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一个leftjoinSQL简单优化分析
有个关联查询的sql,需要2秒多，于是进行查看一番：
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SELECT a.id, a.brand_id, a.series_id, a.product_id, a.material_id, a.custom_category_id, a.price, a.product_url, a.organ_id, ..... FROM pm_brand_xxxx a LEFT JOIN pm_brand_yyyyy d ON a.series_id = d.id WHERE a.is_delete = 0 AND d.is_delete = 0 AND a.organ_id = 'Cxxx' AND a.brand_id = 6491603 AND d.brand_id = 6491603 AND a.model_flag = 14;
MySQL> show profile for query 4; +----------------------+----------+ | Status | Duration | +----------------------+----------+ | starting | 0.000072 | | checking permissions | 0.000002 | | checking permissions | 0.000002 | | Opening tables | 0.000011 | | init | 0.000026 | | System lock | 0.000007 | | optimizing | 0.000016 | | statistics | 0.000142 | | preparing | 0.000018 | | executing | 0.000002 | | Sending data | 2.281192 |<<<<<<<执行的主要时间消耗 | end | 0.000007 | | query end | 0.000011 | | closing tables | 0.000011 | | freeing items | 0.000030 | | logging slow query | 0.000003 | | logging slow query | 0.000102 | | cleaning up | 0.000022 | +----------------------+----------+
+----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------+-------+----------+------------------------------------------------------------------------------------------------------------------------------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------+-------+----------+------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | SIMPLE | d | NULL | ref | PRIMARY,idx_pm_yyyy_bid | idx_pm_yyyyy_bid | 9 | const | 1 | 10.00 | Using where | | 1 | SIMPLE | a | NULL | index_merge | idx_pm_xxxx_sid,idx_pm_xxx_bid,idx_pm_brand_xxxx_organ | idx_pm_xxx_organ,idx_pm_brand_xxxx_bid | 99,9 | NULL | 11314 | 0.04 | Using intersect(idx_pm_xxxxx_organ,idx_pm_xxxx_bid); Using where; Using join buffer (Block Nested Loop) | +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------+-------+----------+------------------------------------------------------------------------------------------------------------------------------------------------+ 2 rows in set, 1 warning (0.00 sec)
从执行计划来看，d表是做了驱动表，a做了被驱动表
d表 type = ref ，使用非唯一性索引或者唯一索引的前缀扫描，返回匹配某个单独值的记录行,这里使用了索引idx_pm_yyyyy_bid，该索引正是brand_id上的索引，
即是说，在和a表的关联中d先通过brand_id来查找记录行，再通过相应记录的id去和a表的series_id做匹配。
我查看相应的记录数，发现a表145万的大表,d表是4075的小表。
a表
mysql> select count(*) from pm_xxxxxx;
+----------+
| count(*) |
+----------+
| 1459777 |
+----------+
1 row in set (0.27 sec)
d表：
mysql> select count(*) from pm_yyyyyy;
+----------+
| count(*) |
+----------+
| 4075 |
+----------+
1 row in set (0.00 sec)
而 a表是type=index_merge 索引合并,这里走了idx_pm_xxx_organ(organ_id),idx_pm_brand_xxxx_bid(brand_id) ,extra 是
Using intersect(idx_pm_xxxxx_organ,idx_pm_xxxx_bid); Using where; Using join buffer (Block Nested Loop)
Using intersect正说明了这里使用了(idx_pm_xxxxx_organ,idx_pm_xxxx_bid)的交集
Using where 是用model_flag等这些其他条件的过滤
Using join buffer (Block Nested Loop) 说明使用BNL的算法进行匹配
BNL 算法是将外层循环的行/结果集（驱动表）存入join buffer, 内层循环的每一行与整个buffer中的记录做比较，从而减少内层循环的次数.
举例来说，外层循环的结果集是100行，使用NLJ 算法需要扫描内部表100次，如果使用BNL算法，先把对Outer Loop表(外部表)每次读取的10行记录放到join buffer,然后在InnerLoop表(内部表)中直接匹配这10行数据，内存循环就可以一次与这10行进行比较, 这样只需要比较10次，对内部表的扫描减少了9/10。所以BNL算法就能够显著减少内层循环表扫描的次数.
在这里就是d表中取得结果集分批放入buffer中与a表进行匹配。
而这个语句无论如何都要2秒中，也在我们的认识中小表驱动大表并没错，我的猜想应该就是在进行BNL时消耗了时间，表现到过程中就是 Sending data 的时间消耗增多。
吐槽的是mysql中貌似没有什么办法来多方面看查询消耗了。
我想到的是如果该表现有sql关联的顺序是否性能能改善，在该sql中，我发现了两个条件：
AND a.brand_id = 6491603
AND d.brand_id = 6491603
在业务逻辑上这两个表的字段应该是一致的，如果我将d表的d.brand_id = 6491603去掉，以上的执行计划应该会改变，于是去掉之后执行，执行时间非常小。
mysql> show profile for query 1; +----------------------+----------+ | Status | Duration | +----------------------+----------+ | starting | 0.000080 | | checking permissions | 0.000002 | | checking permissions | 0.000002 | | Opening tables | 0.000012 | | init | 0.000030 | | System lock | 0.000006 | | optimizing | 0.000014 | | statistics | 0.000130 | | preparing | 0.000016 | | executing | 0.000001 | | Sending data | 0.027325 | | end | 0.000003 | | query end | 0.000015 | | closing tables | 0.000005 | | freeing items | 0.000014 | | cleaning up | 0.000009 | +----------------------+----------+ 16 rows in set, 1 warning (0.00 sec)
看其执行计划： +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------------------------+-------+----------+---------------------------------------------------------------------------------------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------------------------+-------+----------+---------------------------------------------------------------------------------------------------------+ | 1 | SIMPLE | a | NULL | index_merge | idx_pm_xxxxx_sid,idx_pm_xxxxx_bid,idx_pm_xxxx_organ | idx_pm_xxxxx_organ,idx_pm_xxxx_bid | 99,9 | NULL | 11315 | 1.00 | Using intersect(idx_pm_xxxxx_organ,idx_pm_xxxx_bid); Using where | | 1 | SIMPLE | d | NULL | eq_ref | PRIMARY | PRIMARY | 8 | xxxx.a.series_id | 1 | 10.00 | Using where | +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------------------------+-------+----------+---------------------------------------------------------------------------------------------------------+ 2 rows in set, 1 warning (0.00 sec)
发现变成了a表做驱动表，d表做被驱动表,从extra列看
a表是Using intersect(idx_pm_xxxxx_organ,idx_pm_xxxx_bid); Using where 依然是使用索引合并，where条件来取结果，使用了idx_pm_xxxxx_organ,idx_pm_xxxx_bid 连个索引。
d表走PRIMARY 主键索引，从ref列来看是通过a表的series_id 来关联，这样效率表提升了。
需要说的一点是，小结果集并不代表就是小表，大表也可以有小结果集，当大表用来被匹配并被扫描多次，自然效率并不高.
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