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一个小操作,SQL查询速度翻了1000倍。

2022-05-10 12:00 https://my.oschina.net/u/5674736/blog/5523226 TiDB社区干货传送门 次阅读 条评论

背景介绍

某一天早上来到公司,接到业务同学反馈,线上某个SQL之前查询速度很快,从某个时间点开始查询速度突然变慢了,希望DBA帮忙查看下。业务同学反馈的原话如下:

image.png

看到这个问题,我第一时间询问了业务对这个表的基本操作,得到的反馈如下:

  • 这个表的SQL语法没有发生过变化

  • 这个表的表结构近期未发生变更

  • 这个表是个日志表,近期只有写入insert,没有大量delete、update操作

 

分析过程

1、SQL分析

首先,我们来看下这条SQL(脱敏之后):

SELECT

xxx, xxx, xxx, xxx, ....

FROM log_xxxx_2022_4

WHERE  1=1

AND `l_mid` = 'xxxxxxx-E527B8CD-84B-960'

AND `l_opertime` < '2022-04-20 10:56:37'

AND `l_opertime` >= '2022-03-20 10:56:37'

ORDER BY `l_opertime` DESC LIMIT 0,20;

SQL的语义本身比较简单,是一个单表查询,不涉及复杂查询:

从某一张表里面,利用l_mid和l_opertime这两个字段作为过滤条件,输出表里面的其他字段,并按照l_opertime排序。

 

2、表结构分析

这样一条简单的SQL,如果有索引的话,应该不会出现问题才对,我们看下表结构:

show index from  log_xxxx_2022_4;

+-----------------+------------+---------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+-----------+

| Table           | Non_unique | Key_name            | Seq_in_index | Column_name   | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment | Visible | Expression | Clustered |

+-----------------+------------+---------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+-----------+

| log_xxxx_2022_4 |          0 | PRIMARY             |            1 | l_id          | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | YES       |

| log_xxxx_2022_4 |          1 | l_oper              |            1 | l_oper        | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_channel           |            1 | l_channel     | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_xxxxid            |            1 | l_xxxxid      | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_mid               |            1 | l_mid         | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_user              |            1 | l_user        | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_opertime          |            1 | l_opertime    | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | l_xxxstatus         |            1 | l_xxxstatus   | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

| log_xxxx_2022_4 |          1 | index_l_submit_time |            1 | l_submit_time | A         |           0 |     NULL | NULL   |      | BTREE      |         |               | YES     | NULL       | NO        |

+-----------------+------------+---------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+---------------+---------+------------+-----------+

9 rows in set (0.00 sec)

从上述索引结构,可以看出来,我们的l_mid字段和l_opertime字段,都有索引。

从索引原理上看,这个SQL的执行计划至少应该是一个IndexRangeScan(索引范围扫描)。

 

3、执行计划分析

传统的MySQL中,使用Explain语句来分析MySQL的执行计划。在TiDB中,我们可以使用2种方法查看TiDB的执行计划:

a、Explain + SQL :这种方法不会真正执行语句,会直接返回执行计划

b、Explain Analyze + SQL : 这种方法会执行SQL语句,并返回SQL的执行计划

 

我们使用上述方法b来查看执行计划(原因是这种方法可以看到SQL的执行时间),上述SQL的执行计划如下:

+----------------------------------+----------+----------+-----------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------+----------+------+

| id                               | estRows  | actRows  | task      | access object                                       | execution info                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | operator info                                                                        | memory   | disk |

+----------------------------------+----------+----------+-----------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------+----------+------+

| Limit_12                         | 20.00    | 0        | root      |                                                     | time:26.2s, loops:1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | offset:0, count:20                                                                   | N/A      | N/A  |

| └─IndexLookUp_28                 | 20.00    | 0        | root      |                                                     | time:26.2s, loops:1, index_task: {total_time: 26.1s, fetch_handle: 1.95s, build: 3.39s, wait: 20.7s}, table_task: {total_time: 2m6.3s, num: 1043, concurrency: 5}                                                                                                                                                                                                                                                                                                                                                                                                                      |                                                                                      | 167.2 MB | N/A  |

|   ├─IndexRangeScan_25(Build)     | 20000.00 | 21180838 | cop[tikv] | table:log_xxxx_2022_4, index:l_opertime(l_opertime) | time:848.9ms, loops:20703, cop_task: {num: 23, max: 1.42s, min: 2.14ms, avg: 712.3ms, p95: 1.15s, max_proc_keys: 969873, p95_proc_keys: 960000, tot_proc: 15.1s, tot_wait: 41ms, rpc_num: 23, rpc_time: 16.4s, copr_cache_hit_ratio: 0.04}, tikv_task:{proc max:763ms, min:31ms, p80:729ms, p95:747ms, iters:20788, tasks:23}, scan_detail: {total_process_keys: 20220838, total_process_keys_size: 930158548, total_keys: 20220861, rocksdb: {delete_skipped_count: 0, key_skipped_count: 20220839, block: {cache_hit_count: 12975, read_count: 28, read_byte: 1.35 MB}}}             | range:[2022-03-20 10:56:37,2022-04-20 10:56:37), keep order:true, desc, stats:pseudo | N/A      | N/A  |

|   └─Selection_27(Probe)          | 20.00    | 0        | cop[tikv] |                                                     | time:1m57.9s, loops:1043, cop_task: {num: 1441, max: 891.8ms, min: 848.6µs, avg: 91.2ms, p95: 286.5ms, max_proc_keys: 20992, p95_proc_keys: 20480, tot_proc: 1m51.3s, tot_wait: 17.1s, rpc_num: 1441, rpc_time: 2m11.3s, copr_cache_hit_ratio: 0.00}, tikv_task:{proc max:235ms, min:0s, p80:78ms, p95:98ms, iters:27477, tasks:1441}, scan_detail: {total_process_keys: 21180838, total_process_keys_size: 7841770073, total_keys: 21184733, rocksdb: {delete_skipped_count: 0, key_skipped_count: 55260873, block: {cache_hit_count: 239289, read_count: 83, read_byte: 622.7 KB}}}  | eq(comment5_log.log_xxxx_2022_4.l_mid, "625F70C0-ABD4F004-E527B8CD-84B-960")         | N/A      | N/A  |

|     └─TableRowIDScan_26          | 20000.00 | 21180838 | cop[tikv] | table:log_xxxx_2022_4                               | tikv_task:{proc max:231ms, min:0s, p80:76ms, p95:95ms, iters:27477, tasks:1441}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | keep order:false, stats:pseudo                                                       | N/A      | N/A  |

+----------------------------------+----------+----------+-----------+-----------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------+----------+------+

5 rows in set (26.15 sec)

上述SQL的执行时间是:26.15 sec

我们对TiDB的执行计划进行分析:

id 列:算子名称.

从图中可以看出,我们当前的SQL算子包含:

IndexLookUp:先汇总 Build 端 TiKV 扫描上来的 RowID,再去 Probe 端上根据这些 RowID 精确地读取 TiKV 上的数据。

IndexFullScan:另一种“全表扫描”,扫的是索引数据,不是表数据。

TableRowIDScan:根据上层传递下来的 RowID 扫描表数据。时常在索引读操作后检索符合条件的行。

estRows 列:显示TiDB预计会处理的行数

actRows 列:显示TiDB算子实际输出的数据条数

预估扫描行数最多是2w行,但是实际的输出条数是2000w行。

task 列:显示算子在执行语句时的所在位置,root代表tidb,cop代表tikv

access object 列:代表被访问的表对象和索引

execution info 列:算子的实际执行信息,包含执行时间等

这部分内容可以看到每个步骤的执行时间,但是不是特别直观,后面我们会通过Dashboard页面去分析执行时间。

operator info 列:显示访问表、分区、索引的其他信息

range: [2022-03-20 10:56:37,2022-04-20 10:56:37] 表示查询的 WHERE 字句 (l_opertime = 2022-04-20 10:56:37) 被下推到了 TiKV,对应的 task 为 cop[tikv]

keep order:true 表示这个查询需要TiKV按照顺序返回结果

stats:pseudo 它表示estRows显示的预估行数可能不准,TiDB定期在后台更新统计信息,也可以通过Analyze table 来手动更新信息。

memory 列:算子占用的内存空间大小

disk 列:算子占用磁盘空间的大小

 

4、TiDB DashBoard分析

上述Explain Analyze分析的执行计划内容,execution info列不够直观。我们看下TiDB 的Dashboard,其实也能发现一些端倪。

进入TiDB 的 Dashboard页面--->点击左侧的慢查询--->按照SQL语句(或者提炼的SQL指纹)进行搜索--->查看SQL执行耗时情况,看到类似的SQL执行耗时情况如下:

image.png

可以看到,大部分执行耗时都在Coprocessor执行耗时阶段,其他阶段占用的时间非常少。

值得注意的是,Coprocessor累计执行耗时看起来大于SQL执行时间,这个是因为TiKV 会并行处理任务,因此累计执行耗时不是自然流逝时间

我们再看看SQL的基本信息:

image.png

从SQL基本信息上,也可以看到,当前SQL使用的统计信息是pseudo,而pseudo代表统计信息不准确,就有可能导致TiDB基于成本的执行计划选择错误。

 

解决办法

有了上述的理论基础,问题的解决就变得简单了。

image.png

根据官方文档描述,我们使用Analyze table log_xxxx_2022_4 来重新收集下这个表的统计信息,然后重新执行查询:

analyze table log_cmnt_2022_4;

Query OK, 0 rows affected, 1 warning (51.11 sec)



再次利用Explain Analyze查看SQL执行计划:     

+----------------------------------+---------+---------+-----------+-------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------+------+

| id                               | estRows | actRows | task      | access object                             | execution info                                                                                                                                                                                                                                                                                                                                                                                | operator info                                                                                                                                    | memory    | disk |

+----------------------------------+---------+---------+-----------+-------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------+------+

| TopN_9                           | 2.15    | 0       | root      |                                           | time:977µs, loops:1                                                                                                                                                                                                                                                                                                                                                                           | coxxxx5_log.log_xxxx_2022_4.l_opertime:desc, offset:0, count:20                                                                                 | 0 Bytes   | N/A  |

| └─IndexLookUp_24                 | 2.15    | 0       | root      |                                           | time:939.3µs, loops:2,                                                                                                                                                                                                                                                                                                                                                                        |                                                                                                                                                  | 236 Bytes | N/A  |

|   ├─IndexRangeScan_17(Build)     | 2.15    | 0       | cop[tikv] | table:log_xxxx_2022_4, index:l_mid(l_mid) | time:822.3µs, loops:1, cop_task: {num: 1, max: 749.8µs, proc_keys: 0, tot_proc: 1ms, rpc_num: 1, rpc_time: 734.8µs, copr_cache_hit_ratio: 0.00}, tikv_task:{time:1ms, loops:1}, scan_detail: {total_process_keys: 0, total_process_keys_size: 0, total_keys: 1, rocksdb: {delete_skipped_count: 0, key_skipped_count: 0, block: {cache_hit_count: 11, read_count: 0, read_byte: 0 Bytes}}}    | range:["625F70C0-ABD4F004-E527B8CD-84B-960","625F70C0-ABD4F004-E527B8CD-84B-960"], keep order:false                                              | N/A       | N/A  |

|   └─TopN_23(Probe)               | 2.15    | 0       | cop[tikv] |                                           |                                                                                                                                                                                                                                                                                                                                                                                               | comment5_log.log_xxxx_2022_4.l_opertime:desc, offset:0, count:20                                                                                 | N/A       | N/A  |

|     └─Selection_19               | 2.15    | 0       | cop[tikv] |                                           |                                                                                                                                                                                                                                                                                                                                                                                               | ge(comxxxx5_log.log_xxxx_2022_4.l_opertime, 2022-03-20 10:56:37.000000), lt(coxxxxx5_log.log_xxxx_2022_4.l_opertime, 2022-04-20 10:56:37.000000) | N/A       | N/A  |

|       └─TableRowIDScan_18        | 2.15    | 0       | cop[tikv] | table:log_xxxx_2022_4                     |                                                                                                                                                                                                                                                                                                                                                                                               | keep order:false                                                                                                                                 | N/A       | N/A  |

+----------------------------------+---------+---------+-----------+-------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------+-----------+------+

6 rows in set (0.00 sec)

从最新的SQL执行计划中,我们不难发现:

1、执行计划中,预估的行数estRows,从一开始的2w行到现在的2.15行,实际执行行数actRows,从一开始的2000w行,到现在的0行,有了很大的一个改善。

2、SQL的执行时间变成了0.00s,意味着执行时间在10ms之内。

现在我们对比下执行时间:

统计信息收集之前:SQL执行26s

统计信息收集之后:SQL执行0.00s

一个Analyze操作,让整个SQL执行时间,足足翻了1000倍还多!!!

修改之后,业务同学反馈查询速度提升明显,监控肉眼可见:

image.png

image.png

 

Pseudo状态的SQL如何主动排查?如何解决?

从我们上述案例中可以发现,如果一个表的统计信息采用了pseudo,很可能造成查询慢的情况。因此,在实际应用中,我们需要对使用了pseudo统计信息的SQL进行摸排,可以使用下面的方法来进行摸排:

方案1、SQL排查并手动analyze

select 

query, query_time, stats

from 

information_schema.slow_query

where is_internal = false

and stats like '%pseudo%';

使用上述SQL查找到所有的使用了pseudo统计信息的SQL,并对它们访问的表,手动做一次analyze table操作。

上述SQL的输出样例如下:

+-----------------------------+-------------+---------------------------------+

| query                       | query_time  | stats                           |

+-----------------------------+-------------+---------------------------------+

| select * from t1 where a=1; | 0.302558006 | t1:pseudo                       |

| select * from t1 where a=2; | 0.401313532 | t1:pseudo                       |

| select * from t1 where a>2; | 0.602011247 | t1:pseudo                       |

| select * from t1 where a>3; | 0.50077719  | t1:pseudo                       |

| select * from t1 join t2;   | 0.931260518 | t1:407872303825682445,t2:pseudo |

+-----------------------------+-------------+---------------------------------+

 

方案2、修改参数:pseudo-estimate-ratio

这个参数代表修改的行数/表的总行数的比值,超过该值的时候,系统会认为统计信息已经过期,就会使用pseudo,这个值的默认值是0.8,最小值是0,最大值是1。它是统计信息是否失效的判断标准

可以将这个参数调整成1,从而让TiKV执行SQL的时候不选择pseudo统计信息。

 

方案3、修改参数:tidb_enable_pseudo_for_outdated_stats

这个变量用来控制TiDB优化器在某一张表上的统计信息过期之后的行为,默认值是On。

如果使用默认值On,在某张表的统计信息过期之后,代表优化器认为当前表除了总行数之外,其他的统计信息已经失效,所以会采用pseudo统计信息;

如果使用Off,即使一张表上的统计信息失效,也会使用当前表的统计信息,不会使用pseudo。如果你的表更新频繁,又没有即使对表进行analyze table,那么建议使用off选项

 

方案4、TiDB Dashboard排查

登录TiDB的Dashboard,点击TiDB--->statistics--->pseudo estimation OPS面板即可。

image.png

如果监控中使用Pseudo统计信息的SQL过多,那么说明我们的统计信息存在大量失效的情况,需要对这类SQL访问的表重新进行信息统计。

 

总结

到这里,上面的问题算是解决了,我们也知道了如何对使用了Pseudo统计信息的SQL进行排查了。

我们先尝试写一些总结:

1、遇到慢查询,我们一般需要进行一系列分析,包括SQL历史运行状态了解、SQL语义分析、SQL访问的表对应的表结构分析、执行计划分析等等

2、TiDB的Dashboard中的慢日志模块已经帮用户整理了相关信息,要学会借助已有的功能去排查问题。

3、问题解决后,还应该想办法从源头上杜绝问题再次发生。

 

其实如果更近一步去思考,既然TiDB本身会进行统计信息收集,那么它的收集策略又是怎样的呢???为什么它有收集统计信息的功能,我们的表还会使用到pseudo统计信息呢???这些,其实都是值得思考的问题。这里我给出一点官方文档的提示:

image.png

image.png

 

关于统计信息的更多细节,等待大家在实践中去探索,去发现。:)

 

原作者:Asiaye  发表时间:2022/4/26 原文链接:https://tidb.net/blog/df697598

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