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http://blog.163.com/li_hx/blog/static/18399141320147212521455/
2014-08-21 15:07:05| 分类： 数据库 | 标签：查询优化 mysql sql优化 |举报 |字号大中小 订阅
一 表结构如下:
MySQL 5.5.30 5.6.20 版本,表大概有815万行
CREATE TABLE t_audit_operate_log ( Fid bigint(16) AUTO_INCREMENT, Fcreate_time int(10) unsigned NOT NULL DEFAULT ‘0‘, Fuser varchar(50) DEFAULT ‘‘, Fip bigint(16) DEFAULT NULL, Foperate_object_id bigint(20) DEFAULT ‘0‘, PRIMARY KEY (Fid), KEY indx_ctime (Fcreate_time), KEY indx_user (Fuser), KEY indx_objid (Foperate_object_id), KEY indx_ip (Fip) ) ENGINE=InnoDB DEFAULT CHARSET=utf8;
执行查询:
mysql> explain select count(*) from t_audit_operate_log where Fuser=‘XX@XX.com‘ and Fcreate_time>=1407081600 and Fcreate_time<=1407427199\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t_audit_operate_log
type: ref
possible_keys: indx_ctime,indx_user
key: indx_user
key_len: 153
ref: const
rows: 2007326
Extra: Using where
发现,使用了一个不合适的索引, 不是很理想，于是改成指定索引：
mysql> explain select count(*) from t_audit_operate_log use index(indx_ctime) where Fuser=‘CY6016@cyou-inc.com‘ and Fcreate_time>=1407081600 and Fcreate_time<=1407427199\G
select count(*) from t_audit_operate_log use index(indx_ctime) where Fuser=‘CY6016@cyou-inc.com‘ and Fcreate_time>=1407081600 and Fcreate_time<=1407427199\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t_audit_operate_log
type: range
possible_keys: indx_ctime
key: indx_ctime
key_len: 5
ref: NULL
rows: 670092
Extra: Using where
实际执行耗时，后者比前者快了接近10
问题: 很奇怪，优化器为何不选择使用 indx_ctime 索引，而选择了明显会扫描更多行的 indx_user 索引。
分析2个索引的数据量如下: 两个条件的唯一性对比：
select count(*) from t_audit_operate_log where Fuser=‘XX@XX.com‘; +----------+ | count(*) | +----------+ | 1238382 | +----------+
select count(*) from t_audit_operate_log where Fcreate_time>=1407254400 and Fcreate_time<=1407427199; +----------+ | count(*) | +----------+ | 198920 | +----------+
显然,使用索引indx_ctime好于indx_user,但MySQL却选择了indx_user. 为什么?
于是,使用 OPTIMIZER_TRACE进一步探索.
二 OPTIMIZER_TRACE的过程说明
以本处事例简要说明OPTIMIZER_TRACE的过程.
{\ "steps": [\ {\ "join_preparation": {\ ---优化准备工作 "select#": 1,\ "steps": [\ {\ "expanded_query": "/* select#1 */ select count(0) AS `count(*)` from `t_audit_operate_log` where ((`t_audit_operate_log`.`Fuser` = ‘XX@XX.com‘) and (`t_audit_operate_log`.`Fcreate_time` >= 1407081600) and (`t_audit_operate_log`.`Fcreate_time` <= 1407427199))"\ }\ ] /* steps */\ } /* join_preparation */\ },\ {\ "join_optimization": {\ ---优化工作的主要阶段,包括逻辑优化和物理优化两个阶段 "select#": 1,\ "steps": [\ ---优化工作的主要阶段, 逻辑优化阶段 {\ "condition_processing": {\ ---逻辑优化,条件化简 "condition": "WHERE",\ "original_condition": "((`t_audit_operate_log`.`Fuser` = ‘XX@XX.com‘) and (`t_audit_operate_log`.`Fcreate_time` >= 1407081600) and (`t_audit_operate_log`.`Fcreate_time` <= 1407427199))",\ "steps": [\ {\ "transformation": "equality_propagation",\ ---逻辑优化,条件化简,等式处理 "resulting_condition": "((`t_audit_operate_log`.`Fuser` = ‘XX@XX.com‘) and (`t_audit_operate_log`.`Fcreate_time` >= 1407081600) and (`t_audit_operate_log`.`Fcreate_time` <= 1407427199))"\ },\ {\ "transformation": "constant_propagation",\ ---逻辑优化,条件化简,常量处理 "resulting_condition": "((`t_audit_operate_log`.`Fuser` = ‘XX@XX.com‘) and (`t_audit_operate_log`.`Fcreate_time` >= 1407081600) and (`t_audit_operate_log`.`Fcreate_time` <= 1407427199))"\ },\ {\ "transformation": "trivial_condition_removal",\ ---逻辑优化,条件化简,条件去除 "resulting_condition": "((`t_audit_operate_log`.`Fuser` = ‘XX@XX.com‘) and (`t_audit_operate_log`.`Fcreate_time` >= 1407081600) and (`t_audit_operate_log`.`Fcreate_time` <= 1407427199))"\ }\ ] /* steps */\ } /* condition_processing */\ },\ ---逻辑优化,条件化简,结束 {\ "table_dependencies": [\ ---逻辑优化, 找出表之间的相互依赖关系. 非直接可用的优化方式. {\ "table": "`t_audit_operate_log`",\ "row_may_be_null": false,\ "map_bit": 0,\ "depends_on_map_bits": [\ ] /* depends_on_map_bits */\ }\ ] /* table_dependencies */\ },\ {\ "ref_optimizer_key_uses": [\