MySQL查询优化之性能提升一个数量级
这段时间一直在用kettle做数据抽取和报表，写SQL便是家常便饭了，200行+SQL经常要写。甚至写过最长的一个SQL500多行将近600行。这么长的SQL估计大部分人连看的意愿都没有，读起来也比较坑爹，我一般是把这种长SQL分成几个子SQL，测试好了再组装起来。SQL语句写的越多也就越可能出现性能问题。优化SQL可以从很多细节入手，比如加索引，但也不是万能的，当SQL达到一定规模，从结构上优化才是根本解决问题的办法，当然前提是改加的索引已经加了，大部分可以从局部优化的细节已经注意到了。
和往常一样，一个新的需求需要从大概10个表中抽取数据，大部分表数据量都在四十万左右，最多的表有100万左右。说真的数据并不算多，但是这么多遍连接后，如果SQL有的有问题查询效率也是非常低的。一开始我按照自己的思路写了一个SQL，只考虑需求和最短时间内实现。
部分SQL如下图，SQL已经超过200行了：
执行结果如下图：
只查询了38行记录，尽然花了将近10s，感觉已经很慢了。
此时我精简SQL的大概结构如下:
SELECT
*
FROM
(SELECT
*
FROM
A m
INNER JOIN B pm ON pm.id_sour = m.pk_id
LEFT JOIN (SELECT
*
FROM
C
WHERE
is_bring IS NULL OR is_bring = 0
GROUP BY id_m) pd ON m.pk_id = pd.id_m
LEFT JOIN (SELECT
*
FROM
D sd
INNER JOIN E si ON sd.id_ser = si.pk_id
GROUP BY sd.id_m) sd ON m.pk_id = sd.id_m
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN G pm ON pm.id_sour = m.pk_id
LEFT JOIN (SELECT
*
FROM
H
WHERE
is_bring IS NULL OR is_bring = 0
GROUP BY id_m) pd ON m.pk_id = pd.id_m
LEFT JOIN (SELECT
*
FROM
I sd
INNER JOIN E si ON sd.id_ser = si.pk_id
GROUP BY sd.id_m) sd ON m.pk_id = sd.id_m
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN G pm ON pm.id_sour = m.pk_id
LEFT JOIN (SELECT
*
FROM
H
WHERE
is_bring IS NULL OR is_bring = 0
GROUP BY id_m) pd ON m.pk_id = pd.id_m
LEFT JOIN (SELECT
*
FROM
I sd
INNER JOIN E si ON sd.id_ser = si.pk_id
GROUP BY sd.id_m) sd ON m.pk_id = sd.id_m
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('')) t1
LEFT JOIN
(SELECT
*
FROM
J sb
INNER JOIN (SELECT
m.pk_id AS pk_id, pm.m_time AS m_time
FROM
A m
INNER JOIN B pm ON pm.id_sour = m.pk_id
WHERE
pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND pm.status = '' UNION ALL SELECT
m.from_mid_sn AS pk_id,
pm.m_time AS m_time
FROM
F m
INNER JOIN G pm ON pm.id_sour = m.pk_id
WHERE
pm.time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND pm.status = '') mp ON mp.pk_id = sb.id_sour
WHERE
sb.c_time <= mp.m_time
GROUP BY sb.id_sour , mp.m_time) t2 ON t1.id_m = CAST(t2.id_sour AS CHAR)
AND t1.m_time_cost = t2.m_time
再精简一下结构如下：
SELECT
*
FROM
(SELECT
*
FROM
A UNION ALL SELECT
*
FROM
B UNION ALL SELECT
*
FROM
C) t1
LEFT JOIN
((SELECT
*
FROM
D)
INNER JOIN (SELECT
*
FROM
E UNION ALL SELECT
*
FROM
F) t2 ON t1.id = t2.id) t3 ON t1.tid = t3.id
其中上面的A、B、C、D、E、F都是10个表中多个表的连接查询的结果。其实以上SQL在我们实现的时候就做过简单的优化了，t3其实可以放进t1中分别和A、B、C连接。但其实A、B、C、已经连接好多表了，在分别连接t3性能会产生更多的数据，效率会更低。
由于是数据抽取，数据只是存储到指定的事实表中。因此对效率没太高的要求，一分钟之内都是可以接受的。本来想这样就算了，还有堆事要干。恰好手里有一段类似逻辑的SQL，但是不完全一样。然后我就跑了一下。发现比我写的快一个数量级，大吃一惊之余我决定探索一下原因。
精简优化过的SQL代码如下：
SELECT
*
FROM
(SELECT
*
FROM
A m
INNER JOIN (SELECT * FROM B where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN (SELECT * FROM G where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN (SELECT * FROM G where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('')) mm
LEFT JOIN
(SELECT
*
FROM
J sb
INNER JOIN (SELECT
m.pk_id AS pk_id, pm.m_time AS m_time
FROM
A m
INNER JOIN B pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59' UNION ALL SELECT
m.from_mid_sn AS pk_id,
pm.m_time AS m_time
FROM
F m
INNER JOIN (SELECT * FROM G where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') mp ON mp.pk_id = sb.id_sour
WHERE
sb.c_time <= mp.m_time
GROUP BY sb.id_sour , mp.m_time) cost ON cost.id_sour = mm.id_m
AND cost.m_time = mm.m_time_cost
LEFT JOIN
(SELECT
*
FROM
D sd
INNER JOIN E si ON sd.id_ser = si.pk_id
INNER JOIN (SELECT DISTINCT
*
FROM
A m
INNER JOIN B pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') ms ON sd.id_m = ms.pk_id
GROUP BY sd.id_m UNION ALL SELECT
*
FROM
I sd
INNER JOIN E si ON sd.id_ser = si.pk_id
INNER JOIN (SELECT DISTINCT
m.pk_id, from_mid_sn, pm.m_time
FROM
F m
INNER JOIN G pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') ms ON sd.id_m = ms.pk_id
GROUP BY sd.id_m) ser ON ser.id_m = mm.id_m
AND ser.m_time = mm.m_time_cost
LEFT JOIN
(SELECT
*
FROM
C pd
INNER JOIN (SELECT DISTINCT
m.pk_id, pm.m_time
FROM
A m
INNER JOIN B pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') ms ON ms.pk_id = pd.id_m
WHERE
is_bring IS NULL OR is_bring = 0
GROUP BY pd.id_m , ms.m_time UNION ALL SELECT
*
FROM
H pd
INNER JOIN (SELECT DISTINCT
m.pk_id, pm.m_time, from_mid_sn
FROM
F m
INNER JOIN G pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND m.type IN ('')
AND m.is_del = 0
AND m.is_mig = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') ms ON ms.pk_id = pd.id_m
WHERE
is_bring IS NULL OR is_bring = 0
GROUP BY pd.id_m) part ON part.id_m = mm.id_m
AND part.m_time = mm.m_time_cost
运行此代码结果如下：
同样的结果，效率整整提升了一个数量级，哇咔咔。。。其实写出之前让我参考的效率较高的SQL的一位妹子。在我公司，大家称之为SQL女神，果然名不虚传。佩服之余我要要要学习一下。
仔细分析以上优化过的SQL，其实是巧妙的使用了某种规律，我称之为---SQL分配率和结合律。
最左侧的子SQL（或者临时表：mm）如下：
SELECT
*
FROM
A m
INNER JOIN (SELECT * FROM B where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN (SELECT * FROM G where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('') UNION ALL SELECT
*
FROM
F m
INNER JOIN (SELECT * FROM G where is_del = 0 AND m_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59') pm ON pm.id_sour = m.pk_id
WHERE
pm.status = ''
AND pm.is_del = 0
AND pm.s_time BETWEEN '2017-05-25 00:00:00' AND '2017-05-25 23:59:59'
AND m.type IN ('')
其实38条数据的结果，在以上子SQL就已经确定了，因此后面的LEFT JOIN或INNER JOIN，JOIN的数据都会比较少，效率自然高。相对于优化前的写法，以上子SQL各自还连接了一堆相同的表。现在把这堆相同的表提到最外面做一次连接。这里体现的是SQL结合律。
总结：当SQL规模比较庞大时，良好的SQL结构能大大提升执行的效率。并且SQL的优化也不是一蹴而就，也是一个循序渐进不断尝试的过程。以上SQL不一定就是最优，此处并没有谈SQL语法最佳使用细节。具体可参考以下链接。
https://dev.mysql.com/doc/refman/5.7/en/optimization.html
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