mysql 执行计划分析三看， explain,profiling,optimizer_trace

step 1

 使用explain 查看执行计划， 5.6后可以加参数 explain format=json xxx 输出json格式的信息

step 2 

使用profiling详细的列出在每一个步骤消耗的时间，前提是先执行一遍语句。

#打开profiling 的设置
SET profiling = 1;
SHOW VARIABLES LIKE '%profiling%';

#查看队列的内容
show profiles;  
#来查看统计信息
show profile block io,cpu for query 3;

step 3  

Optimizer trace是MySQL5.6添加的新功能，可以看到大量的内部查询计划产生的信息, 先打开设置，然后执行一次sql,最后查看`information_schema`.`OPTIMIZER_TRACE`的内容

#打开设置
SET optimizer_trace='enabled=on';  
#最大内存根据实际情况而定， 可以不设置
SET OPTIMIZER_TRACE_MAX_MEM_SIZE=1000000;
SET END_MARKERS_IN_JSON=ON;
SET optimizer_trace_limit = 1;
SHOW VARIABLES LIKE '%optimizer_trace%';

#执行所需sql后，查看该表信息即可看到详细的执行过程
SELECT * FROM `information_schema`.`OPTIMIZER_TRACE`;

MySQL索引选择不正确并详细解析OPTIMIZER_TRACE格式

http://blog.csdn.net/melody_mr/article/details/48950601

一 表结构如下: 

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='[email protected]' 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='[email protected]' 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='[email protected]';
+----------+
| 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.

查看OPTIMIZER_TRACE方法：

1.set optimizer_trace='enabled=on';    --- 开启trace

2.set optimizer_trace_max_mem_size=1000000;    --- 设置trace大小

3.set end_markers_in_json=on;    --- 增加trace中注释

4.select * from information_schema.optimizer_trace\G;

 {\
   "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` = '[email protected]') 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` = '[email protected]') 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` = '[email protected]') 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` = '[email protected]') 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` = '[email protected]') 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": "'[email protected]'",\
                 "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": [\
                           "[email protected] <= Fuser <= [email protected]"\
                         ] /* 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.