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 supertable of a
table inheritance hierarchy, the table reference produces rows of
not only that table but all of its subtable successors, unless the
keyword 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
Cross join
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. A cross join is equivalent to an
INNER JOIN ON TRUE.
Tip: FROM T1 CROSS JOIN
T2 is equivalent to
FROM T1,
T2.
Qualified joins
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 ON boolean_expressionT1 { [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:
INNER JOIN
For each row R1 of T1, the joined table has a row for each
row in T2 that satisfies the join condition with R1.
LEFT OUTER JOIN
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.
RIGHT OUTER JOIN
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.
FULL OUTER JOIN
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
further processing. This is called a table
alias.
To create a table alias, write
FROM table_reference AS alias
or
FROM table_referencealias
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 SQL syntax. What will actually happen (this is a
PostgreSQL extension to the standard)
is that an implicit table reference is 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;
which 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 my_table AS a CROSS JOIN my_table AS b ...
Additionally, an alias is required if the table reference is a
subquery (see Section 4.2.1.3).
Parentheses are used to resolve ambiguities. The following
statement will assign the alias b to the
result of the join, unlike the previous example:
SELECT * FROM (my_table AS a CROSS JOIN my_table) AS b ...
Another form of table aliasing also gives temporary names to the columns of the table:
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 4.2.1.2.) 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.
where search_condition is any value
expression as defined in Section 1.2 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 in the table generated in the
FROM clause; this is not required, but otherwise the
WHERE clause will be fairly useless.
Note: Before the implementation of the JOIN syntax, it was
necessary to put the join condition of an inner join in the
WHERE clause. For example, these table expressions
are equivalent:
FROM a, b WHERE a.id = b.id AND b.val > 5
and
FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val > 5
or perhaps even
FROM a NATURAL JOIN b WHERE b.val > 5
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 products. 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 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. Qualifying the column name adds
clarity even when it is not needed. This 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 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
purpose is to reduce each group 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 known constant value per 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 6.14.
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 4.3.3).
Here is another example: sum(sales) on a
table grouped by product code gives the total sales for each
product, not 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 group of sales
of a product. For each product, a summary row is returned 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, 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 everywhere.
