转载原文：https://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。