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](https://en.wikipedia.org/wiki/Relational_algebra#Joins_and_join-like_operators) join. The special case of one table join is often referred to as “self-join”.
Expressions from `ON` clause and columns from `USING` clause are called “join keys”. Unless otherwise stated, join produces a [Cartesian product](https://en.wikipedia.org/wiki/Cartesian_product) from rows with matching “join keys”, which might produce results with much more rows than the source tables.
- Blog: [ClickHouse: A Blazingly Fast DBMS with Full SQL Join Support - Under the Hood - Part 2](https://clickhouse.com/blog/clickhouse-fully-supports-joins-hash-joins-part2)
- Blog: [ClickHouse: A Blazingly Fast DBMS with Full SQL Join Support - Under the Hood - Part 3](https://clickhouse.com/blog/clickhouse-fully-supports-joins-full-sort-partial-merge-part3)
- Blog: [ClickHouse: A Blazingly Fast DBMS with Full SQL Join Support - Under the Hood - Part 4](https://clickhouse.com/blog/clickhouse-fully-supports-joins-direct-join-part4)
`JOIN` without specified type implies `INNER`. Keyword `OUTER` can be safely omitted. Alternative syntax for `CROSS JOIN` is specifying multiple tables in [FROM clause](../../../sql-reference/statements/select/from.md) separated by commas.
-`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.
When [join_algorithm](../../../operations/settings/settings.md#settings-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).
The default join type can be overridden using [join_default_strictness](../../../operations/settings/settings.md#settings-join_default_strictness) setting.
The behavior of ClickHouse server for `ANY JOIN` operations depends on the [any_join_distinct_right_table_keys](../../../operations/settings/settings.md#any_join_distinct_right_table_keys) setting.
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".
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.
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:
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 │
└──────┴───────┘
```
```sql
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.
```sql
SELECT A.name, B.score FROM A LEFT JOIN B ON isNotDistinctFrom(A.id, B.id)
- Can be one of the following types: [Int, UInt](../../../sql-reference/data-types/int-uint.md), [Float](../../../sql-reference/data-types/float.md), [Date](../../../sql-reference/data-types/date.md), [DateTime](../../../sql-reference/data-types/datetime.md), [Decimal](../../../sql-reference/data-types/decimal.md).
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`:
``` sql
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.
`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.
- 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](../../../sql-reference/operators/in.md#select-distributed-subqueries) section.
`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`:
```text
┌─a─┬─b─┬─toTypeName(a)─┬─toTypeName(b)─┐
│ 1 │ 1 │ UInt16 │ UInt8 │
│ 2 │ 2 │ UInt16 │ UInt8 │
└───┴───┴───────────────┴───────────────┘
```
and the table `t_2`:
```text
┌──a─┬────b─┬─toTypeName(a)─┬─toTypeName(b)───┐
│ -1 │ 1 │ Int16 │ Nullable(Int64) │
│ 1 │ -1 │ Int16 │ Nullable(Int64) │
│ 1 │ 1 │ Int16 │ Nullable(Int64) │
└────┴──────┴───────────────┴─────────────────┘
```
The query
```sql
SELECT a, b, toTypeName(a), toTypeName(b) FROM t_1 FULL JOIN t_2 USING (a, b);
While joining tables, the empty cells may appear. The setting [join_use_nulls](../../../operations/settings/settings.md#join_use_nulls) define how ClickHouse fills these cells.
If the `JOIN` keys are [Nullable](../../../sql-reference/data-types/nullable.md) fields, the rows where at least one of the keys has the value [NULL](../../../sql-reference/syntax.md#null-literal) are not joined.
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.
- 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.
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](../../../engines/table-engines/special/join.md) table engine, which is a prepared array for joining that is always in RAM.
In some cases, it is more efficient to use [IN](../../../sql-reference/operators/in.md) 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](../../../sql-reference/dictionaries/index.md) section.
By default, ClickHouse uses the [hash join](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/Sort-merge_join) join algorithm. For `JOIN` algorithms description see the [join_algorithm](../../../operations/settings/settings.md#settings-join_algorithm) setting.
If you need to restrict `JOIN` operation memory consumption use the following settings:
When any of these limits is reached, ClickHouse acts as the [join_overflow_mode](../../../operations/settings/query-complexity.md#settings-join_overflow_mode) setting instructs.
