一条SQL语句执行成本的计算

川长思鸟来 2022-09-09 00:25 194阅读 0赞

在一条单表查询语句真正执行之前，MySQL的查询优化器会找出执行该语句所有可能使用的方案，对比之后找出成本最低的方案，这个成本最低的方案就是所谓的执行计划，之后才会调用存储引擎提供的接口真正的执行查询。

# 准备数据 #

employees数据库来自MySQL官方示例数据库employees。

待分析的SQL：

为什么走的是全表扫描，而不是索引idx\_hire\_date或idx\_birth\_date呢？

# 成本的计算过程 #

1.  根据搜索条件，找出所有可能使用的索引
2.  计算全表扫描的代价
3.  计算使用不同索引执行查询的代价
4.  对比各种执行方案的代价，找出成本最低的那个

## 计算全表扫描的代价 ##

全表扫描即将聚簇索引从对应的页面加载到内存，然后检测记录是否满足条件计算。

计算公式：页面数 x 1 + 记录数 x 0.2

页面数和记录数如何获得？查看表的统计信息。

mysql> show table status like 't_emp'\G;
    *************************** 1. row ***************************
               Name: t_emp
             Engine: InnoDB
            Version: 10
         Row_format: Dynamic
               Rows: 299645
     Avg_row_length: 50
        Data_length: 15220736
    Max_data_length: 0
       Index_length: 9469952
          Data_free: 2097152
     Auto_increment: NULL
        Create_time: 2021-08-19 07:19:44
        Update_time: 2021-08-19 07:19:31
         Check_time: NULL
          Collation: latin1_swedish_ci
           Checksum: NULL
     Create_options:
            Comment:
    1 row in set (0.00 sec)

从上面可以看到`Rows: 299645`，记录数为299645，数据的大小为Data\_length=15220736  
，页面数=数据大小/16/1024=929。

从下面的统计表的数据中也可以直接看出记录数和页面数：

mysql> select * from mysql.innodb_table_stats where table_name='t_emp'\G;
    *************************** 1. row ***************************
               database_name: employees
                  table_name: t_emp
                 last_update: 2021-08-19 07:19:44
                      n_rows: 299645
        clustered_index_size: 929
    sum_of_other_index_sizes: 578
    1 row in set (0.00 sec)

因此全表扫描的总成本为：929x1.0+299645x0.2=60858。

来看一下MySQL算的是多少：

mysql> explain format=json  select * from t_emp where hire_date > '1990-11-20' and birth_date > '1840-11-20';
    +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | EXPLAIN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
    +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | {
      "query_block": {
        "select_id": 1,
        "cost_info": {
          "query_cost": "60858.00"
        },
        "table": {
          "table_name": "t_emp",
          "access_type": "ALL",
          "possible_keys": [
            "idx_hire_date",
            "idx_birth_date"
          ],
          "rows_examined_per_scan": 299645,
          "rows_produced_per_join": 74910,
          "filtered": "25.00",
          "cost_info": {
            "read_cost": "45875.85",
            "eval_cost": "14982.15",
            "prefix_cost": "60858.00",
            "data_read_per_join": "3M"
          },
          "used_columns": [
            "emp_no",
            "birth_date",
            "first_name",
            "last_name",
            "gender",
            "hire_date"
          ],
          "attached_condition": "((`employees`.`t_emp`.`hire_date` > '1990-11-20') and (`employees`.`t_emp`.`birth_date` > '1840-11-20'))"
        }
      }
    } |
    +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    1 row in set, 1 warning (0.00 sec)

## 计算使用索引idx\_hire\_date的代价 ##

计算二级索引成本的大步骤为：

1.  计算搜索二级索引树中满足条件的记录
2.  回表查询

搜索二级索引树代价计算：

*  IO成本：范围区间占用页面数量  
    ，查询优化器粗暴的认为读取索引的一个范围区间的I/O成本和读取一个页面是相同的。
 *

CPU成本：需要回表的记录数。

如何计算idx\_hire\_date在hire\_date > '1990-11-20’这个范围区间中包含多少二级索引记录  
：

*  当左边界记录和右边界记录相隔较小时  
    ，直接遍历这些页面的PAGE HEADER中的PAGE\_N\_RECS字段（记录该页面有多少条数据）可以获得精确值。
 *  当左边界记录和右边界记录相隔较大时  
    ，从左边界所在页面向右读取10个页面，计算平均每个页面中包含多少记录。再乘以左边界和右边界之间的页面数量即可  
    ，左边界和右边界之间的页面数量可以从B+树中的父节点获取（若跨越太多页面则需要递归）。

这里我们可以用count(\*)计算一下：

mysql> select count(*) from t_emp where hire_date > '1990-11-20';
    +----------+
    | count(*) |
    +----------+
    |   112374 |
    +----------+
    1 row in set (0.05 sec)

搜索二级索引树的成本：1x1.0+112374x0.2=22475.8

回表查询代价计算  
：回表查询聚簇索引需加载的页面数量(用于计算IO成本)  
，设计MySQL的大叔评估回表操作的I/O成本依旧很豪放，他们认为每次回表操作都相当于访问一个页面  
，回表查询定位页面后，需要定位记录并且判断其他过滤条件（用于计算CPU成本）。

回表的成本：112374x1.0+112374x0.2=134848.8

总成本：22475.8+134848.8=157324.6

再来看一下MySQL算的是多少：

mysql> explain format=json  select * from t_emp force index(idx_hire_date) where hire_date > '1990-11-20' and birth_date > '1840-11-20';
    +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | EXPLAIN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
    +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | {
      "query_block": {
        "select_id": 1,
        "cost_info": {
          "query_cost": "209751.81"
        },
        "table": {
          "table_name": "t_emp",
          "access_type": "range",
          "possible_keys": [
            "idx_hire_date"
          ],
          "key": "idx_hire_date",
          "used_key_parts": [
            "hire_date"
          ],
          "key_length": "3",
          "rows_examined_per_scan": 149822,
          "rows_produced_per_join": 49935,
          "filtered": "33.33",
          "index_condition": "(`employees`.`t_emp`.`hire_date` > '1990-11-20')",
          "cost_info": {
            "read_cost": "199764.68",
            "eval_cost": "9987.13",
            "prefix_cost": "209751.81",
            "data_read_per_join": "2M"
          },
          "used_columns": [
            "emp_no",
            "birth_date",
            "first_name",
            "last_name",
            "gender",
            "hire_date"
          ],
          "attached_condition": "(`employees`.`t_emp`.`birth_date` > '1840-11-20')"
        }
      }
    } |
    +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    1 row in set, 1 warning (0.00 sec)

为什么和我们算的不一样？因为MySQL使用的是统计数据，计算出来的记录数为149822。

总成本=1 x 1.0 + 149822 x 0.2 + 149822 x 1.0 + 149822 x 0.2 = 209751.8

## 计算使用索引idx\_birth\_date的代价 ##

二级索引idx\_birth\_date的成本的计算方式类似。

记录数：

mysql> select count(*) from t_emp where birth_date > '1840-11-20';
    +----------+
    | count(*) |
    +----------+
    |   300024 |
    +----------+
    1 row in set (0.08 sec)

二级索引扫描成本：1 x 1.0 + 300024 x 0.2=60005.8

回表成本：300024 x 1.0 + 300024 x 0.2=360,028.8

总成本：420034.6

通过对比各种执行方案的代价，找出成本最低的那个为**全表扫描**。

看下MySQL算出来的成本：

mysql> explain format=json  select * from t_emp force index(idx_birth_date) where hire_date > '1990-11-20' and birth_date > '1840-11-20';
    +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | EXPLAIN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
    +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | {
      "query_block": {
        "select_id": 1,
        "cost_info": {
          "query_cost": "209751.81"
        },
        "table": {
          "table_name": "t_emp",
          "access_type": "range",
          "possible_keys": [
            "idx_birth_date"
          ],
          "key": "idx_birth_date",
          "used_key_parts": [
            "birth_date"
          ],
          "key_length": "3",
          "rows_examined_per_scan": 149822,
          "rows_produced_per_join": 49935,
          "filtered": "33.33",
          "index_condition": "(`employees`.`t_emp`.`birth_date` > '1840-11-20')",
          "cost_info": {
            "read_cost": "199764.68",
            "eval_cost": "9987.13",
            "prefix_cost": "209751.81",
            "data_read_per_join": "2M"
          },
          "used_columns": [
            "emp_no",
            "birth_date",
            "first_name",
            "last_name",
            "gender",
            "hire_date"
          ],
          "attached_condition": "(`employees`.`t_emp`.`hire_date` > '1990-11-20')"
        }
      }
    } |
    +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    1 row in set, 1 warning (0.00 sec)

这个索引可以看出MySQL的统计数据很不准确。

# optimizer\_trace #

上面只能看到MySQL选择的方案的成本，要想看到MySQL对SQL的执行过程可以使用optimizer\_trace来查看。

参数介绍：

*  QUERY：跟踪语句的文本。
 *  TRACE：跟踪，JSON格式。
 *  MISSING\_BYTES\_BEYOND\_MAX\_MEM\_SIZE：每个记住的跟踪都是一个字符串，随着优化的进行扩展并将其附加数据。该optimizer\_trace\_max\_mem\_size 变量设置所有当前记忆的跟踪所使用的内存总量的限制。如果达到此限制，则当前跟踪不会扩展（因此是不完整的），并且该MISSING\_BYTES\_BEYOND\_MAX\_MEM\_SIZE列显示该跟踪丢失的字节数。
 *  INSUFFICIENT\_PRIVILEGES：如果跟踪的查询使用SQL SECURITY值为的视图或存储的例程DEFINER，则可能是拒绝了除定义者之外的其他用户查看查询的跟踪。在这种情况下，跟踪显示为空，INSUFFICIENT\_PRIVILEGES值为1。否则值为0。

我们可以对SQL进行optimizer\_trace ：

-- optimizer_trace默认不开启，开启会影响性能，用完记得关闭
    mysql> show variables like '%optimizer_trace%';
    +------------------------------+----------------------------------------------------------------------------+
    | Variable_name                | Value                                                                      |
    +------------------------------+----------------------------------------------------------------------------+
    | optimizer_trace              | enabled=off,one_line=off                                                   |
    | optimizer_trace_features     | greedy_search=on,range_optimizer=on,dynamic_range=on,repeated_subselect=on |
    | optimizer_trace_limit        | 1                                                                          |
    | optimizer_trace_max_mem_size | 16384                                                                      |
    | optimizer_trace_offset       | -1                                                                         |
    +------------------------------+----------------------------------------------------------------------------+
    5 rows in set (0.01 sec)
    
    -- 设置开启optimizer_trace
    mysql> set optimizer_trace="enabled=on";
    Query OK, 0 rows affected (0.00 sec)
    
    mysql> explain select * from t_emp where hire_date > '1990-11-20' and birth_date > '1840-11-20';
    +----+-------------+-------+------------+------+------------------------------+------+---------+------+--------+----------+-------------+
    | id | select_type | table | partitions | type | possible_keys                | key  | key_len | ref  | rows   | filtered | Extra       |
    +----+-------------+-------+------------+------+------------------------------+------+---------+------+--------+----------+-------------+
    |  1 | SIMPLE      | t_emp | NULL       | ALL  | idx_hire_date,idx_birth_date | NULL | NULL    | NULL | 299468 |    25.00 | Using where |
    +----+-------------+-------+------------+------+------------------------------+------+---------+------+--------+----------+-------------+
    1 row in set, 1 warning (0.00 sec)
    
    mysql> select * from INFORMATION_SCHEMA.OPTIMIZER_TRACE\G;
    *************************** 1. row ***************************
                                QUERY: explain select * from t_emp where hire_date > '1990-11-20' and birth_date > '1840-11-20'
                                TRACE: {
      "steps": [
        {
          "join_preparation": {
            "select#": 1,
            "steps": [
              {
                "expanded_query": "/* select#1 */ select `t_emp`.`emp_no` AS `emp_no`,`t_emp`.`birth_date` AS `birth_date`,`t_emp`.`first_name` AS `first_name`,`t_emp`.`last_name` AS `last_name`,`t_emp`.`gender` AS `gender`,`t_emp`.`hire_date` AS `hire_date` from `t_emp` where ((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))"
              }
            ]
          }
        },
        {
          "join_optimization": {
            "select#": 1,
            "steps": [
              {
                "condition_processing": {
                  "condition": "WHERE",
                  "original_condition": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))",
                  "steps": [
                    {
                      "transformation": "equality_propagation",
                      "resulting_condition": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))"
                    },
                    {
                      "transformation": "constant_propagation",
                      "resulting_condition": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))"
                    },
                    {
                      "transformation": "trivial_condition_removal",
                      "resulting_condition": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))"
                    }
                  ]
                }
              },
              {
                "substitute_generated_columns": {
                }
              },
              {
                "table_dependencies": [
                  {
                    "table": "`t_emp`",
                    "row_may_be_null": false,
                    "map_bit": 0,
                    "depends_on_map_bits": [
                    ]
                  }
                ]
              },
              {
                "ref_optimizer_key_uses": [
                ]
              },
              {
                "rows_estimation": [
                  {
                    "table": "`t_emp`",
                    "range_analysis": {
                      "table_scan": {
                        "rows": 299645,
                        "cost": 60860
                      },
                      "potential_range_indexes": [
                        {
                          "index": "PRIMARY",
                          "usable": false,
                          "cause": "not_applicable"
                        },
                        {
                          "index": "idx_hire_date",
                          "usable": true,
                          "key_parts": [
                            "hire_date",
                            "emp_no"
                          ]
                        },
                        {
                          "index": "idx_birth_date",
                          "usable": true,
                          "key_parts": [
                            "birth_date",
                            "emp_no"
                          ]
                        }
                      ],
                      "setup_range_conditions": [
                      ],
                      "group_index_range": {
                        "chosen": false,
                        "cause": "not_group_by_or_distinct"
                      },
                      "analyzing_range_alternatives": {
                        "range_scan_alternatives": [
                          {
                            "index": "idx_hire_date",
                            "ranges": [
                              "0x748d0f < hire_date"
                            ],
                            "index_dives_for_eq_ranges": true,
                            "rowid_ordered": false,
                            "using_mrr": false,
                            "index_only": false,
                            "rows": 149822,
                            "cost": 179787,
                            "chosen": false,
                            "cause": "cost"
                          },
                          {
                            "index": "idx_birth_date",
                            "ranges": [
                              "0x74610e < birth_date"
                            ],
                            "index_dives_for_eq_ranges": true,
                            "rowid_ordered": false,
                            "using_mrr": false,
                            "index_only": false,
                            "rows": 149822,
                            "cost": 179787,
                            "chosen": false,
                            "cause": "cost"
                          }
                        ],
                        "analyzing_roworder_intersect": {
                          "usable": false,
                          "cause": "too_few_roworder_scans"
                        }
                      }
                    }
                  }
                ]
              },
              {
                "considered_execution_plans": [
                  {
                    "plan_prefix": [
                    ],
                    "table": "`t_emp`",
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "rows_to_scan": 299645,
                          "access_type": "scan",
                          "resulting_rows": 74911,
                          "cost": 60858,
                          "chosen": true
                        }
                      ]
                    },
                    "condition_filtering_pct": 100,
                    "rows_for_plan": 74911,
                    "cost_for_plan": 60858,
                    "chosen": true
                  }
                ]
              },
              {
                "attaching_conditions_to_tables": {
                  "original_condition": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))",
                  "attached_conditions_computation": [
                  ],
                  "attached_conditions_summary": [
                    {
                      "table": "`t_emp`",
                      "attached": "((`t_emp`.`hire_date` > '1990-11-20') and (`t_emp`.`birth_date` > '1840-11-20'))"
                    }
                  ]
                }
              },
              {
                "refine_plan": [
                  {
                    "table": "`t_emp`"
                  }
                ]
              }
            ]
          }
        },
        {
          "join_explain": {
            "select#": 1,
            "steps": [
            ]
          }
        }
      ]
    }
    MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0
              INSUFFICIENT_PRIVILEGES: 0
    1 row in set (0.00 sec)

细心的你肯定会发现通过optimizer\_trace打印的idx\_hire\_date成本与explain打印的不一致，差别在于回表的CPU成本。

explain：总成本=1 x 1.0 + 149822 x 0.2 + 149822 x 1.0 + 149822 x 0.2 = 209751.8

optimizer\_trace：总成本=1 x 1.0 + 149822 x 0.2 + 149822 x 1.0 = 179787.4