OPTIMIZE Statement
This query tries to initialize an unscheduled merge of data parts for tables. Note that we generally recommend against using OPTIMIZE TABLE ... FINAL (see these docs) as its use case is meant for administration, not for daily operations.
OPTIMIZE can't fix the Too many parts error.
Syntax
The OPTIMIZE query is supported for MergeTree family (including materialized views) and the Buffer engines. Other table engines aren't supported.
When OPTIMIZE is used with the ReplicatedMergeTree family of table engines, ClickHouse creates a task for merging and waits for execution on all replicas (if the alter_sync setting is set to 2) or on current replica (if the alter_sync setting is set to 1).
- If
OPTIMIZEdoes not perform a merge for any reason, it does not notify the client. To enable notifications, use the optimize_throw_if_noop setting. - If you specify a
PARTITION, only the specified partition is optimized. How to set partition expression. - If you specify
FINALorFORCE, optimization is performed even when all the data is already in one part. You can control this behaviour with optimize_skip_merged_partitions. Also, the merge is forced even if concurrent merges are performed. - If you specify
DEDUPLICATE, then completely identical rows (unless by-clause is specified) will be deduplicated (all columns are compared), it makes sense only for the MergeTree engine.
You can specify how long (in seconds) to wait for inactive replicas to execute OPTIMIZE queries by the replication_wait_for_inactive_replica_timeout setting.
If the alter_sync is set to 2 and some replicas are not active for more than the time, specified by the replication_wait_for_inactive_replica_timeout setting, then an exception UNFINISHED is thrown.
DRY RUN
The DRY RUN clause simulates a merge of the specified parts without committing the result. The merged part is written to a temporary location, validated, and then discarded. The original parts and table data remain unchanged.
This is useful for:
- Testing merge correctness across ClickHouse versions.
- Reproducing merge-related bugs deterministically.
- Benchmarking merge performance.
DRY RUN is only supported for MergeTree family tables. The PARTS keyword with a list of part names is required. All specified parts must exist, be active, and belong to the same partition.
DRY RUN is incompatible with FINAL and PARTITION. It can be combined with DEDUPLICATE (with optional column specification) and CLEANUP (for ReplacingMergeTree tables).
Syntax
By default, the resulting merged part is validated in a way similar to CHECK TABLE query. This behavior is controlled by the optimize_dry_run_check_part setting (enabled by default). Disabling it skips validation, which can be useful for benchmarking the merge itself.
Example
BY expression
If you want to perform deduplication on custom set of columns rather than on all, you can specify list of columns explicitly or use any combination of *, COLUMNS or EXCEPT expressions. The explicitly written or implicitly expanded list of columns must include all columns specified in row ordering expression (both primary and sorting keys) and partitioning expression (partitioning key).
Notice that * behaves just like in SELECT: MATERIALIZED and ALIAS columns are not used for expansion.
Also, it is an error to specify empty list of columns, or write an expression that results in an empty list of columns, or deduplicate by an ALIAS column.
Syntax
Examples
Consider the table:
Result:
All following examples are executed against this state with 5 rows.
DEDUPLICATE
When columns for deduplication are not specified, all of them are taken into account. The row is removed only if all values in all columns are equal to corresponding values in the previous row:
Result:
DEDUPLICATE BY *
When columns are specified implicitly, the table is deduplicated by all columns that are not ALIAS or MATERIALIZED. Considering the table above, these are primary_key, secondary_key, value, and partition_key columns:
Result:
DEDUPLICATE BY * EXCEPT
Deduplicate by all columns that are not ALIAS or MATERIALIZED and explicitly not value: primary_key, secondary_key, and partition_key columns.
Result:
DEDUPLICATE BY <list of columns>
Deduplicate explicitly by primary_key, secondary_key, and partition_key columns:
Result:
DEDUPLICATE BY COLUMNS(<regex>)
Deduplicate by all columns matching a regex: primary_key, secondary_key, and partition_key columns:
Result: