相同执行计划,为何有执行快慢的差别
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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。
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 | <derived2> | 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)
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
参考链接
https://dev.mysql.com/doc/refman/8.0/en/explain.html
Enjoy GreatSQL 😃
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