Pre
有的时候，明明某个字段有索引，那我们一般认为走索引好一些，结果mysql走了全表扫描 ， 那怎么看mysql是怎么选择的呢？ 来 今天来看一看MySQL是如何循着合适的执行计划的？
演示Demo
还是那个老表 employees
CREATE TABLE `employees` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(24) NOT NULL DEFAULT '' COMMENT '姓名',
`age` int(11) NOT NULL DEFAULT '0' COMMENT '年龄',
`position` varchar(20) NOT NULL DEFAULT '' COMMENT '职位',
`hire_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '入职时间',
PRIMARY KEY (`id`),
KEY `idx_name_age_position` (`name`,`age`,`position`) USING BTREE
) ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8 COMMENT='员工记录表';
来看个执行计划
mysql> EXPLAIN select * from employees where name > 'a';
+----+-------------+-----------+------------+------+-----------------------+------+---------+------+--------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-----------+------------+------+-----------------------+------+---------+------+--------+----------+-------------+
| 1 | SIMPLE | employees | NULL | ALL | idx_name_age_position | NULL | NULL | NULL | 100175 | 50 | Using where |
+----+-------------+-----------+------------+------+-----------------------+------+---------+------+--------+----------+-------------+
1 row in set
推测一下，为何走了全表扫描
MySQL - 索引优化案例实操 # Case 1 : 联合索引第一个字段用范围不一定会走索引
name > ‘a’ , 一般都不会用到索引。 如果用name索引需要遍历name字段联合索引树，然后还需要根据遍历出来的主键值去主键索引树里再去查出最终数据，成本比全表扫描还高 。
优化呢？
- 强制走索引 （不一定效率高）
- 使用覆盖索引， 这样只需要遍历name字段的联合索引树就能拿到所有结果 ， 来看下
mysql> EXPLAIN select name,age,position from employees where name > 'a' ;
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+-------+----------+--------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+-------+----------+--------------------------+
| 1 | SIMPLE | employees | NULL | range | idx_name_age_position | idx_name_age_position | 74 | NULL | 50087 | 100 | Using where; Using index |
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+-------+----------+--------------------------+
1 row in set
mysql>
再来看个执行计划 ，仅仅把比对的value 换一下
mysql> EXPLAIN select * from employees where name > 'zzz' ;
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+------+----------+-----------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+------+----------+-----------------------+
| 1 | SIMPLE | employees | NULL | range | idx_name_age_position | idx_name_age_position | 74 | NULL | 1 | 100 | Using index condition |
+----+-------------+-----------+------------+-------+-----------------------+-----------------------+---------+------+------+----------+-----------------------+
1 row in set
搞得我一脸懵逼。。。。。 这样也行？
很明显，第二个 > zzz , key_len=74 = 3 * 24(表定义的varch长度) +2 ， 使用了联合索引中的name ， Extra也可以知一二，使用了部分索引条件。
那咋办？ 到底是为啥？ 仅仅是因为value的变化，导致mysql选择了不同的执行计划？
trace工具使用
开启trace工具会影响mysql性能，所以只能临时分析sql使用，用完之后立即关闭 .
mysql> set session optimizer_trace="enabled=on",end_markers_in_json=on; --开启trace
mysql> select * from employees where name > 'a' order by position; --- 执行SQL
mysql> SELECT * FROM information_schema.OPTIMIZER_TRACE; ---查看trace
mysql> set session optimizer_trace="enabled=off"; --关闭trace
第二条和第三条 一起执行 ，切记
Trace结论分析
把结果copy出来
{
"steps": [
{
"join_preparation": {
"select#": 1,
"steps": [
{
"expanded_query": "/* select#1 */ select `employees`.`id` AS `id`,`employees`.`name` AS `name`,`employees`.`age` AS `age`,`employees`.`position` AS `position`,`employees`.`hire_time` AS `hire_time` from `employees` where (`employees`.`name` > 'a') order by `employees`.`position`"
}
] /* steps */
} /* join_preparation */
},
{
"join_optimization": {
"select#": 1,
"steps": [
{
"condition_processing": {
"condition": "WHERE",
"original_condition": "(`employees`.`name` > 'a')",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(`employees`.`name` > 'a')"
},
{
"transformation": "constant_propagation",
"resulting_condition": "(`employees`.`name` > 'a')"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "(`employees`.`name` > 'a')"
}
] /* steps */
} /* condition_processing */
},
{
"substitute_generated_columns": {
} /* substitute_generated_columns */
},
{
"table_dependencies": [
{
"table": "`employees`",
"row_may_be_null": false,
"map_bit": 0,
"depends_on_map_bits": [
] /* depends_on_map_bits */
}
] /* table_dependencies */
},
{
"ref_optimizer_key_uses": [
] /* ref_optimizer_key_uses */
},
{
"rows_estimation": [
{
"table": "`employees`",
"range_analysis": {
"table_scan": {
"rows": 100175,
"cost": 20390
} /* table_scan */,
"potential_range_indexes": [
{
"index": "PRIMARY",
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_name_age_position",
"usable": true,
"key_parts": [
"name",
"age",
"position",
"id"
] /* key_parts */
}
] /* potential_range_indexes */,
"setup_range_conditions": [
] /* setup_range_conditions */,
"group_index_range": {
"chosen": false,
"cause": "not_group_by_or_distinct"
} /* group_index_range */,
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
"index": "idx_name_age_position",
"ranges": [
"a < name"
] /* ranges */,
"index_dives_for_eq_ranges": true,
"rowid_ordered": false,
"using_mrr": false,
"index_only": false,
"rows": 50087,
"cost": 60105,
"chosen": false,
"cause": "cost"
}
] /* range_scan_alternatives */,
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
} /* analyzing_roworder_intersect */
} /* analyzing_range_alternatives */
} /* range_analysis */
}
] /* rows_estimation */
},
{
"considered_execution_plans": [
{
"plan_prefix": [
] /* plan_prefix */,
"table": "`employees`",
"best_access_path": {
"considered_access_paths": [
{
"rows_to_scan": 100175,
"access_type": "scan",
"resulting_rows": 100175,
"cost": 20388,
"chosen": true,
"use_tmp_table": true
}
] /* considered_access_paths */
} /* best_access_path */,
"condition_filtering_pct": 100,
"rows_for_plan": 100175,
"cost_for_plan": 20388,
"sort_cost": 100175,
"new_cost_for_plan": 120563,
"chosen": true
}
] /* considered_execution_plans */
},
{
"attaching_conditions_to_tables": {
"original_condition": "(`employees`.`name` > 'a')",
"attached_conditions_computation": [
] /* attached_conditions_computation */,
"attached_conditions_summary": [
{
"table": "`employees`",
"attached": "(`employees`.`name` > 'a')"
}
] /* attached_conditions_summary */
} /* attaching_conditions_to_tables */
},
{
"clause_processing": {
"clause": "ORDER BY",
"original_clause": "`employees`.`position`",
"items": [
{
"item": "`employees`.`position`"
}
] /* items */,
"resulting_clause_is_simple": true,
"resulting_clause": "`employees`.`position`"
} /* clause_processing */
},
{
"reconsidering_access_paths_for_index_ordering": {
"clause": "ORDER BY",
"steps": [
] /* steps */,
"index_order_summary": {
"table": "`employees`",
"index_provides_order": false,
"order_direction": "undefined",
"index": "unknown",
"plan_changed": false
} /* index_order_summary */
} /* reconsidering_access_paths_for_index_ordering */
},
{
"refine_plan": [
{
"table": "`employees`"
}
] /* refine_plan */
}
] /* steps */
} /* join_optimization */
},
{
"join_execution": {
"select#": 1,
"steps": [
{
"filesort_information": [
{
"direction": "asc",
"table": "`employees`",
"field": "position"
}
] /* filesort_information */,
"filesort_priority_queue_optimization": {
"usable": false,
"cause": "not applicable (no LIMIT)"
} /* filesort_priority_queue_optimization */,
"filesort_execution": [
] /* filesort_execution */,
"filesort_summary": {
"rows": 100003,
"examined_rows": 100003,
"number_of_tmp_files": 31,
"sort_buffer_size": 262056,
"sort_mode": "<sort_key, packed_additional_fields>"
} /* filesort_summary */
}
] /* steps */
} /* join_execution */
}
] /* steps */
}
三大步
- 准备阶段
- 优化阶段
- 执行阶段
重点呢 就是第二步 优化阶段
