The Show Hidden Cubes SSAS Connection String Property

If you need to write queries in SQL Server Management Studio against an SSAS cube that has its Visible property set to false, you have a problem because when you connect to your database you can’t see the cube or its metadata! Luckily you can override this and make the cube visible by setting the following connection string property when you connect:

Show Hidden Cubes=true

image

Connection string properties can be set in the Additional Connection Parameters tab of the connection dialog that appears when you open a new MDX query window.

Unfortunately this doesn’t make any objects in the cube that are not visible, like measures or dimensions, visible again – it just makes the cube itself visible. However, if you’re working on the Calculations tab of the Cube Editor in SSDT it is possible to make all hidden objects visible as I show here.

SSAS Documentation With Squarehouse Commentary

A few months ago I found out about a new tool for managing and viewing Analysis Services Multidimensional documentation called Squarehouse Commentary. I’ve now had the chance to play around with it and talk to the company that makes it and I have to say I’m impressed: in my opinion it’s by far the best tool of its type that I’ve seen. Rather than generating a large pile of documentation that no-one will read and which will get left, forgotten on a fileshare or a SharePoint site, it allows you to synch the descriptions created in its own web application with the description properties in your SSAS cubes and dimensions. It also automatically generates actions so that end users can easily view rich web-based documentation for hierarchies and measures direct from whatever client tool they are using in a few clicks.

This video does a good job of showing off the main features:

Overall, definitely something to check out. It doesn’t support Tabular models yet, but I’m told that this is in the planning stages.

SSAS Multidimensional Caching-Related Performance Problems With Power BI Tables

Last week I was doing some performance tuning for a customer using Power BI on top of a large SSAS Multidimensional cube via a Live connection. Some of their reports were performing particularly badly and I uncovered a problem with the DAX generated by Power BI for tables with a large number of rows, fields from two or more large hierarchies and totals turned on.

The problem is very easy to reproduce; I’m going to do it using a simplified version of the Adventure Works cube that contains only the Date and Product dimensions. Take a look at the following table from a Power BI report:

image

It has the Date attribute from the Date dimension, and the Product attribute from the Product dimension, on rows and two measures on columns. The table has a large number of rows in it (both Date and Product are fairly large hierarchies) and if you look at the DAX generated by Power BI you can see that it only requests the first 501 rows. That’s fine – the real problem is that Power BI also generates a second DAX query to get the two values displayed in the Total line at the bottom of the table. The DAX looks something like this, and is easily identifiable because it uses the Row() function:

EVALUATE
  CALCULATETABLE(
    ROW(
      "Sales_Amount", 'Internet Sales Facts'[Sales Amount],
      "Tax_Amt", 'Internet Sales Facts'[Tax Amt]
    ),
    KEEPFILTERS(
      GENERATE(
        KEEPFILTERS(VALUES('Product'[Product.Key0])),
        CALCULATETABLE(
          FILTER(
            KEEPFILTERS(VALUES('Order Date'[Date.Key0])),
            OR(
              NOT(ISBLANK('Internet Sales Facts'[Sales Amount])),
              NOT(ISBLANK('Internet Sales Facts'[Tax Amt]))
            )
          )
        )
      )
    )
  )

This query has something in it – I don’t know what – that means that it cannot make use of the Analysis Services Storage Engine cache. Every time you run it SSAS will go to disk, read the data that it needs and then aggregate it, which means you’ll get cold-cache performance all the time. On a big cube this can be a big problem. This is very similar to problems I’ve seen with MDX queries on Multidimensional and which I blogged about here; it’s the first time I’ve seen this happen with a DAX query though. I suspect a lot of people using Power BI on SSAS Multidimensional will have this problem without realising it.

This problem does not occur for all tables – as far as I can see it only happens with tables that have a large number of rows and two or more hierarchies in. The easy way to check whether you have this problem is to refresh your report, run a Profiler trace that includes the Progress Report Begin/End and Query Subcube Verbose events (and any others you find useful) and then refresh the report again by pressing the Refresh button in Power BI Desktop without changing it at all. In your trace, if you see any of the Progress Report events appear when that second refresh happens, as well as Query Subcube Verbose events with an Event Subclass of Non-cache data, then you know that the Storage Engine cache is not being used.

image

Also look at the Duration column in the trace for these events which shows the time in milliseconds that they took to execute. This will tell you how much of an impact this problem is having on your report refresh times.

The easy way to stop this happening is to turn off the totals row in the table:

image

Displaying the totals in a separate table also seems to avoid the problem, although of course it doesn’t look as good. Only putting one hierarchy in the table apart from your measures, also seems to solve the problem. You could also try all the usual methods to improve SSAS performance such as building aggregations on the cube.

The dev team is aware of this problem but it’s unlikely to be fixed in the short term.

Deprecated And Discontinued Functionality In SSAS 2016

Some time ago I blogged about the deprecated and discontinued functionality in SSAS 2014, so I thought it would be a good idea to follow my last post on what’s new in SSAS 2016 Multidimensional with a discussion of what’s going or gone from it.

The same page that I linked to last time has been updated for 2016, and there are four more subpages with all the details. There’s nothing much interesting to say about the breaking changes (basically AMO has been rejigged) or behaviour changes (there’s no in-place upgrade for Tabular models using DirectQuery – you have to open the project and edit some settings) but the other two pages do have some news worthy of comment:

Discontinued Functionality

Here’s the official definition of a discontinued feature:

A discontinued feature is one that is no longer supported. It might also be physically removed from the product.

A few comments about what is now discontinued:

  1. The Non_Empty_Behavior property for calculated measures. To be honest, I’m happy to see this go: it doesn’t usually make much difference to performance and in most cases people use it incorrectly too. It really should be removed from Form View in the SSDT cube editor.
  2. COM assemblies. Note that this is not the same thing as .NET assemblies like the Analysis Services Stored Procedure Project! If you are using a custom MDX function implemented in a COM assembly you will probably find that the equivalent function implemented in a .NET assembly is a lot slower, but in my experience it’s almost always possible to avoid custom functions completely and use pure MDX – and this will give you the best query performance.
  3. I’m a bit sad to see the CalculationCurrentPass() and CalculationPassValue() functions die because you could do cool things like this with them, but I haven’t actually needed to use them for a long, long time.

Deprecated Functionality

Here’s the official definition of a deprecated feature:

A deprecated feature is a feature will be cut from the product in a future release, but is still supported and included in the current release to maintain backward compatibility. Typically, a deprecated feature is removed in a major release, often within two releases of the original announcement. For example, deprecated features announced in SQL Server 2012 are likely to be unsupported by SQL Server 2016.

A few comments on what’s been deprecated:

  1. The only thing that someone may possibly be using on the list of features that will not be supported in the next major release is linked dimensions; remote partitions and remote linked dimensions were always a very bad idea.
  2. The death of dimension writeback (note: not the same thing as writing values back to cells) is a bit of a shame: I never needed to use it, and most client tools didn’t support it, but it always struck me as one of those features that people might have used more if they had known about it.
  3. I see session cubes (as used by Excel PivotTables’ grouping functionality) will not be supported in a future release, which is probably a good thing given all the problems they cause. However I bet there are a lot of Excel workbooks out there that will be affected when this does go.
  4. Local cubes will also go in a future release, though I doubt anyone uses them anymore. That said, I think it would be really useful if there was something similar that allows you to create a Power Pivot model that was a local copy of an SSAS Tabular database, with the option to filter the data in it. Offline access is not so important these days but this would allow users to create their own customised Power Pivot models from a properly designed, central model rather than always having to start from scratch.
  5. Profiler for trace capture is also deprecated, and I discussed this in-depth here.

 

What’s New In Analysis Services 2016 Multidimensional?

With the recent release of SQL Server 2016 some of you old faithful SSAS Multidimensional users out there might be wondering if it’s worth upgrading. There is an official page describing what’s new in Analysis Services 2016 here:

https://msdn.microsoft.com/en-us/library/bb522628.aspx

…but it’s mostly concerned with SSAS Tabular and, unfortunately, it’s somewhat misleading. For example, it says:

a number of enhancements have been made to multidimensional models; for example, distinct count ROLAP optimization for data sources like DB2 and Oracle, drill-through multi-selection support with Excel 2016, and Excel query optimizations.

In fact the drillthrough/multi-select improvements (which I blogged about here) already shipped as part of SSAS 2014 and are reliant on improvements in Excel 2016 as much as in SSAS; similarly the Excel 2016 query optimisations are not reliant on any changes in SSAS 2016 and will benefit users of all versions of SSAS.

So what has actually changed with SSAS 2016 Multidimensional? I don’t know all the details on every change, but here’s what I know right now:

  • As the above quote shows, there have been improvements in ROLAP distinct count performance for DB2 and Oracle. I’ve also heard there are improvements for the SQL generated in ROLAP mode to take advantage of SQL Server’s columnstore indexes.
  • We now have Database Consistency Checker for SSAS, which you can read more about here or in Dustin Ryan’s post here. For Multidimensional it will only tell you whether your partition indexes are corrupt (it does a lot more for Tabular) and if they are you need to delete the database and then either restore it or re-deploy/reprocess.
  • Extended Events for SSAS now have UI support in SQL Server Management Studio, although the UI still looks like it needs some work. I still see myself using Profiler for my SSAS performance tuning work for the foreseeable future – or at least until I work out what the extra information that Extended Events give you is useful for. I know other people, like Bill Anton, are more excited about Extended Events and their possibilities though.
  • You can now use computer accounts to be members of the Analysis Services Administrators group in SQL Server Management Studio.
  • If you are developing applications that need to create, alter, process or otherwise do stuff to an Analysis Services database in .NET code then you need to be aware that Analysis Management Objects (AMO) has been refactored to include a second assembly which “paves the way for future extensions to AMO, with clear division between generic and context-specific APIs”.
  • There’s a new default setting for the MemoryHeapType server property that helps to avoid memory fragmentation and is relevant to Tabular and Multidimensional; Marco Russo has the details here.

Not the most exciting or inspiring set of changes, I have to say, even if there’s lots of cool new stuff in SSAS Tabular 2016. There are also usually other fixes and improvements like the MemoryHeapType property that get added but are never documented, and when I find out about them I’ll be sure to blog or tweet. If you find any please let me know!

Excel 2016 PivotTable MDX Changes Lead To Big Query Performance Gains

Yesterday, the Excel team announced a bunch of performance improvements in Excel 2016 for PivotTables connected to Analysis Services (Multidimensional or Tabular), Power Pivot and Power BI. If you haven’t read the official blog post already, here it is:

https://blogs.office.com/2016/07/07/faster-olap-pivottables-in-excel-2016/

In that post, Microsoft point out that how much of a performance increase you get will depend on a number of factors. I guess they have to do this to manage expectations. However I’m going to come right out and say that these changes are probably the most important thing that has happened in the last five years for Analysis Services or Power Pivot query performance and if you are using Excel PivotTables with SSAS or Power Pivot for reporting, you should upgrade to Excel 2016 (you’ll need the Office 365 click-to-run version) just for these changes.

The key improvement that Microsoft have made here is to stop Excel bringing back unnecessary subtotal values in the MDX queries that it generates. This has been a gigantic problem for years and several people have blogged about it: I did here, and among other people Rui Quintino and Richard Lees have posts on the subject that are worth reading. A lot of my consultancy work involves SSAS and MDX performance tuning and I think I see around five to ten customers every year who are seriously affected by this – and I’m sure this is just the tip of the iceberg. Even if you don’t think you are affected, you may have users who are putting up with slow refresh times for Excel-based reports without your knowledge.

Let’s go into some technical detail, see what the original problem was and what has changed in Excel 2016. I’m going to provide some examples using Excel 2013/2016 against SSAS Multidimensional but as I said the same problems occur (although less severely) with SSAS Tabular and Power Pivot.

Consider the following Excel 2013 PivotTable connected to the Adventure Works cube:

image

image

It’s typical of the kind of thing an end user wants to build, and there are two things to point out:

  1. Notice there are three fields that have been placed on the Rows axis of the PivotTable: the Gender and Marital Status hierarchies from the Customer dimension, and the Category hierarchy from the Product dimension. It’s pretty common for users to put many more fields together on an axis when they want to display detailed data.
  2. The user has also turned off grand totals and subtotals in the PivotTable so that only the detailed data is shown. This often happens in combination with (1) because if the user only wants to see the detailed data the subtotals and grand totals are a distraction.

In this case the PivotTable layout has been switched Tabular and the Repeat All Item Labels option is selected to make the PivotTable look like a table, but these are just aesthetic changes.

This PivotTable contains 12 rows, but if you look at the MDX query that is generated by Excel 2013 to populate it, it returns 36 rows of data (actually the MDX in this case returns 36 columns, but that’s a quirk of Excel) because the subtotals and grand total that Excel is not displaying are still being returned. Here’s the query:

SELECT 
NON EMPTY 
CrossJoin(
CrossJoin(
Hierarchize(
{DrilldownLevel(
{[Customer].[Gender].[All Customers]}
,,,INCLUDE_CALC_MEMBERS)}), 
Hierarchize(
{DrilldownLevel({[Customer].[Marital Status].[All Customers]}
,,,INCLUDE_CALC_MEMBERS)})), 
Hierarchize(
{DrilldownLevel({[Product].[Category].[All Products]}
,,,INCLUDE_CALC_MEMBERS)})) 
DIMENSION PROPERTIES PARENT_UNIQUE_NAME,HIERARCHY_UNIQUE_NAME 
ON COLUMNS  
FROM 
[Adventure Works] 
WHERE ([Measures].[Internet Sales Amount]) 
CELL PROPERTIES VALUE, FORMAT_STRING, 
LANGUAGE, BACK_COLOR, FORE_COLOR, FONT_FLAGS

And here’s a sample of what it returns, with the unwanted values highlighted:

image

You can probably guess that these unwanted subtotals and grand totals make the query slower, and the more fields you put together on the rows or columns of a PivotTable the greater the number of subtotals/grand totals are returned and slower things get. PivotTables connected to SSAS or Power Pivot often become completely unusable with more than six or seven fields put together on the same axis, and the only workarounds before Excel 2016 are either to write custom MDX in a named set (I show how to do this here) or if you are using SSAS Multidimensional try to use member properties instead of separate fields (as Richard Lees shows here), but neither are really great alternatives.

Building the same PivotTable using Excel 2016, however, gives the following MDX:

SELECT 
NON EMPTY 
CrossJoin(
CrossJoin(
Hierarchize(
{[Customer].[Gender].[Gender].AllMembers}), 
Hierarchize(
{[Customer].[Marital Status].[Marital Status].AllMembers})), 
Hierarchize(
{[Product].[Category].[Category].AllMembers})) 
DIMENSION PROPERTIES PARENT_UNIQUE_NAME,HIERARCHY_UNIQUE_NAME 
ON COLUMNS  FROM [Adventure Works] 
WHERE ([Measures].[Internet Sales Amount]) 
CELL PROPERTIES VALUE, FORMAT_STRING, 
LANGUAGE, BACK_COLOR, FORE_COLOR, FONT_FLAGS

Which returns just the data needed for the PivotTable:

image

What has changed? Focusing on just the Gender hierarchy, the difference between the two queries is that in Excel 2013 the selection on Gender is given using the DrillDownLevel() function on the All Member of the hierarchy. Isolated and put in its own query, the set expression used looks like this:

SELECT 
{[Measures].[Internet Sales Amount]}
ON COLUMNS,
DrilldownLevel(
{[Customer].[Gender].[All Customers]}
,,,INCLUDE_CALC_MEMBERS)
ON ROWS
FROM
[Adventure Works]

image

Notice how the All Member, used by Excel to return subtotals and grand totals, is returned by the set. Excel 2016 instead just asks for the members on the Gender level of the Gender hierarchy, not including the All Member. Again, put inside its own query, you can see what it returns:

SELECT 
{[Measures].[Internet Sales Amount]}
ON COLUMNS,
{[Customer].[Gender].[Gender].AllMembers}
ON ROWS
FROM
[Adventure Works]

image

Apart from the fact that no All Member is returned, it’s also a lot cleaner and easier to read.

I’ve been very excited about this change since I first heard it was happening, and I think it will benefit a lot of people. A big thanks to Alexander Lahuerta and the rest of the Excel team for making this happen!

Monitoring SSAS Multidimensional Non Empty Filtering Using Profiler, Part 3

In Part 1 of this series I introduced the different types of non empty filtering that occur in Analysis Services Multidimensional and in Part 2 I showed how you can use monitor this activity using Profiler. In this, the final part of the series, I’m going to show some examples of how you can use this information while tuning MDX queries.

Let’s start by looking at the following query:

SELECT
{[Measures].[Internet Sales Amount]}
ON 0,
NON EMPTY
[Customer].[Customer].[Customer].MEMBERS
*
[Product].[Subcategory].[Subcategory].MEMBERS
ON 1
FROM
[Adventure Works]
WHERE([Date].[Calendar Year].&[2003])

 

It returns 19004 rows – all of the combinations of Customer and Subcategory that have a value in the year 2003:

image

Here’s what you can see in Profiler:

image

There are two Non Empty operations here: the ProgressTotal column shows that first is the NON EMPTY statement on the rows axis; the second we can ignore because it’s the evaluation of the WHERE clause. The Duration column shows that the first Non Empty operation takes just 54ms and the query as a whole takes 1021ms.

Now, let’s make things a bit more complicated by adding an extra filter so we only see the Customer/Subcategory combinations where Internet Sales Amount is less than $10:

SELECT
{[Measures].[Internet Sales Amount]}
ON 0,
NON EMPTY
FILTER(
[Customer].[Customer].[Customer].MEMBERS
*
[Product].[Subcategory].[Subcategory].MEMBERS
,
[Measures].[Internet Sales Amount]<10)
ON 1
FROM
[Adventure Works]
WHERE([Date].[Calendar Year].&[2003])

image

Here’s what Profiler shows:

image

The query now takes 2512ms. But why is it slower? The obvious assumption to make is that it’s the Filter() that has slowed things down, but it looks like the Filter() and the NON EMPTY are now being evaluated as a single operation because the first Non Empty operation in the trace is now taking 2408ms – the majority of the query duration.

Removing the NON EMPTY statement from the rows axis and putting the logic to filter out the customers with no sales into the Filter() function, like so:

SELECT
{[Measures].[Internet Sales Amount]}
ON 0,
FILTER(
[Customer].[Customer].[Customer].MEMBERS
*
[Product].[Subcategory].[Subcategory].MEMBERS
,
[Measures].[Internet Sales Amount]<10 
AND 
(NOT ISEMPTY([Measures].[Internet Sales Amount])))
ON 1
FROM
[Adventure Works]
WHERE([Date].[Calendar Year].&[2003])

 

…only makes things worse, increasing query duration to 4139ms. This confirms our suspicion that Filter() is the problem here and that NON EMPTY can remove the empty customers faster than Filter() can.

The problem with the last query but one is that the NON EMPTY statement is being applied after the Filter(). Wouldn’t it be faster to remove the empty customers first and then filter out the ones where Internet Sales Amount is less than $10, so the slower Filter() can be applied over a smaller set?

There are two ways we can do this. First of all, we can use the NonEmpty() function instead of the NON EMPTY statement to remove the empty customers. NonEmpty() is not faster than the NON EMPTY statement per se, but it does allow us to change the order that the different types of filtering are applied here, and that can make all the difference to performance. Here’s a new version of the query:

SELECT
{[Measures].[Internet Sales Amount]}
ON 0,
FILTER(
NONEMPTY(
[Customer].[Customer].[Customer].MEMBERS
*
[Product].[Subcategory].[Subcategory].MEMBERS,
[Measures].[Internet Sales Amount]),
[Measures].[Internet Sales Amount]<10)
ON 1
FROM
[Adventure Works]
WHERE([Date].[Calendar Year].&[2003])

image

Query duration is now down to 217ms and the first Non Empty operation is only 57ms.

There’s another way of doing this. For MDX geek-points you could use the ultra-obscure HAVING clause in your query to do the filtering after the NON EMPTY, like so:

SELECT
{[Measures].[Internet Sales Amount]}
ON 0,
NON EMPTY
[Customer].[Customer].[Customer].MEMBERS
*
[Product].[Subcategory].[Subcategory].MEMBERS
HAVING
[Measures].[Internet Sales Amount]<1000
ON 1
FROM
[Adventure Works]
WHERE([Date].[Calendar Year].&[2003])

From what I can see, the HAVING clause performs a few milliseconds faster than the previous query – measurable but not something a user would notice. I also tested a variation on Mosha’s classic calculated measure approach for Count/Filter optimisation but that performed worse than the two previous queries.