這些配置參數提供了影響查詢最佳化程序選擇的查詢計劃決策方法。如果最佳化程序為特定查詢選擇的預設計劃並非最佳，則臨時的解決方案是使用這些配置參數來強制最佳化程序選擇不同的計劃。提高最佳化程序選擇的計劃素質的有效方法包括了調整計劃程序成本常數（請參閱第 19.7.2 節），手動執行 ANALYZE，增加 default_statistics_target 配置參數的值，以及增加為特定欄位收集的統計訊息量，使用 ALTER TABLE SET STATISTICS。
enable_bitmapscan (boolean)啟用或停用查詢計劃程序使用 bitmap 掃描計劃類型。預設為開啓。
enable_gathermerge (boolean)啟用或停用查詢計劃程序使用 gather merge 計劃類型。預設為開啓。
enable_hashagg (boolean)啟用或停用查詢計劃程序使用 hashed aggregation 計劃類型。預設為開啓。
enable_hashjoin (boolean)啟用或停用查詢計劃程序使用 hash-join 計劃類型。預設為開啓。
enable_indexscan (boolean)啟用或停用查詢計劃程序使用 index-scan 計劃類型。預設為開啓。
enable_indexonlyscan (boolean)啟用或停用查詢計劃程序使用 index-only 掃描計劃類型（請參閱第 11.11 節）。預設為開啓。
enable_material (boolean)啟用或停用查詢計劃程序對實作的使用。完全抑制實作是不可能的，但是關閉此變數會阻止計劃程序插入實體化的節點，除非真的需要它。預設為開啓。
enable_mergejoin (boolean)啟用或停用查詢計劃程序使用 merge-join 計劃類型。預設為開啓。
enable_nestloop (boolean)啟用或停用查詢計劃程序使用 nested-loop join 計劃。完全抑制 nested-loop join 是不可能的，但如果有其他可用方法，則關閉此變數會阻止規劃器使用它。預設為開啓。
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)啟用或停用查詢計劃程序從查詢計劃中修剪分割資料表分割區的功能。 這也控制了計劃程序產生查詢計劃的功能，此功能使查詢執行程序可以在查詢執行期間刪除（忽略）分割區。 預設為 on。有關詳細資訊，請參閱第 5.11.4 節。
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 exactly matching sets of child partitions. Because partitionwise join planning can use significantly more CPU time and memory during planning, the default 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 a partitioned tables 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. Because partitionwise grouping or aggregation can use significantly more CPU time and memory during planning, the default is off.
enable_seqscan (boolean)啟用或停用查詢計劃程序使用循序掃描計劃類型。完全抑制循序掃描是不可能的，但如果有其他方法可用，則關閉此變數會阻止計劃程序使用。預設為開啓。
enable_sort (boolean)啟用或停用查詢計劃程序使用明確的排序步驟。完全抑制明確排序是不可能的，但如果有其他可用方法，則關閉此變數會阻止計劃程序使用。預設為開啓。
enable_tidscan (boolean)啟用或停用查詢計劃程序使用 TID 掃描計劃類型。預設為開啓。
本節中描述的成本變數是以比例來使用的。只有它們的相對值很重要，因此按相同因子放大或縮小它們將不會讓規劃程式的選擇有所變化。預設情況下，這些成本變數基於連續頁面讀取的成本；也就是說，seq_page_cost 通常設定為 1.0，其他成本變數是相對參考其設定的。 但是，如果您願意，可以使用不同的比例，例如特定主機上的實際執行時間（以毫秒為單位）。
注意 不幸的是，並沒有明確定義的方法來決定成本變數的理想值。它們最好被視為特定安裝環境可能接受的所有查詢組合的平均值。這意味著僅僅根據一些實驗來改變它們都不是真正的最佳。
seq_page_cost (floating point)設定計劃程序對磁碟頁面讀取的成本估計，此成本是一系列連續讀取的一部分。預設值為 1.0。透過設定同名的 tablespace 參數，可以為特定資料表空間中的資料表和索引覆寫此值（請參閱 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.
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)設定計劃程序在查詢期間處理每個資料列的成本估算。預設值為 0.01。
cpu_index_tuple_cost (floating point)設定計劃程序在索引掃描期間處理每個索引項目的成本估計。預設值為 0.005。
cpu_operator_cost (floating point)設定計劃程序對查詢期間執行的每個運算子或函數的處理成本的估計。 預設值為 0.0025。
parallel_setup_cost (floating point)設定計劃程序對啟動平行工作程序的成本估計。預設值為 1000。
parallel_tuple_cost (floating point)設定計劃程序對從一個平行工作程序轉移到另一個程序的一個 tuple 的成本估算。預設值為 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. 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. 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. 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. The default is 4 gigabytes (4GB).
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 inheritance and partitioned tables 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:
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. 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 5.10.4 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 non-optimal plans. See Section 19.7.3.
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.
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 regresssuppresses 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.