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_async_append
(boolean
)
#
Enables or disables the query planner's use of async-aware
append plan types. The default is on
.
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_group_by_reordering
(boolean
)
#
Controls if the query planner will produce a plan which will provide
GROUP BY
keys sorted in the order of keys of
a child node of the plan, such as an index scan. When disabled, the
query planner will produce a plan with GROUP BY
keys only sorted to match the ORDER BY
clause,
if any. When enabled, the planner will try to produce a more
efficient plan. The default value 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 and
index-only-scan plan types. The default is on
.
Also see enable_indexonlyscan.
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
. The
enable_indexscan setting must also be
enabled to have the query planner consider index-only-scans.
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_memoize
(boolean
)
#
Enables or disables the query planner's use of memoize plans for
caching results from parameterized scans inside nested-loop joins.
This plan type allows scans to the underlying plans to be skipped when
the results for the current parameters are already in the cache. Less
commonly looked up results may be evicted from the cache when more
space is required for new entries. 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 5.12.4 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_presorted_aggregate
(boolean
)
#
Controls if the query planner will produce a plan which will provide
rows which are presorted in the order required for the query's
ORDER BY
/ DISTINCT
aggregate
functions. When disabled, the query planner will produce a plan which
will always require the executor to perform a sort before performing
aggregation of each aggregate function containing an
ORDER BY
or DISTINCT
clause.
When enabled, the planner will try to produce a more efficient plan
which provides input to the aggregate functions which is presorted in
the order they require for aggregation. The default value is
on
.
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 30).
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 60.
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 5.12.5 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 30).
The default is on
.
join_collapse_limit
(integer
)
#
The planner will rewrite explicit JOIN
constructs (except FULL JOIN
s) 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 JOIN
s. 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.
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.
recursive_worktable_factor
(floating point
)
#
Sets the planner's estimate of the average size of the working
table of a recursive
query, as a multiple of the estimated size of the initial
non-recursive term of the query. This helps the planner choose
the most appropriate method for joining the working table to the
query's other tables.
The default value is 10.0
. A smaller value
such as 1.0
can be helpful when the recursion
has low “fan-out” from one step to the next, as for
example in shortest-path queries. Graph analytics queries may
benefit from larger-than-default values.