
SELECT
======

Synopsis
--------

``` sql
[ 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

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

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

and `join_type` is one of

``` sql
[ INNER ] JOIN
LEFT [ OUTER ] JOIN
RIGHT [ OUTER ] JOIN
FULL [ OUTER ] JOIN
CROSS JOIN
```

and `grouping_element` is one of

``` sql
()
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;


**Warning**

*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.

``` sql
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:

``` sql
expression [ [ AS ] column_alias ]
```

``` sql
relation.*
```

``` sql
*
```

In the case of `expression [ [ AS ] column_alias ]`, a single output column is defined.

In the case of `relation.*`, all columns of `relation` are included in the result set.

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.

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**


openLooKeng 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:

``` sql
query UNION [ALL | DISTINCT] query
```

``` sql
query INTERSECT [DISTINCT] query
```

``` sql
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:

``` sql
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.

OFFSET Clause
-------------

The `OFFSET` clause is used to discard a number of leading rows from the result set:

``` sql
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.

``` sql
LIMIT { count | ALL }
```

``` sql
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.


**Note**

*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 [ARRAY](../language/types.md#array) or [MAP](../language/types.md#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)
```

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.


**Note**

*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)



**Note**

*Currently only single column can be returned from the scalar subquery.*
