MDX优化Set操作—SUM中的CrossJoin
1. 优化Set操作的关键在于：把大的SET操作变成小的SET操作。
2. 由于CrossJoin代价（CPU、内存）巨大，所以最好用其他操作代替CrossJoin操作。
SUM中的CrossJoin
作者认为：要避免SUM一个包含多个CrossJoin的Set，你可以用其他的操作（比如嵌套SUM）进行替换。据此，我测试了一下 两组语句：
WITH
MEMBER MEASURES.ABC
AS
Sum
(
CrossJoin (
Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
Crossjoin (
Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
),
Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
)
)
)
,[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
)
SELECT
MEASURES.ABC
ON
0
,
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.MEMBERS
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.MEMBERS
ON
1
FROM
[
Adventure Works
]
WITH
MEMBER MEASURES.ABC
AS
Sum
(
Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
SUM
(
Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
),
SUM
(
Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
)
,[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
)
)
)
SELECT
MEASURES.ABC
ON
0
,
{
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members}
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.Members
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.Members
ON
1
FROM
[
Adventure Works
]
WITH
MEMBER MEASURES.ABC
AS
Sum
(
Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
),
SUM
(
Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
),
SUM
(
Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
)
)
)
SELECT
MEASURES.ABC
ON
0
,
{
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members}
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.Members
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.Members
ON
1
FROM
[
Adventure Works
]
WITH
MEMBER MEASURES.ABC
AS
Sum
(
CrossJoin (
Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
Crossjoin (
Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
),
Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
)
)
)
AS
MYABC
,
[
Measures
]
.
[
Internet Sales Amount
]
)
-
Sum
(
MYABC
,
[
Measures
]
.
[
Internet Tax Amount
]
)
SELECT
MEASURES.ABC
ON
0
,
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.MEMBERS
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.MEMBERS
ON
1
FROM
[
Adventure Works
]
2. 由于CrossJoin代价（CPU、内存）巨大，所以最好用其他操作代替CrossJoin操作。
SUM中的CrossJoin
作者认为：要避免SUM一个包含多个CrossJoin的Set，你可以用其他的操作（比如嵌套SUM）进行替换。据此，我测试了一下 两组语句：
WITH
MEMBER MEASURES.ABC
AS
Sum
( CrossJoin ( Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
), Crossjoin ( Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
), Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
) ) ) ,[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
)
SELECT
MEASURES.ABC
ON
0
,
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.MEMBERS
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.MEMBERS
ON
1
FROM
[
Adventure Works
]
WITH
MEMBER MEASURES.ABC
AS
Sum
( Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
SUM
( Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
),
SUM
( Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
) ,[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
) ) )
SELECT
MEASURES.ABC
ON
0
,{
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members}
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.Members
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.Members
ON
1
FROM
[
Adventure Works
]
以上语句中，作者认为第一个语句慢于第二个语句（理由是嵌套的SUM每次操作的SET更小），可实际的结果（测了10次）恰恰相反，第一个语句平均花费的时间51.654秒，而第二个语句平均花费的时间在55.912秒，这是何故呢？此外，书中认为在第二个语句的嵌套SUM中，如果把大的Set放在里面，这样会快一些。也就是说下面的语句比上面第二个语句要慢5%-20%。
WITH
MEMBER MEASURES.ABC
AS
Sum
( Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
),
SUM
( Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
),
SUM
( Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
),
[Measures].[Internet Sales Amount]-[Measures].[Internet Tax Amount]
) ) )
SELECT
MEASURES.ABC
ON
0
,{
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members}
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.Members
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.Members
ON
1
FROM
[
Adventure Works
]
以上测试语句中，关于[Date].[Calendar].[Date]的Set其Turple个数在365左右，关于[Product].[Product Categories].[Product Name]的Set其Turple个数在几十个左右，而关于[Customer].[Customer Geography].[State-Province]的Set其成员个数大多在十几个左右
经过测试发现上面这条语句平均时间在57.858秒。也就是说，测试结果和书中的观点是一致的,只是幅度没有那么大。此外，我还尝试了一下这样的写法。
WITH
MEMBER MEASURES.ABC
AS
Sum
( CrossJoin ( Descendants (
[
Customer
]
.
[
Customer Geography
]
.CurrentMember,
[
Customer
]
.
[
Customer Geography
]
.
[
State-Province
]
), Crossjoin ( Descendants (
[
Date
]
.
[
Calendar
]
.CurrentMember,
[
Date
]
.
[
Calendar
]
.
[
Date
]
), Descendants (
[
Product
]
.
[
Product Categories
]
.CurrentMember,
[
Product
]
.
[
Product Categories
]
.
[
Product Name
]
) ) )
AS
MYABC ,
[
Measures
]
.
[
Internet Sales Amount
]
)
-
Sum
( MYABC ,
[
Measures
]
.
[
Internet Tax Amount
]
)
SELECT
MEASURES.ABC
ON
0
,
[
Customer
]
.
[
Customer Geography
]
.
[
Country
]
.Members
*
[
Date
]
.
[
Calendar
]
.
[
Calendar Year
]
.MEMBERS
*
[
Product
]
.
[
Product Categories
]
.
[
Category
]
.MEMBERS
ON
1
FROM
[
Adventure Works
]
上面语句的不同之处在于，把要计算的内容分散开来了，令人惊异的是，这个语句只要2-3秒种就能运行完成。
总结
由上面两次测试我们可以得出以下结论：
1）SUM中的CrossJoin并不一定会降低速度，书中的观点可能是错误的。看来MDX解析器对CrossJoin有很多有优化，在上面的测试中CrossJoin比嵌套的SUM要快8%左右。
2）嵌套SUM中，把大的SET放在里层的SUM中，这样速度能够快一些。上面的测试中，把小的Set放在里层比把大的Set放在里层慢3.5%。
3）在做SUM等统计计算时，如果能够把计算项分解到每个单独的Measure,这个时候性能提升非常明显，速度将会大大提高。上面的测试中，速度提高了20多倍。