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
*************************** 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": [\ ---逻辑优化, 找出备选的索引
{\
"table": "`t_audit_operate_log`",\
"field": "Fuser",\
"equals": "'XX@XX.com'",\
"null_rejecting": false\
}\
] /* ref_optimizer_key_uses */\
},\
{\
"rows_estimation": [\ ---逻辑优化, 估算每个表的元组个数. 单表上进行全表扫描和索引扫描的代价估算. 每个索引都估算索引扫描代价
{\
"table": "`t_audit_operate_log`",\
"range_analysis": {\
"table_scan": {\---逻辑优化, 估算每个表的元组个数. 单表上进行全表扫描的代价
"rows": 8150516,\
"cost": 1.73e6\
} /* table_scan */,\
"potential_range_indices": [\ ---逻辑优化, 列出备选的索引. 后续版本字符串变为potential_range_indexes
{\
"index": "PRIMARY",\---逻辑优化, 本行表明主键索引不可用
"usable": false,\
"cause": "not_applicable"\
},\
{\
"index": "indx_ctime",\---逻辑优化, 索引indx_ctime
"usable": true,\
"key_parts": [\
"Fcreate_time",\
"Fid"\
] /* key_parts */\
},\
{\
"index": "indx_user",\---逻辑优化, 索引indx_user
"usable": true,\
"key_parts": [\
"Fuser",\
"Fid"\
] /* key_parts */\
},\
{\
"index": "indx_objid",\---逻辑优化, 索引
"usable": false,\
"cause": "not_applicable"\
},\
{\
"index": "indx_ip",\---逻辑优化, 索引
"usable": false,\
"cause": "not_applicable"\
}\
] /* potential_range_indices */,\
"setup_range_conditions": [\ ---逻辑优化,如果有可下推的条件,则带条件考虑范围查询
]/* setup_range_conditions */,\
"group_index_range":{\---逻辑优化,如带有GROUPBY或DISTINCT,则考虑是否有索引可优化这种操作.并考虑带有MIN/MAX的情况
"chosen":false,\
"cause":"not_group_by_or_distinct"\
}/* group_index_range */,\
"analyzing_range_alternatives":{\---逻辑优化,开始计算每个索引做范围扫描的花费(等值比较是范围扫描的特例)
"range_scan_alternatives":[\
{\
"index":"indx_ctime",\ ---[A]
"ranges":[\
"1407081600 <= Fcreate_time <= 1407427199"\
]/* ranges */,\
"index_dives_for_eq_ranges":true,\
"rowid_ordered":false,\
"using_mrr":true,\
"index_only":false,\
"rows":688362,\
"cost":564553,\ ---逻辑优化,这个索引的代价最小
"chosen":true\ ---逻辑优化,这个索引的代价最小,被选中.(比前面的table_scan 和其他索引的代价都小)
},\
{\
"index":"indx_user",\
"ranges":[\
"XX@XX.com <= Fuser <= XX@XX.com"\
]/* ranges */,\
"index_dives_for_eq_ranges":true,\
"rowid_ordered":true,\
"using_mrr":true,\
"index_only":false,\
"rows":1945894,\
"cost":1.18e6,\
"chosen":false,\
"cause":"cost"\
}\
]/* range_scan_alternatives */,\
"analyzing_roworder_intersect":{\
"usable":false,\
"cause":"too_few_roworder_scans"\
}/* analyzing_roworder_intersect */\
}/* analyzing_range_alternatives */,\---逻辑优化,开始计算每个索引做范围扫描的花费.这项工作结算
"chosen_range_access_summary":{\---逻辑优化,开始计算每个索引做范围扫描的花费.总结本阶段最优的.
"range_access_plan":{\
"type":"range_scan",\
"index":"indx_ctime",\
"rows":688362,\
"ranges":[\
"1407081600 <= Fcreate_time <= 1407427199"\
]/* ranges */\
}/* range_access_plan */,\
"rows_for_plan":688362,\
"cost_for_plan":564553,\
"chosen":true\ --这里看到的cost和rows都比 indx_user 要来的小很多---这个和[A]处是一样的,是信息汇总.
}/* chosen_range_access_summary */\
}/* range_analysis */\
}\
]/* rows_estimation */\ ---逻辑优化,估算每个表的元组个数.行估算结束
},\
{\
"considered_execution_plans":[\ ---物理优化,开始多表连接的物理优化计算
{\
"plan_prefix":[\
]/* plan_prefix */,\
"table":"`t_audit_operate_log`",\
"best_access_path":{\
"considered_access_paths":[\
{\
"access_type":"ref",\ ---物理优化,计算indx_user索引上使用ref方查找的花费,
"index":"indx_user",\
"rows":1.95e6,\
"cost":683515,\
"chosen":true\
},\ ---物理优化,本应该比较所有的可用索引,即打印出多个格式相同的但索引名不同的内容,这里却没有。推测是bug--没有遍历每一个索引.
{\
"access_type":"range",\---物理优化,猜测对应的是indx_time（没有实例可进行调试，对比5.7的跟踪信息猜测而得）
"rows":516272,\
"cost":702225,\---物理优化，代价大于了ref方式的683515，所以没有被选择
"chosen":false\ -- cost比上面看到的增加了很多，但rows没什么变化---物理优化，此索引没有被选择
}\
]/* considered_access_paths */\
}/* best_access_path */,\
"cost_for_plan":683515,\ ---物理优化，汇总在best_access_path 阶段得到的结果
"rows_for_plan":1.95e6,\
"chosen":true\ -- cost比上面看到的竟然小了很多？虽然rows没啥变化 ---物理优化，汇总在best_access_path 阶段得到的结果
}\
]/* considered_execution_plans */\
},\
{\
"attaching_conditions_to_tables":{\---逻辑优化，尽量把条件绑定到对应的表上
}/* attaching_conditions_to_tables */\
},\
{\
"refine_plan":[\
{\
"table":"`t_audit_operate_log`",\---逻辑优化，下推索引条件"pushed_index_condition"；其他条件附加到表上做为过滤条件"table_condition_attached"
}\
]/* refine_plan */\
}\
]/* steps */\
}/* join_optimization */\ \---逻辑优化和物理优化结束
},\
{\
"join_explain":{}/* join_explain */\
}\
]/* steps */\
三 其他一个相似问题
单表扫描，使用ref和range从索引获取数据一例
http://blog.163.com/li_hx/blog/static/183991413201461853637715/
四 问题的解决方式
遇到单表上有多个索引的时候,在MySQL5.6.20版本之前的版本,需要人工强制使用索引,以达到最好的效果.