```shell script mysql> show variables like ‘optimizer_trace’; +—————–+————————–+ | Variable_name | Value | +—————–+————————–+ | optimizer_trace | enabled=off,one_line=off | +—————–+————————–+ 1 row in set (0.04 sec)
使用步驟
```shell script
# 1. 打开optimizer trace功能 (默认情况下它是关闭的):
SET optimizer_trace="enabled=on";
# 2. 这里输入你自己的查询语句
SELECT ...;
# 3. 从OPTIMIZER_TRACE表中查看上一个查询的优化过程
SELECT * FROM information_schema.OPTIMIZER_TRACE;
# 4. 可能你还要观察其他语句执行的优化过程，重复上边的第2、3步
...
# 5. 当你停止查看语句的优化过程时，把optimizer trace功能关闭
SET optimizer_trace="enabled=off";
示例 ```shell script SET optimizer_trace=”enabled=on”;
SELECT * FROM s1 WHERE key1 > ‘z’ AND key2 < 1000000 AND key3 IN (‘a’, ‘b’, ‘c’) AND common_field = ‘abc’;
SELECT * FROM information_schema.OPTIMIZER_TRACE\G
********* 1. row *************
QUERY: SELECT * FROM s1 WHERE key1 > ‘z’ AND key2 < 1000000 AND key3 IN (‘a’, ‘b’, ‘c’) AND common_field = ‘abc’
TRACE: {
“steps”: [
{
“join_preparation”: { # prepare阶段
“select#”: 1,
“steps”: [
{
“IN_uses_bisection”: true
},
{
“expanded_query”: “/* select#1 / select s1.id AS id,s1.key1 AS key1,s1.key2 AS key2,s1.key3 AS key3,s1.key_part1 AS key_part1,s1.key_part2 AS key_part2,s1.key_part3 AS key_part3,s1.common_field AS common_field from s1 where ((s1.key1 > ‘z’) and (s1.key2 < 1000000) and (s1.key3 in (‘a’,’b’,’c’)) and (s1.common_field = ‘abc’))”
}
] / steps /
} / join_preparation /
},
{
“join_optimization”: { # optimize阶段
“select#”: 1,
“steps”: [
{
“condition_processing”: { # 处理搜索条件
“condition”: “WHERE”,
# 原始搜索条件
“original_condition”: “((s1.key1 > ‘z’) and (s1.key2 < 1000000) and (s1.key3 in (‘a’,’b’,’c’)) and (s1.common_field = ‘abc’))”,
“steps”: [
{
# 等值传递转换
“transformation”: “equality_propagation”,
“resulting_condition”: “((s1.key1 > ‘z’) and (s1.key2 < 1000000) and (s1.key3 in (‘a’,’b’,’c’)) and (s1.common_field = ‘abc’))”
},
{
# 常量传递转换
“transformation”: “constant_propagation”,
“resulting_condition”: “((s1.key1 > ‘z’) and (s1.key2 < 1000000) and (s1.key3 in (‘a’,’b’,’c’)) and (s1.common_field = ‘abc’))”
},
{
# 去除没用的条件
“transformation”: “trivial_condition_removal”,
“resulting_condition”: “((s1.key1 > ‘z’) and (s1.key2 < 1000000) and (s1.key3 in (‘a’,’b’,’c’)) and (s1.common_field = ‘abc’))”
}
] / steps /
} / condition_processing /
},
{
# 替换虚拟生成列
“substitute_generated_columns”: {
} / substitute_generated_columns /
},
{
# 表的依赖信息
“table_dependencies”: [
{
“table”: “s1”,
“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 */
},
{
# 预估不同单表访问方法的访问成本
"rows_estimation": [
{
"table": "`s1`",
"range_analysis": {
"table_scan": { # 全表扫描的行数以及成本
"rows": 9688,
"cost": 2036.7
} /* table_scan */,
# 分析可能使用的索引
"potential_range_indexes": [
{
"index": "PRIMARY", # 主键不可用
"usable": false,
"cause": "not_applicable"
},
{
"index": "idx_key2", # idx_key2可能被使用
"usable": true,
"key_parts": [
"key2"
] /* key_parts */
},
{
"index": "idx_key1", # idx_key1可能被使用
"usable": true,
"key_parts": [
"key1",
"id"
] /* key_parts */
},
{
"index": "idx_key3", # idx_key3可能被使用
"usable": true,
"key_parts": [
"key3",
"id"
] /* key_parts */
},
{
"index": "idx_key_part", # idx_keypart不可用
"usable": false,
"cause": "not_applicable"
}
] /* potential_range_indexes */,
"setup_range_conditions": [
] /* setup_range_conditions */,
"group_index_range": {
"chosen": false,
"cause": "not_group_by_or_distinct"
} /* group_index_range */,
# 分析各种可能使用的索引的成本
"analyzing_range_alternatives": {
"range_scan_alternatives": [
{
# 使用idx_key2的成本分析
"index": "idx_key2",
# 使用idx_key2的范围区间
"ranges": [
"NULL < key2 < 1000000"
] /* ranges */,
"index_dives_for_eq_ranges": true, # 是否使用index dive
"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, # 是否是索引覆盖访问
"rows": 12, # 使用该索引获取的记录条数
"cost": 15.41, # 使用该索引的成本
"chosen": true # 是否选择该索引
},
{
# 使用idx_key1的成本分析
"index": "idx_key1",
# 使用idx_key1的范围区间
"ranges": [
"z < key1"
] /* ranges */,
"index_dives_for_eq_ranges": true, # 同上
"rowid_ordered": false, # 同上
"using_mrr": false, # 同上
"index_only": false, # 同上
"rows": 266, # 同上
"cost": 320.21, # 同上
"chosen": false, # 同上
"cause": "cost" # 因为成本太大所以不选择该索引
},
{
# 使用idx_key3的成本分析
"index": "idx_key3",
# 使用idx_key3的范围区间
"ranges": [
"a <= key3 <= a",
"b <= key3 <= b",
"c <= key3 <= c"
] /* ranges */,
"index_dives_for_eq_ranges": true, # 同上
"rowid_ordered": false, # 同上
"using_mrr": false, # 同上
"index_only": false, # 同上
"rows": 21, # 同上
"cost": 28.21, # 同上
"chosen": false, # 同上
"cause": "cost" # 同上
}
] /* range_scan_alternatives */,
# 分析使用索引合并的成本
"analyzing_roworder_intersect": {
"usable": false,
"cause": "too_few_roworder_scans"
} /* analyzing_roworder_intersect */
} /* analyzing_range_alternatives */,
# 对于上述单表查询s1最优的访问方法
"chosen_range_access_summary": {
"range_access_plan": {
"type": "range_scan",
"index": "idx_key2",
"rows": 12,
"ranges": [
"NULL < key2 < 1000000"
] /* ranges */
} /* range_access_plan */,
"rows_for_plan": 12,
"cost_for_plan": 15.41,
"chosen": true
} /* chosen_range_access_summary */
} /* range_analysis */
}
] /* rows_estimation */
},
{
# 分析各种可能的执行计划
#（对多表查询这可能有很多种不同的方案，单表查询的方案上边已经分析过了，直接选取idx_key2就好）
"considered_execution_plans": [
{
"plan_prefix": [
] /* plan_prefix */,
"table": "`s1`",
"best_access_path": {
"considered_access_paths": [
{
"rows_to_scan": 12,
"access_type": "range",
"range_details": {
"used_index": "idx_key2"
} /* range_details */,
"resulting_rows": 12,
"cost": 17.81,
"chosen": true
}
] /* considered_access_paths */
} /* best_access_path */,
"condition_filtering_pct": 100,
"rows_for_plan": 12,
"cost_for_plan": 17.81,
"chosen": true
}
] /* considered_execution_plans */
},
{
# 尝试给查询添加一些其他的查询条件
"attaching_conditions_to_tables": {
"original_condition": "((`s1`.`key1` > 'z') and (`s1`.`key2` < 1000000) and (`s1`.`key3` in ('a','b','c')) and (`s1`.`common_field` = 'abc'))",
"attached_conditions_computation": [
] /* attached_conditions_computation */,
"attached_conditions_summary": [
{
"table": "`s1`",
"attached": "((`s1`.`key1` > 'z') and (`s1`.`key2` < 1000000) and (`s1`.`key3` in ('a','b','c')) and (`s1`.`common_field` = 'abc'))"
}
] /* attached_conditions_summary */
} /* attaching_conditions_to_tables */
},
{
# 再稍稍的改进一下执行计划
"refine_plan": [
{
"table": "`s1`",
"pushed_index_condition": "(`s1`.`key2` < 1000000)",
"table_condition_attached": "((`s1`.`key1` > 'z') and (`s1`.`key3` in ('a','b','c')) and (`s1`.`common_field` = 'abc'))"
}
] /* refine_plan */
}
] /* steps */
} /* join_optimization */
},
{
"join_execution": { # execute阶段
"select#": 1,
"steps": [
] /* steps */
} /* join_execution */
} ] /* steps */ }
MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0
INSUFFICIENT_PRIVILEGES: 0
1 row in set (0.00 sec) ```
对于单表查询来说，我们主要关注optimize阶段的”rows_estimation”这个过程，这个过程深入分析了对单表查询的各种执行方案的成本；
对于多表连接查询来说，我们更多需要关注”considered_execution_plans”这个过程，这个过程里会写明各种不同的连接方式所对应的成本。
对使用EXPLAIN语句展示出的对某个查询的执行计划很不理解，大家可以尝试使用optimizer trace功能来详细了解每一种执行方案对应的成本