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 may be found by using one
of these configuration parameters to force the optimizer to
choose a different plan. Turning one of these settings off
permanently is seldom a good idea, however.
Better ways to improve the quality of the
plans chosen by the optimizer include adjusting the Planner Cost Constants
, running ANALYZE more
frequently, 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_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_indexscan
(boolean
)
Enables or disables the query planner's use of index-scan plan
types. 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's not possible 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_seqscan
(boolean
)
Enables or disables the query planner's use of sequential scan
plan types. It's not possible 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's not possible 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
.
Unfortunately, there is no well-defined method for determining ideal values for the family of “cost” variables that appear below. You are encouraged to experiment and share your findings.
effective_cache_size
(floating point
)
Sets the planner's assumption about the effective size of the disk cache that is available to a single index scan. 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. Also, take into account the expected number of concurrent queries using different indexes, 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 value is measured in disk pages, which are normally 8192 bytes each. The default is 1000.
random_page_cost
(floating point
)
Sets the planner's estimate of the cost of a nonsequentially fetched disk page. This is measured as a multiple of the cost of a sequential page fetch. A higher value makes it more likely a sequential scan will be used, a lower value makes it more likely an index scan will be used. The default is four.
cpu_tuple_cost
(floating point
)
Sets the planner's estimate of the cost of processing each row during a query. This is measured as a fraction of the cost of a sequential page fetch. The default is 0.01.
cpu_index_tuple_cost
(floating point
)
Sets the planner's estimate of the cost of processing each index row during an index scan. This is measured as a fraction of the cost of a sequential page fetch. The default is 0.001.
cpu_operator_cost
(floating point
)
Sets the planner's estimate of the cost of processing each
operator in a WHERE
clause. This is measured as a fraction of
the cost of a sequential page fetch. The default is 0.0025.
geqo
(boolean
)
Enables or disables genetic query optimization, which is an
algorithm that attempts to do query planning without
exhaustive searching. This is on by default. The
geqo_threshold
variable provides a more
granular way to disable GEQO for certain classes of queries.
geqo_threshold
(integer
)
Use genetic query optimization to plan queries with at least
this many FROM
items involved. (Note that an outer
JOIN
construct counts as only one FROM
item.) The default is 12. For simpler queries it is usually best
to use the deterministic, exhaustive planner, but for queries with
many tables the deterministic planner takes too long.
geqo_effort
(integer
)
Controls the trade off between planning time and query plan efficiency in GEQO. This variable must be an integer in the range from 1 to 10. The default value is 5. 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. The pool size 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
default 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. Generations
specifies 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 default 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.
default_statistics_target
(integer
)
Sets the default statistics target for table columns that have
not had a column-specific target set via ALTER TABLE
SET STATISTICS
. Larger values increase the time needed to
do ANALYZE
, but may improve the quality of the
planner's estimates. The default is 10. For more information
on the use of statistics by the PostgreSQL
query planner, refer to Section 13.2, “Statistics Used by the Planner”.
constraint_exclusion
(boolean
)
Enables or disables the query planner's use of table constraints to
optimize queries. The default is off
.
When this parameter is on
, the planner compares
query conditions with table CHECK
constraints, and
omits scanning tables for which the conditions contradict the
constraints. (Presently this is done only for child tables of
inheritance scans.) 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. This can
improve performance when inheritance is used to build
partitioned tables.
Currently, constraint_exclusion
is disabled by
default because it risks incorrect results if query plans are
cached [mdash ] if a table constraint is changed or dropped,
the previously generated plan might now be wrong, and there is
no built-in mechanism to force re-planning. (This deficiency
will probably be addressed in a future
PostgreSQL release.) Another reason for
keeping it off is that the constraint checks are relatively
expensive, and in many circumstances will yield no savings.
It is recommended to turn this on only if you are actually
using partitioned tables designed to take advantage of the
feature.
Refer to Section 5.9, “Partitioning” for more information on using constraint exclusion and partitioning.
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 may
yield inferior query plans. The default is 8. It is usually
wise to keep this less than geqo_threshold.
join_collapse_limit
(integer
)
The planner will rewrite explicit inner JOIN
constructs into lists of FROM
items whenever a
list of no more than this many items in total would
result. Prior to PostgreSQL 7.4, joins
specified via the JOIN
construct would
never be reordered by the query planner. The query planner has
subsequently been improved so that inner joins written in this
form can be reordered; this configuration parameter controls
the extent to which this reordering is performed.
At present, the order of outer joins specified via the
JOIN
construct is never adjusted by the query
planner; therefore, join_collapse_limit
has no
effect on this behavior. The planner may be improved to
reorder some classes of outer joins in a future release of
PostgreSQL.
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
inner JOIN
s. Thus, the explicit join order
specified in the query will be the actual order in which the
relations are joined. The query planner does not always choose
the optimal join order; advanced users may elect to
temporarily set this variable to 1, and then specify the join
order they desire explicitly. Another consequence of setting
this variable to 1 is that the query planner will behave more
like the PostgreSQL 7.3 query
planner, which some users might find useful for backward
compatibility reasons.
Setting this variable to a value between 1 and
from_collapse_limit
might be useful to
trade off planning time against the quality of the chosen plan
(higher values produce better plans).