新特性解读 | MySQL 8.0 新增 HINT 模式

原创做者： 杨涛涛

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在开始演示以前，咱们先介绍下两个概念。

 

概念一，数据的可选择性基数，也就是常说的cardinality值。

查询优化器在生成各类执行计划以前，得先从统计信息中取得相关数据，这样才能估算每步操做所涉及到的记录数，而这个相关数据就是cardinality。简单来讲，就是每一个值在每一个字段中的惟一值分布状态。

好比表t1有100行记录，其中一列为f1。f1中惟一值的个数能够是100个，也能够是1个，固然也能够是1到100之间的任何一个数字。这里惟一值越的多少，就是这个列的可选择基数。

那看到这里咱们就明白了，为何要在基数高的字段上创建索引，而基数低的的字段创建索引反而没有全表扫描来的快。固然这个只是一方面，至于更深刻的探讨就不在我这篇探讨的范围了。

 

概念二，关于HINT的使用。

这里我来讲下HINT是什么，在何时用。

HINT简单来讲就是在某些特定的场景下人工协助MySQL优化器的工做，使她生成最优的执行计划。通常来讲，优化器的执行计划都是最优化的，不过在某些特定场景下，执行计划可能不是最优化。

好比：表t1通过大量的频繁更新操做，（UPDATE,DELETE,INSERT），cardinality已经很不许确了，这时候恰好执行了一条SQL，那么有可能这条SQL的执行计划就不是最优的。为何说有可能呢？

来看下具体演示

譬如，如下两条SQL，

• A：

select * from t1 where f1 = 20;

• B：

select * from t1 where f1 = 30;

若是f1的值恰好频繁更新的值为30，而且没有达到MySQL自动更新cardinality值的临界值或者说用户设置了手动更新又或者用户减小了sample page等等，那么对这两条语句来讲，可能不许确的就是B了。

 

这里顺带说下，MySQL提供了自动更新和手动更新表cardinality值的方法，因篇幅有限，须要的能够查阅手册。

那回到正题上，MySQL 8.0 带来了几个HINT，我今天就举个index_merge的例子。

示例表结构：

mysql> desc t1;
+------------+--------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+------------+--------------+------+-----+---------+----------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| rank1 | int(11) | YES | MUL | NULL | |
| rank2 | int(11) | YES | MUL | NULL | |
| log_time | datetime | YES | MUL | NULL | |
| prefix_uid | varchar(100) | YES | | NULL | |
| desc1 | text | YES | | NULL | |
| rank3 | int(11) | YES | MUL | NULL | |
+------------+--------------+------+-----+---------+----------------+
7 rows in set (0.00 sec)

表记录数：

mysql> select count(*) from t1;
+----------+
| count(*) |
+----------+
| 32768 |
+----------+
1 row in set (0.01 sec)

这里咱们两条经典的SQL：

• SQL C：

select * from t1 where rank1 = 1 or rank2 = 2 or rank3 = 2;

• SQL D：

select * from t1 where rank1 =100 and rank2 =100 and rank3 =100;

表t1实际上在rank1,rank2,rank3三列上分别有一个二级索引。

 

那咱们来看SQL C的查询计划。

mysql> explain format=json select * from t1 where rank1 =1 or rank2 = 2 or rank3 = 2\G
*************************** 1. row ***************************
EXPLAIN: {
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "3243.65"
},
"table": {
"table_name": "t1",
"access_type": "ALL",
"possible_keys": [
"idx_rank1",
"idx_rank2",
"idx_rank3"
],
"rows_examined_per_scan": 32034,
"rows_produced_per_join": 115,
"filtered": "0.36",
"cost_info": {
"read_cost": "3232.07",
"eval_cost": "11.58",
"prefix_cost": "3243.65",
"data_read_per_join": "49K"
},
"used_columns": [
"id",
"rank1",
"rank2",
"log_time",
"prefix_uid",
"desc1",
"rank3"
],
"attached_condition": "((`ytt`.`t1`.`rank1` = 1) or (`ytt`.`t1`.`rank2` = 2) or (`ytt`.`t1`.`rank3` = 2))"
}
}
}
1 row in set, 1 warning (0.00 sec)

显然，没有用到任何索引，扫描的行数为32034，cost为3243.65。

 

咱们加上hint给相同的查询，再次看看查询计划。

这个时候用到了index_merge,union了三个列。扫描的行数为1103，cost为441.09，明显比以前的快了好几倍。

mysql> explain format=json select /*+ index_merge(t1) */ * from t1 where rank1 =1 or rank2 = 2 or rank3 = 2\G
*************************** 1. row ***************************
EXPLAIN: {
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "441.09"
},
"table": {
"table_name": "t1",
"access_type": "index_merge",
"possible_keys": [
"idx_rank1",
"idx_rank2",
"idx_rank3"
],
"key": "union(idx_rank1,idx_rank2,idx_rank3)",
"key_length": "5,5,5",
"rows_examined_per_scan": 1103,
"rows_produced_per_join": 1103,
"filtered": "100.00",
"cost_info": {
"read_cost": "330.79",
"eval_cost": "110.30",
"prefix_cost": "441.09",
"data_read_per_join": "473K"
},
"used_columns": [
"id",
"rank1",
"rank2",
"log_time",
"prefix_uid",
"desc1",
"rank3"
],
"attached_condition": "((`ytt`.`t1`.`rank1` = 1) or (`ytt`.`t1`.`rank2` = 2) or (`ytt`.`t1`.`rank3` = 2))"
}
}
}
1 row in set, 1 warning (0.00 sec)

 

咱们再看下SQL D的计划：

• 不加HINT，

mysql> explain format=json select * from t1 where rank1 =100 and rank2 =100 and rank3 =100\G
*************************** 1. row ***************************
EXPLAIN: {
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "534.34"
},
"table": {
"table_name": "t1",
"access_type": "ref",
"possible_keys": [
"idx_rank1",
"idx_rank2",
"idx_rank3"
],
"key": "idx_rank1",
"used_key_parts": [
"rank1"
],
"key_length": "5",
"ref": [
"const"
],
"rows_examined_per_scan": 555,
"rows_produced_per_join": 0,
"filtered": "0.07",
"cost_info": {
"read_cost": "478.84",
"eval_cost": "0.04",
"prefix_cost": "534.34",
"data_read_per_join": "176"
},
"used_columns": [
"id",
"rank1",
"rank2",
"log_time",
"prefix_uid",
"desc1",
"rank3"
],
"attached_condition": "((`ytt`.`t1`.`rank3` = 100) and (`ytt`.`t1`.`rank2` = 100))"
}
}
}
1 row in set, 1 warning (0.00 sec)

• 加了HINT，

mysql> explain format=json select /*+ index_merge(t1)*/ * from t1 where rank1 =100 and rank2 =100 and rank3 =100\G
*************************** 1. row ***************************
EXPLAIN: {
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "5.23"
},
"table": {
"table_name": "t1",
"access_type": "index_merge",
"possible_keys": [
"idx_rank1",
"idx_rank2",
"idx_rank3"
],
"key": "intersect(idx_rank1,idx_rank2,idx_rank3)",
"key_length": "5,5,5",
"rows_examined_per_scan": 1,
"rows_produced_per_join": 1,
"filtered": "100.00",
"cost_info": {
"read_cost": "5.13",
"eval_cost": "0.10",
"prefix_cost": "5.23",
"data_read_per_join": "440"
},
"used_columns": [
"id",
"rank1",
"rank2",
"log_time",
"prefix_uid",
"desc1",
"rank3"
],
"attached_condition": "((`ytt`.`t1`.`rank3` = 100) and (`ytt`.`t1`.`rank2` = 100) and (`ytt`.`t1`.`rank1` = 100))"
}
}
}
1 row in set, 1 warning (0.00 sec)

对比下以上两个，加了HINT的比不加HINT的cost小了100倍。

 

总结下，就是说表的cardinality值影响这张的查询计划，若是这个值没有正常更新的话，就须要手工加HINT了。相信MySQL将来的版本会带来更多的HINT。