list of column names, which the joined tables must have in common, and forms a join condition specifying equality of each of these pairs of columns. Furthermore, the output of a JOIN USING has one column for each of the equated pairs of input columns, followed by all of the other columns from each table. Thus, USING (a, b, c) is equivalent to ON (t1.a = t2.a AND t1.b = t2.b AND t1.c = t2.c) with the exception that if ON is used there will be two columns a, b, and c in the result, whereas with USING there will be only one of each.
Finally, NATURAL is a shorthand form of USING: it forms a USING list consisting of exactly those column names that appear in both input tables. As with USING, these columns appear only once in the output table.
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 unconditionally 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 unconditionally 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.
Joins of all types can be chained together or nested: either or both of T1 and T2 may be joined tables. Parentheses may be used around JOIN clauses to control the join order. In the absence of parentheses, JOIN clauses nest left-to-right.
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:
=> SELECT * FROM t1 CROSS JOIN t2;
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)
=> SELECT * FROM t1 INNER JOIN t2 USING (num);
num | name | value
-----+------+-------
1 | a | xxx
3 | c | yyy
(2 rows)
=> SELECT * FROM t1 NATURAL INNER JOIN t2;
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)
=> SELECT * FROM t1 LEFT JOIN t2 USING (num);
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)
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
or
FROM table_reference alias
The AS key word is 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 for the current query — it is no longer possible to refer to the table by the original name. Thus
SELECT * FROM my_table AS m WHERE my_table.a > 5;
is not valid according to the SQL standard. In PostgreSQL this will draw an error if the add_missing_from configuration variable is off (as it is by default). If it is on, an implicit table reference will be added to the FROM clause, so the query is processed as if it were written as
SELECT * FROM my_table AS m, my_table AS my_table WHERE my_table.a > 5;
That will result in a cross join, which is usually not what you want.
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 7.2.1.3).
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, using any of these forms, the alias hides the original names 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 specifying a derived table must be enclosed in parentheses and must be assigned a table alias name. (See Section 7.2.1.2.) For example:
FROM (SELECT * FROM table1) AS alias_name
This example is equivalent to FROM table1 AS alias_name. More interesting cases, which can't 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 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 may be included in SELECT, JOIN, or WHERE clauses in the same manner as a table, view, or subquery column.
If a table function returns a base data type, the single result column is named like the function. If the function returns a composite type, the result columns get the same names as the individual attributes of the type.
A table function may be aliased in the FROM clause, but it also may be left unaliased. If a function is used in the FROM clause with no alias, the function name is used as the resulting table name.
Some examples:
CREATE TABLE foo (fooid int, foosubid int, fooname text);
CREATE FUNCTION getfoo(int) RETURNS SETOF foo AS $$
SELECT * FROM foo WHERE fooid = $1;
$$ LANGUAGE SQL;
SELECT * FROM getfoo(1) AS t1;
SELECT * FROM foo
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 pseudotype record. 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. Consider this example:
SELECT *
FROM dblink('dbname=mydb', 'select proname, prosrc from pg_proc')
AS t1(proname name, prosrc text)
WHERE proname LIKE 'bytea%';
The dblink function executes a remote query (see contrib/dblink). 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.
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 (that is, if the result is false or null) it is discarded. The search condition typically references at least some column of the table generated in the FROM clause; this is not required, but otherwise the WHERE clause will be fairly useless.
Note: 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 > 5and
FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val > 5or perhaps even
FROM a NATURAL JOIN b WHERE b.val > 5Which 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. For outer joins there is no choice in any case: they must be done in the FROM clause. An ON/USING clause of an outer join is not equivalent to a WHERE condition, because it determines the addition of rows (for unmatched input rows) as well as the removal of rows from 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 c1 FROM t2) 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 SELECT ... FROM fdt WHERE EXISTS (SELECT c1 FROM t2 WHERE c2 > fdt.c1)
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.
After passing the WHERE filter, the derived input table may 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 share 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 sharing common values into one group row that is representative of 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; x --- a b c (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 used in the grouping 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 9.15.
Tip: 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 on 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. (Depending on how exactly the products table is set up, name and price may be fully dependent on the product ID, so the additional groupings could theoretically be unnecessary, but this is not implemented yet.) 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.
In strict SQL, GROUP BY can only group by columns of the source table but PostgreSQL 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 a GROUP BY clause, but then only certain groups are of interest, the HAVING clause can be used, much like a WHERE clause, to eliminate groups from a grouped table. 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).
Example:
=> 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.