完整 SELECT 語法
所有操作都為 ANSI SQL compliant 的語法。ANSI SQL 相關介紹可以參考 W3School SQL Tutorial。
為了企業數據安全,我們在 Canner Enterprise 資料調用解決方案只能讓使用者 READ-only 也就是目前僅支援 SELECT statement,不支援 Update, Delete 等操作。
以下為 Canner Enterprise 所支援的 SELECT statement 相關用法。
Synopsis
[ WITH with_query [, ...] ]
SELECT [ ALL | DISTINCT ] select_expression [, ...]
[ FROM from_item [, ...] ]
[ WHERE condition ]
[ GROUP BY [ ALL | DISTINCT ] grouping_element [, ...] ]
[ HAVING condition]
[ { UNION | INTERSECT | EXCEPT } [ ALL | DISTINCT ] select ]
[ ORDER BY expression [ ASC | DESC ] [, ...] ]
[ OFFSET count [ ROW | ROWS ] ]
[ LIMIT { count | ALL } | FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } { ONLY | WITH TIES } ]
where from_item
is one of
table_name [ [ AS ] alias [ ( column_alias [, ...] ) ] ]
from_item join_type from_item [ ON join_condition | USING ( join_column [, ...] ) ]
and join_type
is one of
[ INNER ] JOIN
LEFT [ OUTER ] JOIN
RIGHT [ OUTER ] JOIN
FULL [ OUTER ] JOIN
CROSS JOIN
and grouping_element
is one of
()
expression
GROUPING SETS ( ( column [, ...] ) [, ...] )
CUBE ( column [, ...] )
ROLLUP ( column [, ...] )
Description
Retrieve rows from zero or more tables.
WITH Clause
The WITH
clause defines named relations for use within a query. It
allows flattening nested queries or simplifying subqueries. For example,
the following queries are equivalent:
SELECT a, b
FROM (
SELECT a, MAX(b) AS b FROM t GROUP BY a
) AS x;
WITH x AS (SELECT a, MAX(b) AS b FROM t GROUP BY a)
SELECT a, b FROM x;
This also works with multiple subqueries:
WITH
t1 AS (SELECT a, MAX(b) AS b FROM x GROUP BY a),
t2 AS (SELECT a, AVG(d) AS d FROM y GROUP BY a)
SELECT t1.*, t2.*
FROM t1
JOIN t2 ON t1.a = t2.a;
Additionally, the relations within a WITH
clause can chain:
WITH
x AS (SELECT a FROM t),
y AS (SELECT a AS b FROM x),
z AS (SELECT b AS c FROM y)
SELECT c FROM z;
Currently, the SQL for the WITH
clause will be inlined
anywhere the named relation is used. This means that if the relation is
used more than once and the query is non-deterministic, the results may
be different each time.
SELECT Clause
The SELECT
clause specifies the output of the query. Each
select_expression
defines a column or columns to be included in the
result.
SELECT [ ALL | DISTINCT ] select_expression [, ...]
The ALL
and DISTINCT
quantifiers determine whether duplicate rows
are included in the result set. If the argument ALL
is specified, all
rows are included. If the argument DISTINCT
is specified, only unique
rows are included in the result set. In this case, each output column
must be of a type that allows comparison. If neither argument is
specified, the behavior defaults to ALL
.
Select expressions
Each select_expression
must be in one of the following forms:
expression [ [ AS ] column_alias ]
row_expression.* [ AS ( column_alias [, ...] ) ]
relation.*
*
In the case of expression [ [ AS ] column_alias ]
, a single output
column is defined.
In the case of row_expression.* [ AS ( column_alias [, ...] ) ]
, the
row_expression
is an arbitrary expression of type ROW
. All fields of
the row define output columns to be included in the result set.
In the case of relation.*
, all columns of relation
are included in
the result set. In this case column aliases are not allowed.
In the case of *
, all columns of the relation defined by the query are
included in the result set.
In the result set, the order of columns is the same as the order of their specification by the select expressions. If a select expression returns multiple columns, they are ordered the same way they were ordered in the source relation or row type expression.
If column aliases are specified, they override any preexisting column or row field names:
SELECT (CAST(ROW(1, true) AS ROW(field1 bigint, field2 boolean))).* AS (alias1, alias2);
alias1 | alias2
--------+--------
1 | true
(1 row)
Otherwise, the existing names are used:
SELECT (CAST(ROW(1, true) AS ROW(field1 bigint, field2 boolean))).*;
field1 | field2
--------+--------
1 | true
(1 row)
and in their absence, anonymous columns are produced:
SELECT (ROW(1, true)).*;
_col0 | _col1
-------+-------
1 | true
(1 row)
GROUP BY Clause
The GROUP BY
clause divides the output of a SELECT
statement into
groups of rows containing matching values. A simple GROUP BY
clause
may contain any expression composed of input columns or it may be an
ordinal number selecting an output column by position (starting at one).
The following queries are equivalent. They both group the output by the
nationkey
input column with the first query using the ordinal position
of the output column and the second query using the input column name:
SELECT count(*), nationkey FROM customer GROUP BY 2;
SELECT count(*), nationkey FROM customer GROUP BY nationkey;
GROUP BY
clauses can group output by input column names not appearing
in the output of a select statement. For example, the following query
generates row counts for the customer
table using the input column
mktsegment
:
SELECT count(*) FROM customer GROUP BY mktsegment;
_col0
-------
29968
30142
30189
29949
29752
(5 rows)
When a GROUP BY
clause is used in a SELECT
statement all output
expressions must be either aggregate functions or columns present in the
GROUP BY
clause.
Complex Grouping Operations
Presto also supports complex aggregations using the GROUPING SETS
,
CUBE
and ROLLUP
syntax. This syntax allows users to perform analysis
that requires aggregation on multiple sets of columns in a single query.
Complex grouping operations do not support grouping on expressions
composed of input columns. Only column names or ordinals are allowed.
Complex grouping operations are often equivalent to a UNION ALL
of
simple GROUP BY
expressions, as shown in the following examples. This
equivalence does not apply, however, when the source of data for the
aggregation is non-deterministic.
GROUPING SETS
Grouping sets allow users to specify multiple lists of columns to group
on. The columns not part of a given sublist of grouping columns are set
to NULL
. :
SELECT * FROM shipping;
origin_state | origin_zip | destination_state | destination_zip | package_weight
--------------+------------+-------------------+-----------------+----------------
California | 94131 | New Jersey | 8648 | 13
California | 94131 | New Jersey | 8540 | 42
New Jersey | 7081 | Connecticut | 6708 | 225
California | 90210 | Connecticut | 6927 | 1337
California | 94131 | Colorado | 80302 | 5
New York | 10002 | New Jersey | 8540 | 3
(6 rows)
GROUPING SETS
semantics are demonstrated by this example query:
SELECT origin_state, origin_zip, destination_state, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state),
(origin_state, origin_zip),
(destination_state));
origin_state | origin_zip | destination_state | _col0
--------------+------------+-------------------+-------
New Jersey | NULL | NULL | 225
California | NULL | NULL | 1397
New York | NULL | NULL | 3
California | 90210 | NULL | 1337
California | 94131 | NULL | 60
New Jersey | 7081 | NULL | 225
New York | 10002 | NULL | 3
NULL | NULL | Colorado | 5
NULL | NULL | New Jersey | 58
NULL | NULL | Connecticut | 1562
(10 rows)
The preceding query may be considered logically equivalent to a
UNION ALL
of multiple GROUP BY
queries:
SELECT origin_state, NULL, NULL, sum(package_weight)
FROM shipping GROUP BY origin_state
UNION ALL
SELECT origin_state, origin_zip, NULL, sum(package_weight)
FROM shipping GROUP BY origin_state, origin_zip
UNION ALL
SELECT NULL, NULL, destination_state, sum(package_weight)
FROM shipping GROUP BY destination_state;
However, the query with the complex grouping syntax (GROUPING SETS
,
CUBE
or ROLLUP
) will only read from the underlying data source once,
while the query with the UNION ALL
reads the underlying data three
times. This is why queries with a UNION ALL
may produce inconsistent
results when the data source is not deterministic.
CUBE
The CUBE
operator generates all possible grouping sets (i.e. a power
set) for a given set of columns. For example, the query:
SELECT origin_state, destination_state, sum(package_weight)
FROM shipping
GROUP BY CUBE (origin_state, destination_state);
is equivalent to:
SELECT origin_state, destination_state, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state, destination_state),
(origin_state),
(destination_state),
());
origin_state | destination_state | _col0
--------------+-------------------+-------
California | New Jersey | 55
California | Colorado | 5
New York | New Jersey | 3
New Jersey | Connecticut | 225
California | Connecticut | 1337
California | NULL | 1397
New York | NULL | 3
New Jersey | NULL | 225
NULL | New Jersey | 58
NULL | Connecticut | 1562
NULL | Colorado | 5
NULL | NULL | 1625
(12 rows)
ROLLUP
The ROLLUP
operator generates all possible subtotals for a given set
of columns. For example, the query:
SELECT origin_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY ROLLUP (origin_state, origin_zip);
origin_state | origin_zip | _col2
--------------+------------+-------
California | 94131 | 60
California | 90210 | 1337
New Jersey | 7081 | 225
New York | 10002 | 3
California | NULL | 1397
New York | NULL | 3
New Jersey | NULL | 225
NULL | NULL | 1625
(8 rows)
is equivalent to:
SELECT origin_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS ((origin_state, origin_zip), (origin_state), ());
Combining multiple grouping expressions
Multiple grouping expressions in the same query are interpreted as having cross-product semantics. For example, the following query:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY
GROUPING SETS ((origin_state, destination_state)),
ROLLUP (origin_zip);
which can be rewritten as:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY
GROUPING SETS ((origin_state, destination_state)),
GROUPING SETS ((origin_zip), ());
is logically equivalent to:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state, destination_state, origin_zip),
(origin_state, destination_state));
origin_state | destination_state | origin_zip | _col3
--------------+-------------------+------------+-------
New York | New Jersey | 10002 | 3
California | New Jersey | 94131 | 55
New Jersey | Connecticut | 7081 | 225
California | Connecticut | 90210 | 1337
California | Colorado | 94131 | 5
New York | New Jersey | NULL | 3
New Jersey | Connecticut | NULL | 225
California | Colorado | NULL | 5
California | Connecticut | NULL | 1337
California | New Jersey | NULL | 55
(10 rows)
The ALL
and DISTINCT
quantifiers determine whether duplicate
grouping sets each produce distinct output rows. This is particularly
useful when multiple complex grouping sets are combined in the same
query. For example, the following query:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY ALL
CUBE (origin_state, destination_state),
ROLLUP (origin_state, origin_zip);
is equivalent to:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state, destination_state, origin_zip),
(origin_state, origin_zip),
(origin_state, destination_state, origin_zip),
(origin_state, origin_zip),
(origin_state, destination_state),
(origin_state),
(origin_state, destination_state),
(origin_state),
(origin_state, destination_state),
(origin_state),
(destination_state),
());
However, if the query uses the DISTINCT
quantifier for the
GROUP BY
:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY DISTINCT
CUBE (origin_state, destination_state),
ROLLUP (origin_state, origin_zip);
only unique grouping sets are generated:
SELECT origin_state, destination_state, origin_zip, sum(package_weight)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state, destination_state, origin_zip),
(origin_state, origin_zip),
(origin_state, destination_state),
(origin_state),
(destination_state),
());
The default set quantifier is ALL
.
GROUPING Operation
grouping(col1, ..., colN) -> bigint
The grouping operation returns a bit set converted to decimal,
indicating which columns are present in a grouping. It must be used in
conjunction with GROUPING SETS
, ROLLUP
, CUBE
or GROUP BY
and its
arguments must match exactly the columns referenced in the corresponding
GROUPING SETS
, ROLLUP
, CUBE
or GROUP BY
clause.
To compute the resulting bit set for a particular row, bits are assigned to the argument columns with the rightmost column being the least significant bit. For a given grouping, a bit is set to 0 if the corresponding column is included in the grouping and to 1 otherwise. For example, consider the query below:
SELECT origin_state, origin_zip, destination_state, sum(package_weight),
grouping(origin_state, origin_zip, destination_state)
FROM shipping
GROUP BY GROUPING SETS (
(origin_state),
(origin_state, origin_zip),
(destination_state));
origin_state | origin_zip | destination_state | _col3 | _col4
--------------+------------+-------------------+-------+-------
California | NULL | NULL | 1397 | 3
New Jersey | NULL | NULL | 225 | 3
New York | NULL | NULL | 3 | 3
California | 94131 | NULL | 60 | 1
New Jersey | 7081 | NULL | 225 | 1
California | 90210 | NULL | 1337 | 1
New York | 10002 | NULL | 3 | 1
NULL | NULL | New Jersey | 58 | 6
NULL | NULL | Connecticut | 1562 | 6
NULL | NULL | Colorado | 5 | 6
(10 rows)
The first grouping in the above result only includes the origin_state
column and excludes the origin_zip
and destination_state
columns.
The bit set constructed for that grouping is 011
where the most
significant bit represents origin_state
.
HAVING Clause
The HAVING
clause is used in conjunction with aggregate functions and
the GROUP BY
clause to control which groups are selected. A HAVING
clause eliminates groups that do not satisfy the given conditions.
HAVING
filters groups after groups and aggregates are computed.
The following example queries the customer
table and selects groups
with an account balance greater than the specified value:
SELECT count(*), mktsegment, nationkey,
CAST(sum(acctbal) AS bigint) AS totalbal
FROM customer
GROUP BY mktsegment, nationkey
HAVING sum(acctbal) > 5700000
ORDER BY totalbal DESC;
_col0 | mktsegment | nationkey | totalbal
-------+------------+-----------+----------
1272 | AUTOMOBILE | 19 | 5856939
1253 | FURNITURE | 14 | 5794887
1248 | FURNITURE | 9 | 5784628
1243 | FURNITURE | 12 | 5757371
1231 | HOUSEHOLD | 3 | 5753216
1251 | MACHINERY | 2 | 5719140
1247 | FURNITURE | 8 | 5701952
(7 rows)
UNION | INTERSECT | EXCEPT Clause
UNION
INTERSECT
and EXCEPT
are all set operations. These clauses
are used to combine the results of more than one select statement into a
single result set:
query UNION [ALL | DISTINCT] query
query INTERSECT [DISTINCT] query
query EXCEPT [DISTINCT] query
The argument ALL
or DISTINCT
controls which rows are included in the
final result set. If the argument ALL
is specified all rows are
included even if the rows are identical. If the argument DISTINCT
is
specified only unique rows are included in the combined result set. If
neither is specified, the behavior defaults to DISTINCT
. The ALL
argument is not supported for INTERSECT
or EXCEPT
.
Multiple set operations are processed left to right, unless the order is
explicitly specified via parentheses. Additionally, INTERSECT
binds
more tightly than EXCEPT
and UNION
. That means
A UNION B INTERSECT C EXCEPT D
is the same as
A UNION (B INTERSECT C) EXCEPT D
.
UNION
UNION
combines all the rows that are in the result set from the first
query with those that are in the result set for the second query. The
following is an example of one of the simplest possible UNION
clauses.
It selects the value 13
and combines this result set with a second
query that selects the value 42
:
SELECT 13
UNION
SELECT 42;
_col0
-------
13
42
(2 rows)
The following query demonstrates the difference between UNION
and
UNION ALL
. It selects the value 13
and combines this result set with
a second query that selects the values 42
and 13
:
SELECT 13
UNION
SELECT * FROM (VALUES 42, 13);
_col0
-------
13
42
(2 rows)
SELECT 13
UNION ALL
SELECT * FROM (VALUES 42, 13);
_col0
-------
13
42
13
(2 rows)
INTERSECT
INTERSECT
returns only the rows that are in the result sets of both
the first and the second queries. The following is an example of one of
the simplest possible INTERSECT
clauses. It selects the values 13
and 42
and combines this result set with a second query that selects
the value 13
. Since 42
is only in the result set of the first query,
it is not included in the final results.:
SELECT * FROM (VALUES 13, 42)
INTERSECT
SELECT 13;
_col0
-------
13
(2 rows)
EXCEPT
EXCEPT
returns the rows that are in the result set of the first query,
but not the second. The following is an example of one of the simplest
possible EXCEPT
clauses. It selects the values 13
and 42
and
combines this result set with a second query that selects the value
13
. Since 13
is also in the result set of the second query, it is
not included in the final result.:
SELECT * FROM (VALUES 13, 42)
EXCEPT
SELECT 13;
_col0
-------
42
(2 rows)
ORDER BY Clause
The ORDER BY
clause is used to sort a result set by one or more output
expressions:
ORDER BY expression [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...]
Each expression may be composed of output columns, or it may be an
ordinal number selecting an output column by position, starting at one.
The ORDER BY
clause is evaluated after any GROUP BY
or HAVING
clause, and before any OFFSET
, LIMIT
or FETCH FIRST
clause. The
default null ordering is NULLS LAST
, regardless of the ordering
direction.
Note that, following the SQL specification, an ORDER BY
clause only
affects the order of rows for queries that immediately contain the
clause. Presto follows that specification, and drops redundant usage of
the clause to avoid negative performance impacts.
In the following example, the clause only applies to the select statement.
INSERT INTO some_table
SELECT * FROM another_table
ORDER BY field
Since tables in SQL are inherently unordered, and the ORDER BY
clause
in this case does not result in any difference, but negatively impacts
performance of running the overall insert statement, Presto skips the
sort operation.
Another example where the ORDER BY
clause is redundant, and does not
affect the outcome of the overall statement, is a nested query:
SELECT *
FROM some_table
JOIN (SELECT * FROM another_table ORDER BY field) u
ON some_table.key = u.key
OFFSET Clause
The OFFSET
clause is used to discard a number of leading rows from the
result set:
OFFSET count [ ROW | ROWS ]
If the ORDER BY
clause is present, the OFFSET
clause is evaluated
over a sorted result set, and the set remains sorted after the leading
rows are discarded:
SELECT name FROM nation ORDER BY name OFFSET 22;
name
----------------
UNITED KINGDOM
UNITED STATES
VIETNAM
(3 rows)
Otherwise, it is arbitrary which rows are discarded. If the count
specified in the OFFSET
clause equals or exceeds the size of the
result set, the final result is empty.
LIMIT or FETCH FIRST Clauses
The LIMIT
or FETCH FIRST
clause restricts the number of rows in the
result set.
LIMIT { count | ALL }
FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } { ONLY | WITH TIES }
The following example queries a large table, but the LIMIT
clause
restricts the output to only have five rows (because the query lacks an
ORDER BY
, exactly which rows are returned is arbitrary):
SELECT orderdate FROM orders LIMIT 5;
orderdate
------------
1994-07-25
1993-11-12
1992-10-06
1994-01-04
1997-12-28
(5 rows)
LIMIT ALL
is the same as omitting the LIMIT
clause.
The FETCH FIRST
clause supports either the FIRST
or NEXT
keywords
and the ROW
or ROWS
keywords. These keywords are equivalent and the
choice of keyword has no effect on query execution.
If the count is not specified in the FETCH FIRST
clause, it defaults
to 1
:
SELECT orderdate FROM orders FETCH FIRST ROW ONLY;
orderdate
------------
1994-02-12
(1 row)
If the OFFSET
clause is present, the LIMIT
or FETCH FIRST
clause
is evaluated after the OFFSET
clause:
SELECT * FROM (VALUES 5, 2, 4, 1, 3) t(x) ORDER BY x OFFSET 2 LIMIT 2;
x
---
3
4
(2 rows)
For the FETCH FIRST
clause, the argument ONLY
or WITH TIES
controls which rows are included in the result set.
If the argument ONLY
is specified, the result set is limited to the
exact number of leading rows determined by the count.
If the argument WITH TIES
is specified, it is required that the
ORDER BY
clause be present. The result set consists of the same set of
leading rows and all of the rows in the same peer group as the last of
them ('ties') as established by the ordering in the ORDER BY
clause.
The result set is sorted:
SELECT name, regionkey FROM nation ORDER BY regionkey FETCH FIRST ROW WITH TIES;
name | regionkey
------------+-----------
ETHIOPIA | 0
MOROCCO | 0
KENYA | 0
ALGERIA | 0
MOZAMBIQUE | 0
(5 rows)
TABLESAMPLE
There are multiple sample methods:
BERNOULLI
Each row is selected to be in the table sample with a
probability of the sample percentage. When a table is sampled using the
Bernoulli method, all physical blocks of the table are scanned and
certain rows are skipped (based on a comparison between the sample
percentage and a random value calculated at runtime).
The probability of a row being included in the result is independent from any other row. This does not reduce the time required to read the sampled table from disk. It may have an impact on the total query time if the sampled output is processed further.
SYSTEM
This sampling method divides the table into logical segments of
data and samples the table at this granularity. This sampling method
either selects all the rows from a particular segment of data or skips
it (based on a comparison between the sample percentage and a random
value calculated at runtime).
The rows selected in a system sampling will be dependent on which connector is used. For example, when used with Hive, it is dependent on how the data is laid out on HDFS. This method does not guarantee independent sampling probabilities.
Neither of the two methods allow deterministic bounds on the number of rows returned.
Examples:
SELECT *
FROM users TABLESAMPLE BERNOULLI (50);
SELECT *
FROM users TABLESAMPLE SYSTEM (75);
Using sampling with joins:
SELECT o.*, i.*
FROM orders o TABLESAMPLE SYSTEM (10)
JOIN lineitem i TABLESAMPLE BERNOULLI (40)
ON o.orderkey = i.orderkey;
UNNEST
UNNEST
can be used to expand an :ref:array_type
or :ref:map_type
into a relation. Arrays are expanded into a single column, and maps are
expanded into two columns (key, value). UNNEST
can also be used with
multiple arguments, in which case they are expanded into multiple
columns, with as many rows as the highest cardinality argument (the
other columns are padded with nulls). UNNEST
can optionally have a
WITH ORDINALITY
clause, in which case an additional ordinality column
is added to the end. UNNEST
is normally used with a JOIN
and can
reference columns from relations on the left side of the join.
Using a single column:
SELECT student, score
FROM tests
CROSS JOIN UNNEST(scores) AS t (score);
Using multiple columns:
SELECT numbers, animals, n, a
FROM (
VALUES
(ARRAY[2, 5], ARRAY['dog', 'cat', 'bird']),
(ARRAY[7, 8, 9], ARRAY['cow', 'pig'])
) AS x (numbers, animals)
CROSS JOIN UNNEST(numbers, animals) AS t (n, a);
numbers | animals | n | a
-----------+------------------+------+------
[2, 5] | [dog, cat, bird] | 2 | dog
[2, 5] | [dog, cat, bird] | 5 | cat
[2, 5] | [dog, cat, bird] | NULL | bird
[7, 8, 9] | [cow, pig] | 7 | cow
[7, 8, 9] | [cow, pig] | 8 | pig
[7, 8, 9] | [cow, pig] | 9 | NULL
(6 rows)
WITH ORDINALITY
clause:
SELECT numbers, n, a
FROM (
VALUES
(ARRAY[2, 5]),
(ARRAY[7, 8, 9])
) AS x (numbers)
CROSS JOIN UNNEST(numbers) WITH ORDINALITY AS t (n, a);
numbers | n | a
-----------+---+---
[2, 5] | 2 | 1
[2, 5] | 5 | 2
[7, 8, 9] | 7 | 1
[7, 8, 9] | 8 | 2
[7, 8, 9] | 9 | 3
(5 rows)
Joins
Joins allow you to combine data from multiple relations.
CROSS JOIN
A cross join returns the Cartesian product (all combinations) of two
relations. Cross joins can either be specified using the explit
CROSS JOIN
syntax or by specifying multiple relations in the FROM
clause.
Both of the following queries are equivalent:
SELECT *
FROM nation
CROSS JOIN region;
SELECT *
FROM nation, region;
The nation
table contains 25 rows and the region
table contains 5
rows, so a cross join between the two tables produces 125 rows:
SELECT n.name AS nation, r.name AS region
FROM nation AS n
CROSS JOIN region AS r
ORDER BY 1, 2;
nation | region
----------------+-------------
ALGERIA | AFRICA
ALGERIA | AMERICA
ALGERIA | ASIA
ALGERIA | EUROPE
ALGERIA | MIDDLE EAST
ARGENTINA | AFRICA
ARGENTINA | AMERICA
...
(125 rows)
LATERAL
Subqueries appearing in the FROM
clause can be preceded by the keyword
LATERAL
. This allows them to reference columns provided by preceding
FROM
items.
A LATERAL
join can appear at the top level in the FROM
list, or
anywhere within a parenthesized join tree. In the latter case, it can
also refer to any items that are on the left-hand side of a JOIN
for
which it is on the right-hand side.
When a FROM
item contains LATERAL
cross-references, evaluation
proceeds as follows: for each row of the FROM
item providing the
cross-referenced columns, the LATERAL
item is evaluated using that row
set's values of the columns. The resulting rows are joined as usual with
the rows they were computed from. This is repeated for set of rows from
the column source tables.
LATERAL
is primarily useful when the cross-referenced column is
necessary for computing the rows to be joined:
SELECT name, x, y
FROM nation
CROSS JOIN LATERAL (SELECT name || ' :-' AS x)
CROSS JOIN LATERAL (SELECT x || ')' AS y)
Qualifying Column Names
When two relations in a join have columns with the same name, the column references must be qualified using the relation alias (if the relation has an alias), or with the relation name:
SELECT nation.name, region.name
FROM nation
CROSS JOIN region;
SELECT n.name, r.name
FROM nation AS n
CROSS JOIN region AS r;
SELECT n.name, r.name
FROM nation n
CROSS JOIN region r;
The following query will fail with the error
Column 'name' is ambiguous
:
SELECT name
FROM nation
CROSS JOIN region;
Subqueries
A subquery is an expression which is composed of a query. The subquery is correlated when it refers to columns outside of the subquery. Logically, the subquery will be evaluated for each row in the surrounding query. The referenced columns will thus be constant during any single evaluation of the subquery.
Support for correlated subqueries is limited. Not every standard form is supported.
EXISTS
The EXISTS
predicate determines if a subquery returns any rows:
SELECT name
FROM nation
WHERE EXISTS (SELECT * FROM region WHERE region.regionkey = nation.regionkey)
IN
The IN
predicate determines if any values produced by the subquery are
equal to the provided expression. The result of IN
follows the
standard rules for nulls. The subquery must produce exactly one column:
SELECT name
FROM nation
WHERE regionkey IN (SELECT regionkey FROM region)
Scalar Subquery
A scalar subquery is a non-correlated subquery that returns zero or one
row. It is an error for the subquery to produce more than one row. The
returned value is NULL
if the subquery produces no rows:
SELECT name
FROM nation
WHERE regionkey = (SELECT max(regionkey) FROM region)
Currently only single column can be returned from the scalar subquery.