Cost-Based Optimizer

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Warning:
CockroachDB v2.1 is no longer supported as of April 30, 2020. For more details, refer to the Release Support Policy.

New in v2.1: The cost-based optimizer seeks the lowest cost for a query, usually related to time.

In versions 2.1 and later, CockroachDB's cost-based optimizer is enabled by default. In versions prior to v2.1, a heuristic planner was used to generate query execution plans. The heuristic planner will only be used in the following cases:

Warning:

This is a beta feature. It is currently undergoing continued testing. Please file a Github issue with us if you identify a bug.

How is cost calculated?

A given SQL query can have thousands of equivalent query plans with vastly different execution times. The cost-based optimizer enumerates these plans and chooses the lowest cost plan.

Cost is roughly calculated by:

  • Estimating how much time each node in the query plan will use to process all results
  • Modeling how data flows through the query plan

The most important factor in determining the quality of a plan is cardinality (i.e., the number of rows); the fewer rows each SQL operator needs to process, the faster the query will run.

View query plan

To see whether a query will be run with the cost-based optimizer, run the query with EXPLAIN (OPT). The OPT option displays a query plan tree, along with some information that was used to plan the query. If the query is unsupported (i.e., it returns an error like pq: unsupported statement: *tree.Insert or pq: aggregates with FILTER are not supported yet), the query will not be run with the cost-based optimizer and will be run with the legacy heuristic planner.

For example, the following query (which uses CockroachDB's TPC-H data set) returns the query plan tree, which means that it will be run with the cost-based optimizer:

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> EXPLAIN(OPT) SELECT l_shipmode, avg(l_extendedprice) from lineitem GROUP BY l_shipmode;
                                     text
+-----------------------------------------------------------------------------+
group-by
├── columns: l_shipmode:15(string!null) avg:17(float)
├── grouping columns: l_shipmode:15(string!null)
├── stats: [rows=700, distinct(15)=700]
├── cost: 1207
├── key: (15)
├── fd: (15)-->(17)
├── prune: (17)
├── scan lineitem
│    ├── columns: l_extendedprice:6(float!null) l_shipmode:15(string!null)
│    ├── stats: [rows=1000, distinct(15)=700]
│    ├── cost: 1180
│    └── prune: (6,15)
└── aggregations [outer=(6)]
└── avg [type=float, outer=(6)]
└── variable: l_extendedprice [type=float, outer=(6)]
(16 rows)

In contrast, this query returns pq: unsupported statement: *tree.Insert, which means that it will use the legacy heuristic planner instead of the cost-based optimizer:

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> EXPLAIN (OPT) INSERT INTO l_shipmode VALUES ("truck");
pq: unsupported statement: *tree.Insert

How to turn the optimizer off

With the optimizer turned on, the performance of some workloads may change. If your workload performs worse than expected (e.g., lower throughput or higher latency), you can turn off the cost-based optimizer and use the heuristic planner.

To turn the cost-based optimizer off for the current session:

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> SET optimizer = 'off';

To turn the cost-based optimizer off for all sessions:

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> SET CLUSTER SETTING sql.defaults.optimizer = 'off';
Note:

Changing the cluster setting does not immediately turn the optimizer off; instead, it changes the default session setting to off. To see the change, restart your session.

Known limitations

  • The cost-based optimizer will not support automated use of statistics during this time period. To manually generate table statistics, use the CREATE STATISTICS statement.
  • Some features present in v2.0 are not supported by the cost-based optimizer; however, the optimizer will fall back to the v2.0 code path for this functionality. If performance in the new alpha is worse than v2.0, you can turn the optimizer off to manually force it to fallback to the heuristic planner.
  • Some correlated subqueries are not supported by the cost-based optimizer yet. If you come across an unsupported correlated subquery, please file a Github issue.

See also


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