Cost based optimizations#

Trino supports several cost based optimizations, described below.

Join enumeration#

The order in which joins are executed in a query can have a significant impact on the query’s performance. The aspect of join ordering that has the largest impact on performance is the size of the data being processed and transferred over the network. If a join, that produces a lot of data, is performed early in the execution, then subsequent stages need to process large amounts of data for longer than necessary, increasing the time and resources needed for the query.

With cost based join enumeration, Trino uses Table statistics provided by connectors to estimate the costs for different join orders and automatically picks the join order with the lowest computed costs.

The join enumeration strategy is governed by the join_reordering_strategy session property, with the optimizer.join-reordering-strategy configuration property providing the default value.

The valid values are:
  • AUTOMATIC (default) - full automatic join enumeration enabled

  • ELIMINATE_CROSS_JOINS - eliminate unnecessary cross joins

  • NONE - purely syntactic join order

If using AUTOMATIC and statistics are not available, or if for any other reason a cost could not be computed, the ELIMINATE_CROSS_JOINS strategy is used instead.

Join distribution selection#

Trino uses a hash based join algorithm. That implies that for each join operator a hash table must be created from one join input, called build side. The other input, called probe side, is then iterated, and for each row the hash table is queried to find matching rows.

There are two types of join distributions:
  • Partitioned: each node participating in the query builds a hash table from only fraction of the data

  • Broadcast: each node participating in the query builds a hash table from all of the data (data is replicated to each node)

Each type have their trade offs. Partitioned joins require redistributing both tables using a hash of the join key. This can be slower, sometimes substantially slower, than broadcast joins, but allows much larger joins. In particular, broadcast joins are faster if the build side is much smaller than the probe side. However, broadcast joins require that the tables on the build side of the join after filtering fit in memory on each node, whereas distributed joins only need to fit in distributed memory across all nodes.

With cost based join distribution selection, Trino automatically chooses to use a partitioned or broadcast join. With cost based join enumeration, Trino automatically chooses which side is the probe and which is the build.

The join distribution strategy is governed by the join_distribution_type session property, with the join-distribution-type configuration property providing the default value.

The valid values are:
  • AUTOMATIC (default) - join distribution type is determined automatically for each join

  • BROADCAST - broadcast join distribution is used for all joins

  • PARTITIONED - partitioned join distribution is used for all join

Capping replicated table size#

Join distribution type will be chosen automatically when join reordering strategy is set to AUTOMATIC or when join distribution type is set to AUTOMATIC. In such case it is possible to cap the maximum size of replicated table via join-max-broadcast-table-size config property (e.g. join-max-broadcast-table-size=100MB) or via join_max_broadcast_table_size session property (e.g. set session join_max_broadcast_table_size='100MB';) This allows to improve cluster concurrency and to prevent bad plans when CBO misestimates size of joined tables.

By default replicated table size is capped to 100MB.

Connector implementations#

In order for the Trino optimizer to use the cost based strategies, the connector implementation must provide Table statistics.