cost量化分析

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原创最新推荐文章于 2026-10-26 10:42:14 发布 207 阅读 AI 写同款· GEO 优化 ›

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作者： xryz
文章来源：GreatSQL社区原创

前言：

我们在日常维护数据库的时候，经常会遇到查询慢的语句，这时候一般会通过执行EXPLAIN去查看它的执行计划，但是执行计划往往只给我们带来了最基础的分析信息，比如是否有使用索引，还有一些其他供我们分析的信息，比如使用了临时表、排序等等，却无法展示为什么一些其他的执行计划未被选择，比如说明明有索引，或者好几个索引，但是为什么查询时未使用到期望的索引等

explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| id | select_type | table | partitions | type   | possible_keys                        | key           | key_len | ref            | rows | filtered | Extra                 |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
|  1 | SIMPLE      | t2    | NULL       | range  | id_num_unique,idx_age,idx_age_id_num | idx_age       | 1       | NULL           | 9594 |   100.00 | Using index condition |
|  1 | SIMPLE      | t1    | NULL       | eq_ref | id_num_unique,idx_age                | id_num_unique | 60      | test.t2.id_num |    1 |    50.00 | Using where           |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
2 rows in set, 1 warning (0.01 sec)

如上面这个例子，为什么t2表上列出了多个可能使用的索引，却选择了idx_age，优化器为什么选择了指定的索引，这时候并不能直观的看出问题，这时候我们就可以开启optimizer_trace跟踪分析MySQL具体是怎么选择出最优的执行计划的。

OPTIMIZER_TRACE：

optimizer_trace是什么：

optimizer_trace是一个具有跟踪功能的工具，可以跟踪执行的语句的解析优化执行过程，并将跟踪到的信息记录到INFORMATION_SCHEMA.OPTIMIZER_TRACE表中，但是每个会话都只能跟踪它自己执行的语句，并且表中默认只记录最后一个查询的跟踪结果

使用方法：

# 打开optimizer trace功能 (默认情况下它是关闭的):
set optimizer_trace="enabled=on";
select ...; # 这里输入你自己的查询语句
SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;
# 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";

optimizer_trace表信息：

该表总共有4个字段

QUERY 表示我们的查询语句。
TRACE 表示优化过程的JSON格式文本。(重点关注)
MISSING_BYTES_BEYOND_MAX_MEM_SIZE 由于优化过程可能会输出很多，如果超过某个限制时，多余的文本将不会被显示，这个字段展示了被忽略的文本字节数。
INSUFFICIENT_PRIVILEGES 表示是否没有权限查看优化过程，默认值是0，只有某些特殊情况下才会是 1，我们暂时不关心这个字段的值。

信息解读：

通过 optimizer_trace表的query字段可以看到，一条语句的执行过程主要分为三个步骤：

"join_preparation": {},（准备阶段）
"join_optimization": {},（优化阶段）
"join_execution": {},（执行阶段）

各个步骤的详细内容解读：

preparation：

expanded_query ：将语句进行格式化，补充隐藏的列名和表名等
transformations_to_nested_joins ：查询重写，比如join的on改为where语句

optimization：

condition_processing{ ：条件句处理。
    transformation{：转换类型句。这三次的转换分别是
        equality_propagation（等值条件句转换），如：a = b and b = c and c = 5
        constant_propagation（常量条件句转换），如：a = 1 AND b > a
        trivial_condition_removal（无效条件移除的转换），如：1 = 1
    }
}
substitute_generated_columns ：替换虚拟生成列，测试了很多sql，这一列都没有看到有用的信息
table_dependencies ：梳理表之间的依赖关系。
ref_optimizer_key_uses ：如果优化器认为查询可以使用ref的话，在这里列出可以使用的索引。
rows_estimation{ ：估算表行数和扫描的代价。如果查询中存在range扫描的话，对range扫描进行计划分析及代价估算。
  table_scan：全表扫描的行数(rows)以及所需要的代价(cost)。
  potential_range_indexes：该阶段会列出表中所有的索引并分析其是否可用，并且还会列出索引中可用的列字段。
  analyzing_range_alternatives ：分析可选方案的代价。
}
considered_execution_plans{ ：对比各可行计划的代价，选择相对最优的执行计划。
  plan_prefix：前置的执行计划。
  best_access_path：当前最优的执行顺序信息结果集。
  access_type表示使用索引的方式，可参照为explain中的type字段。
  condition_filtering_pct：类似于explain中的filtered列，这是一个估算值。
  rows_for_plan：该执行计划最终的扫描行数，这里的行数其实也是估算值，是由considered_access_paths的resulting_rows相乘之后再乘以condition_filtering_pct获得。
  cost_for_plan：该执行计划的执行代价，由considered_access_paths的cost相加而得。
  chosen：是否选择了该执行计划。
}
attaching_conditions_to_tables ：添加附加条件，使得条件尽可能筛选单表数据。
refine_plan ：优化后的执行计划。

trace信息中的json信息很长，因为我们关心的是不同执行计划的cost区别，所以只需要重点关注两个部分rows_estimation 和considered_execution_plans

代价模型计算：

统计信息和cost计算参数：

计算cost会涉及到表的主键索引数据页(聚簇索引)数量和表中的记录数，两个信息都可以通过innodb的表统计信息mysql.innodb_table_stats查到，n_rows是记录数，clustered_index_size是聚簇索引页数。

mysql> select * from mysql.innodb_table_stats where table_name='basic_person_info';
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
| database_name | table_name        | last_update         | n_rows | clustered_index_size | sum_of_other_index_sizes |
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
| test          | basic_person_info | 2022-12-23 18:27:24 |  86632 |                  737 |                     1401 |
+---------------+-------------------+---------------------+--------+----------------------+--------------------------+
1 row in set (0.01 sec)

代价模型将操作分为Server层和Engine（存储引擎）层两类，Server层主要是CPU代价，Engine层主要是IO代价，比如MySQL从磁盘读取一个数据页的代价io_block_read_cost为1，从buffer pool读取的代价memory_block_read_cost为0.25，计算符合条件的行代价为row_evaluate_cost为0.1，除此之外还有：

memory_temptable_create_cost (default 1.0) 内存临时表的创建代价。
memory_temptable_row_cost (default 0.1) 内存临时表的行代价。
key_compare_cost (default 0.1) 键比较的代价，例如排序。
disk_temptable_create_cost (default 20.0) 内部myisam或innodb临时表的创建代价。
disk_temptable_row_cost (default 0.5) 内部myisam或innodb临时表的行代价。

这些都可以通过mysql.server_cost、mysql.engine_cost查看defalt值和设置值

mysql> select * from mysql.server_cost;
+------------------------------+------------+---------------------+---------+---------------+
| cost_name                    | cost_value | last_update         | comment | default_value |
+------------------------------+------------+---------------------+---------+---------------+
| disk_temptable_create_cost   |       NULL | 2022-05-11 16:09:37 | NULL    |            20 |
| disk_temptable_row_cost      |       NULL | 2022-05-11 16:09:37 | NULL    |           0.5 |
| key_compare_cost             |       NULL | 2022-05-11 16:09:37 | NULL    |          0.05 |
| memory_temptable_create_cost |       NULL | 2022-05-11 16:09:37 | NULL    |             1 |
| memory_temptable_row_cost    |       NULL | 2022-05-11 16:09:37 | NULL    |           0.1 |
| row_evaluate_cost            |       NULL | 2022-05-11 16:09:37 | NULL    |           0.1 |
+------------------------------+------------+---------------------+---------+---------------+
mysql> select * from mysql.engine_cost;
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| engine_name | device_type | cost_name              | cost_value | last_update         | comment | default_value |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| default     |           0 | io_block_read_cost     |       NULL | 2022-05-11 16:09:37 | NULL    |             1 |
| default     |           0 | memory_block_read_cost |       NULL | 2023-01-09 11:17:39 | NULL    |          0.25 |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+

计算公式：

如上面介绍的一样，代价模型将操作分为两类io_cost和cpu_cost，io_cost+cpu_cost就是总的cost，下面是具体的计算方法：

全表扫描：

全表扫描成本 = io_cost + 1.1 + cpu_cost + 1 (io_cost +1.1和cpu_cost +1在代码里是直接硬加上的，不知道为什么，计算的时候直接加上)

io_cost = clustered_index_size (统计信息中的主键页数) * avg_single_page_cost(读取一个页的平均成本)

avg_single_page_cost = pages_in_memory_percent * 0.25(memory_block_read_cost) + pages_on_disk_percent * 1.0(io_block_read_cost)

pages_in_memory_percent 表示已经加载到 Buffer Pool 中的叶结点占所有叶结点的比例 pages_on_disk_percent 表示没有加载到 Buffer Pool 中的叶结点占所有叶结点的比例

所以当数据已经全部读取到buffer pool中的时候：

io_cost=clustered_index_size * 0.25

都没有读取到buffer pool中的时候：

io_cost=clustered_index_size * 1.0

当部分数据在buffer pool中，部分数据需要从磁盘读取时，这时的系数介于0.25到1之间

cpu_cost = n_rows(统计信息中记录数) * 0.1(row_evaluate_cost)

走索引的成本：

和全表扫描的计算方法类似，其中io_cost与搜索的区间数有关，比如扫描三个区间where a between 1 and 10 or a between 20 and 30 or a between 40 and 50，此时：

io_cost=3 * avg_single_page_cost

cpu_cost=记录数 * 0.1(row_evaluate_cost)+0.01(代码中的微调参数)

针对二级索引还会有回表的操作：

MySQL认为每次回表都相当于是访问一个页面，所以每次回表都会进行一次IO，这部分成本：

io_cost=rows(记录数)*avg_single_page_cost

对回表查询的数据还需要进行一次计算：

cpu_cost=rows(记录数) * 0.1(row_evaluate_cost)(需要注意的是当索引需要回表扫描时，在rows_estimation阶段并不会计算这个值，在considered_execution_plans阶段会重新加上这部分成本)

所以针对需要回表的查询：

io_cost=查询区间 * avg_single_page_cost + rows(记录数) * avg_single_page_cost

cpu_cost=记录数 * 0.1(row_evaluate_cost) + 0.01(代码中的微调参数) + rows(记录数) * 0.1(row_evaluate_cost)

例子：

mysql> set optimizer_trace='enabled=on';
Query OK, 0 rows affected (0.00 sec)

mysql>explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
| id | select_type | table | partitions | type   | possible_keys                        | key           | key_len | ref            | rows | filtered | Extra                 |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
|  1 | SIMPLE      | t2    | NULL       | range  | id_num_unique,idx_age,idx_age_id_num | idx_age       | 1       | NULL           | 9594 |   100.00 | Using index condition |
|  1 | SIMPLE      | t1    | NULL       | eq_ref | id_num_unique,idx_age                | id_num_unique | 60      | test.t2.id_num |    1 |    50.00 | Using where           |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+------+----------+-----------------------+
2 rows in set, 1 warning (0.04 sec)

查看optimizer_trace的内容

select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20 | {
  "steps": [
    {
      "join_preparation": {
        "select#": 1,
        "steps": [
          {
            "expanded_query": "/* select#1 */ select `t1`.`id` AS `id`,`t1`.`id_num` AS `id_num`,`t1`.`lastname` AS `lastname`,`t1`.`firstname` AS `firstname`,`t1`.`mobile` AS `mobile`,`t1`.`sex` AS `sex`,`t1`.`birthday` AS `birthday`,`t1`.`age` AS `age`,`t1`.`top_education` AS `top_education`,`t1`.`address` AS `address`,`t1`.`income_by_year` AS `income_by_year`,`t1`.`create_time` AS `create_time`,`t1`.`update_time` AS `update_time`,`t2`.`id` AS `id`,`t2`.`id_num` AS `id_num`,`t2`.`lastname` AS `lastname`,`t2`.`firstname` AS `firstname`,`t2`.`mobile` AS `mobile`,`t2`.`sex` AS `sex`,`t2`.`birthday` AS `birthday`,`t2`.`age` AS `age`,`t2`.`top_education` AS `top_education`,`t2`.`address` AS `address`,`t2`.`income_by_year` AS `income_by_year`,`t2`.`create_time` AS `create_time`,`t2`.`update_time` AS `update_time` from (`basic_person_info` `t1` join `basic_person_info2` `t2` on((`t1`.`id_num` = `t2`.`id_num`))) where ((`t1`.`age` > 10) and (`t2`.`age` < 20))"
          },
          {
            "transformations_to_nested_joins": {
              "transformations": [
                "JOIN_condition_to_WHERE",
                "parenthesis_removal"
              ],
              "expanded_query": "/* select#1 */ select `t1`.`id` AS `id`,`t1`.`id_num` AS `id_num`,`t1`.`lastname` AS `lastname`,`t1`.`firstname` AS `firstname`,`t1`.`mobile` AS `mobile`,`t1`.`sex` AS `sex`,`t1`.`birthday` AS `birthday`,`t1`.`age` AS `age`,`t1`.`top_education` AS `top_education`,`t1`.`address` AS `address`,`t1`.`income_by_year` AS `income_by_year`,`t1`.`create_time` AS `create_time`,`t1`.`update_time` AS `update_time`,`t2`.`id` AS `id`,`t2`.`id_num` AS `id_num`,`t2`.`lastname` AS `lastname`,`t2`.`firstname` AS `firstname`,`t2`.`mobile` AS `mobile`,`t2`.`sex` AS `sex`,`t2`.`birthday` AS `birthday`,`t2`.`age` AS `age`,`t2`.`top_education` AS `top_education`,`t2`.`address` AS `address`,`t2`.`income_by_year` AS `income_by_year`,`t2`.`create_time` AS `create_time`,`t2`.`update_time` AS `update_time` from `basic_person_info` `t1` join `basic_person_info2` `t2` where ((`t1`.`age` > 10) and (`t2`.`age` < 20) and (`t1`.`id_num` = `t2`.`id_num`))"
            }
          }
        ]
      }
    },
    {
      "join_optimization": {
        "select#": 1,
        "steps": [
          {
            "condition_processing": {
              "condition": "WHERE",
              "original_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and (`t1`.`id_num` = `t2`.`id_num`))",
              "steps": [
                {
                  "transformation": "equality_propagation",
                  "resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
                },
                {
                  "transformation": "constant_propagation",
                  "resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
                },
                {
                  "transformation": "trivial_condition_removal",
                  "resulting_condition": "((`t1`.`age` > 10) and (`t2`.`age` < 20) and multiple equal(`t1`.`id_num`, `t2`.`id_num`))"
                }
              ]
            }
          },
          {
            "substitute_generated_columns": {
            }
          },
          {
            "table_dependencies": [
              {
                "table": "`basic_person_info` `t1`",
                "row_may_be_null": false,
                "map_bit": 0,
                "depends_on_map_bits": [
                ]
              },
              {
                "table": "`basic_person_info2` `t2`",
                "row_may_be_null": false,
                "map_bit": 1,
                "depends_on_map_bits": [
                ]
              }
            ]
          },
          {
            "ref_optimizer_key_uses": [
              {
                "table": "`basic_person_info` `t1`",
                "field": "id_num",
                "equals": "`t2`.`id_num`",
                "null_rejecting": true
              },
              {
                "table": "`basic_person_info2` `t2`",
                "field": "id_num",
                "equals": "`t1`.`id_num`",
                "null_rejecting": true
              }
            ]
          },
          {
            "rows_estimation": [
              {
                "table": "`basic_person_info` `t1`",
                "range_analysis": {
                  "table_scan": {
                    "rows": 86734,
                    "cost": 8859.75

                    t1表的scan成本=聚簇索引页数*0.25 + 行数 * 0.1 +1.1+1
                    737*0.25+1.1+86734*0.1+1=8859.75

                  },
                  "potential_range_indexes": [
                    {
                      "index": "PRIMARY",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "id_num_unique",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "mobile_unique",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_name",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_top_education",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_create_time",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_mobile",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_age",
                      "usable": true,
                      "key_parts": [
                        "age",
                        "id"
                      ]
                    }
                  ],
                  "setup_range_conditions": [
                  ],
                  "group_index_range": {
                    "chosen": false,
                    "cause": "not_single_table"
                  },
                  "skip_scan_range": {
                    "chosen": false,
                    "cause": "not_single_table"
                  },
                  "analyzing_range_alternatives": {
                    "range_scan_alternatives": [
                      {
                        "index": "idx_age",
                        "ranges": [
                          "10 < age"
                        ],
                        "index_dives_for_eq_ranges": true,
                        "rowid_ordered": false,
                        "using_mrr": false,
                        "index_only": false,
                        "rows": 43367,
                        "cost": 15178.7,

                        通过索引idx_age读取数据：
                        io_cost=区间数* 0.25 +记录数* 0.25
                        io_cost=1*0.25+43367*0.25=10,842  
                        cpu_cost=记录数* 0.1 (没有回表的cost)
                        cpu_cost=43367*0.1=4,336.7 
                        cost=10842+4,336.7=15178.7

                        "chosen": false,
                        "cause": "cost"
                      }
                    ],
                    "analyzing_roworder_intersect": {
                      "usable": false,
                      "cause": "too_few_roworder_scans"
                    }
                  }
                }
              },
              {
                "table": "`basic_person_info2` `t2`",
                "range_analysis": {
                  "table_scan": {
                    "rows": 73845,
                    "cost": 7538.85

                    t2表的scan成本=聚簇索引页数*0.25 + 行数 * 0.1 +1.1+1
                    609*0.25+1+73845*0.1+1.1=7538.85

                  },
                  "potential_range_indexes": [
                    {
                      "index": "PRIMARY",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "id_num_unique",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "mobile_unique",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_name",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_top_education",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_create_time",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_basic_person_info_mobile",
                      "usable": false,
                      "cause": "not_applicable"
                    },
                    {
                      "index": "idx_age",
                      "usable": true,
                      "key_parts": [
                        "age",
                        "id"
                      ]
                    },
                    {
                      "index": "idx_age_id_num",
                      "usable": true,
                      "key_parts": [
                        "age",
                        "id_num",
                        "id"
                      ]
                    }
                  ],
                  "setup_range_conditions": [
                  ],
                  "group_index_range": {
                    "chosen": false,
                    "cause": "not_single_table"
                  },
                  "skip_scan_range": {
                    "chosen": false,
                    "cause": "not_single_table"
                  },
                  "analyzing_range_alternatives": {
                    "range_scan_alternatives": [
                      {
                        "index": "idx_age",
                        "ranges": [
                          "age < 20"
                        ],
                        "index_dives_for_eq_ranges": true,
                        "rowid_ordered": false,
                        "using_mrr": false,
                        "index_only": false,
                        "rows": 9594,
                        "cost": 3358.16,

                        通过索引idx_age读取数据：
                        io_cost=区间数* 0.25 +记录数* 0.25
                        io_cost=1*0.25+9594*0.25=2,398.75        
                        cpu_cost=记录数* 0.1   (没有回表的cost) 
                        cpu_cost=9594*0.1959.4  
                        cost=2,398.75+959.4=3,358.15

                        "chosen": true
                      },
                      {
                        "index": "idx_age_id_num",
                        "ranges": [
                          "age < 20"
                        ],
                        "index_dives_for_eq_ranges": true,
                        "rowid_ordered": false,
                        "using_mrr": false,
                        "index_only": false,
                        "rows": 19086,
                        "cost": 6680.36,

                        通过索引idx_age_id_num读取数据：
                        io_cost=区间数* 0.25 +记录数* 0.25
                        io_cost=1*0.25+19086*0.25=4,771.75           
                        cpu_cost=记录数* 0.1  (没有回表的cost)  
                        cpu_cost=19086*0.1=1908.6
                        cost=4,771.75+1908.6=6,680.35

                        "chosen": false,
                        "cause": "cost"
                      }
                    ],
                    "analyzing_roworder_intersect": {
                      "usable": false,
                      "cause": "too_few_roworder_scans"
                    }
                  },
                  "chosen_range_access_summary": {
                    "range_access_plan": {
                      "type": "range_scan",
                      "index": "idx_age",
                      "rows": 9594,
                      "ranges": [
                        "age < 20"
                      ]
                    },
                    "rows_for_plan": 9594,
                    "cost_for_plan": 3358.16,
                    "chosen": true
                  }
                }
              }
            ]
          },
          {
            "considered_execution_plans": [
              {
                "plan_prefix": [
                ],
                "table": "`basic_person_info2` `t2`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "access_type": "ref",
                      "index": "id_num_unique",
                      "usable": false,
                      "chosen": false
                    },
                    {
                      "rows_to_scan": 9594,
                      "filtering_effect": [
                      ],
                      "final_filtering_effect": 1,
                      "access_type": "range",
                      "range_details": {
                        "used_index": "idx_age"
                      },
                      "resulting_rows": 9594,
                      "cost": 4317.56,

                        通过索引idx_age读取数据：
                        io_cost=区间数* 0.25 +记录数* 0.25
                        io_cost=1*0.25+9594*0.25=2,398.75        
                        cpu_cost=记录数* 0.1 + 记录数* 0.1   
                        cpu_cost=9594*0.1*2=1,918.8  
                        cost=2,398.75+1,918.8=4317.56

                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100,
                "rows_for_plan": 9594,
                "cost_for_plan": 4317.56,
                "rest_of_plan": [
                  {
                    "plan_prefix": [
                      "`basic_person_info2` `t2`"
                    ],
                    "table": "`basic_person_info` `t1`",
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "access_type": "eq_ref",
                          "index": "id_num_unique",
                          "rows": 1,
                          "cost": 3357.9,

                          io_cost=t2表记录数*0.25=9594*0.25=2398.5
                          cpu_cost=记录数*0.1=9594*0.1=959.4
                          cost=2398.5+959.4=3357.9

                          "chosen": true
                        },
                        {
                          "rows_to_scan": 86734,
                          "filtering_effect": [
                          ],
                          "final_filtering_effect": 0.5,
                          "access_type": "scan",
                          "using_join_cache": true,
                          "buffers_needed": 14,
                          "resulting_rows": 43367,
                          "cost": 4.16701e+07,
                          "chosen": false
                        }
                      ]
                    },
                    "condition_filtering_pct": 100,
                    "rows_for_plan": 9594,
                    "cost_for_plan": 7675.46,

                   总cost=4,317.56+3,357.9=7,675.46

                    "chosen": true
                  }
                ]
              },
              {
                "plan_prefix": [
                ],
                "table": "`basic_person_info` `t1`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "access_type": "ref",
                      "index": "id_num_unique",
                      "usable": false,
                      "chosen": false
                    },
                    {
                      "rows_to_scan": 86734,
                      "filtering_effect": [
                      ],
                      "final_filtering_effect": 0.5,
                      "access_type": "scan",
                      "resulting_rows": 43367,
                      "cost": 8857.65,

                      t1的scan成本

                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100,
                "rows_for_plan": 43367,
                "cost_for_plan": 8857.65,
                "pruned_by_cost": true

                放弃后续的计算

              }
            ]
          },
          {
            "attaching_conditions_to_tables": {
              "original_condition": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10) and (`t2`.`age` < 20))",
              "attached_conditions_computation": [
              ],
              "attached_conditions_summary": [
                {
                  "table": "`basic_person_info2` `t2`",
                  "attached": "(`t2`.`age` < 20)"
                },
                {
                  "table": "`basic_person_info` `t1`",
                  "attached": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10))"
                }
              ]
            }
          },
          {
            "finalizing_table_conditions": [
              {
                "table": "`basic_person_info2` `t2`",
                "original_table_condition": "(`t2`.`age` < 20)",
                "final_table_condition   ": "(`t2`.`age` < 20)"
              },
              {
                "table": "`basic_person_info` `t1`",
                "original_table_condition": "((`t1`.`id_num` = `t2`.`id_num`) and (`t1`.`age` > 10))",
                "final_table_condition   ": "(`t1`.`age` > 10)"
              }
            ]
          },
          {
            "refine_plan": [
              {
                "table": "`basic_person_info2` `t2`",
                "pushed_index_condition": "(`t2`.`age` < 20)",
                "table_condition_attached": null
              },
              {
                "table": "`basic_person_info` `t1`"
              }
            ]
          }
        ]
      }
    },
    {
      "join_execution": {
        "select#": 1,
        "steps": [
        ]
      }
    }
  ]
}

成本常数修改：

前面已经介绍了成本常量值实际上存放在MySQL自带的系统库MySQL中的server_cost和engine_cost表中，其中server_cost表存放server层的成本常量，engine_cost表存放engine层成本常量

mysql> select * from mysql.server_cost;
+------------------------------+------------+---------------------+---------+---------------+
| cost_name                    | cost_value | last_update         | comment | default_value |
+------------------------------+------------+---------------------+---------+---------------+
| disk_temptable_create_cost   |       NULL | 2022-05-11 16:09:37 | NULL    |            20 |
| disk_temptable_row_cost      |       NULL | 2022-05-11 16:09:37 | NULL    |           0.5 |
| key_compare_cost             |       NULL | 2022-05-11 16:09:37 | NULL    |          0.05 |
| memory_temptable_create_cost |       NULL | 2022-05-11 16:09:37 | NULL    |             1 |
| memory_temptable_row_cost    |       NULL | 2022-05-11 16:09:37 | NULL    |           0.1 |
| row_evaluate_cost            |       NULL | 2022-05-11 16:09:37 | NULL    |           0.1 |
+------------------------------+------------+---------------------+---------+---------------+

mysql> select * from mysql.engine_cost;
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| engine_name | device_type | cost_name              | cost_value | last_update         | comment | default_value |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+
| default     |           0 | io_block_read_cost     |       NULL | 2022-05-11 16:09:37 | NULL    |             1 |
| default     |           0 | memory_block_read_cost |       NULL | 2023-01-09 11:17:39 | NULL    |          0.25 |
+-------------+-------------+------------------------+------------+---------------------+---------+---------------+

其中 default_value的值是系统默认的，不能修改，cost_value列的值我们可以修改，如果cost_value列的值不为空系统将用该值覆盖默认值，我们可以通过update语句来修改

mysql> update mysql.engine_cost set cost_value=10 where cost_name='memory_block_read_cost';
Query OK, 0 rows affected (0.00 sec)
mysql> update mysql.engine_cost set cost_value=10 where cost_name='io_block_read_cost';
Query OK, 0 rows affected (0.00 sec)

很多资料都说执行flush optimizer_costs就可以生效，不过我在修改完后并执行flush optimizer_costs并不能马上生效，最后是通过重启数据库实例才生效，这个可能是数据库版本的差异，大家可以自行验证。

mysql> explain select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+
| id | select_type | table | partitions | type   | possible_keys                        | key           | key_len | ref            | rows  | filtered | Extra       |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+
|  1 | SIMPLE      | t2    | NULL       | ALL    | id_num_unique,idx_age,idx_age_id_num | NULL          | NULL    | NULL           | 73990 |    12.97 | Using where |
|  1 | SIMPLE      | t1    | NULL       | eq_ref | id_num_unique,idx_age                | id_num_unique | 60      | test.t2.id_num |     1 |    50.00 | Using where |
+----+-------------+-------+------------+--------+--------------------------------------+---------------+---------+----------------+-------+----------+-------------+

"table": "`basic_person_info2` `t2`",
                "range_analysis": {
                  "table_scan": {
                    "rows": 73990,
                    "cost": 13491.1

                   全表扫描cost=609*10+73990*0.1+1.1+1= 13491.1

                  },
"index": "idx_age",
                        "ranges": [
                          "age < 20"
                        ],
                        "index_dives_for_eq_ranges": true,
                        "rowid_ordered": false,
                        "using_mrr": false,
                        "index_only": false,
                        "rows": 9594,
                        "cost": 96909.4,

                        idx_age索引扫描cost=1*10+9594*10+9594*0.1=96,909.4

                        "chosen": false,
                        "cause": "cost"
                      },

修改后的执行计划，发现t2表走了全表扫描了而没有走idx_age索引，分别查看一下t2表走全表扫描和idx_age索引的cost发现全表扫描的cost为13491.1，而走索引的cost为96,909.4，因为全表扫描的cost比走索引低，所以优化器没有选择idx_age索引。

从这个例子可以看出，更改成本常量值会直接影响优化器的方案选择，所以一定要慎重，没有特殊原因建议不要修改。

explain format=json

虽然通过optimizer_trace可以看到很多详细的优化器选择过程，但是使用起来起来还是比较麻烦，需要过滤的信息很多，这时explain format=json输出json格式的分析数据也是一个不错的选择，它也包含语句将要执行的成本信息，如下：

query_cost  总查询成本
read_cost   IO成本+除 eval_cost以外cpu成本
eval_cost   检测rows * filter条记录的成本
prefix_cost 单次查询的成本,等于read_cost+eval_cost
mysql> explain format=json select * from basic_person_info t1 join basic_person_info2 t2 on t1.id_num=t2.id_num where t1.age >10 and t2.age<20;
{
  "query_block": {
    "select_id": 1,
    "cost_info": {
      "query_cost": "7675.46"
    },
    "nested_loop": [
      {
        "table": {
          "table_name": "t2",
          "access_type": "range",
          "possible_keys": [
            "id_num_unique",
            "idx_age",
            "idx_age_id_num"
          ],
          "key": "idx_age",
          "used_key_parts": [
            "age"
          ],
          "key_length": "1",
          "rows_examined_per_scan": 9594,
          "rows_produced_per_join": 9594,
          "filtered": "100.00",
          "index_condition": "(`test`.`t2`.`age` < 20)",
          "cost_info": {
            "read_cost": "3358.16",
            包含所有io成本+(cpu成本-eval_cost)
            "eval_cost": "959.40",
            计算扇出的cpu成本，优化器利用启发式规则估算出满足所有条件的的比例(filtered)
            =rows_examined_per_scan*filtered*0.1
            "prefix_cost": "4317.56",
            单表查询的总成本

            "data_read_per_join": "3M"
          },
          "used_columns": [
            "id",
            "id_num",
            "lastname",
            "firstname",
            "mobile",
            "sex",
            "birthday",
            "age",
            "top_education",
            "address",
            "income_by_year",
            "create_time",
            "update_time"
          ]
        }
      },
      {
        "table": {
          "table_name": "t1",
          "access_type": "eq_ref",
          "possible_keys": [
            "id_num_unique",
            "idx_age"
          ],
          "key": "id_num_unique",
          "used_key_parts": [
            "id_num"
          ],
          "key_length": "60",
          "ref": [
            "test.t2.id_num"
          ],
          "rows_examined_per_scan": 1,
          "rows_produced_per_join": 4797,
          "filtered": "50.00",
          "cost_info": {
            "read_cost": "2398.50",
            包含所有io成本+(cpu成本-eval_cost)
            "eval_cost": "479.70",
            计算扇出的cpu成本，优化器利用启发式规则估算出满足所有条件的的比例(filtered)
            =rows_examined_per_scan*filtered*0.1
            "prefix_cost": "7675.46",
            两表查询的总cost
            "data_read_per_join": "1M"
          },
          "used_columns": [
            "id",
            "id_num",
            "lastname",
            "firstname",
            "mobile",
            "sex",
            "birthday",
            "age",
            "top_education",
            "address",
            "income_by_year",
            "create_time",
            "update_time"
          ],
          "attached_condition": "(`test`.`t1`.`age` > 10)"
        }
      }
    ]
  }
}

另外，explain结合show warnings语句一起使用还可以得知优化器改写后的语句

mysql> show warnings;
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Level | Code | Message                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Note  | 1003 | /* select#1 */ select `test`.`t1`.`id` AS `id`,`test`.`t1`.`id_num` AS `id_num`,`test`.`t1`.`lastname` AS `lastname`,`test`.`t1`.`firstname` AS `firstname`,`test`.`t1`.`mobile` AS `mobile`,`test`.`t1`.`sex` AS `sex`,`test`.`t1`.`birthday` AS `birthday`,`test`.`t1`.`age` AS `age`,`test`.`t1`.`top_education` AS `top_education`,`test`.`t1`.`address` AS `address`,`test`.`t1`.`income_by_year` AS `income_by_year`,`test`.`t1`.`create_time` AS `create_time`,`test`.`t1`.`update_time` AS `update_time`,`test`.`t2`.`id` AS `id`,`test`.`t2`.`id_num` AS `id_num`,`test`.`t2`.`lastname` AS `lastname`,`test`.`t2`.`firstname` AS `firstname`,`test`.`t2`.`mobile` AS `mobile`,`test`.`t2`.`sex` AS `sex`,`test`.`t2`.`birthday` AS `birthday`,`test`.`t2`.`age` AS `age`,`test`.`t2`.`top_education` AS `top_education`,`test`.`t2`.`address` AS `address`,`test`.`t2`.`income_by_year` AS `income_by_year`,`test`.`t2`.`create_time` AS `create_time`,`test`.`t2`.`update_time` AS `update_time` from `test`.`basic_person_info` `t1` join `test`.`basic_person_info2` `t2` where ((`test`.`t1`.`id_num` = `test`.`t2`.`id_num`) and (`test`.`t1`.`age` > 10) and (`test`.`t2`.`age` < 20)) |
+-------+------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)

总结：

MySQL的优化器是基于成本来选择最优执行方案的，哪个成本最少就选哪个，所以重点在于计算出各个执行计划的cost
成本由CPU成本和IO成本组成，每个成本常数值可以自己调整，非必要的情况下不要调整，以免影响整个数据库的执行计划选择
通过开启optimizer_trace可以跟踪优化器的各个环节的分析步骤，可以判断有时候为什么没有走索引而走了全表扫描
explain加上format=json选项后可以查看成本信息分为read_cost和eval_cost，但只能看到当前已经选择的执行计划，另外通过show warnings可以看到优化器改写后的语句

Enjoy GreatSQL :)