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Query syntax

JOIN

A JOIN operation combines rows from two tables — or other table-like sources, such as views or table functions — to create a new combined row that can be used in the query. For a conceptual explanation of joins, see Working with joins.

This topic describes how to use the JOIN subclause in the FROM clause. The JOIN subclause specifies, explicitly or implicitly, how to relate rows in one table to the corresponding rows in the other table. You can also use the ASOF JOIN subclause, which is used to join time-series data on timestamp columns when their values closely follow each other, precede each other, or match exactly.

Although the recommended way to join tables is to use JOIN with the ON subclause of the FROM clause, an alternative way to join tables is to use the WHERE clause. For details, see the documentation for the WHERE clause.

Syntax

Use one of the following:

SELECT ...
FROM <object_ref1> [
                     {
                       INNER
                       | { LEFT | RIGHT | FULL } [ OUTER ]
                     }
                     [ DIRECTED ]
                   ]
                   JOIN <object_ref2>
  [ ON <condition> ]
[ ... ]
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SELECT *
FROM <object_ref1> [
                     {
                       INNER
                       | { LEFT | RIGHT | FULL } [ OUTER ]
                     }
                     [ DIRECTED ]
                   ]
                   JOIN <object_ref2>
  [ USING( <column_list> ) ]
[ ... ]
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SELECT ...
FROM <object_ref1> [
                     {
                       NATURAL [
                                 {
                                   INNER
                                   | { LEFT | RIGHT | FULL } [ OUTER ]
                                 }
                                 [ DIRECTED ]
                               ]
                       | CROSS  [ DIRECTED ]
                     }
                   ]
                   JOIN <object_ref2>
[ ... ]
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Parameters

object_ref1 and object_ref2

Each object reference is a table or table-like data source.

JOIN

Use the JOIN keyword to specify that the tables should be joined. Combine JOIN with other join-related keywords — for example, INNER or OUTER — to specify the type of join.

The semantics of joins are as follows (for brevity, this topic uses o1 and o2 for object_ref1 and object_ref2, respectively).

Join Type

Semantics

o1 INNER JOIN o2

For each row of o1, a row is produced for each row of o2 that matches according to the ON condition subclause. (You can also use a comma to specify an inner join. For an example, see the examples section.) If you use INNER JOIN without the ON clause, or if you use a comma without a WHERE clause, the result is the same as using CROSS JOIN: a Cartesian product; every row of o1 paired with every row of o2.

o1 LEFT OUTER JOIN o2

The result of the inner join is augmented with a row for each row of o1 that has no matches in o2. The result columns referencing o2 contain null.

o1 RIGHT OUTER JOIN o2

The result of the inner join is augmented with a row for each row of o2 that has no matches in o1. The result columns referencing o1 contain null.

o1 FULL OUTER JOIN o2

Returns all joined rows, plus one row for each unmatched left side row (extended with nulls on the right), plus one row for each unmatched right side row (extended with nulls on the left).

o1 CROSS JOIN o2

For every possible combination of rows from o1 and o2 (that is, Cartesian product), the joined table contains a row consisting of all columns in o1 followed by all columns in o2. A CROSS JOIN can’t be combined with an ON condition clause. However, you can use a WHERE clause to filter the results.

o1 NATURAL JOIN o2

A NATURAL JOIN is identical to an explicit JOIN on the common columns of the two tables, except that the common columns are included only once in the output. (A natural join assumes that columns with the same name, but in different tables, contain corresponding data.) For examples, see the examples section. A NATURAL JOIN can be combined with an OUTER JOIN. A NATURAL JOIN can’t be combined with an ON condition clause because the JOIN condition is already implied. However, you can use a WHERE clause to filter the results.

The DIRECTED keyword specifies a directed join, which enforces the join order of the tables. The first, or left, table is scanned before the second, or right, table. For example, o1 INNER DIRECTED JOIN o2 scans the o1 table before the o2 table. Directed joins are useful in the following situations:

  • You are migrating workloads into Snowflake that have join order directives.

  • You want to improve performance by scanning join tables in a specific order.

Note

Directed join is a preview feature that is available to all accounts.

Default: INNER JOIN

If the word JOIN is used without specifying INNER or OUTER, then the JOIN is an inner join.

If the DIRECTED keyword is added, the join type — for example, INNER or OUTER — is required.

See also:

ON condition

A Boolean expression that defines the rows from the two sides of the JOIN that are considered to match, for example:

ON object_ref2.id_number = object_ref1.id_number
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Conditions are discussed in more detail in the WHERE clause documentation.

The ON clause is prohibited for CROSS JOIN.

The ON clause is unnecessary, and prohibited, for NATURAL JOIN because the join columns are implied.

For other joins, the ON clause is optional. However, omitting the ON clause results in a Cartesian product; every row of object_ref1 paired with every row of object_ref2. A Cartesian product can produce a very large volume of output, almost all of which consists of pairs of rows that aren’t actually related, which consumes a lot of resources and is often a user error.

USING( column_list )

A list of columns in common between the two tables being joined. These columns are used as the join columns. The columns must have the same name and meaning in each of the tables being joined.

For example, suppose that the SQL statement contains:

... o1 JOIN o2
    USING (key_column)
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In the simple case, this would be equivalent to:

... o1 JOIN o2
    ON o2.key_column = o1.key_column
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In the standard JOIN syntax, the projection list (the list of columns and other expressions after the SELECT keyword) is *. This causes the query to return the key_column exactly once. The columns are returned in the following order:

  • The columns in the USING clause in the order specified.

  • The left table columns not specified in the USING clause.

  • The right table columns not specified in the USING clause.

For examples of standard and nonstandard usage, see the examples section.

Usage notes

  • The following restrictions apply to table functions other than SQL UDTFs:

    • You can’t specify the ON, USING, or NATURAL JOIN clause in a lateral table function, other than a SQL UDTF.

      For example, the following syntax is not allowed:

      SELECT ... FROM my_table
  JOIN TABLE(FLATTEN(input=>[col_a]))
  ON ... ;
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      SELECT ... FROM my_table
  INNER JOIN TABLE(FLATTEN(input=>[col_a]))
  ON ... ;
      Copy
      SELECT ... FROM my_table
  JOIN TABLE(my_js_udtf(col_a))
  ON ... ;
      Copy
      SELECT ... FROM my_table
  INNER JOIN TABLE(my_js_udtf(col_a))
  ON ... ;
      Copy

    • You can’t specify the ON, USING, or NATURAL JOIN clause in an outer lateral join to a table function, other than a SQL UDTF.

      For example, the following syntax is not allowed:

      SELECT ... FROM my_table
  LEFT JOIN TABLE(FLATTEN(input=>[a]))
  ON ... ;
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      SELECT ... FROM my_table
  FULL JOIN TABLE(FLATTEN(input=>[a]))
  ON ... ;
      Copy
      SELECT ... FROM my_table
  LEFT JOIN TABLE(my_js_udtf(a))
  ON ... ;
      Copy
      SELECT ... FROM my_table
  FULL JOIN TABLE(my_js_udtf(a))
  ON ... ;
      Copy

      Using this syntax results in the following error:

      000002 (0A000): Unsupported feature
  'lateral table function called with OUTER JOIN syntax
   or a join predicate (ON clause)'

    • These restrictions don’t apply if you are using a comma, rather than a JOIN keyword:

      SELECT ... FROM my_table,
  TABLE(FLATTEN(input=>[col_a]))
  ON ... ;
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Examples

Many of the JOIN examples use two tables: t1 and t2. Create these tables and insert data:

CREATE TABLE t1 (col1 INTEGER);

INSERT INTO t1 (col1) VALUES
  (2),
  (3),
  (4);

CREATE TABLE t2 (col1 INTEGER);

INSERT INTO t2 (col1) VALUES
  (1),
  (2),
  (2),
  (3);
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The following examples run queries with joins:

Run a query with an inner join

The following example runs a query with an inner join:

SELECT t1.col1, t2.col1
  FROM t1 INNER JOIN t2
    ON t2.col1 = t1.col1
  ORDER BY 1,2;
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+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
+------+------+

Run the same query with an inner-directed join to enforce the join order so that the left table is scanned first:

Note

Directed join is a preview feature that is available to all accounts.

SELECT t1.col1, t2.col1
  FROM t1 INNER DIRECTED JOIN t2
    ON t2.col1 = t1.col1
  ORDER BY 1,2;
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+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
+------+------+

Run a query with a left outer join

The following example runs a query with a left outer join:

SELECT t1.col1, t2.col1
  FROM t1 LEFT OUTER JOIN t2
    ON t2.col1 = t1.col1
  ORDER BY 1,2;
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In the output, there is a NULL value for the row in table t1 that doesn’t have a matching row in table t2:

+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
|    4 | NULL |
+------+------+

Run a query with a right outer join

The following example runs a query with a right outer join:

SELECT t1.col1, t2.col1
  FROM t1 RIGHT OUTER JOIN t2
    ON t2.col1 = t1.col1
  ORDER BY 1,2;
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In the output, there is a NULL value for the row in table t1 that doesn’t have a matching row in table t2.

+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
| NULL |    1 |
+------+------+

Run a query with a full outer join

The following example runs a query with a full outer join:

SELECT t1.col1, t2.col1
  FROM t1 FULL OUTER JOIN t2
    ON t2.col1 = t1.col1
  ORDER BY 1,2;
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Each table has a row that doesn’t have a matching row in the other table, so the output contains two rows with NULL values:

+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
|    4 | NULL |
| NULL |    1 |
+------+------+

Run a query with a cross join

The following example runs a query with a cross join:

Note

A cross join doesn’t have an ON clause.

SELECT t1.col1, t2.col1
  FROM t1 CROSS JOIN t2
  ORDER BY 1, 2;
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The output shows that the query produces a Cartesian product:

+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    1 |
|    2 |    2 |
|    2 |    2 |
|    2 |    3 |
|    3 |    1 |
|    3 |    2 |
|    3 |    2 |
|    3 |    3 |
|    4 |    1 |
|    4 |    2 |
|    4 |    2 |
|    4 |    3 |
+------+------+

A cross join can be filtered by a WHERE clause, as shown in the following example:

SELECT t1.col1, t2.col1
  FROM t1 CROSS JOIN t2
  WHERE t2.col1 = t1.col1
  ORDER BY 1, 2;
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+------+------+
| COL1 | COL1 |
|------+------|
|    2 |    2 |
|    2 |    2 |
|    3 |    3 |
+------+------+

Run a query with a natural join

The following example shows a query with a natural join. First, create two tables and insert data:

CREATE OR REPLACE TABLE d1 (
  id NUMBER,
  name VARCHAR);

INSERT INTO d1 (id, name) VALUES
  (1,'a'),
  (2,'b'),
  (4,'c');

CREATE OR REPLACE TABLE d2 (
  id NUMBER,
  value VARCHAR);

INSERT INTO d2 (id, value) VALUES
  (1,'xx'),
  (2,'yy'),
  (5,'zz');
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Run a query with a natural join:

SELECT *
  FROM d1 NATURAL INNER JOIN d2
  ORDER BY id;
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The output shows that a natural join produces the same output as the corresponding inner join, except that the output doesn’t include a second copy of the join column:

+----+------+-------+
| ID | NAME | VALUE |
|----+------+-------|
|  1 | a    | xx    |
|  2 | b    | yy    |
+----+------+-------+

The following example shows that you can combine natural joins with outer joins:

SELECT *
  FROM d1 NATURAL FULL OUTER JOIN d2
  ORDER BY id;
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+----+------+-------+
| ID | NAME | VALUE |
|----+------+-------|
|  1 | a    | xx    |
|  2 | b    | yy    |
|  4 | c    | NULL  |
|  5 | NULL | zz    |
+----+------+-------+

Run a query that combines joins in the FROM clause

You can combine in the FROM clause. Create a third table:

CREATE TABLE t3 (col1 INTEGER);

INSERT INTO t3 (col1) VALUES
  (2),
  (6);
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Run a query that chains together two joins in the FROM clause:

SELECT t1.*, t2.*, t3.*
  FROM t1
    LEFT OUTER JOIN t2 ON (t1.col1 = t2.col1)
    RIGHT OUTER JOIN t3 ON (t3.col1 = t2.col1)
  ORDER BY t1.col1;
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+------+------+------+
| COL1 | COL1 | COL1 |
|------+------+------|
|    2 |    2 |    2 |
|    2 |    2 |    2 |
| NULL | NULL |    6 |
+------+------+------+

In such a query, the results are determined based on the joins taking place from left to right, although the optimizer might reorder the joins if a different join order produces the same result. If the right outer join is meant to take place before the left outer join, then write the query in the following way:

SELECT t1.*, t2.*, t3.*
FROM t1
  LEFT OUTER JOIN
    (t2 RIGHT OUTER JOIN t3 ON (t3.col1 = t2.col1))
  ON (t1.col1 = t2.col1)
ORDER BY t1.col1;
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+------+------+------+
| COL1 | COL1 | COL1 |
|------+------+------|
|    2 |    2 |    2 |
|    2 |    2 |    2 |
|    3 | NULL | NULL |
|    4 | NULL | NULL |
+------+------+------+

Run queries with joins that use the USING clause

The next two examples show standard (ISO 9075) and nonstandard usage of the USING clause. Both are supported by Snowflake.

This first example shows standard usage. Specifically, the projection list contains exactly *:

WITH
  l AS (
       SELECT 'a' AS userid
       ),
  r AS (
       SELECT 'b' AS userid
       )
SELECT *
  FROM l LEFT JOIN r USING(userid);
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Even though the example query joins two tables, and each table has one column, and the query asks for all columns, the output contains one column, not two:

+--------+
| USERID |
|--------|
| a      |
+--------+

The following example shows nonstandard usage. The projection list contains something other than *:

WITH
  l AS (
       SELECT 'a' AS userid
     ),
  r AS (
       SELECT 'b' AS userid
       )
SELECT l.userid as UI_L,
       r.userid as UI_R
  FROM l LEFT JOIN r USING(userid);
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The output contains two columns, and the second column contains either a value from the second table or NULL:

+------+------+
| UI_L | UI_R |
|------+------|
| a    | NULL |
+------+------+
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