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

2017-06-27 18:09 615 查看
http://blog.csdn.net/xj626852095/article/details/52767963

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='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.

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;

[plain] view plain copy

{\

"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.
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