ClickHouse/docs/en/sql-reference/statements/select/join.md

toc_title
JOIN

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|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.

Settings

The default join type can be overriden 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

• join_algorithm
• join_any_take_last_row
• join_use_nulls
• partial_merge_join_optimizations
• partial_merge_join_rows_in_right_blocks
• join_on_disk_max_files_to_merge
• any_join_distinct_right_table_keys

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*.
• 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 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 "Note" ASOF join is not supported in the Join table engine.

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.

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 an “external dictionaries” feature that you should use instead of JOIN. For more information, see the External 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 │
└───────────┴────────┴────────┘