今天同事咨询一个SQL语句,如下所示,SQL语句本身并不复杂,但是执行效率非常糟糕,糟糕到一塌糊涂(执行计划也是相当复杂)。如果查询条件中没有NOT EXISTS部分,倒是不要一秒就能查询出来。
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SELECT * FROM dbo.UVW_PDATest a WITH(NOLOCK)
WHERE
Remark='前纺' AND Operation_Name='粗纱' AND One_Status_Code='0047'
AND a.Createtime >='2015-9-23'
AND NOT EXISTS
(
SELECT 1 FROM dbo.UVW_PDATest c WITH(NOLOCK)
WHERE a.Task_NO =c.Task_NO AND c.One_Status_Code='0014'
)
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为什么如此简单的SQL语句,执行效率却一塌糊涂呢,因为UVW_PDATest是一个视图,而且该视图是由8个表关联组成。
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SELECT ..........
From dbo.PDA_TB_Produce a With(Nolock)
Join dbo.DctOperationList b With(Nolock)
On a.Operation_Code=b.Operation_Code
Join dbo.DctOneStatusList c With(Nolock)
On a.One_Status_Code=c.One_Status_Code
Left join dbo.DctTwoStatusList d With(Nolock)
On c.One_Status_Code=d.One_Status_Code and a.Two_Status_Code=d.Two_Status_Code
left Join dbo.DctMachineList e With(Nolock)
On a.Operation_Code=e.Operation_Code and a.Machine_Code=e.Machine_Code
left Join dbo.DctOperationList f With(Nolock)
On a.Next_Operation_Code=f.Operation_Code
Join dbo.DctUserList g With(Nolock)
On a.User_ID_Operating=g.User_ID
Join dbo.DctUserList h With(Nolock)
On a.User_ID=h.User_ID
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刚开始我想从索引上去优化,加上一两个索引后发现其实并无多大益处。为什么性能会如此糟糕呢?原因是什么呢?
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大量复杂的Join
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该类查询模式包含了大量连接,尤其是连接条件是不等连接,由于统计信息随着表连接的增多精度逐渐下降,这会导致低效的查询性能。解决这类情况可以通过分解查询,并将中间解决存入临时表解决。 具体参考博客:什么情况下应该分解复杂的查询来提升性能
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于是我拆分上面SQL语句(如下所示),先将执行结果保存到临时表,然后关联取数,结果一秒钟的样子就执行出来了。真可谓是化繁为简。
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SELECT Task_NO INTO #TMP_MID_UVW_PDATest
FROM dbo.UVW_PDATest c WITH(NOLOCK)
WHERE One_Status_Code='0014' and Remark='前纺' AND Operation_Name='粗纱'
SELECT * INTO #TMP_UVW_PDATest
FROM dbo.UVW_PDATest a WITH(NOLOCK)
WHERE Remark='前纺'
AND Operation_Name='粗纱'
AND One_Status_Code='0047'
AND Create_Date>='2015-9-23' ;
SELECT * FROM #TMP_UVW_PDATest a
WHERE NOT EXISTS(SELECT 1 FROM #TMP_MID_UVW_PDATest c WHERE a.Task_NO =c.Task_NO );
DROPTABLE#TMP_UVW_PDATest
DROP TABLE #TMP_MID_UVW_PDATest
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第二个案例是ORACLE
数据库的一个优化案例,具体SQL语句如下所示,执行时间非常长,一般都是二十多秒左右。
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SELECT A.SC_NO,
A.MRP_GROUP_CD,
A.DIMM_ID,
A.JOB_ORDER_NO,
DECODE(SIGN(A.DEMAND_QTY),-1,0,A.DEMAND_QTY) AS DIFF_QTY,
A.ASSIGNED_TYPE
FROM
(
SELECT CC.SC_NO,
BB.MRP_GROUP_CD,
BB.DIMM_ID,
BB.JOB_ORDER_NO,
NVL (SUM (BB.DEMAND_QTY), 0) - NVL(SUM(REC.RECV_QTY),0) AS DEMAND_QTY,
CASE
WHEN DD.REQ_DATE0
ORDER BY A.MRP_GROUP_CD,
A.DIMM_ID,
A.JOB_ORDER_NO;
查看执行计划,你会发现COST主要耗费在HASH JOIN上。如下截图所示,表INV_STOCK_ASSIGN来自于视图INVSUBMAT.INV_MRP_JO_AVAILABLE_V。
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将上面复杂SQL拆分后,执行只需要不到一秒解决,如下截图所示,速率提高了几十倍。优化往往有时候很复杂,有时候也很简单,就是将复杂的语句拆分成简单的SQL语句,性能的提升有时候确实令人吃惊!
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CREATE GLOBAL TEMPORARY TABLE TMP_MRP_MID_DATA
( SC_NO VARCHAR2(20) ,
MRP_GROUP_CD VARCHAR2(10) ,
DIMM_ID NUMBER,
JOB_ORDER_NO VARCHAR2(20) ,
DEMAND_QTY NUMBER ,
DIFF_QTY NUMBER ,
ASSIGNED_TYPE VARCHAR(2)
) ON COMMIT PRESERVE ROWS;
INSERT INTO TMP_MRP_MID_DATA
SELECT A.SC_NO,
A.MRP_GROUP_CD,
A.DIMM_ID,
A.JOB_ORDER_NO,
A.DEMAND_QTY,
DECODE(SIGN(A.DEMAND_QTY),-1,0,A.DEMAND_QTY) AS DIFF_QTY,
A.ASSIGNED_TYPE
FROM
(
SELECT CC.SC_NO,
BB.MRP_GROUP_CD,
BB.DIMM_ID,
BB.JOB_ORDER_NO,
NVL (SUM (BB.DEMAND_QTY), 0) - NVL(SUM(REC.RECV_QTY),0) AS DEMAND_QTY,
CASE