在 DAX 中运行/累计总计仅使用度量，没有计算列

问题描述

问题:

创建一个不引用任何计算列而仅引用度量的运行总计度量.

Create a running total measure that does not reference any calculated columns, only measures.

一种度量是计算.另一种措施是自动递增从 RANKX 创建的 ID.

One measure is a calculation.Another measure is auto-incrementing IDs created from a RANKX.

目标是仅使用对 B 的引用为 A 创建一个运行的总度量/总和.

The goal is to create a running total measure/sum for A using reference only to B.

上下文:由于数据限制原因，无法添加计算列.因此，已经创建了许多度量.

Context:For data restriction reasons, there is no ability to add a calculated column. Therefore, there are a number of measures that have been created.

现有试验:

现有知识库(互联网搜索)似乎仅指列和度量混合的度量.但是，无法添加计算列，也不需要/使用此特定运行总计中的现有列.

Existing knowledge base (internet searches) only seem to refer to measures where there is a mixture of columns and measures. There is no ability to add calculated columns, however, nor need for/use of existing columns within this particular running total.

目前已经尝试使用表表达式来构建带有度量的表，为最大值 (MAXX) 创建附加变量并尝试以这种方式进行过滤并在返回中使用 CAlCULATE.这只是返回总数，而不是运行总数.

Currently there has been an attempt to use a table expression to build the table with the measures, creating additional variables for a max (MAXX) and trying to filter this way and use a CAlCULATE in the return. This is only returning the total and not a running total.

示例表:

[...现有列]	测量A	措施B	(需要)测量 C
...	10	1	10
...	60	2	70
...	40	3	110

度量 A 是其他度量的总和度量 B 是其他度量的排名度量 C 是给定度量 B 的度量 A 的运行总计

Measure A is a sum of other measuresMeasure B is a rank of other measuresMeasure C is a running total of measure A given Measure B

推荐答案

基本模式是:

CumulativeMeasureA =
VAR CurrentRank = [MeasureB]
RETURN
    SUMX ( FILTER ( ALL ( Data[Group] ), [MeasureB] <= CurrentRank ), [MeasureA] )

其中 Data[Group] 是您在报表视觉对象中分组的列.

Where Data[Group] is the column that you are grouping on in your report visual.

请注意，如果没有计算列，这不是很有效，因为它必须为视觉中每一行的每个 Group 计算 [MeasureB]，过滤这些结果，并计算 [MeasureA] 每次未被过滤掉的迭代.对于小型数据集，这不是问题，但随着事情变得更大和更复杂，这可能会成为问题.

Note that this isn't very efficient without calculated columns since it is having to compute [MeasureB] for every Group for every row in your visual, filter those results, and compute [MeasureA] every iteration that isn't filtered out. For small datasets, this isn't a problem but can be as things get larger and more complex.

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