需要帮助了解mysql索引的工作原理

Question

需要帮助了解mysql索引的工作原理

Flu*_*ffy 6 mysql indexing performance innodb

我有一个看起来像这样的表:

CREATE TABLE `metric` (
  `metricid` bigint(20) unsigned NOT NULL AUTO_INCREMENT,
  `host` varchar(50) NOT NULL,
  `userid` int(10) unsigned DEFAULT NULL,
  `lastmetricvalue` double DEFAULT NULL,
  `receivedat` int(10) unsigned DEFAULT NULL,
  `name` varchar(255) NOT NULL,
  `sampleid` tinyint(3) unsigned NOT NULL,
  `type` tinyint(3) unsigned NOT NULL DEFAULT '0',
  `lastrawvalue` double NOT NULL,
  `priority` tinyint(3) unsigned NOT NULL DEFAULT '0',
  PRIMARY KEY (`metricid`),
  UNIQUE KEY `unique-metric` (`userid`,`host`,`name`,`sampleid`)
) ENGINE=InnoDB AUTO_INCREMENT=1000000221496 DEFAULT CHARSET=utf8

Run Code Online (Sandbox Code Playgroud)

它目前有177,892行,当我运行以下查询时:

select metricid, lastrawvalue, receivedat, name, sampleid
FROM metric m
WHERE m.userid = 8
  AND (host, name, sampleid) IN (('localhost','0.4350799184758216cpu-3/cpu-nice',0),
  ('localhost','0.4350799184758216cpu-3/cpu-system',0),
  ('localhost','0.4350799184758216cpu-3/cpu-idle',0),
  ('localhost','0.4350799184758216cpu-3/cpu-wait',0),
  ('localhost','0.4350799184758216cpu-3/cpu-interrupt',0),
  ('localhost','0.4350799184758216cpu-3/cpu-softirq',0),
  ('localhost','0.4350799184758216cpu-3/cpu-steal',0),
  ('localhost','0.4350799184758216cpu-4/cpu-user',0),
  ('localhost','0.4350799184758216cpu-4/cpu-nice',0),
  ('localhost','0.4350799184758216cpu-4/cpu-system',0),
  ('localhost','0.4350799184758216cpu-4/cpu-idle',0),
  ('localhost','0.4350799184758216cpu-4/cpu-wait',0),
  ('localhost','0.4350799184758216cpu-4/cpu-interrupt',0),
  ('localhost','0.4350799184758216cpu-4/cpu-softirq',0),
  ('localhost','0.4350799184758216cpu-4/cpu-steal',0),
  ('localhost','_util/billing-bytes',0),('localhost','_util/billing-metrics',0));

Run Code Online (Sandbox Code Playgroud)

返回结果需要0.87秒,解释是:

    *************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: m
         type: ref
possible_keys: unique-metric
          key: unique-metric
      key_len: 5
          ref: const
         rows: 85560
        Extra: Using where
1 row in set (0.00 sec)

Run Code Online (Sandbox Code Playgroud)

个人资料如下:

+--------------------------------+----------+
| Status                         | Duration |
+--------------------------------+----------+
| starting                       | 0.000160 |
| checking permissions           | 0.000010 |
| Opening tables                 | 0.000021 |
| exit open_tables()             | 0.000008 |
| System lock                    | 0.000008 |
| mysql_lock_tables(): unlocking | 0.000005 |
| exit mysqld_lock_tables()      | 0.000007 |
| init                           | 0.000068 |
| optimizing                     | 0.000018 |
| statistics                     | 0.000091 |
| preparing                      | 0.000042 |
| executing                      | 0.000005 |
| Sending data                   | 0.870180 |
| innobase_commit_low():trx_comm | 0.000012 |
| Sending data                   | 0.000111 |
| end                            | 0.000009 |
| query end                      | 0.000009 |
| ha_commit_one_phase(-1)        | 0.000015 |
| innobase_commit_low():trx_comm | 0.000004 |
| ha_commit_one_phase(-1)        | 0.000005 |
| query end                      | 0.000005 |
| closing tables                 | 0.000012 |
| freeing items                  | 0.000562 |
| logging slow query             | 0.000005 |
| cleaning up                    | 0.000005 |
| sleeping                       | 0.000006 |
+--------------------------------+----------+

Run Code Online (Sandbox Code Playgroud)

这对我来说似乎太高了.我试图userid = 8 and (host, name, sampleid) IN将第一个查询的部分替换为,(userid, host, name, sampleid) IN并且此查询运行大约0.5秒 - 几乎快2倍,以供参考,这是查询:

select metricid, lastrawvalue, receivedat, name, sampleid
FROM metric m
WHERE (userid, host, name, sampleid) IN ((8,'localhost','0.4350799184758216cpu-3/cpu-nice',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-system',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-idle',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-wait',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-interrupt',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-softirq',0),
  (8,'localhost','0.4350799184758216cpu-3/cpu-steal',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-user',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-nice',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-system',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-idle',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-wait',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-interrupt',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-softirq',0),
  (8,'localhost','0.4350799184758216cpu-4/cpu-steal',0),
  (8,'localhost','_util/billing-bytes',0),
  (8,'localhost','_util/billing-metrics',0));

Run Code Online (Sandbox Code Playgroud)

它的解释如下:

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: m
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 171121
        Extra: Using where
1 row in set (0.00 sec)

Run Code Online (Sandbox Code Playgroud)

接下来我更新了表以包含一个连接列:

alter table `metric` add `forindex` varchar(120) not null default '';
update metric set forindex = concat(userid,`host`,`name`,sampleid);
alter table metric add index `forindex` (`forindex`);

Run Code Online (Sandbox Code Playgroud)

更新了查询,只搜索了1个字符串:

select metricid, lastrawvalue, receivedat, name, sampleid
FROM metric m
WHERE (forindex) IN (('8localhost0.4350799184758216cpu-3/cpu-nice0'),
  ('8localhost0.4350799184758216cpu-3/cpu-system0'),
  ('8localhost0.4350799184758216cpu-3/cpu-idle0'),
  ('8localhost0.4350799184758216cpu-3/cpu-wait0'),
  ('8localhost0.4350799184758216cpu-3/cpu-interrupt0'),
  ('8localhost0.4350799184758216cpu-3/cpu-softirq0'),
  ('8localhost0.4350799184758216cpu-3/cpu-steal0'),
  ('8localhost0.4350799184758216cpu-4/cpu-user0'),
  ('8localhost0.4350799184758216cpu-4/cpu-nice0'),
  ('8localhost0.4350799184758216cpu-4/cpu-system0'),
  ('8localhost0.4350799184758216cpu-4/cpu-idle0'),
  ('8localhost0.4350799184758216cpu-4/cpu-wait0'),
  ('8localhost0.4350799184758216cpu-4/cpu-interrupt0'),
  ('8localhost0.4350799184758216cpu-4/cpu-softirq0'),
  ('8localhost0.4350799184758216cpu-4/cpu-steal0'),
  ('8localhost_util/billing-bytes0'),
  ('8localhost_util/billing-metrics0'));

Run Code Online (Sandbox Code Playgroud)

现在我在0.00秒内得到相同的结果!说明是:

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: m
         type: range
possible_keys: forindex
          key: forindex
      key_len: 362
          ref: NULL
         rows: 17
        Extra: Using where
1 row in set (0.00 sec)

Run Code Online (Sandbox Code Playgroud)

总结一下,结果如下:

m.userid = X AND (host, name, sampleid) IN - 使用索引,扫描85560行,以0.9s运行
(userid, host, name, sampleid) IN- 索引未使用,扫描171121行,运行0.5秒
复合索引的附加列替换为连接实用程序列上的索引 - 使用索引,扫描17行,以0运行

为什么第二个查询运行速度比第一个快？为什么第三个查询比其他查询快得多？我是否应该保留这样一个专栏,以便更快地搜索？

Mysql版本是: mysqld Ver 5.5.34-55 for Linux on x86_64 (Percona XtraDB Cluster (GPL), wsrep_25.9.r3928)

Answer 1

Bil*_*win 3

索引WHERE通过尽可能缩小搜索范围来帮助您在子句中搜索术语。你可以看到这种情况正在发生...

EXPLAIN字段rows给出了查询必须检查多少行才能找到与查询匹配的行的估计值。通过比较rows每个 EXPLAIN 中的报告，您可以看到优化得更好的查询有多好：

     rows: 85560 -- first query

     rows: 171121 -- second query examines 2x more rows, but it was probably 
                  -- faster because the data was buffered after the first query

     rows: 17 -- third query examines 5,000x fewer rows than first query

Run Code Online (Sandbox Code Playgroud)

如果您为第三个查询运行“显示配置文件”详细信息，您还会注意到“发送数据”对于更快的查询要快得多。此进程状态指示将行从存储引擎复制到 MySQL 的 SQL 层所花费的时间。即使进行内存到内存的复制，对于数千行也需要一段时间。这就是索引如此有益的原因。

有关更有用的解释，请参阅我的演示文稿如何设计索引，真的。

归档时间：	11 年，11 月前
查看次数：	315 次
最近记录：	11 年，11 月前