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<=1407427199G
*************************** 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<=1407427199G
*************************** 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_traceG;
{
"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 */
optimizer_trace有两个字段：
offset=-5,limit=5 将最近的5次trace打印出来
当offset小于0时，则会显示最新的-offset开始的limit个trace，也就是说，只显示新的trace
注意重设变量会导致trace被清空
mysql> show variables like ‘optimizer_trace_features';
http://jorgenloland.blogspot.com/2011/10/optimizer-tracing-query-execution-plan.html