Restrictions on Query Complexity

Restrictions on Query Complexity

Restrictions on query complexity are part of the settings.
They are used to provide safer execution from the user interface.
Almost all the restrictions only apply to SELECT. For distributed query processing, restrictions are applied on each server separately.

ClickHouse checks the restrictions for data parts, not for each row. It means that you can exceed the value of restriction with the size of the data part.

Restrictions on the “maximum amount of something” can take the value 0, which means “unrestricted”.
Most restrictions also have an ‘overflow_mode’ setting, meaning what to do when the limit is exceeded.
It can take one of two values: throw or break. Restrictions on aggregation (group_by_overflow_mode) also have the value any.

throw – Throw an exception (default).

break – Stop executing the query and return the partial result, as if the source data ran out.

any (only for group_by_overflow_mode) – Continuing aggregation for the keys that got into the set, but don’t add new keys to the set.

max_memory_usage

The maximum amount of RAM to use for running a query on a single server.

In the default configuration file, the maximum is 10 GB.

The setting doesn’t consider the volume of available memory or the total volume of memory on the machine.
The restriction applies to a single query within a single server.
You can use SHOW PROCESSLIST to see the current memory consumption for each query.
Besides, the peak memory consumption is tracked for each query and written to the log.

Memory usage is not monitored for the states of certain aggregate functions.

Memory usage is not fully tracked for states of the aggregate functions min, max, any, anyLast, argMin, argMax from String and Array arguments.

Memory consumption is also restricted by the parameters max_memory_usage_for_user and max_server_memory_usage.

max_memory_usage_for_user

The maximum amount of RAM to use for running a user’s queries on a single server.

Default values are defined in Settings.h. By default, the amount is not restricted (max_memory_usage_for_user = 0).

See also the description of max_memory_usage.

max_rows_to_read

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.

A maximum number of rows that can be read from a table when running a query.

max_bytes_to_read

A maximum number of bytes (uncompressed data) that can be read from a table when running a query.

read_overflow_mode

What to do when the volume of data read exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

max_rows_to_read_leaf

The following restrictions can be checked on each block (instead of on each row). That is, the restrictions can be broken a little.

A maximum number of rows that can be read from a local table on a leaf node when running a distributed query. While
distributed queries can issue a multiple sub-queries to each shard (leaf) - this limit will be checked only on the read
stage on the leaf nodes and ignored on results merging stage on the root node. For example, cluster consists of 2 shards
and each shard contains a table with 100 rows. Then distributed query which suppose to read all the data from both
tables with setting max_rows_to_read=150 will fail as in total it will be 200 rows. While query
with max_rows_to_read_leaf=150 will succeed since leaf nodes will read 100 rows at max.

max_bytes_to_read_leaf

A maximum number of bytes (uncompressed data) that can be read from a local table on a leaf node when running
a distributed query. While distributed queries can issue a multiple sub-queries to each shard (leaf) - this limit will
be checked only on the read stage on the leaf nodes and ignored on results merging stage on the root node.
For example, cluster consists of 2 shards and each shard contains a table with 100 bytes of data.
Then distributed query which suppose to read all the data from both tables with setting max_bytes_to_read=150 will fail
as in total it will be 200 bytes. While query with max_bytes_to_read_leaf=150 will succeed since leaf nodes will read
100 bytes at max.

read_overflow_mode_leaf

What to do when the volume of data read exceeds one of the leaf limits: ‘throw’ or ‘break’. By default, throw.

max_rows_to_group_by

A maximum number of unique keys received from aggregation. This setting lets you limit memory consumption when aggregating.

group_by_overflow_mode

What to do when the number of unique keys for aggregation exceeds the limit: ‘throw’, ‘break’, or ‘any’. By default, throw.
Using the ‘any’ value lets you run an approximation of GROUP BY. The quality of this approximation depends on the statistical nature of the data.

max_bytes_before_external_group_by

Enables or disables execution of GROUP BY clauses in external memory. See GROUP BY in external memory.

Possible values:

Maximum volume of RAM (in bytes) that can be used by the single GROUP BY operation.
0 — GROUP BY in external memory disabled.

Default value: 0.

max_rows_to_sort

A maximum number of rows before sorting. This allows you to limit memory consumption when sorting.

max_bytes_to_sort

A maximum number of bytes before sorting.

sort_overflow_mode

What to do if the number of rows received before sorting exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

max_result_rows

Limit on the number of rows in the result. Also checked for subqueries, and on remote servers when running parts of a distributed query.

max_result_bytes

Limit on the number of bytes in the result. The same as the previous setting.

result_overflow_mode

What to do if the volume of the result exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

Using ‘break’ is similar to using LIMIT. Break interrupts execution only at the block level. This means that amount of returned rows is greater than max_result_rows, multiple of max_block_size and depends on max_threads.

Example:

SET max_threads = 3, max_block_size = 3333;
SET max_result_rows = 3334, result_overflow_mode = 'break';
SELECT *
FROM numbers_mt(100000)
FORMAT Null;

Result:

6666 rows in set. ...

max_execution_time

Maximum query execution time in seconds.
At this time, it is not checked for one of the sorting stages, or when merging and finalizing aggregate functions.

timeout_overflow_mode

What to do if the query is run longer than ‘max_execution_time’: ‘throw’ or ‘break’. By default, throw.

min_execution_speed

Minimal execution speed in rows per second. Checked on every data block when ‘timeout_before_checking_execution_speed’ expires. If the execution speed is lower, an exception is thrown.

min_execution_speed_bytes

A minimum number of execution bytes per second. Checked on every data block when ‘timeout_before_checking_execution_speed’ expires. If the execution speed is lower, an exception is thrown.

max_execution_speed

A maximum number of execution rows per second. Checked on every data block when ‘timeout_before_checking_execution_speed’ expires. If the execution speed is high, the execution speed will be reduced.

max_execution_speed_bytes

A maximum number of execution bytes per second. Checked on every data block when ‘timeout_before_checking_execution_speed’ expires. If the execution speed is high, the execution speed will be reduced.

timeout_before_checking_execution_speed

Checks that execution speed is not too slow (no less than ‘min_execution_speed’), after the specified time in seconds has expired.

max_columns_to_read

A maximum number of columns that can be read from a table in a single query. If a query requires reading a greater number of columns, it throws an exception.

max_temporary_columns

A maximum number of temporary columns that must be kept in RAM at the same time when running a query, including constant columns. If there are more temporary columns than this, it throws an exception.

max_temporary_non_const_columns

The same thing as ‘max_temporary_columns’, but without counting constant columns.
Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.

max_subquery_depth

Maximum nesting depth of subqueries. If subqueries are deeper, an exception is thrown. By default, 100.

max_pipeline_depth

Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.

max_ast_depth

Maximum nesting depth of a query syntactic tree. If exceeded, an exception is thrown.
At this time, it isn’t checked during parsing, but only after parsing the query. That is, a syntactic tree that is too deep can be created during parsing, but the query will fail. By default, 1000.

max_ast_elements

A maximum number of elements in a query syntactic tree. If exceeded, an exception is thrown.
In the same way as the previous setting, it is checked only after parsing the query. By default, 50,000.

max_rows_in_set

A maximum number of rows for a data set in the IN clause created from a subquery.

max_bytes_in_set

A maximum number of bytes (uncompressed data) used by a set in the IN clause created from a subquery.

set_overflow_mode

What to do when the amount of data exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

max_rows_in_distinct

A maximum number of different rows when using DISTINCT.

max_bytes_in_distinct

A maximum number of bytes used by a hash table when using DISTINCT.

distinct_overflow_mode

What to do when the amount of data exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

max_rows_to_transfer

A maximum number of rows that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

max_bytes_to_transfer

A maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.

transfer_overflow_mode

What to do when the amount of data exceeds one of the limits: ‘throw’ or ‘break’. By default, throw.

max_rows_in_join

Limits the number of rows in the hash table that is used when joining tables.

This settings applies to SELECT … JOIN operations and the Join table engine.

If a query contains multiple joins, ClickHouse checks this setting for every intermediate result.

ClickHouse can proceed with different actions when the limit is reached. Use the join_overflow_mode setting to choose the action.

Possible values:

Positive integer.
0 — Unlimited number of rows.

Default value: 0.

max_bytes_in_join

Limits the size in bytes of the hash table used when joining tables.

This settings applies to SELECT … JOIN operations and Join table engine.

If the query contains joins, ClickHouse checks this setting for every intermediate result.

ClickHouse can proceed with different actions when the limit is reached. Use join_overflow_mode settings to choose the action.

Possible values:

Positive integer.
0 — Memory control is disabled.

Default value: 0.

join_overflow_mode

Defines what action ClickHouse performs when any of the following join limits is reached:

max_bytes_in_join
max_rows_in_join

Possible values:

THROW — ClickHouse throws an exception and breaks operation.
BREAK — ClickHouse breaks operation and doesn’t throw an exception.

Default value: THROW.

See Also

max_partitions_per_insert_block

Limits the maximum number of partitions in a single inserted block.

Positive integer.
0 — Unlimited number of partitions.

Default value: 100.

Details

When inserting data, ClickHouse calculates the number of partitions in the inserted block. If the number of partitions is more than max_partitions_per_insert_block, ClickHouse throws an exception with the following text:

“Too many partitions for single INSERT block (more than” + toString(max_parts) + “). The limit is controlled by ‘max_partitions_per_insert_block’ setting. A large number of partitions is a common misconception. It will lead to severe negative performance impact, including slow server startup, slow INSERT queries and slow SELECT queries. Recommended total number of partitions for a table is under 1000..10000. Please note, that partitioning is not intended to speed up SELECT queries (ORDER BY key is sufficient to make range queries fast). Partitions are intended for data manipulation (DROP PARTITION, etc).”

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