sql – 当索引扫描是更好的选择时Postgres不使用索引

我使用子查询重写了查询,试图强制使用索引,然后注意到一些有趣的东西.如果您查看下面的两个查询计划,您将看到当我将子查询的结果集限制为43k时,查询计划确实使用了email_activities上的索引,而将子查询中的限制设置为甚至44k将导致查询计划使用seq扫描email_activities.一个显然比另一个更有效,但Postgres似乎并不关心.

什么可能导致这个？如果其中一个集合大于特定大小,它是否在某处强制使用散列连接？

explain analyze SELECT COUNT(DISTINCT "email_activities"."email_recipient_id") FROM "email_activities" where email_recipient_id in (select "email_recipients"."id" from email_recipients WHERE "email_recipients"."email_campaign_id" = 1607 limit 43000);
                                                                                            QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=118261.50..118261.50 rows=1 width=4) (actual time=224.556..224.556 rows=1 loops=1)
   ->  Nested Loop  (cost=3699.03..118147.99 rows=227007 width=4) (actual time=32.586..209.076 rows=40789 loops=1)
         ->  HashAggregate  (cost=3698.94..3827.94 rows=43000 width=4) (actual time=32.572..47.276 rows=43000 loops=1)
               ->  Limit  (cost=0.09..3548.44 rows=43000 width=4) (actual time=0.017..22.547 rows=43000 loops=1)
                     ->  Index Scan using index_email_recipients_on_email_campaign_id on email_recipients  (cost=0.09..5422.47 rows=65710 width=4) (actual time=0.017..19.168 rows=43000 loops=1)
                           Index Cond: (email_campaign_id = 1607)
         ->  Index Only Scan using index_email_activities_on_email_recipient_id on email_activities  (cost=0.09..2.64 rows=5 width=4) (actual time=0.003..0.003 rows=1 loops=43000)
               Index Cond: (email_recipient_id = email_recipients.id)
               Heap Fetches: 40789
 Total runtime: 224.675 ms

和：

explain analyze SELECT COUNT(DISTINCT "email_activities"."email_recipient_id") FROM "email_activities" where email_recipient_id in (select "email_recipients"."id" from email_recipients WHERE "email_recipients"."email_campaign_id" = 1607 limit 50000);
                                                                                            QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=119306.25..119306.25 rows=1 width=4) (actual time=3050.612..3050.613 rows=1 loops=1)
   ->  Hash Semi Join  (cost=4451.08..119174.27 rows=263962 width=4) (actual time=1831.673..3038.683 rows=47935 loops=1)
         Hash Cond: (email_activities.email_recipient_id = email_recipients.id)
         ->  Seq Scan on email_activities  (cost=0.00..107490.96 rows=9359988 width=4) (actual time=0.003..751.988 rows=9360039 loops=1)
         ->  Hash  (cost=4276.08..4276.08 rows=50000 width=4) (actual time=34.058..34.058 rows=50000 loops=1)
               Buckets: 8192  Batches: 1  Memory Usage: 1758kB
               ->  Limit  (cost=0.09..4126.08 rows=50000 width=4) (actual time=0.016..27.302 rows=50000 loops=1)
                     ->  Index Scan using index_email_recipients_on_email_campaign_id on email_recipients  (cost=0.09..5422.47 rows=65710 width=4) (actual time=0.016..22.244 rows=50000 loops=1)
                           Index Cond: (email_campaign_id = 1607)
 Total runtime: 3050.660 ms

>版本：x86_64-unknown-linux-gnu上的PostgreSQL 9.3.10,由gcc编译(Ubuntu / Linaro 4.6.3-1ubuntu5)4.6.3,64位
> email_activities：~10m行
> email_recipients：~11m行

解决方法

对于少数行,运行索引扫描是值得的.如果要返回更多行(表的百分比更高,并且取决于数据分布,值频率和行宽),则更有可能在一个数据页上找到多行.然后切换到位图索引扫描是值得的.无论如何,一旦必须访问大部分数据页,运行顺序扫描,过滤剩余行并完全跳过索引的开销会更便宜.

Postgres切换到顺序扫描,期望找到rows = 263962,这已经是整个表的3％. (虽然实际上只找到了行= 47935,但请参见下文.)

更多相关答案：

> Efficient PostgreSQL query on timestamp using index or bitmap index scan?

小心强制查询计划

您不能直接在Postgres中强制使用某个计划程序方法,但是出于调试目的,您可以使其他方法看起来非常昂贵.请参见手册中的Planner Method Configuration.

SET enable_seqscan = off(如另一个答案中建议的那样)对顺序扫描执行此操作.但这仅用于在会话中进行调试.除非您确切知道自己在做什么,否则不要将其用作生产中的常规设置.它可以强制荒谬的查询计划. Quoting the manual：

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
planer cost constants (see 07003),running 07004 manually,
increasing the value of the 07005 configuration
parameter,and increasing the amount of statistics collected for
specific columns using ALTER TABLE SET STATISTICS.

这已经是你需要的大部分建议了.

> Keep PostgreSQL from sometimes choosing a bad query plan

在这种特殊情况下,Postgres预计email_activities.email_recipient_id的点击次数比实际发现次数多5-6次：

estimated rows=227007 vs. actual ... rows=40789
estimated rows=263962 vs. actual ... rows=47935

如果您经常运行此查询,则需要让ANALYZE查看更大的样本以获得有关特定列的更准确统计信息.你的桌子很大(约10M行),所以要：

ALTER TABLE email_activities ALTER COLUMN email_recipient_id
SET STATISTICS 3000;  -- max 10000,default 100

然后ANALYZE email_activities;

最后的手段

在极少数情况下,您可能会在单独的事务中或在具有自己环境的函数中强制使用SET LOCAL enable_seqscan = off的索引.喜欢：

CREATE OR REPLACE FUNCTION f_count_dist_recipients(_email_campaign_id int,_limit int)
  RETURNS bigint AS
$func$
   SELECT COUNT(DISTINCT a.email_recipient_id)
   FROM   email_activities a
   WHERE  a.email_recipient_id IN (
      SELECT id
      FROM   email_recipients
      WHERE  email_campaign_id = $1
      LIMIT  $2)       -- or consider query below
$func$ LANGUAGE sql VOLATILE COST 100000 SET enable_seqscan = off;

该设置仅适用于函数的本地范围.

警告：这只是一个概念证明.从长远来看,即使是这种不那么激进的人工干预也可能会让你感到痛苦.基数,价值频率,您的架构,全局Postgres设置,一切都随着时间的推移而变化.您将升级到新的Postgres版本.您现在强制执行的查询计划可能会在以后成为一个非常糟糕的主意.

通常,这只是解决您的设置问题的方法.更好地找到并修复它.

替代查询

问题中缺少基本信息,但这个等效查询可能更快,更可能使用索引(email_recipient_id) – 对于更大的LIMIT越来越多.

SELECT COUNT(*) AS ct
FROM  (
   SELECT id
   FROM   email_recipients
   WHERE  email_campaign_id = 1607
   LIMIT  43000
   ) r
WHERE  EXISTS (
   SELECT 1
   FROM   email_activities
   WHERE  email_recipient_id = r.id);

（编辑：李大同）

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