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mysql 5.7.10代价计算相对之前的版本有5.7 代价模型浅析较大的改进。例如
5.7 在代价类型上分为io,cpu和memory, 5.7的代价模型还在完善中，memory的代价虽然已经收集了，但还没有没有计算在最终的代价中。
5.7 在源码上对代价模型进行了大量重构，代价分为server层和engine层。server层主要是cpu的代价，而engine层主要是io的代价。
5.7 引入了两个系统表mysql.server_cost和mysql.engine_cost来分别配置这两个层的代价。
以下分析均基于mysql5.7.10
disk_temptable_row_cost (default 1.0)
内部myisam或innodb临时表的行代价
由上可以看出创建临时表的代价是很高的，尤其是内部的myisam或innodb临时表。
memory_block_read_cost (default 1.0)
从内存读数据的代价，对innodb来说，表示从buffer pool读一个page的代价
目前io_block_read_cost和memory_block_read_cost默认值均为1，实际生产中建议酌情调大memory_block_read_cost，特别是对普通硬盘的场景。
cost参数可以通过修改mysql.server_cost和mysql.engine_cost来实现。初始这两个表中的记录cost_value项均为NULL, 代价值都取上两节介绍的初始值。
当修改cost_value为非NULL时，代价值按设定的值计算。修改方法如下：
# 修改io_block_read_cost值为2 UPDATE mysql.engine_cost SET cost_value = 2.0 WHERE cost_name = ‘io_block_read_cost‘; #FLUSH OPTIMIZER_COSTS 生效，只对新连接有效，老连接无效。 FLUSH OPTIMIZER_COSTS;
另外，在主备环境下，修改cost参数时主备都要修改。因为mysql.server_cost和mysql.engine_cost的更新不会参与复制。
初始化数据
create table t1(c1 int primary key, c2 int unique,c3 int) engine=innodb; let $loop=100; while($loop) { eval insert into t1(c1,c2,c3) values($loop, $loop+1, $loop+2); dec $loop; } set optimizer_trace = "enabled=on";
以下语句选择覆盖索引c2
explain select c1,c2 from t1 where c2 > 10; id select_type table partitions type possible_keys key key_len ref rows filtered Extra 1 SIMPLE t1 NULL range c2 c2 5 NULL 91 100.00 Using where; Using index
查看optimizer_trace, 可以看出全表扫描代价为23.1，通过c2上的索引扫描代价为19.309， 最后选择c2上的索引扫描。
"rows_estimation": [ { "table": "`t1`", "range_analysis": { "table_scan": { "rows": 100, "cost": 23.1 }, "potential_range_indexes": [ { "index": "PRIMARY", "usable": false, "cause": "not_applicable" }, { "index": "c2", "usable": true, "key_parts": [ "c2" ] } ], "best_covering_index_scan": { "index": "c2", "cost": 21.109, "chosen": true }, "setup_range_conditions": [ ], "group_index_range": { "chosen": false, "cause": "not_group_by_or_distinct" }, "analyzing_range_alternatives": { "range_scan_alternatives": [ { "index": "c2", "ranges": [ "10 < c2" ], "index_dives_for_eq_ranges": true, "rowid_ordered": false, "using_mrr": false, "index_only": true, "rows": 91, "cost": 19.309, "chosen": true } ], "analyzing_roworder_intersect": { "usable": false, "cause": "too_few_roworder_scans" } }, "chosen_range_access_summary": { "range_access_plan": { "type": "range_scan", "index": "c2", "rows": 91, "ranges": [ "10 < c2" ] }, "rows_for_plan": 91, "cost_for_plan": 19.309, "chosen": true } } } ] }, { "considered_execution_plans": [ { "plan_prefix": [ ], "table": "`t1`", "best_access_path": { "considered_access_paths": [ { "rows_to_scan": 91, "access_type": "range", "range_details": { "used_index": "c2" }, "resulting_rows": 91, "cost": 37.509, "chosen": true } ] }, "condition_filtering_pct": 100, "rows_for_plan": 91, "cost_for_plan": 37.509, "chosen": true } ]
全表扫描的代价23.1
包括io和cpu的代价
test_quick_select： double scan_time= cost_model->row_evaluate_cost(static_cast<double>(records)) + 1; Cost_estimate cost_est= head->file->table_scan_cost(); cost_est.add_io(1.1);//这里加1.1应该是个调节值 cost_est.add_cpu(scan_time);
其中io代价table_scan_cost会根据buffer pool大小和索引大小来估算page in memory和in disk的比例，分别算出代价。
handler::table_scan_cost() ha_innobase::scan_time()*table->cost_model()->page_read_cost(1.0);//1*1=1 //其中scan_time计算数据所占page数，
page_read_cost计算读取单个page的代价
buffer_block_read_cost(pages_in_mem) + io_block_read_cost(pages_on_disk);
io代价为1+1.1=2.1
cpu代价为row_evaluate_cost
double row_evaluate_cost(double rows) const { DBUG_ASSERT(m_initialized); DBUG_ASSERT(rows >= 0.0); return rows * m_server_cost_constants->row_evaluate_cost(); // 100 * 0.2(row_evaluate_cost)=20; }
cpu代价为20+1=21；
最终代价为2.1+21=23.1
c2索引扫描代价19.309
同样也分为io和cpu代价
multi_range_read_info_const： *cost= index_scan_cost(keyno, static_cast<double>(n_ranges), static_cast<double>(total_rows)); cost->add_cpu(cost_model->row_evaluate_cost(static_cast<double>(total_rows)) + 0.01);
io代价 1.0987925356750823*1=1.0987925356750823
index_scan_cost: const double io_cost= index_only_read_time(index, rows) * //估算index占page个数 = 1.0987925356750823 table->cost_model()->page_read_cost_index(index, 1.0); //根据buffer pool大小和索引大小来估算page in memory和in disk的比例，计算读一个page的代价。 = 1
cpu代价91*0.2+0.01=18.21
cost->add_cpu(cost_model->row_evaluate_cost( static_cast<double>(total_rows)) + 0.01); //这里根据过滤条件算出的total_rows为91
最终代价1.0987925356750823+18.21=19.309
以下语句选择了全表扫描
explain select * from t1 where c2 > 10; id select_type table partitions type possible_keys key key_len ref rows filtered Extra 1 SIMPLE t1 NULL ALL c2 NULL NULL NULL 100 91.00 Using where
查看optimizer_trace, 可以看出全表扫描代价为23.1，通过c2上的索引扫描代价为110.21， 最后选择全表扫描。
"rows_estimation": [ { "table": "`t1`", "range_analysis": { "table_scan": { "rows": 100, "cost": 23.1 }, "potential_range_indexes": [ { "index": "PRIMARY", "usable": false, "cause": "not_applicable" }, { "index": "c2", "usable": true, "key_parts": [ "c2" ] } ], "setup_range_conditions": [ ], "group_index_range": { "chosen": false, "cause": "not_group_by_or_distinct" }, "analyzing_range_alternatives": { "range_scan_alternatives": [ { "index": "c2", "ranges": [ "10 < c2" ], "index_dives_for_eq_ranges": true, "rowid_ordered": false, "using_mrr": false, "index_only": false, "rows": 91, "cost": 110.21, "chosen": false, "cause": "cost" } ], "analyzing_roworder_intersect": { "usable": false, "cause": "too_few_roworder_scans" } } } } ] }, { "considered_execution_plans": [ { "plan_prefix": [ ], "table": "`t1`", "best_access_path": { "considered_access_paths": [ { "rows_to_scan": 100, "access_type": "scan", "resulting_rows": 91, "cost": 21, "chosen": true } ] }, "condition_filtering_pct": 100, "rows_for_plan": 91, "cost_for_plan": 21, "chosen": true } ] },
全表扫描代价23.1
同上一节分析
c2索引扫描代价为110.21
上一节通过c2索引扫描代价为19.309，因为是覆盖索引不需要回表，所以代价较少。而此例是需要回表的。
multi_range_read_info_const： *cost= read_cost(keyno, static_cast<double>(n_ranges), static_cast<double>(total_rows)); cost->add_cpu(cost_model->row_evaluate_cost( static_cast<double>(total_rows)) + 0.01);
io代价需回表
read_cost: //92*1=92 const double io_cost= read_time(index, static_cast<uint>(ranges) static_cast<ha_rows>(rows)) * table->cost_model()->page_read_cost(1.0); read_time: //91+1=92 virtual double read_time(uint index, uint ranges, ha_rows rows) { return rows2double(ranges+rows); }
这里回表时计算代价为每行代价为1，默认认为回表时每行都对于聚集索引的一个page.
io代价为92
cpu代价为91*0.2+0.01=18.21
cost->add_cpu(cost_model->row_evaluate_cost( static_cast<double>(total_rows)) + 0.01);
最后代价为92+18.21=110.21
5.7 代价模型优化还在持续改进中，相信后续的版本会越来越好。代价的参数的配置需谨慎，需要大量的测试和验证。
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原文地址：http://www.cnblogs.com/justfortaste/p/5660568.html
