We have a table with the following values:
ItemName Qty F Bit Date
Item1 100 1/1/2011
Item2 150 1/5/2011
Item3 200 1/14/2011
Item4 250 1/20/2011
Item5 1000 2/1/2011
In a MDX query we require to filter the data based on the date range. Say if we require to fetch the items and its qty for the date range 01/01/2011 to 01/31/2011 we use the following MDX query:
SELECT
NON Empty
{[Final Report View For Scorecards].[F Bit Date].Children} ON ROWS,
NON Empty
{[Measures].[Qty]} ON COLUMNS
FROM
(
SELECT
(
{[Final Report View For Scorecards].[F Bit Date].&[2011-01-01T00:00:00] :
[Final Report View For Scorecards].[F Bit Date].&[2011-01-31T00:00:00]}
) ON COLUMNS
FROM [FinalReportView]
)
Here when the FromDate and ToDate parameters are passed as 01/01/2011 and 01/20/2011, the items 1 to 4 with their respective qty's are retrieved correctly. But when the FromDate and ToDate are passed as 01/01/2011 and 01/31/2011 (or) 01/01/2011 and 01/19/2011, it returns even the value of Item5 which is wrong. i.e., only when the exact dates available in the table are passed as parameters, it returns the correct result. But when dates that are not available in the table is passed, the entire dataset is returned.
It is the same when 'FILTER' is used.
Any pointers to this issue?
If you use a member name that does not exist, then (if you have configured the MdxMissingMemberMode of the dimension to the default setting which is equivalent to Ignore), then Analysis Services converts invalid member names to NULL. And the range operator : interprets null as unbounded, i. e. if you use null on the right side of a range, it means "to the last member of the level", and on the left side of : it means "from the first member of the level".
What could you do to resolve that:
Either take care to only generate names of existing members
Or just fill up the dimension table with a range of dates large enough not to cause problems. You do not need to add any fact for these days, there just should be an entry in the dimension table.
Or use some string logic within MDX to find the next existing (for the left side of the range) or the previous existing (for the right side of the range). You can use VBA string functions in MDX.
Related
I've got a large table of events that have occurred in an inventory of vehicles, which affect whether they are in service or out of service. I would like to create a measure that would be able to count the number of vehicles in the various inventories at any point in time, based on the events in this table.
This table is pulled from a SQL database into an Excel 2016 sheet, and I'm using PowerPivot to try to come up with the DAX measure.
Here is some example data event_list:
vehicle_id event_date event event_sequence inventory
100 2018-01-01 purchase 1 in-service
101 2018-01-01 purchase 1 in-service
102 2018-02-04 purchase 1 in-service
100 2018-02-07 maintenance 2 out-of-service
101 2018-02-14 damage 2 out-of-service
101 2018-02-18 repaired 3 in-service
100 2018-03-15 repaired 3 in-service
102 2018-05-01 damage 2 out-of-service
103 2018-06-03 purchase 1 in-service
I'd like to be able to create a pivot table in Excel (or use CUBE functions, etc) to get an output table like this:
date in-service out-of-service
2018-02-04 3 0
2018-02-14 1 2
2018-03-15 3 0
2018-06-03 3 1
Essentially, I want to be able to calculate the inventory based on any date in time. The example only has a few dates, but hopefully provides enough of a picture.
I've basically come up with this so far, but it counts more vehicles than desired - I can't figure out how to only take the latest event_sequence or event_date and use that to count the inventory.
cumulative_vehicles_at_date:=CALCULATE(
COUNTA([vehicle_id]),
IF(IF(HASONEVALUE (event_list[event_date]), VALUES (event_list[event_date]))>=event_list[event_date],event_list[event_date])
)
I tried using MAX() and EARLIER() functions, but they don't seem to work.
Edit: Added the PowerBI tag as I'm now using that software to attempt to solve this as well. See comments on Alexis Olson's answer.
I think I've found a much cleaner method than I gave previously.
Let's add two columns onto the event_list table. One which counts vehicles "in-service" on that date and one which counts vehicles "out-of-service" on that date.
InService =
VAR Summary = SUMMARIZE(
FILTER(event_list,
event_list[event_date] <= EARLIER(event_list[event_date])),
event_list[vehicle_id],
"MaxSeq", MAX(event_list[event_sequence]))
VAR Filtered = FILTER(event_list,
event_list[event_sequence] =
MAXX(
FILTER(Summary,
event_list[vehicle_id] = EARLIER(event_list[vehicle_id])),
[MaxSeq]))
RETURN SUMX(Filtered, 1 * (event_list[inventory] = "in-service"))
You can create an analogous calculated column for OutOfService or you can just take the total minus the InService count.
OutOfService =
CALCULATE(
DISTINCTCOUNT(event_list[vehicle_id]),
FILTER(event_list,
event_list[event_date] <= EARLIER(event_list[event_date])))
- event_list[InService]
Now all you have to do is put event_date on the matrix visual rows section and add the InService and OutOfService columns to the values section (use Maximum or Minimum for the aggregation option rather than Sum).
Here's the logic behind the calculated column InService:
We first create a Summary table which calculates the maximal event_sequence value for each vehicle. (We filter the event_date to only consider dates up to the current one we are working with.)
Now that we know what the last event_sequence value is for each vehicle, we use that to filter the entire table down to just the rows that correspond to those vehicles and sequence values. The filter goes through the table row by row and checks to see if the sequence value matches the one we calculated in the Summary table. Note that when we filter the Summary table to just the vehicle we are currently working with, we only get a single row. I'm just using MAXX to extract the [MaxSeq] value. (It's kind of like using LOOKUPVALUE, but you can't use that on a variable.)
Now that we've filtered the table just to the most recent events for each vehicle, all we need to do is count how many of them are "in-service". I used a SUMX here where the 1*(True/False) coerces the boolean value to return 1 or 0.
This is pretty difficult. I don't have a great answer, but here's something that kind of works.
You'll create a new calculated table where you'll calculate the status for each vehicle on each date. Start with the base cross join for each vehicle and each date:
= CROSSJOIN(VALUES(event_list[vehicle_id]), VALUES(event_list[event_date]))
Then add a calculated column to find the max sequence number for each vehicle on that date.
Sequence = MAXX(
FILTER(event_list,
event_list[event_date] <= Cross[event_date] &&
event_list[vehicle_id] = Cross[vehicle_id]),
event_list[event_sequence])
Now you can lookup the inventory value for each vehicle/sequence pair with another calculated column:
Inventory = LOOKUPVALUE(
event_list[inventory],
event_list[vehicle_id], Cross[vehicle_id],
event_list[event_sequence], Cross[Sequence])
The result should look something like this:
Once you have this, you can create a matrix using this calculated table. Put the event_date on the rows and Inventory on the columns. Filter out blank inventory values in the visual level filter and put the vehicle_id in the values field, using a count or distinct count as the aggregation method (instead of the default sum).
It should look like this:
I have a SSAS DSV similar to following structure:
Id Type Special
1 A 1
2 B Null
3 A Null
4 C 1
5 C Null
I built a dimension for this DSV including one attribute for Type.
Then I have in my cube three measures
Measure1: Count of rows
Measure2A: Sum of Special
Measure2B: Count of non-empty values for Special
Finally in Excel, I display data as following:
Rows --> Type attribute
Values --> Measure1 / Measure2A / Measure2B
When I look at the results, everything is correct.
For instance, I get a count of 1 for measure2A and measure 2B for row = C
BUT when I attempt to drill through for related cells, instead of getting 1 row, I get 2 (the ones where type = C without considering the value of Special)
I guess I am doing something wrong in my design of the cube but cannot understand what.
When determining what rows to show in drillthrough SSAS only considers the dimension context not which detail rows have a non null measure value.
You could add a new dimension on the Special column and add that dimension as a filter to your PivotTable.
Or you could install ASSP and construct a custom rowset action that fires an MDX query which does a NON EMPTY on your measure.
http://asstoredprocedures.codeplex.com/wikipage?title=Drillthrough&referringTitle=Home
I need to create a "Variance" column in qlikview:
2013 2014 Variance
Measure 1 100 110 10%
Measure 2 105 100 -4.8%
...
Can this be done in Qlikview with just one "Calculated dimension" column that says something like:
[Value for Column2]/[Value for Column 1] - 1
So that it works for any new measures I add in the table and regardless of what the column 1 and column 2 are?
EDIT:
Sample Data:
Year Measure1 Measure2
2012 9750 197
2013 10000 200
2014 11000 210
2015 11500 215
I need the output to be structured as shown below with the Variance column as a calculation between 2 selected Year dimension values.
You can do this with the Column() function.
Column(2) / Column(1)
The number refers to the Expression column -- first Expression is #1, etc. Dimension columns are not counted.
An alternative that is insensitive to column postion, you can use column labels in the expression. Assume expressions with labels of "Sales" & "Margin". The variance expression can be written as:
[Margin] / [Sales]
I am 99% certain there is no built in function to do it.
I've played with a few options I thought might work, but only this is proving of any use.
I've defined the 2 calculated dimensions as variables Year1 and Year2 which I change through an input box. Then the simple calculated dimension =[$(Year2)]-[$(Year1)] gives me a new dimension of the variances.
This assumes the Measure is something that comes from the data, something like this, and now you just want to display it over varying years. I haven't considered what to do if the measures are all expressions.
Here is how to do it using variables and expressions. This will create 2 new columns that will look like a new dimension but will actually be defined by the use of an if() statement.
First step in the script we need to create a dimension that has the Measures in it. this should not associate to any of the column already in the data. (UDR stands for User Defined Report). the dual just allows us to define a sort order that is not alphabetical.
UDR:
load dual(UDR,Sort) as Rows inline [
UDR, Sort
Measure1, 1
Measure2, 2];
the result should be something like this.
Next step is creating the 2 variables for the years.
I do this in the script, but you could use any method of variable creation.
set vMaxYear="=max(Year)";
set vMinYear="=min(Year)";
Now we use the field Rows as the dimension.
And we need to create an expression for the max selected year. Notice that the first if() is testing which rows of the UDR the expression is on and then the definition of the expression for that line is provided. The second if() is testing the year dimension against the variable vMaxYear which will change as selections are made. Min year is the same just replace vMaxYear with vMinYear.
if(Rows='Measure1',sum(if(Year=vMaxYear,Measure1)),
if(Rows='Measure2',sum(if(Year=vMaxYear,Measure2))))
Lastly we use the column() function to calculate the variance in the third expression.
column(1)/column(2)
To make the expression labels dynamic I simply add =vMinYear into the label.
The result is this table, that will respond to my selections in the year list box.
2013 vs 2014
2014 vs 2015
I have a report with a static choice on the prompt page. The user can choose 'Full Detail', or 'Summarised'.
For a simplified example, say my report has these columns: Customer, Product, Date, Quantity, Value.
I would like to be able to show/hide the Date column based on the detail level choice, and have the Quantity and Value columns aggregate into a single Customer/Product line. I know how to show/hide the column (tying the choice variable to the column's Render Variable), but this does not do the aggregation, only makes the column invisible.
I have thought about doing a separate report page for Full Detail and Summary, but in my actual report I have a second choice box with which the user can choose a field to summarise by (e.g. Customer or Product), and the report will section-group by that field. At the moment I am doing that one per page (5 of them). Doing the detail choice the same way would mean I would need 10 pages. There is surely a better way.
Full detail:
Customer Product Date Qty Value
ABCD Things 22/10/2014 10 1.00
21/10/2014 40 4.00
23/10/2014 50 5.00
Summarised (How it looks at the moment, after hiding the Date column):
Customer Product Qty Value
ABCD Things 10 1.00
40 4.00
50 5.00
Summarised (How I would like it to look):
Customer Product Qty Value
ABCD Things 100 10.00
I am using Cognos Report Studio 10.1.1
You should not just hide column.
You should also set same value for this column in all rows
Instead of just [Date] in this column set
if (?HideDate? = 1) then ('') else ([Date])
or, if you prefer CASE
case ?HideDate? when 1 then '' else [Date] end
replace ?HideDate? = 1 with you own condition
Alexey's answer is great if you are using the standard Cognos 'auto-group and summarize' functionality.
If you have a custom aggregate that includes the [Date] column in its definition, you might squeeze a bit of performance gain out of modifying the aggregate function itself to disregard the [Date] column when a summarized total is desired.
If your aggregate function was:
total([Value] for [Customer],[Product],[Date])
..you might change this to a CASE statement like so:
CASE ?HideDate?
WHEN 1 then total([Value] for [Customer],[Product])
ELSE total([Value] for [Customer],[Product],[Date])
END
The data items after a 'for' clause usually end up in a GROUP BY clause in the resultant SQL. Limiting the items grouped, when possible, can help performance. In this case the performance improvement would likely be slight since there will only be one distinct value in Alexey's solution, but it's something to consider.
Anyone have advice on how to build an average measure that is dynamic -- it doesn't specify a particular slice but instead uses your current view? I'm working within a front-end OLAP viewer (Strategy Companion) and I need a "dynamic" implementation based on the dimensions that are currently filtered in the data view.
My fact table looks something like this:
Key AmountA IndicatorA AmountB Other Data
1 5 1 null 25
2 6 1 null 52
3 7 1 2 106
4 null 0 4 108
Now I can specify a simple average for "[Measures].[AmountA]" with "[Measures].[AmountA] / [Measures].[IndicatorA]" which works great - "[IndicatorA]" sums up to the number of non-null values of "[AmountA]". And this also works great no matter what dimensions are selected in the view - it always divides by the count of rows that have been filtered in.
But what about [AmountB]? I don't have a null indicator column. I want to get an average value of [AmountB] for whatever rows have been filtered in for my current view. If I try to use the count of rows as a simple formula (psuedo-code "[Measures].[AmountB] / Count([Measures].[Key])") I get the wrong result, because it is counting all the null rows in the average.
So, I need a way to use the AVG function to specify the average of [AmountB] over the set of "whatever rows I'm currently filtering in, based on whatever dimensions I'm currently using". How do I specify this dynamic set?
I've tried several different uses of the AVG function and they have either returned null or summed up to huge numbers, clearly not the average I'm looking for.
Thanks-
Matt
Sorry, my first suggestion was wrong. If you don't have access to OLAP cube you can't write any mdx-query for this purpose (IMHO). Because, you don't have any detailed data (from your fact table) in this access level and you can use only aggregated data and dimensions from your cube.
Otherwise (if you have access to olap db), you can create this metric (count of not NULL rows) in your measure group and after that use it for AVG calculation (as calculated member in your cube or in section "WITH" in your mdx-query).