Optimizer properties#


  • Type: boolean

  • Default value: false

Enables optimization for aggregations on dictionaries. This can also be specified on a per-query basis using the dictionary_aggregation session property.


  • Type: boolean

  • Default value: true

Compute hash codes for distribution, joins, and aggregations early during execution, allowing result to be shared between operations later in the query. This can reduce CPU usage by avoiding computing the same hash multiple times, but at the cost of additional network transfer for the hashes. In most cases it decreases overall query processing time. This can also be specified on a per-query basis using the optimize_hash_generation session property.

It is often helpful to disable this property, when using EXPLAIN in order to make the query plan easier to read.


  • Type: boolean

  • Default value: false

Enable optimization of some aggregations by using values that are stored as metadata. This allows Trino to execute some simple queries in constant time. Currently, this optimization applies to max, min and approx_distinct of partition keys and other aggregation insensitive to the cardinality of the input,including DISTINCT aggregates. Using this may speed up some queries significantly.

The main drawback is that it can produce incorrect results, if the connector returns partition keys for partitions that have no rows. In particular, the Hive connector can return empty partitions, if they were created by other systems. Trino cannot create them.


  • Type: boolean

  • Default value: true

When an aggregation is above an outer join and all columns from the outer side of the join are in the grouping clause, the aggregation is pushed below the outer join. This optimization is particularly useful for correlated scalar subqueries, which get rewritten to an aggregation over an outer join. For example:

SELECT * FROM item i
    WHERE i.i_current_price > (
        SELECT AVG(j.i_current_price) FROM item j
            WHERE i.i_category = j.i_category);

Enabling this optimization can substantially speed up queries by reducing the amount of data that needs to be processed by the join. However, it may slow down some queries that have very selective joins. This can also be specified on a per-query basis using the push_aggregation_through_join session property.


  • Type: boolean

  • Default value: true

Parallelize writes when using UNION ALL in queries that write data. This improves the speed of writing output tables in UNION ALL queries, because these writes do not require additional synchronization when collecting results. Enabling this optimization can improve UNION ALL speed, when write speed is not yet saturated. However, it may slow down queries in an already heavily loaded system. This can also be specified on a per-query basis using the push_table_write_through_union session property.


  • Type: string


  • Default value: AUTOMATIC

The join reordering strategy to use. NONE maintains the order the tables are listed in the query. ELIMINATE_CROSS_JOINS reorders joins to eliminate cross joins, where possible, and otherwise maintains the original query order. When reordering joins, it also strives to maintain the original table order as much as possible. AUTOMATIC enumerates possible orders, and uses statistics-based cost estimation to determine the least cost order. If stats are not available, or if for any reason a cost could not be computed, the ELIMINATE_CROSS_JOINS strategy is used. This can be specified on a per-query basis using the join_reordering_strategy session property.


  • Type: integer

  • Default value: 9

When optimizer.join-reordering-strategy is set to cost-based, this property determines the maximum number of joins that can be reordered at once.


The number of possible join orders scales factorially with the number of relations, so increasing this value can cause serious performance issues.


  • Type: boolean

  • Default value: true

Reduces number of rows produced by joins when optimizer detects that duplicated join output rows can be skipped.


  • Type: boolean

  • Default value: true

Use connector provided table node partitioning when reading tables. For example, table node partitioning corresponds to Hive table buckets. When set to true and minimal partition to task ratio is matched or exceeded, each table partition is read by a separate worker. The minimal ratio is defined in optimizer.table-scan-node-partitioning-min-bucket-to-task-ratio.

Partition reader assignments are distributed across workers for parallel processing. Use of table scan node partitioning can improve query performance by reducing query complexity. For example, cluster wide data reshuffling might not be needed when processing an aggregation query. However, query parallelism might be reduced when partition count is low compared to number of workers.


  • Type: double

  • Default value: 0.5

Specifies minimal bucket to task ratio that has to be matched or exceeded in order to use table scan node partitioning. When the table bucket count is small compared to the number of workers, then the table scan is distributed across all workers for improved parallelism.