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【聚看点】相同执行计划,为何有执行快慢的差别

2022-08-15 16:49:26来源:GreatSQL社区

前言

今天遇到一个很神奇的现象,在数据库中,相同的执行计划,执行SQL所需要的时间相差很大,执行快的SQL瞬间出结果,执行慢的SQL要几十秒才出结果,一度让我怀疑是数据库抽风了,后面才发现是见识不足,又进入了知识空白区。


【资料图】

场景复现

数据库版本使用的是8.0.23 MySQL Community Server - GPL。

由于生产环境数据敏感,禁止随意折腾,我在自己的测试环境,通过如下步骤,构造了一批数据,勉强能够复现出相同的场景来。

使用sysbench构造一万张表,每张表10行记录即可。create table test.test_col as select * from information_schema.columns;create table test.test_tab as select * from information_schema.tables;create table test.test_tc as select * from information_schema.table_constraints;执行10次 insert into test.test_tab select * from test.test_tab;创建必要的索引
alter table test_col add key(table_schema, table_name);alter table test_col add key(column_name);alter table test_tab add key(table_schema, table_name);alter table test_tc add key(table_name);

最终我测试表的数据如下:

mysql> select count(1) from test_col;+----------+| count(1) |+----------+|   1395616|+----------+1 row in set (3.29 sec)mysql> select count(1) from test_tab;+----------+| count(1) |+----------+|    10338 |+----------+1 row in set (0.12 sec)mysql> select count(1) from test_tc;+----------+| count(1) |+----------+|    10143 |+----------+1 row in set (0.06 sec)

先看执行快的SQL和它的执行计划:

mysql> select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME limit 3 ) t;+----------+| count(1) |+----------+|        3 |+----------+1 row in set (0.00 sec)mysql> explain select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME limit 3 ) t;+----+-------------+------------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+| id | select_type | table      | partitions | type  | possible_keys | key          | key_len | ref                                     | rows  | filtered | Extra                    |+----+-------------+------------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+|  1 | PRIMARY     |  | NULL       | ALL   | NULL          | NULL         | NULL    | NULL                                    |     3 |   100.00 | NULL                     ||  2 | DERIVED     | t2         | NULL       | index | TABLE_SCHEMA  | TABLE_SCHEMA | 390     | NULL                                    | 10240 |   100.00 | Using where; Using index ||  2 | DERIVED     | t3         | NULL       | ref   | TABLE_NAME    | TABLE_NAME   | 195     | test.t2.TABLE_NAME                      |     1 |    10.00 | Using where              ||  2 | DERIVED     | t1         | NULL       | ref   | TABLE_SCHEMA  | TABLE_SCHEMA | 390     | test.t2.TABLE_SCHEMA,test.t2.TABLE_NAME |    61 |   100.00 | NULL                     |+----+-------------+------------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+4 rows in set, 1 warning (0.00 sec)mysql>

再看执行慢的SQL和它的执行计划:

mysql> select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME ) t;+----------+| count(1) |+----------+|   1333088|+----------+1 row in set (2.45 sec)mysql> explain select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME ) t;+----+-------------+-------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+| id | select_type | table | partitions | type  | possible_keys | key          | key_len | ref                                     | rows  | filtered | Extra                    |+----+-------------+-------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+|  1 | SIMPLE      | t2    | NULL       | index | TABLE_SCHEMA  | TABLE_SCHEMA | 390     | NULL                                    | 10240 |   100.00 | Using where; Using index ||  1 | SIMPLE      | t3    | NULL       | ref   | TABLE_NAME    | TABLE_NAME   | 195     | test.t2.TABLE_NAME                      |     1 |    10.00 | Using where              ||  1 | SIMPLE      | t1    | NULL       | ref   | TABLE_SCHEMA  | TABLE_SCHEMA | 390     | test.t2.TABLE_SCHEMA,test.t2.TABLE_NAME |    61 |   100.00 | Using index              |+----+-------------+-------+------------+-------+---------------+--------------+---------+-----------------------------------------+-------+----------+--------------------------+3 rows in set, 1 warning (0.00 sec)

对比两个SQL执行计划,选择索引相同,表关联顺序相同,快的执行0.00秒,慢的执行2.45秒,生产环境数据量更多,差异更大。两条SQL差别是执行快的SQL子查询中多了limit 3。

从上述执行计划,我们可以看出,t2表为驱动表,先与t3做关联,得到结果后再与t1做关联,最后将结果集返回给客户端。

我们都知道,MySQL从server层返回数据给client,是一行一行返回的。也就是上层结果集与t1表每关联一行,有结果后,在没有排序的情况下,就是直接返回,并不会等所有行关联完后一起返回。

那么整个关联路径,是怎么样的呢,简化流程后应该是下面两种情况的一个。

从t2取出所有数据,与t3表关联得到所有结果集后;再从t1中取一行关联,每得到一行结果,返回一次数据。从t2取一行数据,与t3表关联得到一行结果后,再从t1中取一行关联,每得到一行结果,返回一次数据。新的技巧

由于上面两个SQL执行计划、预估成本都相同,无法看出具体执行过程中差异点在什么地方导致执行性能差这么多。

在MySQL 8.0.18及之后,有一个新功能explain analyze,可以定量分析SQL执行过程中的耗时及实际数据访问条数,拿到我们的场景具体使用一下。

mysql> explain analyze select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME limit 3 ) t \G*************************** 1. row ***************************EXPLAIN: -> Aggregate: count(1)  (actual time=0.348..0.349 rows=1 loops=1)    -> Table scan on t  (cost=2.84 rows=3) (actual time=0.003..0.004 rows=3 loops=1)        -> Materialize  (cost=75298.09 rows=3) (actual time=0.339..0.340 rows=3 loops=1)            -> Limit: 3 row(s)  (cost=75298.09 rows=3) (actual time=0.179..0.205 rows=3 loops=1)                -> Nested loop inner join  (cost=75298.09 rows=132366) (actual time=0.177..0.203 rows=3 loops=1)                    -> Nested loop inner join  (cost=4648.25 rows=1024) (actual time=0.130..0.130 rows=1 loops=1)                        -> Filter: ((t2.`TABLE_NAME` is not null) and (t2.TABLE_SCHEMA is not null))  (cost=1064.25 rows=10240) (actual time=0.065..0.065 rows=1 loops=1)                            -> Index scan on t2 using TABLE_SCHEMA  (cost=1064.25 rows=10240) (actual time=0.053..0.053 rows=1 loops=1)                        -> Filter: (t3.TABLE_SCHEMA = t2.TABLE_SCHEMA)  (cost=0.25 rows=0) (actual time=0.062..0.062 rows=1 loops=1)                            -> Index lookup on t3 using TABLE_NAME (TABLE_NAME=t2.`TABLE_NAME`)  (cost=0.25 rows=1) (actual time=0.059..0.059 rows=1 loops=1)                    -> Index lookup on t1 using TABLE_SCHEMA (TABLE_SCHEMA=t2.TABLE_SCHEMA, TABLE_NAME=t2.`TABLE_NAME`)  (cost=56.08 rows=129) (actual time=0.044..0.070 rows=3 loops=1)1 row in set (0.00 sec)mysql> explain analyze select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME ) t \G*************************** 1. row ***************************EXPLAIN: -> Aggregate: count(1)  (actual time=2130.310..2130.311 rows=1 loops=1)    -> Nested loop inner join  (cost=19704.44 rows=132366) (actual time=0.114..2006.259 rows=1333088 loops=1)        -> Nested loop inner join  (cost=4648.25 rows=1024) (actual time=0.094..108.093 rows=10143 loops=1)            -> Filter: ((t2.`TABLE_NAME` is not null) and (t2.TABLE_SCHEMA is not null))  (cost=1064.25 rows=10240) (actual time=0.051..17.021 rows=10338 loops=1)                -> Index scan on t2 using TABLE_SCHEMA  (cost=1064.25 rows=10240) (actual time=0.049..12.845 rows=10338 loops=1)            -> Filter: (t3.TABLE_SCHEMA = t2.TABLE_SCHEMA)  (cost=0.25 rows=0) (actual time=0.007..0.008 rows=1 loops=10338)                -> Index lookup on t3 using TABLE_NAME (TABLE_NAME=t2.`TABLE_NAME`)  (cost=0.25 rows=1) (actual time=0.007..0.008 rows=1 loops=10338)        -> Index lookup on t1 using TABLE_SCHEMA (TABLE_SCHEMA=t2.TABLE_SCHEMA, TABLE_NAME=t2.`TABLE_NAME`)  (cost=1.79 rows=129) (actual time=0.010..0.172 rows=131 loops=10143)1 row in set (2.13 sec)mysql>

从上面的分析结果来看,在驱动表t2执行Index scan on t2 using TABLE_SCHEMA这一步的时候,就存在很大的差异了,执行快的SQL在这一步只扫描了一行记录,耗时0.053毫秒,而执行快的SQL在这一步扫描数量基本上和执行计划估计的一致,扫描了10338行记录,耗时12.845毫秒;驱动表扫描记录越多,那么和后续表关联的nested loop join次数也越多,导致两条SQL执行时间差异巨大。

加大limit的返回限制为5000,驱动表t2扫描的行数增加至99行,执行时间增加至0.201毫秒

mysql> explain analyze select count(1) from (select t1.TABLE_CATALOG, t2.TABLE_SCHEMA, t2.TABLE_NAME, t1.COLUMN_NAME, t1.DATA_TYPE, t3.CONSTRAINT_TYPE   from test_col t1   inner join test_tab t2 on t1.TABLE_SCHEMA = t2.TABLE_SCHEMA and t1.table_name = t2.table_name  inner join test_tc t3 on t2.TABLE_SCHEMA = t3.TABLE_SCHEMA and t2.TABLE_NAME = t3.TABLE_NAME limit 5000) t \G*************************** 1. row ***************************EXPLAIN: -> Aggregate: count(1)  (actual time=33.395..33.396 rows=1 loops=1)    -> Table scan on t  (cost=565.00 rows=5000) (actual time=0.005..0.765 rows=5000 loops=1)        -> Materialize  (cost=75298.09 rows=5000) (actual time=31.863..33.046 rows=5000 loops=1)            -> Limit: 5000 row(s)  (cost=75298.09 rows=5000) (actual time=0.126..25.326 rows=5000 loops=1)                -> Nested loop inner join  (cost=75298.09 rows=132366) (actual time=0.124..24.757 rows=5000 loops=1)                    -> Nested loop inner join  (cost=4648.25 rows=1024) (actual time=0.095..0.834 rows=20 loops=1)                        -> Filter: ((t2.`TABLE_NAME` is not null) and (t2.TABLE_SCHEMA is not null))  (cost=1064.25 rows=10240) (actual time=0.046..0.201 rows=99 loops=1)                            -> Index scan on t2 using TABLE_SCHEMA  (cost=1064.25 rows=10240) (actual time=0.044..0.157 rows=99 loops=1)                        -> Filter: (t3.TABLE_SCHEMA = t2.TABLE_SCHEMA)  (cost=0.25 rows=0) (actual time=0.005..0.006 rows=0 loops=99)                            -> Index lookup on t3 using TABLE_NAME (TABLE_NAME=t2.`TABLE_NAME`)  (cost=0.25 rows=1) (actual time=0.005..0.006 rows=0 loops=99)                    -> Index lookup on t1 using TABLE_SCHEMA (TABLE_SCHEMA=t2.TABLE_SCHEMA, TABLE_NAME=t2.`TABLE_NAME`)  (cost=56.08 rows=129) (actual time=0.011..1.171 rows=250 loops=20)1 row in set (0.04 sec)mysql>

从上面的analyze结果,也可以看出来,在测试使用的SQL结构中,关联顺序是方法2,也就是从t2取一行数据,与t3表关联得到一行结果后,再从t1中取一行关联,每得到一行结果,返回一次数据。

从官方文档中介绍,explain analyze是explain format=tree的补充,两者都是8.0出现的新功能,这里简单介绍一下我个人理解的查看这种执行计划的顺序,如果有不正确的地方,还请指正:最先查看第一个缩进最多的行,没有相同缩进时,再向上一个缩进查看,再查看相同缩进的行(如果它有子缩进行,也是先查看缩进最多的行),以如下SQL为例,它的执行计划查看顺序为10->9->12->11->8->13->7->6->5->4->3。

第一个缩进最多的行是第10行,执行计划判断以索引扫描的方式从t2表读取10240条记录,实际从t2表读取了99条记录,在读取这99条记录的操作过程中,读取到第1条记录耗时0.044毫秒,读取到第99条耗时0.157毫秒,由于它是第一个读取的表,也是查询的驱动表,只会读取一次数据查看第9行,数据从存储引擎获取后,需要在server层过滤,计划是过滤10240条记录,实际上过滤了99条记录,过滤这99条记录的过程中,第1条记录执行完成耗时是0.046毫秒,第99条记录执行完成耗时是0.201毫秒,驱动表过滤操作也只进行一次第11行与第9行缩进相同,但是由于它有子缩进第12行,所以先执行第12行,以普通索引等值查找的方式扫描t3表,这里执行计划每个关联会返回一条记录,但是实际数据返回0条,是由于这个值是平均值,即t2表的99行记录在t3表中查询记录数除以99,取整后得到的值。第12行,对从存储引擎层返回的数据,做进一步过滤,这里也循环99次第8行,t2 表与t3表的关联,关联后返回记录20条,完成关联耗时为0.834毫秒第13行,以普通索引等值查询,从t1表中获取数据,一共要完成20次循环查询,每次循环获取第一条记录的平均时间是0.011毫秒,每次循环获取最后一条记录的时间是1.171毫秒,每次循环平均获取250条记录。第7行,与t1关联查询的方法和结果,一共返回5000条记录,返回第1条记录耗时0.124毫秒,返回第5000条记录耗时24.757毫秒第6行,limit判断,耗时25.326毫秒第5行,物化这5000行记录,物化完成耗时33.046毫秒第4行,扫描物化表数据5000条记录,扫描耗时0.765毫秒第3行,数据做聚合,返回count数量,耗时33.396毫秒,也是整个SQL执行的总耗时

explain analyze将执行过程中的索引、连接方式、过滤等信息嵌入了每个执行步骤,初次接触时,可以使用explain结果进行对比查看,以更容易接受和理解执行过程

总结

相同的SQL执行计划,却有不同的数据获取过程,这个在以前的版本中,是很难分析到的,explain\optimizer_trace\profile都不行,现在通过explain analyze能够轻易实现,通过这个工具,也加深了对多表join的一个执行过程的理解,是一个非常实用的工具。

需要注意点:

explain analyze过程中会实际执行具体SQL,但并不会返回SQL的执行结果,返回的结果集是详细执行步骤目前只支持select语句,对于insert\update \delete未支持,这点和explain有差别

关键词: 执行时间 生产环境 可以看出 查询记录 简单介绍

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