有個關聯(lián)查詢的sql,需要2秒多,于是進行查看一番:
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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 |<<<<<<<執(zhí)行的主要時間消耗 | 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)
從執(zhí)行計劃來看,d表是做了驅(qū)動表,a做了被驅(qū)動表
d表 type = ref ,使用非唯一性索引或者唯一索引的前綴掃描,返回匹配某個單獨值的記錄行,這里使用了索引idx_pm_yyyyy_bid,該索引正是brand_id上的索引,
即是說,在和a表的關聯(lián)中d先通過brand_id來查找記錄行,再通過相應記錄的id去和a表的series_id做匹配。
我查看相應的記錄數(shù),發(fā)現(xiàn)a表145萬的大表,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,這里走了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正說明了這里使用了(idx_pm_xxxxx_organ,idx_pm_xxxx_bid)的交集
Using where 是用model_flag等這些其他條件的過濾
Using join buffer (Block Nested Loop) 說明使用BNL的算法進行匹配
BNL 算法是將外層循環(huán)的行/結(jié)果集(驅(qū)動表)存入join buffer, 內(nèi)層循環(huán)的每一行與整個buffer中的記錄做比較,從而減少內(nèi)層循環(huán)的次數(shù).
舉例來說,外層循環(huán)的結(jié)果集是100行,使用NLJ 算法需要掃描內(nèi)部表100次,如果使用BNL算法,先把對Outer Loop表(外部表)每次讀取的10行記錄放到join buffer,然后在InnerLoop表(內(nèi)部表)中直接匹配這10行數(shù)據(jù),內(nèi)存循環(huán)就可以一次與這10行進行比較, 這樣只需要比較10次,對內(nèi)部表的掃描減少了9/10。所以BNL算法就能夠顯著減少內(nèi)層循環(huán)表掃描的次數(shù).
在這里就是d表中取得結(jié)果集分批放入buffer中與a表進行匹配。
而這個語句無論如何都要2秒中,也在我們的認識中小表驅(qū)動大表并沒錯,我的猜想應該就是在進行BNL時消耗了時間,表現(xiàn)到過程中就是 Sending data 的時間消耗增多。
吐槽的是mysql中貌似沒有什么辦法來多方面看查詢消耗了。
我想到的是如果該表現(xiàn)有sql關聯(lián)的順序是否性能能改善,在該sql中,我發(fā)現(xiàn)了兩個條件:
AND a.brand_id = 6491603
AND d.brand_id = 6491603
在業(yè)務邏輯上這兩個表的字段應該是一致的,如果我將d表的d.brand_id = 6491603去掉,以上的執(zhí)行計劃應該會改變,于是去掉之后執(zhí)行,執(zhí)行時間非常小。
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)
看其執(zhí)行計劃: +----+-------------+-------+------------+-------------+---------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+---------+-------------------------+-------+----------+---------------------------------------------------------------------------------------------------------+ | 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)
發(fā)現(xiàn)變成了a表做驅(qū)動表,d表做被驅(qū)動表,從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條件來取結(jié)果,使用了idx_pm_xxxxx_organ,idx_pm_xxxx_bid 連個索引。
d表走PRIMARY 主鍵索引,從ref列來看是通過a表的series_id 來關聯(lián),這樣效率表提升了。
需要說的一點是,小結(jié)果集并不代表就是小表,大表也可以有小結(jié)果集,當大表用來被匹配并被掃描多次,自然效率并不高.
文章題目:一個leftjoinSQL簡單優(yōu)化分析
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