## Events-In-Progress for Time Periods in DAX

Calculating the Events-In-Progress is a very common requirement and many of my fellow bloggers like Chris Webb, Alberto Ferrari and Jason Thomas already blogged about it and came up with some really nice solutions. Alberto also wrote a white-paper summing up all their findings which is a must-read for every DAX and Tabular/PowerPivot developer.
However, I recently had a slightly different requirement where I needed to calculate the Events-In-Progress for Time Periods – e.g. the Open Orders in a given month – and not only for a single day. The calculations shown in the white-paper only work for a single day so I had to come up with my own calculation to deal with this particular problem.

Before we can start we need to identify which orders we actually want to count if a Time Period is selected. Basically we have to differentiate between 6 types of Orders for our calculation and which of them we want to filter or not:

 Order Definition Order1 (O1) Starts before the Time Period and ends after it Order2 (O2) Starts before the Time Period and ends in it Order3 (O3) Starts in the Time Period and ends after it Order4 (O4) Starts and ends in the Time Period Order5 (O5) Starts and ends after the Time Period Order6 (O6) Starts and ends before the Time Period

For my customer an order was considered as “open” if it was open within the selected Time Period, so in our case we need to count only Orders O1, O2, O3 and O4. The first calculation you would usually come up with may look like this:

1. [MyOpenOrders_FILTER] :=
2. CALCULATE (
3.     DISTINCTCOUNT ( ‘Internet Sales’[Sales Order Number] ),
4.     FILTER (
5.         ‘Internet Sales’,
6.         ‘Internet Sales’[Order Date]
7.             <= CALCULATE ( MAX ( ‘Date’[Date] ) )
8.     ),
9.     FILTER (
10.         ‘Internet Sales’,
11.         ‘Internet Sales’[Ship Date]
12.             >= CALCULATE ( MIN ( ‘Date’[Date] ) )
13.     )
14. )

We apply custom filters here to get all orders that were ordered on or before the last day and were also shipped on or after the first day of the selected Time Period. This is pretty straight forward and works just fine from a business point of view. However, performance could be much better as you probably already guessed if you read Alberto’s white-paper.

So I integrate his logic into my calculation and came up with this formula (Note that I could not use the final Yoda-Solution as I am using a DISTINCTCOUNT here):

1. [MyOpenOrders_TimePeriod] :=
2. CALCULATE (
3.     DISTINCTCOUNT ( ‘Internet Sales’[Sales Order Number] ),
4.     GENERATE (
5.         VALUES ( ‘Date’[Date] ),
6.         FILTER (
7.             ‘Internet Sales’,
8.             CONTAINS (
9.                 DATESBETWEEN (
10.                     ‘Date’[Date],
11.                     ‘Internet Sales’[Order Date],
12.                     ‘Internet Sales’[Ship Date]
13.                 ),
14.                 [Date], ‘Date’[Date]
15.             )
16.         )
17.     )
18. )

To better understand the calculation you may want to rephrase the original requirement to this: “An open order is an order that was open on at least one day in the selected Time Period”.

I am not going to explain the calculations in detail again as the approach was already very well explained by Alberto and the concepts are the very same.

An alternative calculation would also be this one which of course produces the same results but performs “different”:

1. [MyOpenOrders_TimePeriod2] :=
2. CALCULATE (
3.     DISTINCTCOUNT ( ‘Internet Sales’[Sales Order Number] ),
4.     FILTER (
5.         GENERATE (
6.             SUMMARIZE (
7.                 ‘Internet Sales’,
8.                 ‘Internet Sales’[Order Date],
9.                 ‘Internet Sales’[Ship Date]
10.             ),
11.             DATESBETWEEN (
12.                 ‘Date’[Date],
13.                 ‘Internet Sales’[Order Date],
14.                 ‘Internet Sales’[Ship Date]
15.             )
16.         ),
17.         CONTAINS ( VALUES ( ‘Date’[Date] ), [Date], ‘Date’[Date] )
18.     )
19. )

I said it performs “different” as for all DAX calculations, performance also depends on your model, the data and the distribution and granularity of the data. So you should test which calculation performs best in your scenario. I did a simple comparison in terms of query performance for AdventureWorks and also my customer’s model and results are slightly different:

 Calculation (Results in ms) AdventureWorks Customer’s Model [MyOpenOrders_FILTER] 58.0 1,094.0 [MyOpenOrders_TimePeriod] 40.0 390.8 [MyOpenOrders_TimePeriod2] 35.5 448.3

As you can see, the original FILTER-calculation performs worst on both models. The last calculation performs better on the small AdventureWorks-Model whereas on my customer’s model (16 Mio rows) the calculation in the middle performs best. So it’s up to you (and your model) which calculation you should prefer.

The neat thing is that all three calculations can be used with any existing hierarchy or column in your Date-table and of course also on the Date-Level as the original calculation.

## SSAS Dynamic Named Sets in Calculated Members

Recently at one of my customers we were doing some performance tuning on a SSAS Multidimensional cube and I came across something I have not seen before yet in my lifetime as a SSAS developer. Even the simplest select queries where running for several seconds, even if executed on warm cache! So first I thought this may be related to some SCOPE assignments overwriting values etc. Using my MdxScriptDebugger I could easily identify that none of the SCOPE assignments had any impact on the actual result of the query. However, what the result of the MdxScriptDebugger trace also revealed was that the query-time increased after a calculated member was created. Important to note here is that this specific member was NOT used in the test-query at all! Investigating into the calculated member turned out that it was using a Dynamic Named Set.

Dynamic Named Sets are usually used if you want a set to be re-evaluated in the current context opposed to Static Named Sets which are only evaluated once during creation. For example you can create a Dynamic Named Set for your Top 10 customers. Changing the Year or Country would cause a re-evaluation of the set and different customers are returned for your filter selections. These type of calculated sets is usually not a problem.

Another reason to used Dynamic Named Sets is to deal with Sub-Selects. Some client tools, foremost Microsoft Excel Pivot Tables, use Sub-Selects to deal with multiple selections on the same attribute. Lets do a little example on how to use Dynamic Named Sets here. Assume you need to calculate the average yearly sales for the selected years. This may sound very trivial at first sight but can be very tricky. In order to calculated the yearly average we first need to calculated how many years are in the currently selected:

1. CREATE MEMBER CURRENTCUBE.[Measures].[CountYears_EXISTING] AS (
2. COUNT(EXISTING [Date].[Calendar Year].[Calendar Year].members)
3. );

However, this does not work if Sub-Selects are used in the query:

The calculated member returns “6” (the overall number of years) instead of “2” (the actually selected number of years). The issue here is that the calculation is not aware of any Sub-Select or filters within the Sub-Select as it is executed only outside of the Sub-Select.

To work around this issue you can create a Dynamic Name Set and refer to it in your calculated member:

1. CREATE DYNAMIC SET [ExistingYears] AS {
2. EXISTING [Date].[Calendar Year].[Calendar Year].members
3. };
4.
5. CREATE MEMBER CURRENTCUBE.[Measures].[CountYears_DynamicSet] AS (
6. COUNT([ExistingYears])
7. );

Now we get the correct results for our Count of Years calculation and could simply divide our Sales Amount by this value to get average yearly sales. The reason for this is that Dynamic Named Sets are also evaluated within the Sub-Select and therefore a COUNT() on it returns the correct results here.

So this technique is quite powerful and is also the only feasible workaround to deal with this kind of problem. But as I initially said, this can also cause some performance issues!

1. CREATE DYNAMIC SET [ExpensiveSet] AS {
2. Exists(
3.     [Product].[Category].[Category].members,
4.     Filter(
5.         Generate(
7.             CrossJoin(
8.                 {[Date].[Calendar].CURRENTMEMBER},
10.                     Order(
11.                         [Customer].[Customer].[Customer].MEMBERS,
12.                         [Measures].[Internet Sales Amount],
13.                         BDESC),
14.                     10000))),
15.         [Measures].[Internet Order Quantity] > -1
16.     ),
17.     'Internet Sales'
18. )
19. };
20.
21. CREATE MEMBER CURRENTCUBE.[Measures].[UnusedCalc] AS (
22. COUNT([ExpensiveSet])
23. );

The [ExpensiveSet] is just a Dynamic Named Set which needs some time to be evaluated and the [UnusedCalc] measure does a simple count over the [ExpensiveSet]. Having these calculations in place you can now run any query against your cube and will notice that even the simplest query now takes some seconds to execute even if the new calculated member is not used:

1. SELECT
2. [Measures].[Internet Sales Amount] ON 0