PostgreSQL 8.4.2 Documentation
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# 18.6. Query Planning

## 18.6.1. Planner Method Configuration

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 can 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`.

## 18.6.2. Planner Cost Constants

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. Traditionally, these variables have been referenced to sequential page fetches as the unit of cost; 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.

> Note: 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 get. 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.

`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. 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.

  > Tip: 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.

`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. 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 default is 128 megabytes (`128MB`).

## 18.6.3. Genetic Query Optimizer

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. Also, GEQO's searching is randomized and therefore its plans may vary nondeterministically. For more information see Chapter 49.

`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 a more granular way to control use 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 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 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.

## 18.6.4. Other Planner Options

`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 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.

  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. This can improve performance when inheritance is used to build partitioned tables.

  Currently, constraint exclusion is enabled by default only for cases that are often used to implement table partitioning. 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 partitioned tables you might prefer to turn it off entirely.

  Refer to Section 5.9.4 for more information on using constraint exclusion and 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 nondeterministic plans. See Section 18.6.3.

`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. 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 nondeterministic plans. See Section 18.6.3.

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