理解 PostgreSQL 的 count 函数的行为

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关于 count 函数的使用一直存在争议,尤其是在 MySQL 中,作为流行度越来越高的 PostgreSQL 是否也有类似的问题呢,我们通过实践来理解一下 PostgreSQL 中 count 函数的行为。

构建测试数据库

创建测试数据库,并创建测试表。测试表中有自增 ID、创建时间、内容三个字段,自增 ID 字段是主键。

create database performance_test;

create table test_tbl (id serial primary key, created_at timestamp, content varchar(512));
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生成测试数据

使用 generate_series 函数生成自增 ID,使用 now() 函数生成 created_at 列,对于 content 列,使用了 repeat(md5(random()::text), 10) 生成 10 个 32 位长度的 md5 字符串。使用下列语句,插入 1000w 条记录用于测试。

performance_test=# insert into test_tbl select generate_series(1,10000000),now(),repeat(md5(random()::text),10);
INSERT 0 10000000
Time: 212184.223 ms (03:32.184)
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由 count 语句引发的思考

默认情况下 PostgreSQL 不开启 SQL 执行时间的显示,所以需要手动开启一下,方便后面的测试对比。

\timing on
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count(*) 和 count(1) 的性能区别是经常被讨论的问题,分别使用 count(*) 和 count(1) 执行一次查询。

performance_test=# select count(*) from test_tbl;
  count
----------
 10000000
(1 row)

Time: 115090.380 ms (01:55.090)

performance_test=# select count(1) from test_tbl;
  count
----------
 10000000
(1 row)

Time: 738.502 ms
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可以看到两次查询的速度差别非常大,count(1) 真的有这么大的性能提升?接下来再次运行查询语句。

performance_test=# select count(*) from test_tbl;
  count
----------
 10000000
(1 row)

Time: 657.831 ms

performance_test=# select count(1) from test_tbl;
  count
----------
 10000000
(1 row)

Time: 682.157 ms
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可以看到第一次查询时候会非常的慢,后面三次速度非常快并且时间相近,这里就有两个问题出现了:

  • 为什么第一次查询速度这么慢?
  • count(*) 和 count(1) 到底存不存在性能差别?

查询缓存

使用 explain 语句重新执行查询语句

explain (analyze,buffers,verbose) select count(*) from test_tbl;
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可以看到如下输出:

 Finalize Aggregate  (cost=529273.69..529273.70 rows=1 width=8) (actual time=882.569..882.570 rows=1 loops=1)
   Output: count(*)
   Buffers: shared hit=96 read=476095
   ->  Gather  (cost=529273.48..529273.69 rows=2 width=8) (actual time=882.492..884.170 rows=3 loops=1)
         Output: (PARTIAL count(*))
         Workers Planned: 2
         Workers Launched: 2
         Buffers: shared hit=96 read=476095
         ->  Partial Aggregate  (cost=528273.48..528273.49 rows=1 width=8) (actual time=881.014..881.014 rows=1 loops=3)
               Output: PARTIAL count(*)
               Buffers: shared hit=96 read=476095
               Worker 0: actual time=880.319..880.319 rows=1 loops=1
                 Buffers: shared hit=34 read=158206
               Worker 1: actual time=880.369..880.369 rows=1 loops=1
                 Buffers: shared hit=29 read=156424
               ->  Parallel Seq Scan on public.test_tbl  (cost=0.00..517856.98 rows=4166598 width=0) (actual time=0.029..662.165 rows=3333333 loops=3)
                     Buffers: shared hit=96 read=476095
                     Worker 0: actual time=0.026..661.807 rows=3323029 loops=1
                       Buffers: shared hit=34 read=158206
                     Worker 1: actual time=0.030..660.197 rows=3285513 loops=1
                       Buffers: shared hit=29 read=156424
 Planning time: 0.043 ms
 Execution time: 884.207 ms
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注意里面的 shared hit,表示命中了内存中缓存的数据,这就可以解释为什么后面的查询会比第一次快很多。接下来去掉缓存,并重启 PostgreSQL。

service postgresql stop
echo 1 > /proc/sys/vm/drop_caches
service postgresql start
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重新执行 SQL 语句,速度慢了很多。

 Finalize Aggregate  (cost=529273.69..529273.70 rows=1 width=8) (actual time=50604.564..50604.564 rows=1 loops=1)
   Output: count(*)
   Buffers: shared read=476191
   ->  Gather  (cost=529273.48..529273.69 rows=2 width=8) (actual time=50604.508..50606.141 rows=3 loops=1)
         Output: (PARTIAL count(*))
         Workers Planned: 2
         Workers Launched: 2
         Buffers: shared read=476191
         ->  Partial Aggregate  (cost=528273.48..528273.49 rows=1 width=8) (actual time=50591.550..50591.551 rows=1 loops=3)
               Output: PARTIAL count(*)
               Buffers: shared read=476191
               Worker 0: actual time=50585.182..50585.182 rows=1 loops=1
                 Buffers: shared read=158122
               Worker 1: actual time=50585.181..50585.181 rows=1 loops=1
                 Buffers: shared read=161123
               ->  Parallel Seq Scan on public.test_tbl  (cost=0.00..517856.98 rows=4166598 width=0) (actual time=92.491..50369.691 rows=3333333 loops=3)
                     Buffers: shared read=476191
                     Worker 0: actual time=122.170..50362.271 rows=3320562 loops=1
                       Buffers: shared read=158122
                     Worker 1: actual time=14.020..50359.733 rows=3383583 loops=1
                       Buffers: shared read=161123
 Planning time: 11.537 ms
 Execution time: 50606.215 ms
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shared read 表示没有命中缓存,通过这个现象可以推断出,上一小节的四次查询中,第一次查询没有命中缓存,剩下三次查询都命中了缓存。

count(1) 和 count(*) 的区别

接下来探究 count(1) 和 count(*) 的区别是什么,继续思考最开始的四次查询,第一次查询使用了 count(*),第二次查询使用了 count(1) ,却依然命中了缓存,不正是说明 count(1) 和 count(*) 是一样的吗?

事实上,PostgreSQL 官方对于 is there a difference performance-wise between select count(1) and select count(*)? 问题的回复也证实了这一点:

Nope. In fact, the latter is converted to the former during parsing.[2]

既然 count(1) 在性能上没有比 count(*) 更好,那么使用 count(*) 就是更好的选择。

sequence scan 和 index scan

接下来测试一下,在不同数据量大小的情况下 count(*) 的速度,将查询语句写在 count.sql 文件中,使用 pgbench 进行测试。

pgbench -c 5 -t 20 performance_test -r -f count.sql
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分别测试 200w - 1000w 数据量下的 count 语句耗时

数据大小 count耗时(ms)
200w 738.758
300w 1035.846
400w 1426.183
500w 1799.866
600w 2117.247
700w 2514.691
800w 2526.441
900w 2568.240
1000w 2650.434

绘制成耗时曲线

曲线的趋势在 600w - 700w 数据量之间出现了转折,200w - 600w 是线性增长,600w 之后 count 的耗时就基本相同了。使用 explain 语句分别查看 600w 和 700w 数据时的 count 语句执行。

700w:

 Finalize Aggregate  (cost=502185.93..502185.94 rows=1 width=8) (actual time=894.361..894.361 rows=1 loops=1)
   Output: count(*)
   Buffers: shared hit=16344 read=352463
   ->  Gather  (cost=502185.72..502185.93 rows=2 width=8) (actual time=894.232..899.763 rows=3 loops=1)
         Output: (PARTIAL count(*))
         Workers Planned: 2
         Workers Launched: 2
         Buffers: shared hit=16344 read=352463
         ->  Partial Aggregate  (cost=501185.72..501185.73 rows=1 width=8) (actual time=889.371..889.371 rows=1 loops=3)
               Output: PARTIAL count(*)
               Buffers: shared hit=16344 read=352463
               Worker 0: actual time=887.112..887.112 rows=1 loops=1
                 Buffers: shared hit=5459 read=118070
               Worker 1: actual time=887.120..887.120 rows=1 loops=1
                 Buffers: shared hit=5601 read=117051
               ->  Parallel Index Only Scan using test_tbl_pkey on public.test_tbl  (cost=0.43..493863.32 rows=2928960 width=0) (actual time=0.112..736.376 rows=2333333 loops=3)
                     Index Cond: (test_tbl.id < 7000000)
                     Heap Fetches: 2328492
                     Buffers: shared hit=16344 read=352463
                     Worker 0: actual time=0.107..737.180 rows=2344479 loops=1
                       Buffers: shared hit=5459 read=118070
                     Worker 1: actual time=0.133..737.960 rows=2327028 loops=1
                       Buffers: shared hit=5601 read=117051
 Planning time: 0.165 ms
 Execution time: 899.857 ms
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600w:

 Finalize Aggregate  (cost=429990.94..429990.95 rows=1 width=8) (actual time=765.575..765.575 rows=1 loops=1)
   Output: count(*)
   Buffers: shared hit=13999 read=302112
   ->  Gather  (cost=429990.72..429990.93 rows=2 width=8) (actual time=765.557..770.889 rows=3 loops=1)
         Output: (PARTIAL count(*))
         Workers Planned: 2
         Workers Launched: 2
         Buffers: shared hit=13999 read=302112
         ->  Partial Aggregate  (cost=428990.72..428990.73 rows=1 width=8) (actual time=763.821..763.821 rows=1 loops=3)
               Output: PARTIAL count(*)
               Buffers: shared hit=13999 read=302112
               Worker 0: actual time=762.742..762.742 rows=1 loops=1
                 Buffers: shared hit=4638 read=98875
               Worker 1: actual time=763.308..763.308 rows=1 loops=1
                 Buffers: shared hit=4696 read=101570
               ->  Parallel Index Only Scan using test_tbl_pkey on public.test_tbl  (cost=0.43..422723.16 rows=2507026 width=0) (actual time=0.053..632.199 rows=2000000 loops=3)
                     Index Cond: (test_tbl.id < 6000000)
                     Heap Fetches: 2018490
                     Buffers: shared hit=13999 read=302112
                     Worker 0: actual time=0.059..633.156 rows=1964483 loops=1
                       Buffers: shared hit=4638 read=98875
                     Worker 1: actual time=0.038..634.271 rows=2017026 loops=1
                       Buffers: shared hit=4696 read=101570
 Planning time: 0.055 ms
 Execution time: 770.921 ms
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根据以上现象推断,PostgreSQL 似乎在 count 的数据量小于数据表长度的某一比例时,才使用 index scan,通过查看官方 wiki 也可以看到相关描述:

It is important to realise that the planner is concerned with minimising the total cost of the query. With databases, the cost of I/O typically dominates. For that reason, "count(*) without any predicate" queries will only use an index-only scan if the index is significantly smaller than its table. This typically only happens when the table's row width is much wider than some indexes'.[3]

根据 Stackoverflow 上的回答,count 语句查询的数量大于表大小的 3/4 时候就会用使用全表扫描代替索引扫描[4]。

结论

  1. 不要用 count(1) 或 count(列名) 代替 count(*)
  2. count 本身是非常耗时的
  3. count 可能是 index scan 也可能是 sequence scan,取决于 count 数量占表大小的比例

参考资料

[1] 深入理解Postgres中的cache

[2] Re: performance difference in count(1) vs. count(*)?

[3] Is "count(*)" much faster now?

[4] PostgreSQL not using index during count(*)

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