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本篇文章给大家分享的是有关怎么在PostgreSQL 数据库对IN、EXISTS、ANY/ALL和JOIN进行优化，小编觉得挺实用的，因此分享给大家学习，希望大家阅读完这篇文章后可以有所收获，话不多说，跟着小编一起来看看吧。
测试环境：
postgres=# select version(); version --------------------------------------------------------------------------------------------------------- PostgreSQL 11.9 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-39), 64-bit (1 row) postgres=#
数据准备：
$ pgbench -i -s 10
postgres=# \d List of relations Schema | Name | Type | Owner --------+------------------+-------+---------- public | pgbench_accounts | table | postgres public | pgbench_branches | table | postgres public | pgbench_history | table | postgres public | pgbench_tellers | table | postgres (4 rows) postgres=# select * from pgbench_accounts limit 1; aid | bid | abalance | filler -----+-----+----------+-------------------------------------------------------------------------------------- 1 | 1 | 0 | (1 row) postgres=# select * from pgbench_branches limit 1; bid | bbalance | filler -----+----------+-------- 1 | 0 | (1 row) postgres=# select * from pgbench_history limit 1; tid | bid | aid | delta | mtime | filler -----+-----+-----+-------+-------+-------- (0 rows) postgres=# select * from pgbench_tellers limit 1; tid | bid | tbalance | filler -----+-----+----------+-------- 1 | 1 | 0 | (1 row) postgres=# select * from pgbench_branches; bid | bbalance | filler -----+----------+-------- 1 | 0 | 2 | 0 | 3 | 0 | 4 | 0 | 5 | 0 | 6 | 0 | 7 | 0 | 8 | 0 | 9 | 0 | 10 | 0 | (10 rows) postgres=# update pgbench_branches set bbalance=4500000 where bid in (4,7); UPDATE 2 postgres=#
查询要求：找出那些余额(balance)大于0的每个分支(branch)在表在pgbench_accounts中有多少个账户
SELECT count( aid ),bid FROM pgbench_accounts WHERE bid IN ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 ) GROUP BY bid;
SELECT count( aid ),bid FROM pgbench_accounts WHERE bid = ANY ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 ) GROUP BY bid;
SELECT count( aid ),bid FROM pgbench_accounts WHERE EXISTS ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 AND pgbench_accounts.bid = pgbench_branches.bid ) GROUP BY bid;
SELECT count( aid ),a.bid FROM pgbench_accounts a JOIN pgbench_branches b ON a.bid = b.bid WHERE b.bbalance > 0 GROUP BY a.bid;
在完成这个查询要求的时候，有人可能会假设exists和inner join性能可能会更好，因为他们可以使用两表连接的逻辑和优化。而IN和ANY子句需要使用子查询。
然而，PostgreSQL(10版本之后)已经智能的足以对上面四种写法产生相同的执行计划！
所有上面的写法都会产生相同的执行计划：
QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------ Finalize GroupAggregate (cost=23327.73..23330.26 rows=10 width=12) (actual time=97.199..99.014 rows=2 loops=1) Group Key: a.bid -> Gather Merge (cost=23327.73..23330.06 rows=20 width=12) (actual time=97.191..99.006 rows=6 loops=1) Workers Planned: 2 Workers Launched: 2 -> Sort (cost=22327.70..22327.73 rows=10 width=12) (actual time=93.762..93.766 rows=2 loops=3) Sort Key: a.bid Sort Method: quicksort Memory: 25kB Worker 0: Sort Method: quicksort Memory: 25kB Worker 1: Sort Method: quicksort Memory: 25kB -> Partial HashAggregate (cost=22327.44..22327.54 rows=10 width=12) (actual time=93.723..93.727 rows=2 loops=3) Group Key: a.bid -> Hash Join (cost=1.14..22119.10 rows=41667 width=8) (actual time=24.024..83.263 rows=66667 loops=3) Hash Cond: (a.bid = b.bid) -> Parallel Seq Scan on pgbench_accounts a (cost=0.00..20560.67 rows=416667 width=8) (actual time=0.023..43.151 rows=333333 loops=3) -> Hash (cost=1.12..1.12 rows=1 width=4) (actual time=0.027..0.028 rows=2 loops=3) Buckets: 1024 Batches: 1 Memory Usage: 9kB -> Seq Scan on pgbench_branches b (cost=0.00..1.12 rows=1 width=4) (actual time=0.018..0.020 rows=2 loops=3) Filter: (bbalance > 0) Rows Removed by Filter: 8 Planning Time: 0.342 ms Execution Time: 99.164 ms (22 rows)
那么，我们是否可以得出这样的结论:我们可以随意地编写查询，而PostgreSQL的智能将会处理其余的问题?！
等等!
如果我们考虑排除情况，事情会变得不同。
查询要求：找出那些余额(balance)不大于0的每个分支(branch)在表在pgbench_accounts中有多少个账户
SELECT count( aid ),bid FROM pgbench_accounts WHERE bid NOT IN ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 ) GROUP BY bid;
执行计划:
QUERY PLAN ---------------------------------------------------------------------------------------------------------------------------------------------------------- Finalize GroupAggregate (cost=23645.42..23647.95 rows=10 width=12) (actual time=128.606..130.502 rows=8 loops=1) Group Key: pgbench_accounts.bid -> Gather Merge (cost=23645.42..23647.75 rows=20 width=12) (actual time=128.598..130.490 rows=24 loops=1) Workers Planned: 2 Workers Launched: 2 -> Sort (cost=22645.39..22645.42 rows=10 width=12) (actual time=124.960..124.963 rows=8 loops=3) Sort Key: pgbench_accounts.bid Sort Method: quicksort Memory: 25kB Worker 0: Sort Method: quicksort Memory: 25kB Worker 1: Sort Method: quicksort Memory: 25kB -> Partial HashAggregate (cost=22645.13..22645.23 rows=10 width=12) (actual time=124.917..124.920 rows=8 loops=3) Group Key: pgbench_accounts.bid -> Parallel Seq Scan on pgbench_accounts (cost=1.13..21603.46 rows=208333 width=8) (actual time=0.078..83.134 rows=266667 loops=3) Filter: (NOT (hashed SubPlan 1)) Rows Removed by Filter: 66667 SubPlan 1 -> Seq Scan on pgbench_branches (cost=0.00..1.12 rows=1 width=4) (actual time=0.020..0.021 rows=2 loops=3) Filter: (bbalance > 0) Rows Removed by Filter: 8 Planning Time: 0.310 ms Execution Time: 130.620 ms (21 rows) postgres=#
SELECT count( aid ),bid FROM pgbench_accounts WHERE bid <> ALL ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 ) GROUP BY bid;
执行计划:
QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------ Finalize GroupAggregate (cost=259581.79..259584.32 rows=10 width=12) (actual time=418.220..419.913 rows=8 loops=1) Group Key: pgbench_accounts.bid -> Gather Merge (cost=259581.79..259584.12 rows=20 width=12) (actual time=418.212..419.902 rows=24 loops=1) Workers Planned: 2 Workers Launched: 2 -> Sort (cost=258581.76..258581.79 rows=10 width=12) (actual time=413.906..413.909 rows=8 loops=3) Sort Key: pgbench_accounts.bid Sort Method: quicksort Memory: 25kB Worker 0: Sort Method: quicksort Memory: 25kB Worker 1: Sort Method: quicksort Memory: 25kB -> Partial HashAggregate (cost=258581.50..258581.60 rows=10 width=12) (actual time=413.872..413.875 rows=8 loops=3) Group Key: pgbench_accounts.bid -> Parallel Seq Scan on pgbench_accounts (cost=0.00..257539.83 rows=208333 width=8) (actual time=0.054..367.244 rows=266667 loops=3) Filter: (SubPlan 1) Rows Removed by Filter: 66667 SubPlan 1 -> Materialize (cost=0.00..1.13 rows=1 width=4) (actual time=0.000..0.001 rows=2 loops=1000000) -> Seq Scan on pgbench_branches (cost=0.00..1.12 rows=1 width=4) (actual time=0.001..0.001 rows=2 loops=337880) Filter: (bbalance > 0) Rows Removed by Filter: 8 Planning Time: 0.218 ms Execution Time: 420.035 ms (22 rows) postgres=#
SELECT count( aid ),bid FROM pgbench_accounts WHERE NOT EXISTS ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 AND pgbench_accounts.bid = pgbench_branches.bid ) GROUP BY bid;
执行计划:
QUERY PLAN ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Finalize GroupAggregate (cost=28327.72..28330.25 rows=10 width=12) (actual time=152.024..153.931 rows=8 loops=1) Group Key: pgbench_accounts.bid -> Gather Merge (cost=28327.72..28330.05 rows=20 width=12) (actual time=152.014..153.917 rows=24 loops=1) Workers Planned: 2 Workers Launched: 2 -> Sort (cost=27327.70..27327.72 rows=10 width=12) (actual time=147.782..147.786 rows=8 loops=3) Sort Key: pgbench_accounts.bid Sort Method: quicksort Memory: 25kB Worker 0: Sort Method: quicksort Memory: 25kB Worker 1: Sort Method: quicksort Memory: 25kB -> Partial HashAggregate (cost=27327.43..27327.53 rows=10 width=12) (actual time=147.732..147.737 rows=8 loops=3) Group Key: pgbench_accounts.bid -> Hash Anti Join (cost=1.14..25452.43 rows=375000 width=8) (actual time=0.134..101.884 rows=266667 loops=3) Hash Cond: (pgbench_accounts.bid = pgbench_branches.bid) -> Parallel Seq Scan on pgbench_accounts (cost=0.00..20560.67 rows=416667 width=8) (actual time=0.032..45.174 rows=333333 loops=3) -> Hash (cost=1.12..1.12 rows=1 width=4) (actual time=0.036..0.037 rows=2 loops=3) Buckets: 1024 Batches: 1 Memory Usage: 9kB -> Seq Scan on pgbench_branches (cost=0.00..1.12 rows=1 width=4) (actual time=0.025..0.027 rows=2 loops=3) Filter: (bbalance > 0) Rows Removed by Filter: 8 Planning Time: 0.322 ms Execution Time: 154.040 ms (22 rows) postgres=#
SELECT count( aid ),a.bid FROM pgbench_accounts a LEFT JOIN pgbench_branches b ON a.bid = b.bid AND b.bbalance > 0 WHERE b.bid IS NULL GROUP BY a.bid;
执行计划:
QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------ Finalize GroupAggregate (cost=28327.72..28330.25 rows=10 width=12) (actual time=145.298..147.096 rows=8 loops=1) Group Key: a.bid -> Gather Merge (cost=28327.72..28330.05 rows=20 width=12) (actual time=145.288..147.083 rows=24 loops=1) Workers Planned: 2 Workers Launched: 2 -> Sort (cost=27327.70..27327.72 rows=10 width=12) (actual time=141.883..141.887 rows=8 loops=3) Sort Key: a.bid Sort Method: quicksort Memory: 25kB Worker 0: Sort Method: quicksort Memory: 25kB Worker 1: Sort Method: quicksort Memory: 25kB -> Partial HashAggregate (cost=27327.43..27327.53 rows=10 width=12) (actual time=141.842..141.847 rows=8 loops=3) Group Key: a.bid -> Hash Anti Join (cost=1.14..25452.43 rows=375000 width=8) (actual time=0.087..99.535 rows=266667 loops=3) Hash Cond: (a.bid = b.bid) -> Parallel Seq Scan on pgbench_accounts a (cost=0.00..20560.67 rows=416667 width=8) (actual time=0.025..44.337 rows=333333 loops=3) -> Hash (cost=1.12..1.12 rows=1 width=4) (actual time=0.026..0.027 rows=2 loops=3) Buckets: 1024 Batches: 1 Memory Usage: 9kB -> Seq Scan on pgbench_branches b (cost=0.00..1.12 rows=1 width=4) (actual time=0.019..0.020 rows=2 loops=3) Filter: (bbalance > 0) Rows Removed by Filter: 8 Planning Time: 0.231 ms Execution Time: 147.180 ms (22 rows) postgres=#
NOT IN 和 <> ALL生成执行计划都包含了一个子查询。他们是各自独立的。
而NOT EXISTS和LEFT JOIN生成了相同的执行计划。
这些hash连接(或hash anti join)是完成查询要求的最灵活的方式。这也是推荐exists或join的原因。因此，推荐使用exists或join的经验法则是有效的。
但是，我们继续往下看! 即使有了子查询执行计划，NOT IN子句的执行时间也会更好?
是的。PostgreSQL做了出色的优化，PostgreSQL将子查询计划进行了hash处理。因此PostgreSQL对如何处理IN子句有了更好的理解，这是一种逻辑思维方式，因为很多人倾向于使用IN子句。子查询返回的行很少，但即使子查询返回几百行，也会发生同样的情况。
但是，如果子查询返回大量行(几十万行)怎么办?让我们尝试一个简单的测试：
CREATE TABLE t1 AS SELECT * FROM generate_series(0, 500000) id; CREATE TABLE t2 AS SELECT (random() * 4000000)::integer id FROM generate_series(0, 4000000); ANALYZE t1; ANALYZE t2; EXPLAIN SELECT id FROM t1 WHERE id NOT IN (SELECT id FROM t2);
执行计划：
QUERY PLAN -------------------------------------------------------------------------------- Gather (cost=1000.00..15195064853.01 rows=250000 width=4) Workers Planned: 1 -> Parallel Seq Scan on t1 (cost=0.00..15195038853.01 rows=147059 width=4) Filter: (NOT (SubPlan 1)) SubPlan 1 -> Materialize (cost=0.00..93326.01 rows=4000001 width=4) -> Seq Scan on t2 (cost=0.00..57700.01 rows=4000001 width=4) (7 rows) postgres=#
这里，执行计划将子查询进行了物化。代价评估变成了15195038853.01。(PostgreSQL的默认设置，如果t2表的行低于100k，会将子查询进行hash)。这样就会严重影响性能。因此，对于那种子查询返回的行数很少的场景，IN子句可以起到很好的作用。
有的！在我们用不同的方式写查询的时候，可能有数据类型的转换。
比如，语句：
EXPLAIN ANALYZE SELECT * FROM emp WHERE gen = ANY(ARRAY['M', 'F']);
就会发生隐式的类型转换：
Seq Scan on emp (cost=0.00..1.04 rows=2 width=43) (actual time=0.023..0.026 rows=3 loops=1)
Filter: ((gen)::text = ANY ('{M,F}'::text[]))这里的(gen)::text就发生了类型转换。如果在大表上，这种类型转换的代价会很高，因此，PostgreSQL对IN子句做了更好的处理。
EXPLAIN ANALYZE SELECT * FROM emp WHERE gen IN ('M','F');
Seq Scan on emp (cost=0.00..1.04 rows=3 width=43) (actual time=0.030..0.034 rows=3 loops=1)
Filter: (gen = ANY ('{M,F}'::bpchar[]))将IN子句转换成了ANY子句，没有对gen列进行类型转换。而是将M\F转成了bpchar(内部等价于char)
以上就是怎么在PostgreSQL 数据库对IN、EXISTS、ANY/ALL和JOIN进行优化，小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注亿速云行业资讯频道。
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