【GreatSQL优化器-02】索引和Sargable谓词
【GreatSQL优化器-02】索引和Sargable谓词一、Sargable谓词介绍
GreatSQL的优化器在有过滤条件的时候，需要先把条件按照是否有索引来进行区分，可以用索引来加速查询的条件称为Sargable，其中 arge 来源于 Search Argument（搜索参数）的首字母拼成的"SARG"。GreatSQL用keyuse_array索引数组和Sargables数组来储存Sargable谓词，其中Sargable数组是对keyuse_array的补充使用，比如``a, =,SELECT * FROM t1 where c1 in (SELECT cc1 FROM t2) andc1 >c2;+----+------+---------------------+| c1 | c2 | date1 |+----+------+---------------------+|2 | 1 | 2022-03-26 16:44:00 |+----+------+---------------------+> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;| SELECT * FROM t1 where c1 in (SELECT cc1 FROM t2) andc1 >c2 | {"steps": [ {// 1、sql语句转换成更快执行的语句 "join_preparatiON": { "SELECT#": 1, "steps": [ { "join_preparatiON": { "SELECT#": 2, "steps": [ { "expanded_query": "/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`" } ] } }, { "expanded_query": "/* SELECT#1 */ SELECT `t1`.`c1` AS `c1`,`t1`.`c2` AS `c2`,`t1`.`date1` AS `date1` FROM `t1` where (`t1`.`c1` in (/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`) and (`t1`.`c1` > `t1`.`c2`))" }, { "transformatiON": { "SELECT#": 2, "FROM": "IN (SELECT)", "to": "semijoin", "chosen": true, "transformatiON_to_semi_join": { "subquery_predicate": "`t1`.`c1` in (/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`)", "embedded in": "WHERE", "semi-join cONditiON": "(`t1`.`c1` = `t2`.`cc1`)", "decorrelated_predicates": [ { "outer": "`t1`.`c1`", "inner": "`t2`.`cc1`" } ] } } }, { "transformatiONs_to_nested_joins": { "transformatiONs": [ "semijoin" ],// 这个sql语句被转换成以下最终的语句，可以发现最后以semi join的形式查询的。 "expanded_query": "/* SELECT#1 */ SELECT `t1`.`c1` AS `c1`,`t1`.`c2` AS `c2`,`t1`.`date1` AS `date1` FROM `t1` semi join (`t2`) where ((`t1`.`c1` > `t1`.`c2`) and (`t1`.`c1` = `t2`.`cc1`))" } } ] } }, {// 2、优化器执行计划生成 "join_optimizatiON": { "SELECT#": 1, "steps": [ { "cONditiON_processing": { "cONditiON": "WHERE", "original_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and (`t1`.`c1` = `t2`.`cc1`))", "steps": [ { "transformatiON": "equality_propagatiON", "resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))" }, { "transformatiON": "cONstant_propagatiON", "resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))" }, { "transformatiON": "trivial_cONditiON_removal", "resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))" } ] } }, { "substitute_generated_columns": { } }, {// 表依赖，因为是两张表做join，因此这里显示了2张表 "TABLE_dependencies": [ { "TABLE": "`t1`", "row_may_be_null": false, "map_bit": 0, "depends_ON_map_bits": [ ] }, { "TABLE": "`t2`", "row_may_be_null": false, "map_bit": 1, "depends_ON_map_bits": [ ] } ] }, { "ref_optimizer_key_uses": [ //这里就是keyuse_array的结果，实际用到了两个索引，c1和cc2的唯一索引 { "TABLE": "`t1`", "field": "c1", "equals": "`t2`.`cc1`", "null_rejecting": true }, { "TABLE": "`t2`", "field": "cc1", "equals": "`t1`.`c1`", "null_rejecting": true } ] }, // 通过查看系统表可以查看到keyuse_array信息，但是Sargables数组信息没有显示。// 这个通过debug代码发现过程中提取出了一个谓词组，就是c1>c2,field值为cc1列，arg_value值为c2列，num_VALUES为所有Sargable谓词数量，这里为1。// 这个Sargable数组信息在后面函数update_sargable_FROM_cONst补充检查是否可以使用这里面的cc1索引加速查询。struct SARGABLE_PARAM {Field *field; // t2的cc1列Item **arg_value; // t1的c2列uINT num_VALUES;// 值为1，因为数组只有一个值};二、update_ref_and_keys代码执行过程
update_ref_and_keys函数里面通过add_key_fields把查询sql的cond条件包含的索引信息添加到Key_use_array，注意只有等于的条件才会通过add_key_field添加key_field。cond条件分为两种：FUNC_ITEM和COND_ITEM，其中and_level用于在merge_key_fields时候把用不到的key_field删掉。
条件类型说明FUNC_ITEM只由一个条件组成COND_ITEM由若干个 AND 和 OR 连接起来的条件，包含Item_cond_or和Item_cond_and两种 COND_AND_FUNC：同一个and条件的and_level不变 COND_OR_FUNC：处理前and_level需要自增实际代码执行过程：
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');
CREATE TABLE t2 (cc1 INT PRIMARY KEY, cc2 INT);
INSERT INTO t2 VALUES (1,3),(2,1),(3,2),(4,3);
CREATE INDEX idx1 ON t1(c2);
CREATE INDEX idx2 ON t1(c2,date1);
CREATE INDEX idx2_1 ON t2(cc2);
SET optimizer_trace = 'enabled=ON' ;
greatsql> SELECT * FROM t1 where c1 in (SELECT cc1 FROM t2) andc1 >c2;
+----+------+---------------------+
| c1 | c2 | date1 |
+----+------+---------------------+
|2 | 1 | 2022-03-26 16:44:00 |
+----+------+---------------------+
> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;
| SELECT * FROM t1 where c1 in (SELECT cc1 FROM t2) andc1 >c2 | {
"steps": [
{// 1、sql语句转换成更快执行的语句
"join_preparatiON": {
"SELECT#": 1,
"steps": [
{
"join_preparatiON": {
"SELECT#": 2,
"steps": [
{
"expanded_query": "/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`"
}
]
}
},
{
"expanded_query": "/* SELECT#1 */ SELECT `t1`.`c1` AS `c1`,`t1`.`c2` AS `c2`,`t1`.`date1` AS `date1` FROM `t1` where (`t1`.`c1` in (/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`) and (`t1`.`c1` > `t1`.`c2`))"
},
{
"transformatiON": {
"SELECT#": 2,
"FROM": "IN (SELECT)",
"to": "semijoin",
"chosen": true,
"transformatiON_to_semi_join": {
"subquery_predicate": "`t1`.`c1` in (/* SELECT#2 */ SELECT `t2`.`cc1` FROM `t2`)",
"embedded in": "WHERE",
"semi-join cONditiON": "(`t1`.`c1` = `t2`.`cc1`)",
"decorrelated_predicates": [
{
"outer": "`t1`.`c1`",
"inner": "`t2`.`cc1`"
}
]
}
}
},
{
"transformatiONs_to_nested_joins": {
"transformatiONs": [
"semijoin"
],// 这个sql语句被转换成以下最终的语句，可以发现最后以semi join的形式查询的。
"expanded_query": "/* SELECT#1 */ SELECT `t1`.`c1` AS `c1`,`t1`.`c2` AS `c2`,`t1`.`date1` AS `date1` FROM `t1` semi join (`t2`) where ((`t1`.`c1` > `t1`.`c2`) and (`t1`.`c1` = `t2`.`cc1`))"
}
}
]
}
},
{// 2、优化器执行计划生成
"join_optimizatiON": {
"SELECT#": 1,
"steps": [
{
"cONditiON_processing": {
"cONditiON": "WHERE",
"original_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and (`t1`.`c1` = `t2`.`cc1`))",
"steps": [
{
"transformatiON": "equality_propagatiON",
"resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))"
},
{
"transformatiON": "cONstant_propagatiON",
"resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))"
},
{
"transformatiON": "trivial_cONditiON_removal",
"resulting_cONditiON": "((`t1`.`c1` > `t1`.`c2`) and multiple equal(`t1`.`c1`, `t2`.`cc1`))"
}
]
}
},
{
"substitute_generated_columns": {
}
},
{// 表依赖，因为是两张表做join，因此这里显示了2张表
"TABLE_dependencies": [
{
"TABLE": "`t1`",
"row_may_be_null": false,
"map_bit": 0,
"depends_ON_map_bits": [
]
},
{
"TABLE": "`t2`",
"row_may_be_null": false,
"map_bit": 1,
"depends_ON_map_bits": [
]
}
]
},
{
"ref_optimizer_key_uses": [ //这里就是keyuse_array的结果，实际用到了两个索引，c1和cc2的唯一索引
{
"TABLE": "`t1`",
"field": "c1",
"equals": "`t2`.`cc1`",
"null_rejecting": true
},
{
"TABLE": "`t2`",
"field": "cc1",
"equals": "`t1`.`c1`",
"null_rejecting": true
}
]
},
// 通过查看系统表可以查看到keyuse_array信息，但是Sargables数组信息没有显示。
// 这个通过debug代码发现过程中提取出了一个谓词组，就是c1>c2,field值为cc1列，arg_value值为c2列，num_VALUES为所有Sargable谓词数量，这里为1。
// 这个Sargable数组信息在后面函数update_sargable_FROM_cONst补充检查是否可以使用这里面的cc1索引加速查询。
struct SARGABLE_PARAM {
Field *field; // t2的cc1列
Item **arg_value; // t1的c2列
uINT num_VALUES;// 值为1，因为数组只有一个值
};函数的SELECT_optimize属性见下表。
函数的查询优化类型涉及函数对应索引操作OPTIMIZE_NONE无OPTIMIZE_KEY 、between 、IN函数Item_func::BETWEEN : 把between转换为a>1 and a对于 c1=cc1： ->判断c1=5是否可以合并 -> c1相等。可以合并 ->将 c1=5 的Key_field删除，剩下c1=cc1，注意这里返回的end="cc1=c1"->对于 cc1=c1： ->判断 c1=5 是否可以合并 ->cc1不等于c1，不能合并->将所有没有被合并的 Key_field 去掉最终剩下2个 Key_field：Key_field(c1=cc1, and_level=1, optimize=0, null_rejecting=true)Key_field(cc1=c1, and_level=1, optimize=0, null_rejecting=true)最后因为通过merge_key_fields算出来的field==end，因此不加入keyuse_array，注意只有or条件才会执行merge_key_fields。这里条件如果去掉or t1.c1=5这两个key_field就会加入keyuse_array于是看到如下的trace，这里面的access_type全是scan方式，说明没有用索引提升查询性能。 "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", 这里t1的扫描方式是索引扫描 "resulting_rows": 4, "cost": 0.65, "chosen": true } ] }, "rest_of_plan": [ { "plan_prefix": [ "`t1`" ], "TABLE": "`t2`", "best_access_path": { "cONsidered_access_paths": [ { "rows_to_scan": 5, "filtering_effect": [ ], "final_filtering_effect": 1, "access_type": "scan", 这里t2的扫描方式是索引扫描 "using_join_cache": true, "buffers_needed": 1, "resulting_rows": 5, "cost": 2.25005, "chosen": true } ] },-- 下面的结果中，type=index，表明选择了索引扫描，跟上面算出来的结论一致greatsql> EXPLAIN SELECT * FROM t1 join t2 ON t1.c1=t2.cc1 and t2.cc1=t1.c1 or t1.c1=5;+----+-------------+-------+------------+-------+---------------+--------+---------+------+------+----------+---------------------------------------------------------+| 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 | 这里选择了索引扫描，跟上面算出来的结论一致|1 | SIMPLE | t2 | NULL | index | PRIMARY | idx2_1 | 5 | NULL | 5 | 100.00 | Using where; Using index; Using join buffer (hash join) |+----+-------------+-------+------------+-------+---------------+--------+---------+------+------+----------+---------------------------------------------------------+例子2：SELECT * FROM t1 join t2 ON t1.c1=t2.cc1 and t1.c2 Filter: ((t2.cc1 = t1.c1) or (t1.c1 = 5))(cost=2.90 rows=20) -> Inner hash join (no cONditiON)(cost=2.90 rows=20) -> INDEX scan ON t2 using idx2_1(cost=0.19 rows=5) -> Hash -> INDEX scan ON t1 using idx2(cost=0.65 rows=4) |+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+greatsql> EXPLAIN FORMAT=TREE SELECT * FROM t1 join t2 ON t1.c1=t2.cc1 and t1.c1 Nested loop inner join(cost=1.91 rows=3) -> Filter: (t1.c1 < 5)(cost=0.86 rows=3) -> INDEX range scan ON t1 using PRIMARY over (c1 < 5)(cost=0.86 rows=3) -> Single-row INDEX lookup ON t2 using PRIMARY (cc1=t1.c1)(cost=0.28 rows=1) |+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+附录：join_type表扫描方式
JT_UNKNOWNJT_SYSTEM表只有一行，比如SELECT * FROM (SELECT 1)JT_CONST表最多只有一行满足，比如WHERE TABLE.pk = 3JT_EQ_REF=符号用在唯一索引JT_REF=符号用在非唯一索引JT_ALL全表扫描JT_RANGE范围扫描JT_INDEX_SCAN索引扫描JT_FTFulltext索引扫描JT_REF_OR_NULL包含null值，比如"WHERE col = ... OR col IS NULLJT_INDEX_MERGE一张表执行多次范围扫描最后合并结果四、总结
从上面优化器最早的步骤我们认识了Sargable谓词的定义和判定方法，如果查询用到了Sargable谓词是可以进行eq_ref扫描方式的，有效提高了查询效率。通过实际例子发现，在做多表连接的时候用OR条件会降低执行效率，同时用唯一索引列作为连接条件的话会提高效率。因此实际写查询sql的时候，尽量用唯一索引作为连接条件，少用OR条件进行过滤。
Enjoy GreatSQL
来源:https://www.cnblogs.com/greatsql/p/18547608
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