These configuration parameters provide a crude method of
       influencing the query plans chosen by the query optimizer. If
       the default plan chosen by the optimizer for a particular query
       is not optimal, a temporary solution is to use one
       of these configuration parameters to force the optimizer to
       choose a different plan.
       Better ways to improve the quality of the
       plans chosen by the optimizer include adjusting the planner cost
       constants (see Section 19.7.2),
       running ANALYZE manually, increasing
       the value of the default_statistics_target configuration parameter,
       and increasing the amount of statistics collected for
       specific columns using ALTER TABLE SET
       STATISTICS.
      
enable_bitmapscan (boolean)
      
      
      
        Enables or disables the query planner's use of bitmap-scan plan
        types. The default is on.
       
enable_gathermerge (boolean)
      
      
        Enables or disables the query planner's use of gather
        merge plan types. The default is on.
       
enable_hashagg (boolean)
      
      
        Enables or disables the query planner's use of hashed
        aggregation plan types. The default is on.
       
enable_hashjoin (boolean)
      
      
        Enables or disables the query planner's use of hash-join plan
        types. The default is on.
       
enable_incremental_sort (boolean)
      
      
        Enables or disables the query planner's use of incremental sort steps.
        The default is on.
       
enable_indexscan (boolean)
      
      
      
        Enables or disables the query planner's use of index-scan plan
        types. The default is on.
       
enable_indexonlyscan (boolean)
      
      
        Enables or disables the query planner's use of index-only-scan plan
        types (see Section 11.9).
        The default is on.
       
enable_material (boolean)
      
      
        Enables or disables the query planner's use of materialization.
        It is impossible to suppress materialization entirely,
        but turning this variable off prevents the planner from inserting
        materialize nodes except in cases where it is required for correctness.
        The default is on.
       
enable_mergejoin (boolean)
      
      
        Enables or disables the query planner's use of merge-join plan
        types. The default is on.
       
enable_nestloop (boolean)
      
      
        Enables or disables the query planner's use of nested-loop join
        plans. It is impossible to suppress nested-loop joins entirely,
        but turning this variable off discourages the planner from using
        one if there are other methods available. The default is
        on.
       
enable_parallel_append (boolean)
      
      
        Enables or disables the query planner's use of parallel-aware
        append plan types. The default is on.
       
enable_parallel_hash (boolean)
       
      
        Enables or disables the query planner's use of hash-join plan
        types with parallel hash. Has no effect if hash-join plans are not
        also enabled. The default is on.
       
enable_partition_pruning (boolean)
       
      
        Enables or disables the query planner's ability to eliminate a
        partitioned table's partitions from query plans.  This also controls
        the planner's ability to generate query plans which allow the query
        executor to remove (ignore) partitions during query execution.  The
        default is on.
        See Section 5.11.4 for details.
       
enable_partitionwise_join (boolean)
      
      
        Enables or disables the query planner's use of partitionwise join,
        which allows a join between partitioned tables to be performed by
        joining the matching partitions.  Partitionwise join currently applies
        only when the join conditions include all the partition keys, which
        must be of the same data type and have one-to-one matching sets of
        child partitions.  With this setting enabled, the number of nodes
        whose memory usage is restricted by work_mem
        appearing in the final plan can increase linearly according to the
        number of partitions being scanned.  This can result in a large
        increase in overall memory consumption during the execution of the
        query.  Query planning also becomes significantly more expensive in
        terms of memory and CPU.  The default value is off.
       
enable_partitionwise_aggregate (boolean)
      
      
        Enables or disables the query planner's use of partitionwise grouping
        or aggregation, which allows grouping or aggregation on partitioned
        tables to be performed separately for each partition.  If the
        GROUP BY clause does not include the partition
        keys, only partial aggregation can be performed on a per-partition
        basis, and finalization must be performed later.  With this setting
        enabled, the number of nodes whose memory usage is restricted by
        work_mem appearing in the final plan can increase
        linearly according to the number of partitions being scanned.  This
        can result in a large increase in overall memory consumption during
        the execution of the query.  Query planning also becomes significantly
        more expensive in terms of memory and CPU.  The default value is
        off.
       
enable_seqscan (boolean)
      
      
      
        Enables or disables the query planner's use of sequential scan
        plan types. It is impossible to suppress sequential scans
        entirely, but turning this variable off discourages the planner
        from using one if there are other methods available. The
        default is on.
       
enable_sort (boolean)
      
      
        Enables or disables the query planner's use of explicit sort
        steps. It is impossible to suppress explicit sorts entirely,
        but turning this variable off discourages the planner from
        using one if there are other methods available. The default
        is on.
       
enable_tidscan (boolean)
      
      
        Enables or disables the query planner's use of TID
        scan plan types. The default is on.
       
     The cost variables described in this section are measured
     on an arbitrary scale.  Only their relative values matter, hence
     scaling them all up or down by the same factor will result in no change
     in the planner's choices.  By default, these cost variables are based on
     the cost of sequential page fetches; that is,
     seq_page_cost is conventionally set to 1.0
     and the other cost variables are set with reference to that.  But
     you can use a different scale if you prefer, such as actual execution
     times in milliseconds on a particular machine.
    
Unfortunately, there is no well-defined method for determining ideal values for the cost variables. They are best treated as averages over the entire mix of queries that a particular installation will receive. This means that changing them on the basis of just a few experiments is very risky.
seq_page_cost (floating point)
      
      Sets the planner's estimate of the cost of a disk page fetch that is part of a series of sequential fetches. The default is 1.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).
random_page_cost (floating point)
      
      Sets the planner's estimate of the cost of a non-sequentially-fetched disk page. The default is 4.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).
        Reducing this value relative to seq_page_cost
        will cause the system to prefer index scans; raising it will
        make index scans look relatively more expensive.  You can raise
        or lower both values together to change the importance of disk I/O
        costs relative to CPU costs, which are described by the following
        parameters.
       
Random access to mechanical disk storage is normally much more expensive than four times sequential access. However, a lower default is used (4.0) because the majority of random accesses to disk, such as indexed reads, are assumed to be in cache. The default value can be thought of as modeling random access as 40 times slower than sequential, while expecting 90% of random reads to be cached.
        If you believe a 90% cache rate is an incorrect assumption
        for your workload, you can increase random_page_cost to better
        reflect the true cost of random storage reads. Correspondingly,
        if your data is likely to be completely in cache, such as when
        the database is smaller than the total server memory, decreasing
        random_page_cost can be appropriate.  Storage that has a low random
        read cost relative to sequential, e.g., solid-state drives, might
        also be better modeled with a lower value for random_page_cost,
        e.g., 1.1.
       
         Although the system will let you set random_page_cost to
         less than seq_page_cost, it is not physically sensible
         to do so.  However, setting them equal makes sense if the database
         is entirely cached in RAM, since in that case there is no penalty
         for touching pages out of sequence.  Also, in a heavily-cached
         database you should lower both values relative to the CPU parameters,
         since the cost of fetching a page already in RAM is much smaller
         than it would normally be.
        
cpu_tuple_cost (floating point)
      
      Sets the planner's estimate of the cost of processing each row during a query. The default is 0.01.
cpu_index_tuple_cost (floating point)
      
      Sets the planner's estimate of the cost of processing each index entry during an index scan. The default is 0.005.
cpu_operator_cost (floating point)
      
      Sets the planner's estimate of the cost of processing each operator or function executed during a query. The default is 0.0025.
parallel_setup_cost (floating point)
      
      Sets the planner's estimate of the cost of launching parallel worker processes. The default is 1000.
parallel_tuple_cost (floating point)
      
      Sets the planner's estimate of the cost of transferring one tuple from a parallel worker process to another process. The default is 0.1.
min_parallel_table_scan_size (integer)
      
      
        Sets the minimum amount of table data that must be scanned in order
        for a parallel scan to be considered.  For a parallel sequential scan,
        the amount of table data scanned is always equal to the size of the
        table, but when indexes are used the amount of table data
        scanned will normally be less.
        If this value is specified without units, it is taken as blocks,
        that is BLCKSZ bytes, typically 8kB.
        The default is 8 megabytes (8MB).
       
min_parallel_index_scan_size (integer)
      
      
        Sets the minimum amount of index data that must be scanned in order
        for a parallel scan to be considered.  Note that a parallel index scan
        typically won't touch the entire index; it is the number of pages
        which the planner believes will actually be touched by the scan which
        is relevant.  This parameter is also used to decide whether a
        particular index can participate in a parallel vacuum.  See
        VACUUM.
        If this value is specified without units, it is taken as blocks,
        that is BLCKSZ bytes, typically 8kB.
        The default is 512 kilobytes (512kB).
       
effective_cache_size (integer)
      
      
        Sets the planner's assumption about the effective size of the
        disk cache that is available to a single query.  This is
        factored into estimates of the cost of using an index; a
        higher value makes it more likely index scans will be used, a
        lower value makes it more likely sequential scans will be
        used. When setting this parameter you should consider both
        PostgreSQL's shared buffers and the
        portion of the kernel's disk cache that will be used for
        PostgreSQL data files, though some
        data might exist in both places. Also, take
        into account the expected number of concurrent queries on different
        tables, since they will have to share the available
        space.  This parameter has no effect on the size of shared
        memory allocated by PostgreSQL, nor
        does it reserve kernel disk cache; it is used only for estimation
        purposes.  The system also does not assume data remains in
        the disk cache between queries.
        If this value is specified without units, it is taken as blocks,
        that is BLCKSZ bytes, typically 8kB.
        The default is 4 gigabytes (4GB).
        (If BLCKSZ is not 8kB, the default value scales
        proportionally to it.)
       
jit_above_cost (floating point)
      
      
        Sets the query cost above which JIT compilation is activated, if
        enabled (see Chapter 31).
        Performing JIT costs planning time but can
        accelerate query execution.
        Setting this to -1 disables JIT compilation.
        The default is 100000.
       
jit_inline_above_cost (floating point)
      
      
        Sets the query cost above which JIT compilation attempts to inline
        functions and operators.  Inlining adds planning time, but can
        improve execution speed.  It is not meaningful to set this to less
        than jit_above_cost.
        Setting this to -1 disables inlining.
        The default is 500000.
       
jit_optimize_above_cost (floating point)
      
      
        Sets the query cost above which JIT compilation applies expensive
        optimizations.  Such optimization adds planning time, but can improve
        execution speed.  It is not meaningful to set this to less
        than jit_above_cost, and it is unlikely to be
        beneficial to set it to more
        than jit_inline_above_cost.
        Setting this to -1 disables expensive optimizations.
        The default is 500000.
       
The genetic query optimizer (GEQO) is an algorithm that does query planning using heuristic searching. This reduces planning time for complex queries (those joining many relations), at the cost of producing plans that are sometimes inferior to those found by the normal exhaustive-search algorithm. For more information see Chapter 59.
geqo (boolean)
      
      
      
      
        Enables or disables genetic query optimization.
        This is on by default.  It is usually best not to turn it off in
        production; the geqo_threshold variable provides
        more granular control of GEQO.
       
geqo_threshold (integer)
      
      
        Use genetic query optimization to plan queries with at least
        this many FROM items involved. (Note that a
        FULL OUTER JOIN construct counts as only one FROM
        item.) The default is 12. For simpler queries it is usually best
        to use the regular, exhaustive-search planner, but for queries with
        many tables the exhaustive search takes too long, often
        longer than the penalty of executing a suboptimal plan.  Thus,
        a threshold on the size of the query is a convenient way to manage
        use of GEQO.
       
geqo_effort (integer)
      
      Controls the trade-off between planning time and query plan quality in GEQO. This variable must be an integer in the range from 1 to 10. The default value is five. Larger values increase the time spent doing query planning, but also increase the likelihood that an efficient query plan will be chosen.
        geqo_effort doesn't actually do anything
        directly; it is only used to compute the default values for
        the other variables that influence GEQO behavior (described
        below). If you prefer, you can set the other parameters by
        hand instead.
       
geqo_pool_size (integer)
      
      
        Controls the pool size used by GEQO, that is the
        number of individuals in the genetic population.  It must be
        at least two, and useful values are typically 100 to 1000.  If
        it is set to zero (the default setting) then a suitable
        value is chosen based on geqo_effort and
        the number of tables in the query.
       
geqo_generations (integer)
      
      
        Controls the number of generations used by GEQO, that is
        the number of iterations of the algorithm.  It must
        be at least one, and useful values are in the same range as
        the pool size.  If it is set to zero (the default setting)
        then a suitable value is chosen based on
        geqo_pool_size.
       
geqo_selection_bias (floating point)
      
      Controls the selection bias used by GEQO. The selection bias is the selective pressure within the population. Values can be from 1.50 to 2.00; the latter is the default.
geqo_seed (floating point)
      
      Controls the initial value of the random number generator used by GEQO to select random paths through the join order search space. The value can range from zero (the default) to one. Varying the value changes the set of join paths explored, and may result in a better or worse best path being found.
default_statistics_target (integer)
      
      
        Sets the default statistics target for table columns without
        a column-specific target set via ALTER TABLE
        SET STATISTICS.  Larger values increase the time needed to
        do ANALYZE, but might improve the quality of the
        planner's estimates. The default is 100. For more information
        on the use of statistics by the PostgreSQL
        query planner, refer to Section 14.2.
       
constraint_exclusion (enum)
      
      
      
        Controls the query planner's use of table constraints to
        optimize queries.
        The allowed values of constraint_exclusion are
        on (examine constraints for all tables),
        off (never examine constraints), and
        partition (examine constraints only for inheritance
        child tables and UNION ALL subqueries).
        partition is the default setting.
        It is often used with traditional inheritance trees to improve
        performance.
      
        When this parameter allows it for a particular table, the planner
        compares query conditions with the table's CHECK
        constraints, and omits scanning tables for which the conditions
        contradict the constraints.  For example:
CREATE TABLE parent(key integer, ...); CREATE TABLE child1000(check (key between 1000 and 1999)) INHERITS(parent); CREATE TABLE child2000(check (key between 2000 and 2999)) INHERITS(parent); ... SELECT * FROM parent WHERE key = 2400;
        With constraint exclusion enabled, this SELECT
        will not scan child1000 at all, improving performance.
       
Currently, constraint exclusion is enabled by default only for cases that are often used to implement table partitioning via inheritance trees. Turning it on for all tables imposes extra planning overhead that is quite noticeable on simple queries, and most often will yield no benefit for simple queries. If you have no tables that are partitioned using traditional inheritance, you might prefer to turn it off entirely. (Note that the equivalent feature for partitioned tables is controlled by a separate parameter, enable_partition_pruning.)
Refer to Section 5.11.5 for more information on using constraint exclusion to implement partitioning.
cursor_tuple_fraction (floating point)
      
      Sets the planner's estimate of the fraction of a cursor's rows that will be retrieved. The default is 0.1. Smaller values of this setting bias the planner towards using “fast start” plans for cursors, which will retrieve the first few rows quickly while perhaps taking a long time to fetch all rows. Larger values put more emphasis on the total estimated time. At the maximum setting of 1.0, cursors are planned exactly like regular queries, considering only the total estimated time and not how soon the first rows might be delivered.
from_collapse_limit (integer)
      
      
        The planner will merge sub-queries into upper queries if the
        resulting FROM list would have no more than
        this many items.  Smaller values reduce planning time but might
        yield inferior query plans.  The default is eight.
        For more information see Section 14.3.
       
Setting this value to geqo_threshold or more may trigger use of the GEQO planner, resulting in non-optimal plans. See Section 19.7.3.
jit (boolean)
      
      
        Determines whether JIT compilation may be used by
        PostgreSQL, if available (see Chapter 31).
        The default is on.
       
join_collapse_limit (integer)
      
      
        The planner will rewrite explicit JOIN
        constructs (except FULL JOINs) into lists of
        FROM items whenever a list of no more than this many items
        would result.  Smaller values reduce planning time but might
        yield inferior query plans.
       
        By default, this variable is set the same as
        from_collapse_limit, which is appropriate
        for most uses. Setting it to 1 prevents any reordering of
        explicit JOINs. Thus, the explicit join order
        specified in the query will be the actual order in which the
        relations are joined. Because the query planner does not always choose
        the optimal join order, advanced users can elect to
        temporarily set this variable to 1, and then specify the join
        order they desire explicitly.
        For more information see Section 14.3.
       
Setting this value to geqo_threshold or more may trigger use of the GEQO planner, resulting in non-optimal plans. See Section 19.7.3.
parallel_leader_participation (boolean)
       
      
        Allows the leader process to execute the query plan under
        Gather and Gather Merge nodes
        instead of waiting for worker processes.  The default is
        on.  Setting this value to off
        reduces the likelihood that workers will become blocked because the
        leader is not reading tuples fast enough, but requires the leader
        process to wait for worker processes to start up before the first
        tuples can be produced.  The degree to which the leader can help or
        hinder performance depends on the plan type, number of workers and
        query duration.
       
force_parallel_mode (enum)
      
      
        Allows the use of parallel queries for testing purposes even in cases
        where no performance benefit is expected.
        The allowed values of force_parallel_mode are
        off (use parallel mode only when it is expected to improve
        performance), on (force parallel query for all queries
        for which it is thought to be safe), and regress (like
        on, but with additional behavior changes as explained
        below).
       
        More specifically, setting this value to on will add
        a Gather node to the top of any query plan for which this
        appears to be safe, so that the query runs inside of a parallel worker.
        Even when a parallel worker is not available or cannot be used,
        operations such as starting a subtransaction that would be prohibited
        in a parallel query context will be prohibited unless the planner
        believes that this will cause the query to fail.  If failures or
        unexpected results occur when this option is set, some functions used
        by the query may need to be marked PARALLEL UNSAFE
        (or, possibly, PARALLEL RESTRICTED).
       
        Setting this value to regress has all of the same effects
        as setting it to on plus some additional effects that are
        intended to facilitate automated regression testing.  Normally,
        messages from a parallel worker include a context line indicating that,
        but a setting of regress suppresses this line so that the
        output is the same as in non-parallel execution.  Also,
        the Gather nodes added to plans by this setting are hidden
        in EXPLAIN output so that the output matches what
        would be obtained if this setting were turned off.
       
plan_cache_mode (enum)
      
      
        Prepared statements (either explicitly prepared or implicitly
        generated, for example by PL/pgSQL) can be executed using custom or
        generic plans.  Custom plans are made afresh for each execution
        using its specific set of parameter values, while generic plans do
        not rely on the parameter values and can be re-used across
        executions.  Thus, use of a generic plan saves planning time, but if
        the ideal plan depends strongly on the parameter values then a
        generic plan may be inefficient.  The choice between these options
        is normally made automatically, but it can be overridden
        with plan_cache_mode.
        The allowed values are auto (the default),
        force_custom_plan and
        force_generic_plan.
        This setting is considered when a cached plan is to be executed,
        not when it is prepared.
        For more information see PREPARE.