MySQL再深入执行计划之trace工具
博客：https://itwxe.com
不知不觉过年半个多月没更新啦，不得不说老家真冷啊，完全不想伸出小二滴爪子，回到深圳开始更新啦，还是深圳暖和！
上一篇 Explain执行计划详解 中提到执行计划分析查询语句时 possible_keys 列中存在可以使用的索引，但是实际最后 key 列中却并没有使用索引，走的全表扫描，这是为啥捏？
这就涉及小二说的 MySQL 执行查询语句时需要进行的查询成本分析了，而 MySQL 提供的 trace 工具就可以让我们看到 MySQL 计算的查询成本，以及选择索引的大致分析过程。
一、示例表
-- 示例表
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';
INSERT INTO employees(name,age,position,hire_time) VALUES('itwxe',22,'manager',NOW());
INSERT INTO employees(name,age,position,hire_time) VALUES('weiwei', 23,'test',NOW());
INSERT INTO employees(name,age,position,hire_time) VALUES('leilei',23,'dev',NOW());
-- 插入10w条测试数据
drop procedure if exists insert_employees;
delimiter $$
create procedure insert_employees()
begin
declare i int;
set i = 1;
while(i <= 100000)do
insert into employees(name, age, position) values(CONCAT('itwxe', i), rand() * 42 + 18, 'dev');
set i = i + 1;
end while;
end$$
delimiter ;
call insert_employees();二、trace使用步骤
首先需要说明的是开启 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。
使用 trace 工具步骤：
-- 首先开启trace
mysql> set session optimizer_trace="enabled=on", end_markers_in_json=on;
-- 执行查询SQL
mysql> select * from employees where name > 'wei' order by position;
-- 查询trace字段
mysql> SELECT * FROM information_schema.OPTIMIZER_TRACE;使用完成后关闭 trace 工具：
-- 当分析完SQL，关闭trace
mysql> set session optimizer_trace="enabled=off";三、trace字段解析
例如这条语句：
explain select * from employees where name > 'itwxe' order by position;
explain select * from employees where name > 'wei' order by position;可以看到第一条 SQL 中 possible_keys 有可以使用的索引 idx_name_age_position ，但是最后实际上 MySQL 没有使用 idx_name_age_position ，而是使用了全表扫描；而第二条 SQL 中使用到了 idx_name_age_position 中的 name 列，这是为什么呢？
如果非常了解 MySQL 索引底层数据结构的小伙伴应该就知道因为第二条 SQL name > 'itwxe' 扫描的联合索引 idx_name_age_position 的结果太多，回表成本太大，所以 MySQL 选择了全表扫描。要想看到 MySQL 计算的全表扫描成本和使用 idx_name_age_position 成本就得使用咱们这篇的主角 trace 工具了，以第二条SQL为例。
-- 首先开启trace
mysql> set session optimizer_trace="enabled=on", end_markers_in_json=on;
-- 执行查询SQL
mysql> select * from employees where name > 'wei' order by position;
-- 查询trace字段
mysql> SELECT * FROM information_schema.OPTIMIZER_TRACE;执行后可以得到如下的示例结果，重要字段注释如下：
| select * from employees where name > 'wei' order by position | {
"steps": [
{
"join_preparation": { -- 第一阶段：SQL准备阶段，格式化SQL
"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` > 'wei') order by `employees`.`position`"
}
] /* steps */
} /* join_preparation */
},
{
"join_optimization": { -- 第二阶段：SQL优化阶段
"select#": 1,
"steps": [
{
"condition_processing": { -- 条件处理，where，1=1之类的SQL优化
"condition": "WHERE",
"original_condition": "(`employees`.`name` > 'wei')",
"steps": [
{
"transformation": "equality_propagation",
"resulting_condition": "(`employees`.`name` > 'wei')"
},
{
"transformation": "constant_propagation",
"resulting_condition": "(`employees`.`name` > 'wei')"
},
{
"transformation": "trivial_condition_removal",
"resulting_condition": "(`employees`.`name` > 'wei')"
}
] /* steps */
} /* condition_processing */
},
{
"substitute_generated_columns": {
} /* substitute_generated_columns */
},
{
"table_dependencies": [ -- 表依赖详情
{
"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": [ -- 预估表的访问成本
{
"table": "`employees`",
"range_analysis": {
"table_scan": { -- 全表扫描情况
"rows": 100075, -- 扫描行数，innodb中这个是预估值,MyISAM中时准确值
"cost": 20306 -- 查询成本
} /* table_scan */,
"potential_range_indexes": [ -- 查询可能使用的索引
{
"index": "PRIMARY", -- 主键索引
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_name_age_position", -- 联合索引
"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": { -- 分析各个索引使用成本
"range_scan_alternatives": [
{
"index": "idx_name_age_position",
"ranges": [
"wei < name" -- 索引使用范围
] /* ranges */,
"index_dives_for_eq_ranges": true,
"rowid_ordered": false, -- 使用该索引获取的记录是否按照主键排序
"using_mrr": false, -- 是否使用mrr
"index_only": false, -- 是否使用覆盖索引
"rows": 1, -- 索引扫描行数，也是个预估值
"cost": 2.21, -- 索引使用成本
"chosen": true -- 是否选择该索引
}
] /* range_scan_alternatives */,
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
} /* analyzing_roworder_intersect */
} /* analyzing_range_alternatives */,
"chosen_range_access_summary": {
"range_access_plan": {
"type": "range_scan",
"index": "idx_name_age_position",
"rows": 1,
"ranges": [
"wei < name"
] /* ranges */
} /* range_access_plan */,
"rows_for_plan": 1,
"cost_for_plan": 2.21,
"chosen": true
} /* chosen_range_access_summary */
} /* range_analysis */
}
] /* rows_estimation */
},
{
"considered_execution_plans": [
{
"plan_prefix": [
] /* plan_prefix */,
"table": "`employees`",
"best_access_path": { -- 最优访问路径
"considered_access_paths": [ -- 最终访问路径
{
"rows_to_scan": 1,
"access_type": "range", -- 访问类型 range：访问扫描；scan：全表扫描
"range_details": {
"used_index": "idx_name_age_position"
} /* range_details */,
"resulting_rows": 1,
"cost": 2.41,
"chosen": true, -- 确定选择
"use_tmp_table": true
}
] /* considered_access_paths */
} /* best_access_path */,
"condition_filtering_pct": 100,
"rows_for_plan": 1,
"cost_for_plan": 2.41,
"sort_cost": 1,
"new_cost_for_plan": 3.41,
"chosen": true
}
] /* considered_execution_plans */
},
{
"attaching_conditions_to_tables": {
"original_condition": "(`employees`.`name` > 'wei')",
"attached_conditions_computation": [
] /* attached_conditions_computation */,
"attached_conditions_summary": [
{
"table": "`employees`",
"attached": "(`employees`.`name` > 'wei')"
}
] /* 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": "idx_name_age_position",
"plan_changed": false
} /* index_order_summary */
} /* reconsidering_access_paths_for_index_ordering */
},
{
"refine_plan": [
{
"table": "`employees`",
"pushed_index_condition": "(`employees`.`name` > 'wei')",
"table_condition_attached": null
}
] /* refine_plan */
}
] /* steps */
} /* join_optimization */
},
{
"join_execution": { -- 第三阶段：SQL执行阶段
"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": 1,
"examined_rows": 1,
"number_of_tmp_files": 0,
"sort_buffer_size": 262056,
"sort_mode": "<sort_key, packed_additional_fields>"
} /* filesort_summary */
}
] /* steps */
} /* join_execution */
}
] /* steps */
} | 0 | 0 |可以看到预估表的访问成本计算过程中，MySQL 会计算全表扫描和可以使用的索引成本，比较各个可以使用的索引和全表扫描的查询成本，最终选择最优的一种方式去查询。例如这个例子中全表扫描成本为20306，使用 idx_name_age_position 联合索引成本为2.21，所以最终 MySQL 选择使用 idx_name_age_position 索引去执行查询。
当然 trace 工具主要看的是 MySQL 大致的优化和计算成本过程，那么 MySQL 是通过什么去计算出的这个成本呢？小二先在这里挖个坑吧，看下啥时候填上，哈哈哈~~~