【GreatSQL 优化器 - 05】条件过滤 condition_fanout_filter

一、condition_fanout_filter 介绍

GreatSQL 的优化器对于 join 的表需要根据行数和 cost 来确定最后哪张表先执行哪张表后执行，这里面就涉及到预估满足条件的表数据，condition_fanout_filter 会根据一系列方法计算出一个数据过滤百分比，这个比百分比就是 filtered 系数，这个值区间在 [0,1]，值越小代表过滤效果越好。用这个系数乘以总的行数就可以得出最后需要扫描的表行数的数量，可以大幅节省开销和执行时间。

这个功能是由 OPTIMIZER_SWITCH_COND_FANOUT_FILTER这个OPTIMIZER_SWITCH 来控制的，默认是打开的。因此一般情况下不需要特意去关闭，但是如果遇到执行特别慢的一些情况可以考虑关闭。

下面用一个简单的例子来说明 condition_fanout_filter 是什么：

CREATE TABLE t1 (c1 INT PRIMARY KEY, c2 INT,date1 DATETIME);
INSERT INTO t1 VALUES (1,10,'2021-03-25 16:44:00.123456'),(2,1,'2022-03-26 16:44:00.123456'),(3,4,'2023-03-27 16:44:00.123456'),(5,5,'2024-03-25 16:44:00.123456');
CREATE TABLE t2 (cc1 INT PRIMARY KEY, cc2 INT);
INSERT INTO t2 VALUES (1,3),(2,1),(3,2),(4,3),(5,15);
# 为了查看过滤系数，需要创建一张没有主键的表用来做过滤估计。
CREATE TABLE t3 (ccc1 INT, ccc2 varchar(100));
INSERT INTO t3 VALUES (1,'aa1'),(2,'bb1'),(3,'cc1'),(4,'dd1'),(null,'ee');
CREATE INDEX idx1 ON t1(c2);
CREATE INDEX idx2 ON t1(c2,date1);
CREATE INDEX idx2_1 ON t2(cc2);
CREATE INDEX idx3_1 ON t3(ccc1);

看到下面的 t3 的过滤百分比 46.66%，意味着两张表 join 一共 20 行，因为 t3 的过滤百分比为 46.66%，因此最后只需要读取 20*46.66%=9 行数据。

注意，这里没有建立直方图，因此结果不包含直方图过滤的因素，关于直方图后面会专门开一章讲。

greatsql> EXPLAIN SELECT * FROM t1 JOIN t3 ON t1.c1=t3.ccc1 OR t3.ccc1 <3;
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+
| id | select_type | table | partitions | type  | possible_keys | key  | key_len | ref  | rows | filtered | Extra                                      |
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+
|  1 | SIMPLE      | t1    | NULL       | index | PRIMARY       | idx2 | 11      | NULL |    4 |   100.00 | Using index                                |
|  1 | SIMPLE      | t3    | NULL       | ALL   | idx3_1        | NULL | NULL    | NULL |    5 |    46.66 | Using where; Using join buffer (hash join) |
# 这里显示的值就是 t3 的过滤数据百分比
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+

greatsql> EXPLAIN FORMAT=TREE SELECT * FROM t1 JOIN t3 ON t1.c1=t3.ccc1 OR t3.ccc1<3;
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| EXPLAIN                                                                                                                                                                                                                                                       |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| -> Filter: ((t3.ccc1 = t1.c1) or (t3.ccc1 < 3))  (cost=3.65 rows=9)
    -> Inner hash join (no condition)  (cost=3.65 rows=9) # 这里结果为读取9行数据，跟上面算出来的数据一致
        -> Table scan on t3  (cost=0.12 rows=5)
        -> Hash
            -> Index scan on t1 using idx2  (cost=1.40 rows=4)
 |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

二、best_access_path 代码解释

condition_fanout_filte 的计算在 best_access_path 函数实现，用来预估不同 join 连接时候的 join 表的读取行数和可能的 cost。

void Optimize_table_order::best_access_path(JOIN_TAB *tab,
                                            const table_map remaining_tables,
                                            const uint idx, bool disable_jbuf,
                                            const double prefix_rowcount,
                                            POSITION *pos) {
# 如果根据前面的结果keyuse_array数组有值的话，那么根据find_best_ref()函数先找出最优索引，按照索引的方式计算cost
  if (tab->keyuse() != nullptr &&
      (table->file->ha_table_flags() & HA_NO_INDEX_ACCESS) == 0)
    best_ref =
        find_best_ref(tab, remaining_tables, idx, prefix_rowcount,
                      &found_condition, &ref_depend_map, &used_key_parts);
  # 最主要计算下面3个值
  pos->filter_effect = filter_effect = std::  (1.0, tab->found_records * calculate_condition_filter() / rows_after_filtering);
  pos->rows_fetched = rows_fetched = rows_after_filtering;
  pos->read_cost = scan_read_cost = calculate_scan_cost();    
}

下面是代码里面涉及的计算公式，这里是 keyuse_array 数组为空的情况，也就是扫描方式 "access_type" 非 "eq_ref" 和 "ref" 的情况，或者说是没有找到最优索引的情况。keyuse_array 数组有值的情况，在函数 find_best_ref () 计算，结果有可能也走下面的计算方式，详细在后面的章节详细介绍。

表一，rows_after_filtering 计算方式

表二，calculate_condition_filter () 计算方式

注：filter 是条件过滤百分比，这个值区间在 [0,1]，这个值越小代表过滤效果越好

表三，get_filtering_effect () 算出来的过滤系数

表四，calculate_scan_cost () 计算方式

表五 引擎计算相关

三、实际例子说明

接下来看一个例子来说明上面的代码。这里的例子不涉及 keyuse_array 数组有值的情况。

greatsql> SELECT * FROM t1 join t3 ON t1.c1=t3.ccc1 or t3.ccc1<3;
+----+------+---------------------+------+------+
| c1 | c2   | date1               | ccc1 | ccc2 |
+----+------+---------------------+------+------+
|  1 |   10 | 2021-03-25 16:44:00 |    1 | aa1  |
|  5 |    5 | 2024-03-25 16:44:00 |    1 | aa1  |
|  3 |    4 | 2023-03-27 16:44:00 |    1 | aa1  |
|  2 |    1 | 2022-03-26 16:44:00 |    1 | aa1  |
|  1 |   10 | 2021-03-25 16:44:00 |    2 | bb1  |
|  5 |    5 | 2024-03-25 16:44:00 |    2 | bb1  |
|  3 |    4 | 2023-03-27 16:44:00 |    2 | bb1  |
|  2 |    1 | 2022-03-26 16:44:00 |    2 | bb1  |
|  3 |    4 | 2023-03-27 16:44:00 |    3 | cc1  |
+----+------+---------------------+------+------+

greatsql> EXPLAIN FORMAT=TREE SELECT * FROM t1 join t3 ON t1.c1=t3.ccc1 or t3.ccc1<3;
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| EXPLAIN                                                                                                                                                                                                                                                       |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| -> Filter: ((t3.ccc1 = t1.c1) or (t3.ccc1 < 3))  (cost=3.65 rows=9)
    -> Inner hash join (no condition)  (cost=3.65 rows=9)
        -> Table scan on t3  (cost=0.12 rows=5)
        -> Hash
            -> Index scan on t1 using idx2  (cost=1.40 rows=4)
 |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

greatsql> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;
            "considered_execution_plans": [
              {
                "plan_prefix": [
                ],
                "table": "`t1`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "rows_to_scan": 4,
                      "filtering_effect": [
                      ],
                      "final_filtering_effect": 1,
                      "access_type": "scan",
                      "resulting_rows": 4,# 这个值就是rows_after_filtering
                      "cost": 0.65, # 计算公式=read_cost(0.25)+cost_model->row_evaluate_cost(1 * rows_after_filtering)
                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100, # 这个值就是filter_effect * 100
                "rows_for_plan": 4, # 这个值就是prefix_rowcount=rows_to_scan * filter_effect / 100
                "cost_for_plan": 0.65, # 这个值就是prefix_cost=read_cost(0.25) + cm->row_evaluate_cost(prefix_rowcount=4)
                "rest_of_plan": [
                  {
                    "plan_prefix": [ # 当前左连接表为t1
                      "`t1`"
                    ],
                    "table": "`t3`", # 右连接表为t3
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "rows_to_scan": 5,
                          "filtering_effect": [
                          ],
                          "final_filtering_effect": 1,
                          "access_type": "scan",
                          "using_join_cache": true,
                          "buffers_needed": 1,
                          "resulting_rows": 5, # 这个值就是rows_after_filtering
                          "cost": 2.25005, # 计算公式read_cost(0.25)+cost_model->row_evaluate_cost(prefix_rowcount * rows_after_filtering=4 * 5)
                          "chosen": true
                        }
                      ]
                    },
                    "condition_filtering_pct": 46.664, # 这个值计算过程见下面<<附录：t3的filter_effect值计算>> ※bug？用explain看到的和直接敲命令看到的不一样，前者46.664后者为100，而"rows_for_plan"会变为没有过滤的20
                    "rows_for_plan": 9.3328, # 这个值就是prefix_rowcount=4(上一张表的prefix_rowcount) * 5 * 46.664 / 100
                    "cost_for_plan": 2.90005, # 这个值就是prefix_cost=0.65(上一张表的prefix_cost)+read_cost(0.25) + cm->row_evaluate_cost(20=4*5)
                    "chosen": true
                  }
                ]
              },
              {
                "plan_prefix": [
                ],
                "table": "`t3`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "rows_to_scan": 5,
                      "filtering_effect": [
                      ],
                      "final_filtering_effect": 1,
                      "access_type": "scan",
                      "resulting_rows": 5,
                      "cost": 0.75, # 计算公式read_cost(0.25)+5行*0.1
                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100,
                "rows_for_plan": 5, # 这个值就是prefix_rowcount
                "cost_for_plan": 0.75, # 这个值就是prefix_cost
                "rest_of_plan": [
                  {
                    "plan_prefix": [ # 当前左连接表为t3
                      "`t3`"
                    ],
                    "table": "`t1`", # 右连接表为t1
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "rows_to_scan": 4,
                          "filtering_effect": [
                          ],
                          "final_filtering_effect": 1,
                          "access_type": "scan",
                          "using_join_cache": true,
                          "buffers_needed": 1,
                          "resulting_rows": 4,
                          "cost": 3.00776, # 计算公式read_cost(1.007)+cost_model->row_evaluate_cost(prefix_rowcount * rows_after_filtering=5 * 4)
                          "chosen": true
                        }
                      ]
                    },
                    "condition_filtering_pct": 100,
                    "rows_for_plan": 20, # 这个值就是prefix_rowcount=5(上一张表的prefix_rowcount) * 4 * 100 / 100
                    "cost_for_plan": 3.75776, # 这个值就是prefix_cost=0.75(上一张表的prefix_cost)+read_cost(1.007) + cm->row_evaluate_cost(20=5*4)
                    "pruned_by_cost": true # 因为这里算出来的3.75776 > 2.90005，因此被裁剪掉了
                  }
                ]
              }
            ]
          },
          {
            "attaching_conditions_to_tables": { # 添加一些附加条件
              "original_condition": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))",
              "attached_conditions_computation": [
              ],
              "attached_conditions_summary": [ # t1作为驱动表，执行全表扫描，因此不需要任何条件
                {
                  "table": "`t1`",
                  "attached": null
                },
                {
                  "table": "`t3`",
                  "attached": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))" #  t3作为连接表，按照条件过滤
                }
              ]
            }
          },
          {
            "finalizing_table_conditions": [
              {
                "table": "`t3`",
                "original_table_condition": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))",
                "final_table_condition   ": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))"
              }
            ]
          },
          {
            "refine_plan": [
              {
                "table": "`t1`"
              },
              {
                "table": "`t3`"
              }
            ]
          }
        ]
      }
    },
    {
      "join_explain": {
        "select#": 1,
        "steps": [
        ]
      }
    }
  ]
} |           

# 附录：t3的filter_effect值计算
# 这个函数计算t1.c1=t3.ccc1和t3.ccc1<3的filter_effect值，计算过程见下面
float Item_cond_or::get_filtering_effect(THD *thd, table_map filter_for_table,
                                         table_map read_tables,
                                         const MY_BITMAP *fields_to_ignore,
                                         double rows_in_table) {
  while ((item = it++)) {
    const float cur_filter = item->get_filtering_effect(
        thd, filter_for_table, read_tables, fields_to_ignore, rows_in_table);
    # 第一次：计算t1.c1=t3.ccc1，返回 1/5=20%，其中5是总行数。
    # 第二次：计算t3.ccc1<3，返回COND_FILTER_INEQUALITY=0.333
    # 最后的filter=0.2+0.333 - (0.2*0.333)=0.4664
    filter = filter + cur_filter - (filter * cur_filter);
  }
  return filter;
}

下面把 OPTIMIZER_SWITCH_COND_FANOUT_FILTER 模式关闭看看执行效果什么变化

greatsql> explain SELECT /*+ set_var(optimizer_switch='condition_fanout_filter=off') */ * FROM t1 join t3 ON t1.c1=t3.ccc1 or t3.ccc1<3;
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+
| id | select_type | table | partitions | type  | possible_keys | key  | key_len | ref  | rows | filtered | Extra                                      |
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+
|  1 | SIMPLE      | t1    | NULL       | index | PRIMARY       | idx2 | 11      | NULL |    4 |   100.00 | Using index                                |
|  1 | SIMPLE      | t3    | NULL       | ALL   | idx3_1        | NULL | NULL    | NULL |    5 |   100.00 | Using where; Using join buffer (hash join) |
                                                                                            # 看到这里选择了不做条件过滤，全表数据连接
+----+-------------+-------+------------+-------+---------------+------+---------+------+------+----------+--------------------------------------------+

greatsql> EXPLAIN FORMAT=TREE SELECT /*+ set_var(optimizer_switch='condition_fanout_filter=off') */ * FROM t1 join t3 ON t1.c1=t3.ccc1 or t3.ccc1<3;
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| EXPLAIN                                                                                                                                                                                                                                                         |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| -> Filter: ((t3.ccc1 = t1.c1) or (t3.ccc1 < 3))  (cost=3.65 rows=20)# 因为表里数据少，跟刚才比cost没有变化，但是要是数据大的话就很明显
    -> Inner hash join (no condition)  (cost=3.65 rows=20) # 这里变为了20行，意味着读取全部行数据
        -> Table scan on t3  (cost=0.19 rows=5)
        -> Hash
            -> Index scan on t1 using idx2  (cost=1.40 rows=4)
 |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

          {
            "considered_execution_plans": [
              {
                "plan_prefix": [
                ],
                "table": "`t1`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "rows_to_scan": 4,
                      "access_type": "scan",
                      "resulting_rows": 4,
                      "cost": 1.4,
                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100,
                "rows_for_plan": 4,
                "cost_for_plan": 1.4,
                "rest_of_plan": [
                  {
                    "plan_prefix": [ # 当前左连接表为t1
                      "`t1`"
                    ],
                    "table": "`t3`",
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "rows_to_scan": 5,
                          "access_type": "scan",
                          "using_join_cache": true,
                          "buffers_needed": 1,
                          "resulting_rows": 5,
                          "cost": 2.25005,
                          "chosen": true
                        }
                      ]
                    },
                    "condition_filtering_pct": 100, # 这里的过滤系数变为100，说明不进行条件过滤，读取全部行
                    "rows_for_plan": 20, # 由上面的9行变为全部数据连接，20行数据
                    "cost_for_plan": 3.65005, 
                    "chosen": true
                  }
                ]
              },
              {
                "plan_prefix": [
                ],
                "table": "`t3`",
                "best_access_path": {
                  "considered_access_paths": [
                    {
                      "rows_to_scan": 5,
                      "access_type": "scan",
                      "resulting_rows": 5,
                      "cost": 0.75,
                      "chosen": true
                    }
                  ]
                },
                "condition_filtering_pct": 100,
                "rows_for_plan": 5,
                "cost_for_plan": 0.75,
                "rest_of_plan": [
                  {
                    "plan_prefix": [
                      "`t3`"
                    ],
                    "table": "`t1`",
                    "best_access_path": {
                      "considered_access_paths": [
                        {
                          "rows_to_scan": 4,
                          "access_type": "scan",
                          "using_join_cache": true,
                          "buffers_needed": 1,
                          "resulting_rows": 4,
                          "cost": 3.00776,
                          "chosen": true
                        }
                      ]
                    },
                    "condition_filtering_pct": 100,
                    "rows_for_plan": 20,
                    "cost_for_plan": 3.75776,
                    "pruned_by_cost": true
                  }
                ]
              }
            ]
          },
          {
            "attaching_conditions_to_tables": {
              "original_condition": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))",
              "attached_conditions_computation": [
              ],
              "attached_conditions_summary": [
                {
                  "table": "`t1`",
                  "attached": null
                },
                {
                  "table": "`t3`",
                  "attached": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))"
                }
              ]
            }
          },
          {
            "finalizing_table_conditions": [
              {
                "table": "`t3`",
                "original_table_condition": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))",
                "final_table_condition   ": "((`t3`.`ccc1` = `t1`.`c1`) or (`t3`.`ccc1` < 3))"
              }
            ]
          },
          {
            "refine_plan": [
              {
                "table": "`t1`"
              },
              {
                "table": "`t3`"
              }
            ]
          }
        ]
      }
    },
    {
      "join_explain": {
        "select#": 1,
        "steps": [
        ]
      }
    }
  ]
}  

现在把之前 03 的内容拿过来回顾一下当时的结果，看看为何是当时的结果。从下面结果可以看出，当存在 keyuse 的时候，优化器最后走哪条索引是通过 find_best_ref () 函数来决定的。

greatsql> EXPLAIN SELECT * FROM t1 join t2 ON t1.c1=t2.cc1 and t1.c1<5;
+----+-------------+-------+------------+--------+---------------+---------+---------+-----------+------+----------+-------------+
| id | select_type | table | partitions | type   | possible_keys | key     | key_len | ref       | rows | filtered | Extra       |
+----+-------------+-------+------------+--------+---------------+---------+---------+-----------+------+----------+-------------+
|  1 | SIMPLE      | t1    | NULL       | range  | PRIMARY       | PRIMARY | 4       | NULL      |    3 |   100.00 | Using where |
|  1 | SIMPLE      | t2    | NULL       | eq_ref | PRIMARY       | PRIMARY | 4       | db1.t1.c1 |    1 |   100.00 | NULL        |
                                   # t2这个结果看下面的解释
+----+-------------+-------+------------+--------+---------------+---------+---------+-----------+------+----------+-------------+

          {\
            "considered_execution_plans": [\
              {\
                "plan_prefix": [\
                ],\
                "table": "`t1`",\
                "best_access_path": {\
                  "considered_access_paths": [\
                    {\
                      "access_type": "ref",\ # t1表不选择"ref"方式，因为cost比"range"方式大
                      "index": "PRIMARY",\
                      "usable": false,\
                      "chosen": false\
                    },\
                    {\
                      "rows_to_scan": 3,\
                      "filtering_effect": [\
                      ],\
                      "final_filtering_effect": 1,\
                      "access_type": "range",\ # t1表最后选择了"range"方式进行扫描
                      "range_details": {\
                        "used_index": "PRIMARY"\
                      },\
                      "resulting_rows": 3,\
                      "cost": 0.860732,\
                      "chosen": true\
                    }\
                  ]\
                },\
                "condition_filtering_pct": 100,\
                "rows_for_plan": 3,\
                "cost_for_plan": 0.860732,\
                "rest_of_plan": [\
                "rest_of_plan": [\
                  {\
                    "plan_prefix": [\
                      "`t1`"\
                    ],\
                    "table": "`t2`",\
                    "best_access_path": {\
                      "considered_access_paths": [\
                        {\
                          "access_type": "eq_ref",\ # 在t1 join t2的连接方式里，判断用"eq_ref"方式扫描cost更小。这里通过find_best_ref()获取
                          "index": "PRIMARY",\
                          "rows": 1,\
                          "cost": 1.05,\
                          "chosen": true,\
                          "cause": "clustered_pk_chosen_by_heuristics"\
                        },\
                        {\
                          "access_type": "range",\
                          "range_details": {\
                            "used_index": "PRIMARY"\
                          },\
                          "chosen": false,\
                          "cause": "heuristic_index_cheaper"\
                        }\
                      ]\
                    },\
                    "condition_filtering_pct": 100,\
                    "rows_for_plan": 3,\
                    "cost_for_plan": 1.91073,\
                    "chosen": true\
                  }\
          {\
            "refine_plan": [\ # 根据最后比较结果，按照t1,t2方式连接cost最小，因此选择这种顺序进行join
              {\
                "table": "`t1`"\
              },\
              {\
                "table": "`t2`"\
              }\
            ]\
          }\

四、总结

从上面优化器最早的步骤我们认识了条件过滤 condition_fanout_filter 的定义以及使用方法，也学会了计算 join 时候的 cost，然后根据 cost 选择最优的连接顺序。回到开头提到的这个功能在一些场景有可能会带来选择错误的问题，这个下一期展开细讲。用这种过滤方法算出来的结果也不精确，GreatSQL 用了另一个功能直方图来做数据统计，让结果更加精确，这个直方图的分析放后面章节讲。