Project: ClickHouse (App Definition and Development / Database)
File: in.md

slug: /en/sql-reference/operators/in The `IN`, `NOT IN`, `GLOBAL IN`, and `GLOBAL NOT IN` operators are covered separately, since their functionality is quite rich. The left side of the operator is either a single column or a tuple. Examples: ``` sql SELECT UserID IN (123, 456) FROM ... SELECT (CounterID, UserID) IN ((34, 123), (101500, 456)) FROM ... ``` If the left side is a single column that is in the index, and the right side is a set of constants, the system uses the index for processing the query. Dont list too many values explicitly (i.e.millions). If a data set is large, put it in a temporary table (for example, see the section ), then use a subquery. The right side of the operator can be a set of constant expressions, a set of tuples with constant expressions (shown in the examples above), or the name of a database table or `SELECT` subquery in brackets. ClickHouse allows types to differ in the left and the right parts of `IN` subquery. In this case it converts the left side value to the type of the right side, as if the function is applied. That means, that the data type becomes , and if the conversion cannot be performed, it returns . Example Query: ``` sql SELECT '1' IN (SELECT 1); ``` Result: ``` text in('1', _subquery49) 1 ``` If the right side of the operator is the name of a table (for example, `UserID IN users`), this is equivalent to the subquery `UserID IN (SELECT * FROM users)`. Use this when working with external data that is sent along with the query. For example, the query can be sent together with a set of user IDs loaded to the users temporary table, which should be filtered. If the right side of the operator is a table name that has the Set engine (a prepared data set that is always in RAM), the data set will not be created over again for each query. The subquery may specify more than one column for filtering tuples. Example: ``` sql SELECT (CounterID, UserID) IN (SELECT CounterID, UserID FROM ...) FROM ... ``` The columns to the left and right of the `IN` operator should have the same type. The `IN` operator and subquery may occur in any part of the query, including in aggregate functions and lambda functions. Example: ``` sql SELECT EventDate, avg(UserID IN ( SELECT UserID FROM test.hits WHERE EventDate = toDate('2014-03-17') )) AS ratio FROM test.hits GROUP BY EventDate ORDER BY EventDate ASC ``` ``` text EventDateratio 2014-03-17 1 2014-03-18 0.807696 2014-03-19 0.755406 2014-03-20 0.723218 2014-03-21 0.697021 2014-03-22 0.647851 2014-03-23 0.648416 ``` For each day after March 17th, count the percentage of pageviews made by users who visited the site on March

A subquery in the `IN` clause is always run just one time on a single server. There are no dependent subqueries. During request processing, the `IN` operator assumes that the result of an operation with always equals `0`, regardless of whether `NULL` is on the right or left side of the operator. `NULL` values are not included in any dataset, do not correspond to each other and cannot be compared if . Here is an example with the `t_null` table: ``` text xy 1 2 3 ``` Running the query `SELECT x FROM t_null WHERE y IN (NULL,3)` gives you the following result: ``` text x 2 ``` You can see that the row in which `y = NULL` is thrown out of the query results. This is because ClickHouse cant decide whether `NULL` is included in the `(NULL,3)` set, returns `0` as the result of the operation, and `SELECT` excludes this row from the final output. ``` sql SELECT y IN (NULL, 3) FROM t_null ``` ``` text in(y, tuple(NULL, 3)) 0 1 ``` There are two options for `IN` operators with subqueries (similar to `JOIN` operators): normal `IN` / `JOIN` and `GLOBAL IN` / `GLOBAL JOIN`. They differ in how they are run for distributed query processing. :::note Remember that the algorithms described below may work differently depending on the `distributedproductmode` setting. ::: When using the regular `IN`, the query is sent to remote servers, and each of them runs the subqueries in the `IN` or `JOIN` clause. When using `GLOBAL IN` / `GLOBAL JOIN`, first all the subqueries are run for `GLOBAL IN` / `GLOBAL JOIN`, and the results are collected in temporary tables. Then the temporary tables are sent to each remote server, where the queries are run using this temporary data. For a non-distributed query, use the regular `IN` / `JOIN`. Be careful when using subqueries in the `IN` / `JOIN` clauses for distributed query processing. Lets look at some examples. Assume that each server in the cluster has a normal local_table. Each server also has a distributed_table table with the Distributed type, which looks at all the servers in the cluster. For a query to the distributed_table, the query will be sent to all the remote servers and run on them using the local_table. For example, the query ``` sql SELECT uniq(UserID) FROM distributed_table ``` will be sent to all remote servers as ``` sql SELECT uniq(UserID) FROM local_table ``` and run on each of them in parallel, until it reaches the stage where intermediate results can be combined. Then the intermediate results will be returned to the requestor server and merged on it, and the final result will be sent to the

Now lets examine a query with `IN`: ``` sql SELECT uniq(UserID) FROM distributedtable WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM localtable WHERE CounterID = 34) ``` Calculation of the intersection of audiences of two sites. This query will be sent to all remote servers as ``` sql SELECT uniq(UserID) FROM localtable WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM localtable WHERE CounterID = 34) ``` In other words, the data set in the `IN` clause will be collected on each server independently, only across the data that is stored locally on each of the servers. This will work correctly and optimally if you are prepared for this case and have spread data across the cluster servers such that the data for a single UserID resides entirely on a single server. In this case, all the necessary data will be available locally on each server. Otherwise, the result will be inaccurate. We refer to this variation of the query as local IN. To correct how the query works when data is spread randomly across the cluster servers, you could specify distributed_table inside a subquery. The query would look like this: ``` sql SELECT uniq(UserID) FROM distributedtable WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributedtable WHERE CounterID = 34) ``` This query will be sent to all remote servers as ``` sql SELECT uniq(UserID) FROM localtable WHERE CounterID = 101500 AND UserID IN (SELECT UserID FROM distributedtable WHERE CounterID = 34) ``` The subquery will begin running on each remote server. Since the subquery uses a distributed table, the subquery that is on each remote server will be resent to every remote server as: ``` sql SELECT UserID FROM local_table WHERE CounterID = 34 ``` For example, if you have a cluster of 100 servers, executing the entire query will require 10,000 elementary requests, which is generally considered unacceptable. In such cases, you should always use `GLOBAL IN` instead of `IN`. Lets look at how it works for the query: ``` sql SELECT uniq(UserID) FROM distributedtable WHERE CounterID = 101500 AND UserID GLOBAL IN (SELECT UserID FROM distributedtable WHERE CounterID = 34) ``` The requestor server will run the subquery: ``` sql SELECT UserID FROM distributed_table WHERE CounterID = 34 ``` and the result will be put in a temporary table in RAM. Then the request will be sent to each remote server as: ``` sql SELECT uniq(UserID) FROM localtable WHERE CounterID = 101500 AND UserID GLOBAL IN data1 ``` The temporary table `_data1` will be sent to every remote server with the query (the name of the temporary table is implementation-defined). This is more optimal than using the normal `IN`. However, keep the following points in mind: When creating a temporary table, data is not made unique. To reduce the volume of data transmitted over the network, specify DISTINCT in the subquery. (You do not need to do this for a normal

The temporary table will be sent to all the remote servers. Transmission does not account for network topology. For example, if 10 remote servers reside in a datacenter that is very remote in relation to the requestor server, the data will be sent 10 times over the channel to the remote datacenter. Try to avoid large data sets when using `GLOBAL IN`. When transmitting data to remote servers, restrictions on network bandwidth are not configurable. You might overload the network. Try to distribute data across servers so that you do not need to use `GLOBAL IN` on a regular basis. If you need to use `GLOBAL IN` often, plan the location of the ClickHouse cluster so that a single group of replicas resides in no more than one data center with a fast network between them, so that a query can be processed entirely within a single data center. It also makes sense to specify a local table in the `GLOBAL IN` clause, in case this local table is only available on the requestor server and you want to use data from it on remote servers. You can use and to control how much data is transferred during distributed queries. This is specially important if the `GLOBAL IN` query returns a large amount of data. Consider the following SQL: ```sql select * from table1 where col1 global in (select col1 from table2 where <some_predicate>) ``` If `some_predicate` is not selective enough, it will return a large amount of data and cause performance issues. In such cases, it is wise to limit the data transfer over the network. Also, note that is set to `throw` (by default) meaning that an exception is raised when these thresholds are met. When is greater than 1, distributed queries are further transformed. For example, the following: ```sql SELECT CounterID, count() FROM distributedtable1 WHERE UserID IN (SELECT UserID FROM localtable2 WHERE CounterID < 100) SETTINGS maxparallelreplicas=3 ``` is transformed on each server into: ```sql SELECT CounterID, count() FROM localtable1 WHERE UserID IN (SELECT UserID FROM localtable2 WHERE CounterID < 100) SETTINGS parallelreplicascount=3, parallelreplicasoffset=M ``` where `M` is between `1` and `3` depending on which replica the local query is executing on. These settings affect every MergeTree-family table in the query and have the same effect as applying `SAMPLE 1/3 OFFSET (M-1)/3` on each table. Therefore adding the setting will only produce correct results if both tables have the same replication scheme and are sampled by UserID or a subkey of it. In particular, if `localtable2` does not have a sampling key, incorrect results will be produced. The same rule applies to `JOIN`. One workaround if `localtable2` does not meet the requirements, is to use `GLOBAL IN` or `GLOBAL JOIN`. If a table doesn't have a sampling key, more flexible options for can be used that can produce different and more optimal behaviour.