SSRS Mobile Report Drillthrough

Patrick LeBlanc shows how to drill from a mobile SQL Server Reporting Services report to a paginated report (built on Analysis Services):

17. The report appears but does not execute because the parameters are not set. Why not?

Well, after inspecting the URL (http://localhost/ReportServer/Pages/ReportViewer.aspx?%2fHigher+Education+Solution%2fReports%2fAnnual+Enrollment+Details&DateSchoolYear=2007&Term=Spring), it passed the values as expected. What is the problem? Remember, the parameters are populated from and SSAS model, so that means we need to send the values formatted as such. This format is:


No problem, just build that string as part of the URL. Guess what, that doesn’t work either. What you need to do encode certain characters in the URL. For example, to pass year it needs to look like this [Date].[School Year].&[{{SelectionList.SelectedItem}}].

Click through for a step-by-step guide.

SSAS Extended Events

Eugene Polonichko shows how to use Extended Events with SQL Server Analysis Services:

Cubes require frequent monitoring since their productivity decreases quite often (slowdowns during query building, processing time increment). To find out the reason of decrease, we need to monitor our system. For this, we use SQL Server Profiler. However, Microsoft is planning to exclude this SQL tracing tool in subsequent versions. The main disadvantage of the tool is resource intensity, and it should be run on a production server carefully, since it may cause a critical system productivity loss.

Thus, Extended Events is a general event-handling system for server systems. This system supports the correlation of data from SQL Server which allows getting SQL Server state events.

If you’re already familiar with relational engine XEs, this isn’t much of a stretch.

That 53rd Week

Jens Vestergaard notes that you can sometimes have a 53rd week in the year:

There are a lot of great examples out there on how to build your own custom Time Intelligence into Analysis Services (MD). Just have a look at this, this, this, this and this. All good sources for solid Time Intelligence in SSAS.
One thing they have in common though, is that they all make the assumption that there is and will always be 52 weeks in a year. The data set I am currently working with is built on ISO 8601 standard. In short, this means that there is an (re-) occurrence of a 53rd full week as opposed to only 52 in the Gregorian version which is defined by: 1 Gregorian calendar year = 52 weeks + 1 day (2 days in a leap year).

The 53rd occurs approximately every five to six years, though this is not always the case. The last couple of times  we saw 53 weeks in a year was in 1995, 2000, 2006, 2012 and 2015. Next time will be in 2020. This gives you enough time to either forget about the hacks and hard-coded fixes in place to mitigate the issue OR bring your code in a good state, ready for the next time.

Dates and currency are hard problems.

Calculated Dimensions

Ginger Grant shows how calculated dimensions can solve the classic role-playing dimension problem in Analysis Services Tabular:

Working with role playing dimensions, which are found when you have say multiple dates in a table and you want to relate them back to a single date table, have always been problematic in SQL Server Analysis Services Tabular. Tabular models only allow one active relationship to a single column at a time. The picture on the left shows how tabular models represent a role playing dimension, and the model on the right is the recommended method for how to model the relationships in Analysis Services Tabular as then users can filter the data on a number of different date tables.

The big downside to this is one has to import the date table into the model multiple times, meaning the same data is imported again and again. At least that was the case until SQL Server 2016 was released. This weeks TSQL topic Fixing Old Problems with Shiny New Toys is really good reason to describe a better way of handling this problem.

Read on for how to implement calculated dimensions.

MDX Calculation Duration

Chris Webb wants to know how long specific MDX calculations take:

In my last two blog posts (see here and here) I showed how to use the Calculation Evaluation and Calculation Evaluation Detailed Information trace events to work out which MDX calculations are evaluated when a query runs in Analysis Services Multidimensional. That’s very useful, but wouldn’t it be great if you could work out how long any single calculation contributes to the overall duration of a query? If you could, it would make performance tuning MDX calculations much easier.

While you can’t get an exact amount of time taken for each calculation, the good news is that it is possible to get a duration rounded to the next second if your calculation is evaluated in bulk mode.

It’s an interesting way of backing into an answer.

Detail Rows Expression In SSAS Tabular

Chris Webb shows a new feature in SSAS Tabular vNext:

What drillthrough does in SSAS Multidimensional, and what the new Detail Rows Expression property in SSAS Tabular does, is allow an end user to see the detail-level data (usually the rows in the fact table) that was aggregated to give the value the user clicked on in the original PivotTable.

Read through for an example as well as how it’s already an improvement over Multidimensional’s dillthrough.

Basics Of Azure Analysis Services Management

Bill Anton walks through some of the basics of managing an Azure Analysis Services cube:

The quickest win – from an ROI perspective – for Azure AS is the ability to pause the instance during extended periods of inactivity – for example, at night, when there aren’t any users running reports.

This can be achieved via the Suspend-AzureRmAnalysisServicesServer cmdlet we saw in the previous post.

Read on for a few tips of this ilk, including resizing the server.

Wanted: MDX Intellisense

Jens Vestergaard wants Intellisense for MDX:

The Connect Item I have chosen to write about is an old one and is about getting Intellisense for MDX in SQL Server Management Studio [SSMS]. Despite the fact that it was created back in 2009 by Jamie Thomson (b|l|t), it is still active and there has been a public acknowledgement back then, by the Analysis Service Team, that they will consider this request for an upcoming release. 2009, still active… True story.

Read on for more details and be sure to join Jens’s quixotic quest if you’d like to see MDX Intellisense.

Deleting SSAS Partitions

Chris Koester shows how to use TMSL and Powershell to delete an Analysis Services tabular model partition:

The sample script below shows how this is done. The sequence command is used to delete multiple partitions in a single transaction. This is similar to the batch command in XMLA. In this example we’re only performing delete operations, but many different operations can be performed in sequence (And some in parallel).

Click through for a description of the process as well as a script to do the job.

Where Azure Analysis Services Fits

Melissa Coates explains where Azure Analysis Services fits in common BI architectures:

(2) Data Sources

  • From a single source such as a data warehouse. This is the most traditional path for BI development, and still has a very valid place in many BI/analytics deployments. This scenario puts the work of data integration on the ETL process into the data warehouse, which is the most appropriate place.

  • Directly from various systems.  This can be done, but works well only in specific cases – it definitely won’t work well if there are a lot of highly normalized tables, or if there’s not a straightforward way to relate the disparate data together. Trying to go directly to the source systems & skip an intermediary data warehouse puts the “integration” burden on the data source view in Analysis Services, so plan for plenty of time testing if you’re going to try this route (i.e., it can be much harder, not easier). Note that this option only makes sense if the data is stored in Analysis Services because it needs to be related together somehow (i.e., DirectQuery mode, discussed next in #3, with > 1 data source won’t work if a user tries to combine data sources because the data is not inherently related).

If you’re thinking about Azure Analysis Services, this post is a good one.


March 2017
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