JOIN Clause

Join produces a new table by combining columns from one or multiple tables by using values common to each. It is a common operation in databases with SQL support, which corresponds to relational algebra join. The special case of one table join is often referred to as “self-join”.

Syntax

SELECT <expr_list>
FROM <left_table>
[GLOBAL] [INNER|LEFT|RIGHT|FULL|CROSS] [OUTER|SEMI|ANTI|ANY|ALL|ASOF] JOIN <right_table>
(ON <expr_list>)|(USING <column_list>) ...

Expressions from ON clause and columns from USING clause are called “join keys”. Unless otherwise stated, join produces a Cartesian product from rows with matching “join keys”, which might produce results with much more rows than the source tables.

Supported Types of JOIN

All standard SQL JOIN types are supported:

INNER JOIN, only matching rows are returned.
LEFT OUTER JOIN, non-matching rows from left table are returned in addition to matching rows.
RIGHT OUTER JOIN, non-matching rows from right table are returned in addition to matching rows.
FULL OUTER JOIN, non-matching rows from both tables are returned in addition to matching rows.
CROSS JOIN, produces cartesian product of whole tables, “join keys” are not specified.

JOIN without specified type implies INNER. Keyword OUTER can be safely omitted. Alternative syntax for CROSS JOIN is specifying multiple tables in FROM clause separated by commas.

Additional join types available in ClickHouse:

LEFT SEMI JOIN and RIGHT SEMI JOIN, a whitelist on “join keys”, without producing a cartesian product.
LEFT ANTI JOIN and RIGHT ANTI JOIN, a blacklist on “join keys”, without producing a cartesian product.
LEFT ANY JOIN, RIGHT ANY JOIN and INNER ANY JOIN, partially (for opposite side of LEFT and RIGHT) or completely (for INNER and FULL) disables the cartesian product for standard JOIN types.
ASOF JOIN and LEFT ASOF JOIN, joining sequences with a non-exact match. ASOF JOIN usage is described below.
PASTE JOIN, performs a horizontal concatenation of two tables.

Note

When join_algorithm is set to partial_merge, RIGHT JOIN and FULL JOIN are supported only with ALL strictness (SEMI, ANTI, ANY, and ASOF are not supported).

Settings

The default join type can be overridden using join_default_strictness setting.

The behavior of ClickHouse server for ANY JOIN operations depends on the any_join_distinct_right_table_keys setting.

See also

Use the cross_to_inner_join_rewrite setting to define the behavior when ClickHouse fails to rewrite a CROSS JOIN as an INNER JOIN. The default value is 1, which allows the join to continue but it will be slower. Set cross_to_inner_join_rewrite to 0 if you want an error to be thrown, and set it to 2 to not run the cross joins but instead force a rewrite of all comma/cross joins. If the rewriting fails when the value is 2, you will receive an error message stating "Please, try to simplify WHERE section".

ON Section Conditions

An ON section can contain several conditions combined using the AND and OR operators. Conditions specifying join keys must refer both left and right tables and must use the equality operator. Other conditions may use other logical operators but they must refer either the left or the right table of a query.

Rows are joined if the whole complex condition is met. If the conditions are not met, still rows may be included in the result depending on the JOIN type. Note that if the same conditions are placed in a WHERE section and they are not met, then rows are always filtered out from the result.

The OR operator inside the ON clause works using the hash join algorithm — for each OR argument with join keys for JOIN, a separate hash table is created, so memory consumption and query execution time grow linearly with an increase in the number of expressions OR of the ON clause.

Note

If a condition refers columns from different tables, then only the equality operator (=) is supported so far.

Example

Consider table_1 and table_2:

┌─Id─┬─name─┐     ┌─Id─┬─text───────────┬─scores─┐
│  1 │ A    │     │  1 │ Text A         │     10 │
│  2 │ B    │     │  1 │ Another text A │     12 │
│  3 │ C    │     │  2 │ Text B         │     15 │
└────┴──────┘     └────┴────────────────┴────────┘

Query with one join key condition and an additional condition for table_2:

SELECT name, text FROM table_1 LEFT OUTER JOIN table_2
    ON table_1.Id = table_2.Id AND startsWith(table_2.text, 'Text');

Note that the result contains the row with the name C and the empty text column. It is included into the result because an OUTER type of a join is used.

┌─name─┬─text───┐
│ A    │ Text A │
│ B    │ Text B │
│ C    │        │
└──────┴────────┘

Query with INNER type of a join and multiple conditions:

SELECT name, text, scores FROM table_1 INNER JOIN table_2
    ON table_1.Id = table_2.Id AND table_2.scores > 10 AND startsWith(table_2.text, 'Text');

Result:

┌─name─┬─text───┬─scores─┐
│ B    │ Text B │     15 │
└──────┴────────┴────────┘

Query with INNER type of a join and condition with OR:

CREATE TABLE t1 (`a` Int64, `b` Int64) ENGINE = MergeTree() ORDER BY a;

CREATE TABLE t2 (`key` Int32, `val` Int64) ENGINE = MergeTree() ORDER BY key;

INSERT INTO t1 SELECT number as a, -a as b from numbers(5);

INSERT INTO t2 SELECT if(number % 2 == 0, toInt64(number), -number) as key, number as val from numbers(5);

SELECT a, b, val FROM t1 INNER JOIN t2 ON t1.a = t2.key OR t1.b = t2.key;

Result:

┌─a─┬──b─┬─val─┐
│ 0 │  0 │   0 │
│ 1 │ -1 │   1 │
│ 2 │ -2 │   2 │
│ 3 │ -3 │   3 │
│ 4 │ -4 │   4 │
└───┴────┴─────┘

Query with INNER type of a join and conditions with OR and AND:

Note

By default, non-equal conditions are supported as long as they use columns from the same table. For example, t1.a = t2.key AND t1.b > 0 AND t2.b > t2.c, because t1.b > 0 uses columns only from t1 and t2.b > t2.c uses columns only from t2. However, you can try experimental support for conditions like t1.a = t2.key AND t1.b > t2.key, check out section below for more details.

SELECT a, b, val FROM t1 INNER JOIN t2 ON t1.a = t2.key OR t1.b = t2.key AND t2.val > 3;

Result:

┌─a─┬──b─┬─val─┐
│ 0 │  0 │   0 │
│ 2 │ -2 │   2 │
│ 4 │ -4 │   4 │
└───┴────┴─────┘

[experimental] Join with inequality conditions for columns from different tables

Note

This feature is experimental. To use it, set allow_experimental_join_condition to 1 in your configuration files or by using the SET command:

SET allow_experimental_join_condition=1

Otherwise, you'll get INVALID_JOIN_ON_EXPRESSION.

Clickhouse currently supports ALL/ANY/SEMI/ANTI INNER/LEFT/RIGHT/FULL JOIN with inequality conditions in addition to equality conditions. The inequality conditions are supported only for hash and grace_hash join algorithms. The inequality conditions are not supported with join_use_nulls.

Example

Table t1:

┌─key──┬─attr─┬─a─┬─b─┬─c─┐
│ key1 │ a    │ 1 │ 1 │ 2 │
│ key1 │ b    │ 2 │ 3 │ 2 │
│ key1 │ c    │ 3 │ 2 │ 1 │
│ key1 │ d    │ 4 │ 7 │ 2 │
│ key1 │ e    │ 5 │ 5 │ 5 │
│ key2 │ a2   │ 1 │ 1 │ 1 │
│ key4 │ f    │ 2 │ 3 │ 4 │
└──────┴──────┴───┴───┴───┘

Table t2

┌─key──┬─attr─┬─a─┬─b─┬─c─┐
│ key1 │ A    │ 1 │ 2 │ 1 │
│ key1 │ B    │ 2 │ 1 │ 2 │
│ key1 │ C    │ 3 │ 4 │ 5 │
│ key1 │ D    │ 4 │ 1 │ 6 │
│ key3 │ a3   │ 1 │ 1 │ 1 │
│ key4 │ F    │ 1 │ 1 │ 1 │
└──────┴──────┴───┴───┴───┘

SELECT t1.*, t2.* from t1 LEFT JOIN t2 ON t1.key = t2.key and (t1.a < t2.a) ORDER BY (t1.key, t1.attr, t2.key, t2.attr);

key1    a   1   1   2   key1    B   2   1   2
key1    a   1   1   2   key1    C   3   4   5
key1    a   1   1   2   key1    D   4   1   6
key1    b   2   3   2   key1    C   3   4   5
key1    b   2   3   2   key1    D   4   1   6
key1    c   3   2   1   key1    D   4   1   6
key1    d   4   7   2           0   0   \N
key1    e   5   5   5           0   0   \N
key2    a2  1   1   1           0   0   \N
key4    f   2   3   4           0   0   \N

NULL values in JOIN keys

The NULL is not equal to any value, including itself. It means that if a JOIN key has a NULL value in one table, it won't match a NULL value in the other table.

Example

Table A:

┌───id─┬─name────┐
│    1 │ Alice   │
│    2 │ Bob     │
│ ᴺᵁᴸᴸ │ Charlie │
└──────┴─────────┘

Table B:

┌───id─┬─score─┐
│    1 │    90 │
│    3 │    85 │
│ ᴺᵁᴸᴸ │    88 │
└──────┴───────┘

SELECT A.name, B.score FROM A LEFT JOIN B ON A.id = B.id

┌─name────┬─score─┐
│ Alice   │    90 │
│ Bob     │     0 │
│ Charlie │     0 │
└─────────┴───────┘

Notice that the row with Charlie from table A and the row with score 88 from table B are not in the result because of the NULL value in the JOIN key.

In case you want to match NULL values, use the isNotDistinctFrom function to compare the JOIN keys.

SELECT A.name, B.score FROM A LEFT JOIN B ON isNotDistinctFrom(A.id, B.id)

┌─name────┬─score─┐
│ Alice   │    90 │
│ Bob     │     0 │
│ Charlie │    88 │
└─────────┴───────┘

ASOF JOIN Usage

ASOF JOIN is useful when you need to join records that have no exact match.

Algorithm requires the special column in tables. This column:

Must contain an ordered sequence.
Can be one of the following types: Int, UInt, Float, Date, DateTime, Decimal.
For hash join algorithm it can’t be the only column in the JOIN clause.

Syntax ASOF JOIN ... ON:

SELECT expressions_list
FROM table_1
ASOF LEFT JOIN table_2
ON equi_cond AND closest_match_cond

You can use any number of equality conditions and exactly one closest match condition. For example, SELECT count() FROM table_1 ASOF LEFT JOIN table_2 ON table_1.a == table_2.b AND table_2.t <= table_1.t.

Conditions supported for the closest match: >, >=, <, <=.

Syntax ASOF JOIN ... USING:

SELECT expressions_list
FROM table_1
ASOF JOIN table_2
USING (equi_column1, ... equi_columnN, asof_column)

ASOF JOIN uses equi_columnX for joining on equality and asof_column for joining on the closest match with the table_1.asof_column >= table_2.asof_column condition. The asof_column column is always the last one in the USING clause.

For example, consider the following tables:

     table_1                           table_2
  event   | ev_time | user_id       event   | ev_time | user_id
----------|---------|---------- ----------|---------|----------
              ...                               ...
event_1_1 |  12:00  |  42         event_2_1 |  11:59  |   42
              ...                 event_2_2 |  12:30  |   42
event_1_2 |  13:00  |  42         event_2_3 |  13:00  |   42
              ...                               ...

ASOF JOIN can take the timestamp of a user event from table_1 and find an event in table_2 where the timestamp is closest to the timestamp of the event from table_1 corresponding to the closest match condition. Equal timestamp values are the closest if available. Here, the user_id column can be used for joining on equality and the ev_time column can be used for joining on the closest match. In our example, event_1_1 can be joined with event_2_1 and event_1_2 can be joined with event_2_3, but event_2_2 can’t be joined.

Note

ASOF JOIN is supported only by hash and full_sorting_merge join algorithms. It's not supported in the Join table engine.

PASTE JOIN Usage

The result of PASTE JOIN is a table that contains all columns from left subquery followed by all columns from the right subquery. The rows are matched based on their positions in the original tables (the order of rows should be defined). If the subqueries return a different number of rows, extra rows will be cut.

Example:

SELECT *
FROM
(
    SELECT number AS a
    FROM numbers(2)
) AS t1
PASTE JOIN
(
    SELECT number AS a
    FROM numbers(2)
    ORDER BY a DESC
) AS t2

┌─a─┬─t2.a─┐
│ 0 │    1 │
│ 1 │    0 │
└───┴──────┘

Note: In this case result can be nondeterministic if the reading is parallel. Example:

SELECT *
FROM
(
    SELECT number AS a
    FROM numbers_mt(5)
) AS t1
PASTE JOIN
(
    SELECT number AS a
    FROM numbers(10)
    ORDER BY a DESC
) AS t2
SETTINGS max_block_size = 2;

┌─a─┬─t2.a─┐
│ 2 │    9 │
│ 3 │    8 │
└───┴──────┘
┌─a─┬─t2.a─┐
│ 0 │    7 │
│ 1 │    6 │
└───┴──────┘
┌─a─┬─t2.a─┐
│ 4 │    5 │
└───┴──────┘

Distributed JOIN

There are two ways to execute join involving distributed tables:

When using a normal JOIN, the query is sent to remote servers. Subqueries are run on each of them in order to make the right table, and the join is performed with this table. In other words, the right table is formed on each server separately.
When using GLOBAL ... JOIN, first the requestor server runs a subquery to calculate the right table. This temporary table is passed to each remote server, and queries are run on them using the temporary data that was transmitted.

Be careful when using GLOBAL. For more information, see the Distributed subqueries section.

Implicit Type Conversion

INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN queries support the implicit type conversion for "join keys". However the query can not be executed, if join keys from the left and the right tables cannot be converted to a single type (for example, there is no data type that can hold all values from both UInt64 and Int64, or String and Int32).

Example

Consider the table t_1:

┌─a─┬─b─┬─toTypeName(a)─┬─toTypeName(b)─┐
│ 1 │ 1 │ UInt16        │ UInt8         │
│ 2 │ 2 │ UInt16        │ UInt8         │
└───┴───┴───────────────┴───────────────┘

and the table t_2:

┌──a─┬────b─┬─toTypeName(a)─┬─toTypeName(b)───┐
│ -1 │    1 │ Int16         │ Nullable(Int64) │
│  1 │   -1 │ Int16         │ Nullable(Int64) │
│  1 │    1 │ Int16         │ Nullable(Int64) │
└────┴──────┴───────────────┴─────────────────┘

The query

SELECT a, b, toTypeName(a), toTypeName(b) FROM t_1 FULL JOIN t_2 USING (a, b);

returns the set:

┌──a─┬────b─┬─toTypeName(a)─┬─toTypeName(b)───┐
│  1 │    1 │ Int32         │ Nullable(Int64) │
│  2 │    2 │ Int32         │ Nullable(Int64) │
│ -1 │    1 │ Int32         │ Nullable(Int64) │
│  1 │   -1 │ Int32         │ Nullable(Int64) │
└────┴──────┴───────────────┴─────────────────┘

Usage Recommendations

Processing of Empty or NULL Cells

While joining tables, the empty cells may appear. The setting join_use_nulls define how ClickHouse fills these cells.

If the JOIN keys are Nullable fields, the rows where at least one of the keys has the value NULL are not joined.

Syntax

The columns specified in USING must have the same names in both subqueries, and the other columns must be named differently. You can use aliases to change the names of columns in subqueries.

The USING clause specifies one or more columns to join, which establishes the equality of these columns. The list of columns is set without brackets. More complex join conditions are not supported.

Syntax Limitations

For multiple JOIN clauses in a single SELECT query:

Taking all the columns via * is available only if tables are joined, not subqueries.
The PREWHERE clause is not available.
The USING clause is not available.

For ON, WHERE, and GROUP BY clauses:

Arbitrary expressions cannot be used in ON, WHERE, and GROUP BY clauses, but you can define an expression in a SELECT clause and then use it in these clauses via an alias.

Performance

When running a JOIN, there is no optimization of the order of execution in relation to other stages of the query. The join (a search in the right table) is run before filtering in WHERE and before aggregation.

Each time a query is run with the same JOIN, the subquery is run again because the result is not cached. To avoid this, use the special Join table engine, which is a prepared array for joining that is always in RAM.

In some cases, it is more efficient to use IN instead of JOIN.

If you need a JOIN for joining with dimension tables (these are relatively small tables that contain dimension properties, such as names for advertising campaigns), a JOIN might not be very convenient due to the fact that the right table is re-accessed for every query. For such cases, there is a “dictionaries” feature that you should use instead of JOIN. For more information, see the Dictionaries section.

Memory Limitations

By default, ClickHouse uses the hash join algorithm. ClickHouse takes the right_table and creates a hash table for it in RAM. If join_algorithm = 'auto' is enabled, then after some threshold of memory consumption, ClickHouse falls back to merge join algorithm. For JOIN algorithms description see the join_algorithm setting.

If you need to restrict JOIN operation memory consumption use the following settings:

max_rows_in_join — Limits number of rows in the hash table.
max_bytes_in_join — Limits size of the hash table.

When any of these limits is reached, ClickHouse acts as the join_overflow_mode setting instructs.

Examples

Example:

SELECT
    CounterID,
    hits,
    visits
FROM
(
    SELECT
        CounterID,
        count() AS hits
    FROM test.hits
    GROUP BY CounterID
) ANY LEFT JOIN
(
    SELECT
        CounterID,
        sum(Sign) AS visits
    FROM test.visits
    GROUP BY CounterID
) USING CounterID
ORDER BY hits DESC
LIMIT 10

┌─CounterID─┬───hits─┬─visits─┐
│   1143050 │ 523264 │  13665 │
│    731962 │ 475698 │ 102716 │
│    722545 │ 337212 │ 108187 │
│    722889 │ 252197 │  10547 │
│   2237260 │ 196036 │   9522 │
│  23057320 │ 147211 │   7689 │
│    722818 │  90109 │  17847 │
│     48221 │  85379 │   4652 │
│  19762435 │  77807 │   7026 │
│    722884 │  77492 │  11056 │
└───────────┴────────┴────────┘

JOIN Clause

Related Content​

Supported Types of JOIN​

Settings​

ON Section Conditions​

[experimental] Join with inequality conditions for columns from different tables​

NULL values in JOIN keys​

ASOF JOIN Usage​

PASTE JOIN Usage​

Distributed JOIN​

Implicit Type Conversion​

Usage Recommendations​

Processing of Empty or NULL Cells​

Syntax​

Syntax Limitations​

Performance​

Memory Limitations​

Examples​

Related Content

Supported Types of JOIN

Settings

ON Section Conditions

[experimental] Join with inequality conditions for columns from different tables

NULL values in JOIN keys

ASOF JOIN Usage

PASTE JOIN Usage

Distributed JOIN

Implicit Type Conversion

Usage Recommendations

Processing of Empty or NULL Cells

Syntax

Syntax Limitations

Performance

Memory Limitations

Examples