Synopsis

[ WITH with_query [, ...] ]
SELECT [ ALL | DISTINCT ] select_expr [, ...]
[ FROM from_item [, ...] ]
[ WHERE condition ]
[ GROUP BY expression [, ...] ]
[ HAVING condition]
[ UNION [ ALL | DISTINCT ] select ]
[ ORDER BY expression [ ASC | DESC ] [, ...] ]
[ LIMIT count ]

where from_item is one of

table_name [ [ AS ] alias [ ( column_alias [, ...] ) ] ]

from_item join_type from_item [ ON join_condition | USING ( join_column [, ...] ) ]

Description

Retrieve rows from zero or more tables.

GROUP BY Clause

The GROUP BY clause divides the output of a SELECT statement into groups of rows containing matching values. A 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 expression must be either aggregate functions or columns present in the GROUP BY clause.

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 Clause

The UNION clause is used to combine the results of more than one select statement into a single result set:

query UNION [ALL | 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 following is an example of one of the simplest possible UNION clauses. The following query selects the value 13 and combines this result set with a second query which selects the value 42:

SELECT 13
UNION
SELECT 42;

 _col0
-------
    13
    42
(2 rows)

Multiple unions are processed left to right, unless the order is explicitly specified via parentheses.

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 as the last step of a query after any GROUP BY or HAVING clause.

LIMIT Clause

The LIMIT clause restricts the number of rows in the result set. 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;

 o_orderdate
-------------
 1996-04-14
 1992-01-15
 1995-02-01
 1995-11-12
 1992-04-26
(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.

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 ARRAY or MAP 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)