前面介绍了，如果加个format=JOSN会把数据以json的格式返回，如果想看查询的额外信息，还可以在explain之后加个show warning查看，其中如果code为1003，则代表message里的内容是mysql优化器优化之后的sql。
对于mysql5.6之前的版本说，mysql像黑盒子，只能通过explain语句查看最后优化器决定使用的执行计划，却无法知道他做什么决定。在mysql5.6之后，mysql设计为我们贴心的加了optimizer trance，这个功能方便我们执行优化器的过程，这个功能的开启和关闭由系统变量optimizer_trance决定。
mysql> show variables like 'optimizer_trace'; +-----------------+--------------------------+ | Variable_name | Value | +-----------------+--------------------------+ | optimizer_trace | enabled=off,one_line=off | +-----------------+--------------------------+ 1 row in set (0.01 sec)
这里可以看到系统变量默认是关闭的， 如果开启需要改成on。这里one_line表示在一行展示，我们默认就关闭，不然不方便我们观看。
mysql> SET optimizer_trace="enabled=on"; Query OK, 0 rows affected (0.00 sec)
当我们设置为on的时候，则会打开，当我们输入我们想看的sql语句查询之后，就可以看information_schema数据库下的optimizer_trace表中查看完整优化过程。
当我们查看之后，可以看到四个部分：
Query：表示我们的查询语句。
trace：表示优化过程的JSON格式文本。
MISSING_BUTES_BEYOND_MAX_MEM_SIZE：由于优化过程可能输出很多，如果超过某个限制，多余文本将不会被显示，展示被忽略的文本字段。
INSUFFICIENT_PRIVILEGS：表示是否没有权限查看优化过程，默认是0，只有某些特殊情况才是1。
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 之后就可以看到显示： *************************** 1. row *************************** # 分析的查询语句是什么 QUERY: SELECT * FROM s1 WHERE key1 > 'z' AND key2 < 1000000 AND key3 IN ('a', 'b', 'c') AND common_field = 'abc' # 优化的具体过程 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 */ } # 因优化过程文本太多而丢弃的文本字节大小，值为0时表示并没有丢弃 MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0 # 权限字段 INSUFFICIENT_PRIVILEGES: 0 1 row in set (0.00 sec)
优化器追踪主要有三个部分，prepare部分，optimize阶段，execute阶段，看名字都知道，我们要着重看optimize阶段。
