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 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 ; +----------+ | 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` = ) 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` = ) 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` = ) 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` = ) 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` = ) 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": "                 "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": [\                           " <= Fuser <=                         ] /* 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  

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