Cost-Based Optimizer

On this page

The cost-based optimizer seeks the lowest cost for a query, usually related to time.

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.

Table statistics

The cost-based optimizer can often find more performant query plans if it has access to statistical data on the contents of your tables. This data needs to be generated from scratch for new tables, and regenerated periodically for existing tables.

By default, CockroachDB automatically generates table statistics when tables are created, and as they are updated. It does this using a background job that automatically determines which columns to get statistics on — specifically, it chooses:

• Columns that are part of the primary key or an index (in other words, all indexed columns).
• Up to 100 non-indexed columns.

By default, CockroachDB also automatically collects multi-column statistics on columns that prefix an index.

For best query performance, most users should leave automatic statistics enabled with the default settings. Advanced users can follow the steps provided in this section for performance tuning and troubleshooting.

Control statistics refresh rate

Statistics are refreshed in the following cases:

• When there are no statistics.
• When it's been a long time since the last refresh, where "long time" is defined according to a moving average of the time across the last several refreshes.
• After a successful IMPORT or RESTORE into the table.
• After any schema change affecting the table.

• After each mutation operation (INSERT, UPDATE, or DELETE), the probability of a refresh is calculated using a formula that takes the cluster settings shown in the following table as inputs. These settings define the target number of rows in a table that must be stale before statistics on that table are refreshed. Increasing either setting will reduce the frequency of refreshes. In particular, min_stale_rows impacts the frequency of refreshes for small tables, while fraction_stale_rows has more of an impact on larger tables.

  Setting	Default Value	Details
  sql.stats.automatic_collection.fraction_stale_rows	0.2	Target fraction of stale rows per table that will trigger a statistics refresh.
  sql.stats.automatic_collection.min_stale_rows	500	Target minimum number of stale rows per table that will trigger a statistics refresh.

  Note:

  Because the formula for statistics refreshes is probabilistic, you will not see statistics update immediately after changing these settings, or immediately after exactly 500 rows have been updated.

Small versus large table examples

Suppose the clusters settings sql.stats.automatic_collection.fraction_stale_rows and sql.stats.automatic_collection.min_stale_rows have the default values .2 and 500 as shown in the preceding table.

If a table has 100 rows and 20 became stale, a re-collection would not be triggered because, even though 20% of the rows are stale, they do not meet the 500 row minimum.

On the other hand, if a table has 1,500,000,000 rows, 20% of that, or 300,000,000 rows, would have to become stale before auto statistics collection was triggered. With a table this large, you would have to lower sql.stats.automatic_collection.fraction_stale_rows significantly to allow for regular stats collections. This can cause smaller tables to have stats collected much more frequently, because it is a global setting that affects automatic stats collection for all tables.

In such cases we recommend that you use the sql_stats_automatic_collection_enabled storage parameter, which lets you configure auto statistics on a per-table basis.

Enable and disable automatic statistics collection for clusters

Automatic statistics collection is enabled by default. To disable automatic statistics collection, follow these steps:

1. Set the sql.stats.automatic_collection.enabled cluster setting to false:

   > SET CLUSTER SETTING sql.stats.automatic_collection.enabled = false;
   

2. Use the SHOW STATISTICS statement to view automatically generated statistics.

3. Delete the automatically generated statistics:

   > DELETE FROM system.table_statistics WHERE true;
   

4. Restart the nodes in your cluster to clear the statistics caches.

To learn how to manually generate statistics, see the CREATE STATISTICS examples.

Enable and disable automatic statistics collection for tables

Statistics collection can be expensive for large tables, and you may prefer to defer collection until after data is finished loading or during off-peak hours. Tables that are frequently updated, including small tables, may trigger statistics collection more often, which can lead to unnecessary overhead and unpredictable query plan changes.

You can enable and disable automatic statistics collection for individual tables using the sql_stats_automatic_collection_enabled storage parameter. For example:

CREATE TABLE accounts (
    id INT PRIMARY KEY,
    balance DECIMAL)
WITH (sql_stats_automatic_collection_enabled = false);

The table setting takes precedence over the cluster setting described in Enable and disable automatic statistics collection for clusters.

You can set the table settings at table creation time or using ALTER TABLE ... SET:

CREATE TABLE accounts (
    id INT PRIMARY KEY,
    balance DECIMAL);

ALTER TABLE accounts
SET (sql_stats_automatic_collection_enabled = false);

The current table settings are shown in the WITH clause output of SHOW CREATE TABLE:

  table_name |                    create_statement
-------------+---------------------------------------------------------
  accounts   | CREATE TABLE public.accounts (
             |     id INT8 NOT NULL,
             |     balance DECIMAL NULL,
             |     CONSTRAINT accounts_pkey PRIMARY KEY (id ASC)
             | ) WITH (sql_stats_automatic_collection_enabled = false)
(1 row)

ALTER TABLE accounts RESET (sql_stats_automatic_collection_enabled) removes the table setting, in which case the cluster setting is in effect for the table.

The "stale row" cluster settings discussed in Control statistics refresh rate have table setting counterparts sql_stats_automatic_collection_fraction_stale_rows and sql_stats_automatic_collection_min_stale_rows. For example:

CREATE TABLE accounts (
    id INT PRIMARY KEY,
    balance DECIMAL)
WITH (sql_stats_automatic_collection_enabled = true,
sql_stats_automatic_collection_min_stale_rows = 1000000,
sql_stats_automatic_collection_fraction_stale_rows= 0.05
);

ALTER TABLE accounts
SET (sql_stats_automatic_collection_fraction_stale_rows = 0.1,
sql_stats_automatic_collection_min_stale_rows = 2000);

Automatic statistics rules are checked once per minute. While altered automatic statistics table settings take immediate effect for any subsequent DML statements on a table, running row mutations that started prior to modifying the table settings may still trigger statistics collection based on the settings that existed before you ran the ALTER TABLE ... SET statement.

Control histogram collection

By default, the optimizer collects histograms for all index columns (specifically the first column in each index) during automatic statistics collection. If a single column statistic is explicitly requested using manual invocation of CREATE STATISTICS, a histogram will be collected, regardless of whether or not the column is part of an index.

If you are an advanced user and need to disable histogram collection for troubleshooting or performance tuning reasons, change the sql.stats.histogram_collection.enabled cluster setting by running SET CLUSTER SETTING as follows:

SET CLUSTER SETTING sql.stats.histogram_collection.enabled = false;

When sql.stats.histogram_collection.enabled is set to false, histograms are never collected, either as part of automatic statistics collection or by manually invoking CREATE STATISTICS.

Control whether the avg_size statistic is used to cost scans

New in v22.1: The avg_size table statistic represents the average size of a table column. If a table does not have an average size statistic available for a column, it uses the default value of 4 bytes.

The optimizer uses avg_size to cost scans and relevant joins. Costing scans per row regardless of the size of the columns comprising the row doesn't account for time to read or transport a large number of bytes over the network. This can lead to undesirable plans when there are multiple options for scans or joins that read directly from tables.

Cockroach Labs recommends that you allow the optimizer to consider column size when costing plans. If you are an advanced user and need to disable using avg_size for troubleshooting or performance tuning reasons, you can disable it by setting the cost_scans_with_default_col_size session variable to true with SET cost_scans_with_default_col_size=true.

Control whether the optimizer creates a plan with a full scan

Even if you have secondary indexes, the optimizer may determine that a full table scan will be faster. For example, if you add a secondary index to a table with a large number of rows and find that a statement plan is not using the secondary index, then it is likely that performing a full table scan using the primary key is faster than doing a secondary index scan plus an index join.

You can disable statement plans that perform full table scans with the disallow_full_table_scans session variable.

If you disable full scans, you can set the large_full_scan_rows session variable to specify the maximum table size allowed for a full scan. If no alternative plan is possible, the optimizer will return an error.

If you disable full scans, and you provide an index hint, the optimizer will try to avoid a full scan while also respecting the index hint. If this is not possible, the optimizer will return an error. If you do not provide an index hint, the optimizer will return an error, the full scan will be logged, and the sql.guardrails.full_scan_rejected.count metric will be updated.

Locality optimized search in multi-region clusters

In multi-region deployments with regional by row tables, the optimizer, in concert with the SQL engine, may perform a locality optimized search to attempt to avoid high-latency, cross-region communication between nodes. If there is a possibility that the results of a query all live in local rows, the database will first search for rows in the gateway node's region. The search only continues in remote regions if rows in the local region did not satisfy the query. Examples of queries that can use locality optimized search include unique key lookups and queries with LIMIT clauses.

Even if a value cannot be read locally, CockroachDB takes advantage of the fact that some of the other regions are much closer than others and thus can be queried with lower latency. In this case, it performs all lookups against the remote regions in parallel and returns the result once it is retrieved, without having to wait for each lookup to come back. This can lead to increased performance in multi-region deployments, since it means that results can be returned from wherever they are first found without waiting for all of the other lookups to return.

Locality optimized search is supported for scans that are guaranteed to return 100,000 keys or fewer. This optimization allows the execution engine to avoid visiting remote regions if all requested keys are found in the local region, thus reducing the latency of the query.

Limitations

• Locality optimized search works only for queries selecting a limited number of records (up to 100,000 unique keys).

• Locality optimized search does not work for queries that use partitioned unique indexes on virtual computed columns. A workaround for computed columns is to make the virtual computed column a stored computed column. Locality optimized search does not work for queries that use partitioned unique expression indexes. Tracking GitHub Issue

Query plan cache

CockroachDB uses a cache for the query plans generated by the optimizer. This can lead to faster query execution since the database can reuse a query plan that was previously calculated, rather than computing a new plan each time a query is executed.

The query plan cache is enabled by default. To disable it, execute the following statement:

> SET CLUSTER SETTING sql.query_cache.enabled = false;

Only the following statements use the plan cache:

• SELECT
• INSERT
• UPDATE
• UPSERT
• DELETE

The optimizer can use cached plans if they are:

• Prepared statements.
• Non-prepared statements using identical constant values.

Join reordering

For a query involving multiple joins, the cost-based optimizer will explore additional join orderings in an attempt to find the lowest-cost execution plan, which can lead to significantly better performance in some cases.

Because this process leads to an exponential increase in the number of possible execution plans for such queries, it's only used to reorder subtrees containing 8 or fewer joins by default.

To change this setting, which is controlled by the reorder_joins_limit session variable, run the following statement:

> SET reorder_joins_limit = 0;

To disable this feature, set the variable to 0. You can configure the default reorder_joins_limit session setting with the cluster setting sql.defaults.reorder_joins_limit, which has a default value of 8.

For more information about selecting an optimal join ordering, see our blog post An Introduction to Join Ordering.

Reduce planning time for queries with many joins

The cost-based optimizer explores multiple join orderings to find the lowest-cost plan. If there are many joins or join subtrees in the query, this can increase the number of execution plans the optimizer explores, and therefore the exploration and planning time. If the planning phase of a query takes a long time (on the order of multiple seconds or minutes) to plan, or the query plan involves many joins, consider the following alternatives to reduce the planning time:

• To limit the size of the subtree that can be reordered, set the reorder_joins_limit session variable to a lower value, for example:

  SET reorder_joins_limit = 2;
  

  If the join ordering inherent in the query is acceptable, for the shortest planning time, you can set reorder_joins_limit to 0. This disables exploration of join orderings entirely.

  By reducing reorder_joins_limit CockroachDB reduces the number of plans explored, so a less efficient plan may be chosen by the optimizer.

  If one query has a slow planning time, you can avoid interfering with other query plans by setting reorder_joins_limit to the desired lower value before executing that query and resetting the session variable to the default after executing the query.

• If setting and resetting the session variable is cumbersome or if there are multiple independent joins in the query where some may benefit from join reordering, you can use a join hint. If the join has a hint specifying the type of join to something other than the default INNER (i.e., INNER LOOKUP, MERGE, HASH, etc.), join reordering will be disabled and the plan will respect the join order inherent in the way the query is written. This works at the expression level and doesn't affect the entire query (for instance, if you have a union of two joins they are independent join expressions).

Join hints

To force the use of a specific join algorithm even if the optimizer determines that a different plan would have a lower cost, you can use a join hint. You specify a join hint as <join type> <join algorithm> JOIN. For example:

• INNER HASH JOIN
• OUTER MERGE JOIN
• LEFT LOOKUP JOIN
• CROSS HASH JOIN
• INNER INVERTED JOIN
• LEFT INVERTED JOIN

For a join hint example, see Use the right join type.

Supported join algorithms

• HASH: Forces a hash join; in other words, it disables merge and lookup joins. A hash join is always possible, even if there are no equality columns - CockroachDB considers the nested loop join with no index a degenerate case of the hash join (i.e., a hash table with one bucket).

• MERGE: Forces a merge join, even if it requires re-sorting both sides of the join.

• LOOKUP: Forces a lookup join into the right side; the right side must be a table with a suitable index. Note that LOOKUP can only be used with INNER and LEFT joins.

• INVERTED: Forces an inverted join into the right side; the right side must be a table with a suitable GIN index. Note that INVERTED can only be used with INNER and LEFT joins.

  Note:

  You cannot use inverted joins on partial GIN indexes.

If it is not possible to use the algorithm specified in the hint, an error is signaled.

Additional considerations

• This syntax is consistent with the SQL Server syntax for join hints, except that:

  • SQL Server uses LOOP instead of LOOKUP.
  • CockroachDB does not support LOOP and instead supports LOOKUP for the specific case of nested loop joins with an index.

• When you specify a join hint, the two tables will not be reordered by the optimizer. The reordering behavior has the following characteristics, which can be affected by hints:

  • Given a JOIN b, CockroachDB will not try to commute to b JOIN a. This means that you will need to pay attention to this ordering, which is especially important for lookup joins. Without a hint, a JOIN b might be executed as b INNER LOOKUP JOIN a using an index into a, whereas a INNER LOOKUP JOIN b requires an index into b.
  • (a JOIN b) JOIN c might be changed to a JOIN (b JOIN c), but this does not happen if a JOIN b uses a hint; the hint forces that particular join to happen as written in the query.

• You should reconsider hint usage with each new release of CockroachDB. Due to improvements in the optimizer, hints specified to work with an older version may cause decreased performance in a newer version.

Zigzag joins

The optimizer may plan a zigzag join when there are at least two secondary indexes on the same table and the table is filtered in a query with at least two filters constraining different attributes to a constant. A zigzag join works by "zigzagging" back and forth between two indexes and returning only rows with matching primary keys within a specified range. For example:

CREATE TABLE abc (
  a INT,
  b INT,
  INDEX (a),
  INDEX (b)
);

EXPLAIN SELECT * FROM abc WHERE a = 10 AND b = 20;

               info
----------------------------------
  distribution: local
  vectorized: true

  • zigzag join
    pred: (a = 10) AND (b = 20)
    left table: abc@abc_a_idx
    left columns: (a)
    left fixed values: 1 column
    right table: abc@abc_b_idx
    right columns: (b)
    right fixed values: 1 column
(11 rows)

Prevent or force a zigzag join

The optimizer supports index hints to prevent or force a zigzag join. Apply the hints in the same way as other existing index hints.

To prevent the optimizer from planning a zigzag join for the specified table, use the hint NO_ZIGZAG_JOIN. For example:

SELECT * FROM abc@{NO_ZIGZAG_JOIN};

New in v22.1: To force the optimizer to plan a zigzag join for the specified table, use the hint FORCE_ZIGZAG. For example:

SELECT * FROM abc@{FORCE_ZIGZAG};

Inverted join examples

To run these examples, initialize a demo cluster with the MovR workload.

Start the MovR database on a 3-node CockroachDB demo cluster with a larger data set.

cockroach demo movr --num-histories 250000 --num-promo-codes 250000 --num-rides 125000 --num-users 12500 --num-vehicles 3750 --nodes 3

Create a GIN index on the vehicles table's ext column.

CREATE INVERTED INDEX idx_vehicle_details ON vehicles(ext);

Check the statement plan for a SELECT statement that uses an inner inverted join.

EXPLAIN SELECT * FROM vehicles@vehicles_pkey AS v2 INNER INVERTED JOIN vehicles@idx_vehicle_details AS v1 ON v1.ext @> v2.ext;

                    info
---------------------------------------------
  distribution: full
  vectorized: true

  • lookup join
  │ table: vehicles@vehicles_pkey
  │ equality: (city, id) = (city,id)
  │ equality cols are key
  │ pred: ext @> ext
  │
  └── • inverted join
      │ table: vehicles@idx_vehicle_details
      │
      └── • scan
            estimated row count: 3,750 (100% of the table; stats collected 1 hour ago)
            table: vehicles@vehicles_pkey
            spans: FULL SCAN
(16 rows)

You can omit the INNER INVERTED JOIN statement by putting v1.ext on the left side of a @> join condition in a WHERE clause and using an index hint for the GIN index.

EXPLAIN SELECT * FROM vehicles@idx_vehicle_details AS v1, vehicles AS v2 WHERE v1.ext @> v2.ext;

                                            info
--------------------------------------------------------------------------------------------
  distribution: full
  vectorized: true

  • lookup join
  │ table: vehicles@vehicles_pkey
  │ equality: (city, id) = (city,id)
  │ equality cols are key
  │ pred: ext @> ext
  │
  └── • inverted join
      │ table: vehicles@idx_vehicle_details
      │
      └── • scan
            estimated row count: 3,750 (100% of the table; stats collected 1 hour ago)
            table: vehicles@vehicles_pkey
            spans: FULL SCAN
(16 rows)

Use the LEFT INVERTED JOIN hint to perform a left inverted join.

EXPLAIN SELECT * FROM vehicles AS v2 LEFT INVERTED JOIN vehicles AS v1 ON v1.ext @> v2.ext;

                                           info
------------------------------------------------------------------------------------------
  distribution: full
  vectorized: true

  • lookup join (left outer)
  │ table: vehicles@vehicles_pkey
  │ equality: (city, id) = (city,id)
  │ equality cols are key
  │ pred: ext @> ext
  │
  └── • inverted join (left outer)
      │ table: vehicles@idx_vehicle_details
      │
      └── • scan
            estimated row count: 3,750 (100% of the table; stats collected 1 hour ago)
            table: vehicles@vehicles_pkey
            spans: FULL SCAN
(16 rows)

Known limitations

• When a column is dropped from a multi-column index, the optimizer will not collect new statistics for the deleted column. However, the optimizer never deletes the old multi-column statistics. This can cause a buildup of statistics in system.table_statistics leading the optimizer to use stale statistics, which could result in sub-optimal plans. To workaround this issue and avoid these scenarios, explicitly delete those statistics from the system.table_statistics table.

  Tracking GitHub Issue

• Single-column statistics are not deleted when columns are dropped, which could cause minor performance issues.

  Tracking GitHub Issue

• The automatic statistics refresher automatically checks whether it needs to refresh statistics for every table in the database upon startup of each node in the cluster. If statistics for a table have not been refreshed in a while, this will trigger collection of statistics for that table. If statistics have been refreshed recently, it will not force a refresh. As a result, the automatic statistics refresher does not necessarily perform a refresh of statistics after an upgrade. This could cause a problem, for example, if the upgrade moves from a version without histograms to a version with histograms. To refresh statistics manually, use CREATE STATISTICS.

  Tracking GitHub Issue

See also

• JOIN expressions
• SET {session variable}
• SET CLUSTER SETTING
• RESET CLUSTER SETTING
• SHOW {session variable}
• CREATE STATISTICS
• SHOW STATISTICS
• EXPLAIN

Was this helpful?