PostgreSQL 对IN,EXISTS,ANY/ALL,JOIN的sql优化方案-巨人网络通讯

PostgreSQL 对IN,EXISTS,ANY/ALL,JOIN的sql优化方案

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=#

IN语句

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

1.使用IN子句　

SELECT
  count( aid ),bid
FROM
  pgbench_accounts
WHERE
  bid IN ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 )
GROUP BY
  bid;

2.使用ANY子句

SELECT
  count( aid ),bid
FROM
  pgbench_accounts
WHERE
  bid = ANY ( SELECT bid FROM pgbench_branches WHERE bbalance > 0 )
GROUP BY
  bid;

3.使用EXISTS子句

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;

4.使用INNER JOIN

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。

排除查询

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

1.使用NOT IN

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=#

2.使用>ALL

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=#

3.使用NOT EXISTS

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=#

4.使用LEFT JOIN和IS NULL

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。

其它注意点

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)