MySQL 5.7 cost模型浅析
代价模型
mysql 5.7.10 cost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
- cost模型参数可以动态配置，可以适应不同的硬件
- 区分考虑数据在内存和在磁盘中的cost
- cost精度提升为浮点型
- jion计算时不仅要考虑condition,还要考虑condition上的filter，具体参见参数condition_fanout_filter
5.7 在cost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cost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
server_cost
- row_evaluate_cost (default 0.2) 计算符合条件的行的代价，行数越多，此项代价越大
- memory_temptable_create_cost (default 2.0) 内存临时表的创建代价
- memory_temptable_row_cost (default 0.2) 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 40.0) 内部myisam或innodb临时表的创建代价
-
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。
engine_cost
- io_block_read_cost (default 1.0) 从磁盘读数据的cost，对innodb来说，表示从磁盘读一个page的cost
-
memory_block_read_cost (default 1.0)
从内存读数据的cost，对innodb来说，表示从buffer pool读一个page的cost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配置
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。
# 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。cost分析示例
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";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cost为23.1，通过c2上的索引扫描cost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cost 23.1
包括io和cpu的cost
test_quick_select：
double scan_time=
cost_model->row_evaluate_cost(static_cast(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 cost able_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cost。
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cost
buffer_block_read_cost(pages_in_mem) + io_block_read_cost(pages_on_disk);
io cost为1+1.1=2.1
cpu cost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 cost为20+1=21；
最终cost为2.1+21=23.1
c2索引扫描cost19.309
同样也分为io和cpu cost
multi_range_read_info_const：
*cost= index_scan_cost(keyno, static_cast(n_ranges),
static_cast(total_rows));
cost->add_cpu(cost_model->row_evaluate_cost(static_cast(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。 = 1cpu代价91*0.2+0.01=18.21
cost->add_cpu(cost_model->row_evaluate_cost(
static_cast(total_rows)) + 0.01); //这里根据过滤条件算出的total_rows为91 最终cost 1.0987925356750823+18.21=19.309
- 示例2
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cost为23.1，通过c2上的索引扫描cost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cost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cost较少。而此例是需要回表的。
multi_range_read_info_const：
*cost= read_cost(keyno, static_cast(n_ranges),
static_cast(total_rows));
cost->add_cpu(cost_model->row_evaluate_cost(
static_cast(total_rows)) + 0.01); io代价需回表
read_cost: //92*1=92
const double io_cost= read_time(index, static_cast(ranges)
static_cast(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cost为每行cost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(total_rows)) + 0.01); 最后代价为92+18.21=110.21
总结
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。cost的参数的配置需谨慎，需要大量的测试和验证。
From：https://www.cnblogs.com/justfortaste/p/5660568.html