Posts tagged ‘Development Patterns’

SQL Server Analysis Services Projects with Multiple Developers

A topic that often comes up when discussing enterprise level development with SSAS is how to have multiple developers work on the same project at the same time.  This issue doesn’t come up for many installations… a lot of teams get away with just having a single person working on their OLAP capabilities.  However, for a decent sized implementation, you’re going to want to have more than one person working on the solution at the same time.  I’ll be discussing some of the issues, workarounds, and tools you can use to make concurrent SSAS development easier.


Here’s a link to the source code from later in the post if that’s all you’re looking for.


Background


Analysis Services objects (cubes, dimensions, roles, etc.) are manipulated either programmatically or in Visual Studio (Visual Studio is the normal method). These objects are persisted by serializing them to XML. When you deploy an Analysis Services database, you either connect directly to an instance of Analysis Services, or you save off XMLA scripts/files that can be used to create the database on a remote server.


If you have a single person working on an AS project, you don’t have a problem.  If you’re using source control with exclusive locks (i.e., only one person can edit a file at a given time) you can have multiple people working on the same solution, but not on the same object at the same time.  This is somewhat complicated by the fact that modifying one object (such as a dimension) may require a change in associated objects (such as a cube where it is included).  You’re still fairly limited in the amount of work you can do concurrently.


The way to have multiple developers working concurrently is to use source control with non-exclusive check-outs, so multiple people can work on each file at the same time.  The down side is that you eventually have to merge the copies each person is working on back together.  Since the SSAS files are large, complicated XML documents this isn’t necessarily an easy task.  Most source control systems will attempt to automatically merge non-conflicting changes, but this usually doesn’t work very well with SSAS files (for reasons I’ll go into in just a minute).  There are, however, some things we can do to make the task a bit easier.


Challenges with Merging SSAS Files


When SSAS objects are persisted to XML, they contain structural information about the objects (which is required) as well as environmental and formatting metadata (which can be helpful in Visual Studio, but is not required for the solution to work correctly when deployed). The environment and formatting metadata elements tend to be extremely volatile, and vary for each developer. Stripping the volatile fields from the XML files will make the merge process easier without affecting the cubes and dimensions that are deployed.


Ex. A developer checks out “Adventure Works.cube” , fixes an error in a Description field, then deploys the cube to test. When he checks the file in, he will have to merge large XML file. He has only changed one line, but large sections of the file will be different from copies checked out to other developers due to metadata capturing the state of Visual Studio and the local AS server. By stripping this metadata, the developer can focus on merging the one change that matters without having to verify every other change in the file.


SSAS Elements that can be Removed


The following represent the environment and formatting metadata elements that are persisted in Analysis Services files. These fields can all be safely stripped from Analysis Services files prior to merging to remove the large number of unimportant conflicts that normally occur.




























Element


Description


CreatedTimestamp


When the object was created.


LastSchemaUpdate


When the schema was last pushed to a SSAS DB. Updated when an object is deployed using the development environment.


LastProcessed


When the object was last processed. Updated when an object is processed using the development environment.


State


The state (processed, unprocessed) of the object. Updated based on actions in the development environment.


CurrentStorageMode


The current storage mode of the object. Updated based on actions in the development environment.


Annotations


Annotations and metadata around the positioning of various objects (such as tables) on the canvas. This data is usually updated every time an object is opened. This element does have a user impact. The annotations section is where the layout of DSV objects is stored, and there is value in arranging those objects. However, this is where most conflicts occur, so it is often worth removing this section and losing custom positioning.


design-time-name


A GUID assigned to each object. It is generated when an object is created (either by a user in BIDS or by reverse engineering an existing database.


Programmatically Removing SSAS Elements


I’ve create a PowerShell function ‘Clean-SsasProject’ that will iterate over all writable SSAS objects in a directory and remove the volatile elements by manipulating the XML.  The function will make a copy of every file it modifies.  It is written using using PowerShell v2 CTP3, but should be easy to back port if you need to.  I’ve included a commented out section that will process the .ASDatabase file as well… this is used for a particular scenario on our team, just including it in case it is handy for anybody.  Use the $WhatIf and $Debug flags to know what the function will do before you do it for real.  This code is geared to the project I’m working on currently, and you may want to modify it to meet your precise needs. 


I would recommend creating a backup of your solution before you try this script, just in case.  I’ve been using this for awhile with no ill effects, but you could have a scenario I never dreamed about, so…


***DO THIS AT YOUR OWN RISK.  IT WORKS ON MY MACHINE.  ***


Consider comparing the cleaned XML side by side with the original to make sure this process works for you… it’s worked fine for every project I’ve used it on, but better safe than sorry.


You can download the source here.


Using Clean-SsasProject (for an individual)


I have my environment configured to load all files with the pattern ‘*Library.ps1’ when PowerShell loads via the following script in my ‘Profile.ps1’ file:



   1: $powerShellScriptsDirectory = “c:\PowerShellScripts\”
   2: if (!$powerShellScriptsDirectory.EndsWith(“\”)) { $powerShellScriptsDirectory += “\” }
   3:  
   4: Write-Host Welcome $Env:Username
   5:     
   6: foreach($filename in Get-ChildItem $powerShellScriptsDirectory* -Include “*Library.ps1″)
   7: {
   8:     & $filename 
   9: }

I store the .ps1 file with Clean-SsasProject and the other functions it depends on in my PowerShell scripts directory, so it’s loaded every time the PowerShell environment loads.  You can then just run ‘Clean-SsasProject’ from the PowerShell prompt.  I also have a .Cmd file in my path to automatically clean my normal SSAS project.  It just uses the following commands:



   1: SET SSAS_PROJECT_LOCATION=C:\Source\MyProject
   2:  
   3: PowerShell -Command “Clean-SsasProject %SSAS_PROJECT_LOCATION%”

Running that command file will strip the volatile fields out of any file in the directory that is writable (i.e., checked-out of my source control system).


Using Clean-SsasProject (for a team)


This tool is designed to work when every team member does the following:



  1. Check-out all files required for a change.  Remember that modifying one object may require that another object be updated, so make sure and check out all objects that can possibly be affected).

  2. Make the change.

  3. Clean the files.

  4. Merge/Resolve conflicts.

  5. Build project output (if required for your solution… I’ll be posting on how to easy project builds/deployments in a few days)

  6. Check-in all files required for a change.

General Best Practices


There are some other general things you can do to make concurrent development a little bit easier (most of these go for software development in general, not just Analysis Services).  If you’ve attempted to have multiple developers work on a project, you’re probably doing all these things already.  Remember that it is always faster and easier not to have to merge when you don’t have to.


Do Separate AS Cubes and Databases by Subject Area


Including only related objects in a cube/database is a standard best practice. This approach avoids potential performance issues, increases manageability and maintainability, and improves the presentation and understandability for the end user. This design pattern also lessens the chance that multiple developers will need to be working on the same object at the same time.


Don’t Combine Unrelated Attributes in a Single Dimension


Including unrelated attributes in a single dimension causes problems with performance, maintainability, and general use of the solution. Including unrelated attributes also promotes conflicts by increasing the chance that developers working on unrelated areas will need to work on the same file.


Do Communicate and Schedule Work for Minimum Conflicts


Make sure to communicate with other developers to avoid working on the same objects when possible. If you need to work on the same object, ensure the design changes are compatible and that there is no way to optimize the work.


Major changes that will dramatically affect source merging should be performed with an exclusive lock on the file.


Ex. A developer wants to re-order the 200 calculated members in the calculate script. The developer should wait until everyone else has submitted their changes, then make the change and submit it.


Do Check-out late and Check-in Early


Minimize the time you keep AS files checked out. While it may take some time to develop new functionality for AS (modifying the source database, creating an ETL to load the database from a source system, etc.) the work in AS is typically fairly quick to do if properly designed and prepared for. Complete the design and other development before checking out the Analysis Services files.


Do Use Tools to Help Merge


Use a side-by-side differencing tool to compare and merge different versions of Analysis Services files. A good diffing tool will have features to make this operation significantly easier. Consider using a tool such as Beyond Compare for this task.  You can use this process to verify that Clean-SsasProject works for your solution the first time you it.


 


Next Steps


Modify the provided source/process to meet your needs and environment.  There is no 100% “right way” to handle development like this… everyone’s situation will be just a little bit different, and require a little bit of customization.  I’m just trying to give you the tools to make it a little bit easier.


Conclusion


That’s all there is.  If you use the tools, techniques, and approach above it should make developing Analysis Services solutions with multiple developers a bit easier for for you.  You’ll still have some of the headaches normally associated with this type of work, but hopefully you’ll have an easier time of it.


Cheers,


David

Testing and Tuning an Internet Facing PerformancePoint M&A Dashboard

I’ve spent the past 15 months, off and on, working as the technical lead on a performance management project using PerformancePoint Server.  The project was to create a SharePoint Portal where users could view the performance of a school system.  The portal was initially internally facing, but the goal was to make it publicly accessible.  My last project with this particular client was to implement an Internet facing version of the portal. 


Since the school system in question has more than 120,000 students, scalability and performance was definitely an issue.  I spent a week at the Microsoft Technology Center in Atlanta, GA testing and tuning the solution.  We performed our testing in a lab with seven desktop machines (to test with) and three servers to simulate our production environment.  I was joined for the week by one of the PPS M&A Architects, who helped me evaluate the solution.  Everyone at the MTC was super helpful and friendly, and we got a lot of really good advice. 


I want to share some of the things I learned during the performance testing of the solution.


Recommended Reading


A few good things to read before getting started on a venture like this (among all the other white papers about scaling SQL Server, SharePoint, and the like).



Steps


To performance test and tune our SharePoint site, we needed to…



  1. Deploy the site in the test lab.

  2. Create load tests.

  3. Tune Windows Server, SharePoint, IIS, PerformancePoint Server, SQL Server, and Analysis Services as needed.

  4. Run and analyze the load tests.

  5. Modify the PPS design as required.

Just five little items on the list… how hard can that be, right?  Items #2 through #5 were done in iterations.


System Under Test


The application we created uses SharePoint Enterprise Server 2007 (MOSS), PerformancePoint Server Monitor & Analyze 2007 (SP1), and SQL Server 2005 (SP2 CU8).  Both SQL Server RDBMS and SSAS reside on the same server.  The RDBMS supports only SharePoint and PerformancePoint.  Analysis Services is dedicated to the Dashboards. 


Data Source Design


The data source is a SQL Server Analysis Services DB.  It uses a straightforward star schema design.  All of the KPI’s that we’re using in the PPS Dashboards are surfaced using calculated measures in the cube.  This is definitely a good way to go.  This design makes developing the dashboards very easy, it encapsulates the logic of the KPIs, and it helps with efficient caching inside of SSAS.  The cube was less than 1 GB in size for this phase of the project.  Some default aggregations were built, and usage based aggregations were applied against the cube.  Query performance was base lined at a few dozen milliseconds for the largest/most complex queries once the cache was warmed.


Dashboard Design


Our portal has seven dashboards.  Each dashboard has several pages. 



  1. The Overall view contains a scorecard with a Year Member Selection filter.  Each KPI on the scorecard conditionally displays an Analytic Grid and a Web Page report (pointing to a HTML file in SharePoint).

  2. The Compare view contains two scorecards, with up to 14 filters (primarily Member Selection… more on this later).  Seven filters apply to each scorecard; users can make different selections, and compare the two views.

  3. The Look Inside the Data view contains a single scorecard with up to 12 filters (and a few more columns than the Compare view, to show historical values).

The Analytic Charts were all fairly straightforward, with a default view of a calculated measure against time.  The users can drill into the charts. 


The filters were primarily Member Selection, with at most a few dozen members.  We have one MDX filter (for Schools), that returns about 400 members (this will come into play later).


The Dashboard was implemented using Application Security.


SharePoint


I enabled Anonymous Authentication for SharePoint.  The site basically supports all of our Dashboards, as well as a number of Help files and some content to help people use the site and the data.  I customized some of the master pages (to remove features such as SharePoint Help, Search, the breadcrumb trail, and stuff like that), but the site design was fairly trivial for this version of the site.  Excel Services and other MOSS features were not used.


Deploying to a Test Environment


Our friendly neighborhood Microsoft rep arranged for us to have some time in the Microsoft Technology Center in Atlanta, GA.  The provided an environment for us to test in, and they provided three servers (to mimic our production hardware) and seven desktop machines for us to run load tests on.  It took a bit ~1.5 days to install/configure the software, install service packs, and deploy our solution (include a SharePoint site collection, PPS M&A Dashboards, and SSAS DB).


Hardware Under Test


We tested our solution on the following hardware.  Our production hardware was similar.  A hardware network load balancing appliance was used for the web front ends.


Web Front End (1)



  • DL380 G5

  • Dual Core x 2 – 2.66 GHz – 64 bit

  • 8 GB RAM

  • Windows Server 2003 (SP2)

  • IIS 6.0

  • SharePoint Enterprise Server 2007 (SP1+Infrastructure update)

  • PerformancePoint Server Monitor & Analyze (SP1)

Web Front End (2)



  • DL 382 G5

  • Dual Core x 2 – 2.6 GHz – 64 bit

  • 4 GB RAM

  • Windows Server 2003 (SP2)

  • IIS 6.0

  • SharePoint Enterprise Server 2007 (SP1+Infrastructure update)

  • PerformancePoint Server Monitor & Analyze (SP1)

SQL Server (RDBMS and SSAS)



  • DL 585 G2

  • Dual Core x4 – 2.66 GHZ – 64 bit

  • 16 GB RAM

  • Windows Server 2003 (SP2)

  • SQL Server (SP2 CU8)

  • RAID10 disk array

Test Rig


We used Visual Studio 2008 (SP1) Team Systems for Testers (VSTT) to perform our testing.  We used one machine as a Controller and four machines as Agents.  We saved our test results to SQL Server on a separate machine.


Test Machines (x5)



  • Dual core 64 bit

  • 2 GB RAM

  • Vista

This configuration let us scale to 4,000+ users without severely taxing any of the machines.  Due to the network appliance being used for our load balancing, IP Spoofing was not required for our test machines.  We had some issues with using Vista for the agents… it was just a little bit trickier to configure than XP.  We were never able to get the counter collection working for those machines (annoying, but not really an issue since we had so much capacity and all the machines were in the same room).  We didn’t run into any major issues getting our test rig up and running, but it definitely took some time.


Creating the Load Tests


In order to do our tuning and performance testing, we first had to create everything needed to create a load test.  VSTT is a good tool for load testing, but be aware that there is a learning curve.  If you don’t have any experience performing web based load testing, then this could be a significant hurdle.  Luckily, in a previous life I was a Load Test Architect, and I had experience with the tool.


Test Design


I first identified several use cases that I thought would be common for users of our portal.  These consisted of actions such as viewing the top level summary Dashboards, using filters to change the view of the scorecards, clicking on analytic grids, and things like that.  I created a mixture of coded and graphical Web Tests for each small work flow (such as changing a filter four times on a certain Dashboard, or clicking through eight charts on a Dashboard).  I then used Web Tests that call other Web Tests to build up my use cases.  The use cases were then combined in different ratios in each of my load tests so I could simulate a variety of user populations.


Designing your tests correctly is crucial.  If you’re way off on what the users will be doing, all of your performance tuning could be for naught.  I would recommend using a pilot group, or at least some test subjects, to figure out how people will use your portal.


Web Test Implementation


Microsoft Consulting Services provided a test harness for PPS.  The test harness is a coded web test that can parse the XML snippets used by PPS, and perform actions such as changing filter values, activating charts, etc.  It’s a pretty cool tool, and was very helpful.  Unfortunately, it is proprietary, so I can’t share it… you’ll have to get engage MCS if you want to use it.  I did make some modifications to it to change the way it would use filters, to make it work when Web Tests called other Web Tests, etc.  If anyone is using this test harness (or if it does become publicly available), I can share the (minor) modifications that I made.


I constructed all of the visual web tests using Fiddler 2 (in conjunction with a home grown helper application to insert comments, and do a few other useful tasks).  Thanks to the MCS test harness, a few extra tools, and the way the web tests were constructed, the actual test generation was very straightforward.


Testing Considerations


One of the key elements to consider when performing a load test against PPS is that, due to the architecture, the process of loading a single page may involve dozens of calls.  For example, a dashboard that has 15 filters, two scorecards, and two charts would actually make 20 http requests… so if each request takes 1 second, it takes 20 seconds to load the page.  I was primarily interested in the time to last byte for each page, so I used comments while recording my tests to know where to place the transactions, so I could analyze those instead of individual calls.


I implemented reasonable timing and pacing for the individual steps in each of the tests, and made sure to gradually ramp up my users when applying the load.  Testing was slightly weighted towards the Dashboards that were the largest and would have the heaviest usage, though another use case was used to randomly hit every part of the site to bust the cache if possible.


System Tuning


We tuned a few things, or at least reviewed the settings, out of the gate.  During our testing we continuously monitored the servers (and test machines), identified bottlenecks, tweaked settings, then did it all again.


During our initial testing, we found SSAS to be the bottleneck for our solution.  I suspect that our web front ends (SharePoint and/or IIS) could be tweaked a little more, but it wasn’t really necessary in our scenario.


ASP.NET Tuning


We started tuning ASP.NET with some of the suggestions in the article Contention, poor performance, and deadlocks when you make Web service requests from ASP.NET applications.  In the end, I found the performance to be good using the autoConfig=”true” (the formulas listed in the article are automagically applied) with a few tweaks.  I increased the number of threads available significantly… that was a major bottleneck for awhile.  I also increased the minWorkThreads to 50 (otherwise SharePoint stalled after adding a few hundred users while spinning up more threads, even when ramping users up fairly slowly).  I also increased the minFreeThreads and minLocalRequestFreeThreads.  I increased the number of connections to the DB server.  Don’t just blindly apply any of these settings… your mileage will vary.  I went through a number of test iterations carefully monitoring each of the servers and testing these settings. 


I made the following change to the machine.config (on all web front ends):

  1: …
  2:   <system.web>
  3:     <processModel autoConfig=“true” maxWorkerThreads=“400″ maxIoThreads=“400″ minWorkerThreads=“50″ />
  4:     <httpRuntime minFreeThreads=“352″ minLocalRequestFreeThreads=“304″ />
  5:     …
  6:   </system.web>
  7: …
  8:   <system.net>
  9:    <connectionManagement>
 10:      <add address=“10.1.11.11″ maxconnection=“48″/>       ? Database server ip address
 11:   </connectionManagement>
 12:   </system.net>
 13: …
 14: </configuration> 

IIS Tuning


I adjusted the application pool (for both the Performance Management site as well as the Central Administration page). Pinging and Rapid Failure were turned off on the Health tab. I did NOT use web gardens in this configuration, even though I have seen it recommended.  I did not see a difference in performance between using them and not, and was advised by one of the local IIS gurus not to use them.


SQL Server 2005 Tuning


We updated SQL Server to Service Pack 2 – Cumulative Update 8 (build 3257). Before applying the CU, we ran into some significant performance issues.  SSAS did not like some of our very small dimensions, and performance was severely degraded.  The CU resolved the issues. 


Data files, transaction logs, and the Temp DB were spread out over multiple disks.  Our usage of the RDBMS was very light, so we needed minimal tuning here.


SQL Server Analysis Services 2005 Tuning


I updated the Query \ Max Threads element of Analysis Services to 20 to provide additional threads for querying. Adjusting this element needs to be done carefully, as it can improve the performance of the web front ends at the expense of maxing out SQL Server.  I actually ramped this number up quite a bit during testing (with good effect), but found 20 to be adequate after resolving some other issues.  I suggest monitoring the queued requests in IIS along with the queued and active threads in SSAS to determine a good balance here.  I initially found a lot of queuing in the Long Parsing Jobs and Short Parsing Jobs as well, due to some filters.


PerformancePoint Server M&A Tuning


OK, this is important.  Since we Internet facing, we’re supporting anonymous users… a decent number of them, in fact.  I updated the stored procedure [PPSMonitoring]. [dbo].[ParameterValuesCreate] to comment out a section that is not needed for an anonymous site, but that has an extremely negative impact on performance.  This is the piece that stores the current filter selections.  In addition to not being necessary, it was totally thrashing our transaction log. 


I made the following change to the [PPSMonitoring]. [dbo].[ParameterValuesCreate]  sproc:

  1: – init 
  2:     SET @TransactionIsOurs = 0
  3:     IF @@TRANCOUNT = 0
  4:     BEGIN – only if @@TRANCOUNT is 0, we do BEGIN TRAN
  5:         BEGIN TRANSACTION
  6:         SET @TransactionIsOurs = 1
  7:     END
  8: – DD – 20080821 – Commented out the following section to increase performance
  9: –    –If this parameter value already exists (for this login), update. Otherwise, insert.
 10: –    IF(EXISTS(SELECT * FROM [ParameterValues] WHERE Login = @Login AND [ParameterUniqueName] = @ParameterUniqueName))
 11: –    BEGIN
 12: –        SELECT 1
 13: –        UPDATE [ParameterValues]
 14: –        SET 
 15: –            [LastUpdated] = GETDATE(),
 16: –            [SerializedXml] = @SerializedXml
 17: –        WHERE [Login] = @Login AND [ParameterUniqueName] = @ParameterUniqueName
 18: –        IF @@ERROR <> 0 
 19: –        BEGIN
 20: –           RAISERROR (5580001, 16, 1, @tErrMsg, 7, N’ParameterValues’) WITH LOG
 21: –           SET @ReturnCode = -1
 22: –           GOTO exit_label
 23: –        END
 24: –    END
 25: –    ELSE
 26: –    BEGIN
 27: –        –Insert record
 28: –        INSERT INTO [ParameterValues]
 29: –            ([Login], [ParameterUniqueName],[LastUpdated],[SerializedXml])
 30: –        VALUES     
 31: –            (@Login, @ParameterUniqueName, GETDATE(), @SerializedXml)
 32: –        IF @@ERROR <> 0 
 33: –        BEGIN
 34: –           RAISERROR (5580002, 16, 1, @tErrMsg, 8, N’ParameterValues’) WITH LOG
 35: –           SET @ReturnCode = -1
 36: –           GOTO exit_label
 37: –        END
 38: –    END
 39:
 40:     IF @TransactionIsOurs = 1
 41:     BEGIN
 42:         COMMIT TRANSACTION
 43:     END
 44:     RETURN 0


SharePoint Tuning


There is a lot of tuning that can be done to SharePoint; tuning SharePoint was not a focus for us.  We did perform a few changes. 


Allowing the SharePoint Server worker process to consume large amounts of memory can decrease performance. For computers that have more than 4 GB of RAM, the ASP.NET cache size can be constrained with the privateBytesLimit attribute, set on the cache element of the Web.config file. By setting privateBytesLimit=”2576980378” (that is, 60% of 4 GB), you can avoid a scenario in which a server that has more than 4 GB of memory creates an oversized cache.


For a machine with 8 GB of RAM with a limit of 60% of the memory, we would add the following to the web.config. The private bytes limit number is calculated via the formula (<gigabytes of RAM available> * <percentage of RAM to use>* 1,024^3). 


I made the following change to my web.config file for the SharePoint site:

  1: …
  2: <system.web>
  3: …
  4: <caching>
  5:   <cache privateBytesLimit = “5153960755″ />
  6: </caching>
  7: …
  8: </system.web>
  9: …

Tweaking the Dashboard Design


Overall, I was expecting to be able to support about 500 concurrent users on each of the front end servers.  I expected the WFE’s to be the bottleneck.  With the original implementation of the Dashboard, I found we could support up to about 2,000 users… as long as they weren’t using our Comparison and Look Inside the Data pages. 


When testing those pages, SSAS was getting slammed.  I was able to support 1,000 users… but SSAS was at 80% utilization constantly, with occasional spikes and severe performance degradation.  Not a really good situation.


Through testing and profiling, I identified the culprit… it was the School filter.


The School filter is an MDX filter, and it looks like this:

  1: FILTER(
  2:   DESCENDANTS(
  3:     [Entity].[Entity]
  4:     ,[Entity].[Entity]
  5:     ,SELF_AND_BEFORE
  6:   )
  7:   ,[Entity].[Entity Type] = [Entity].[Entity Type].&[SCHOOL]
  8: )

No big deal, right?  Except… each time it is loaded (i.e., a lot) it fires off a few other MDX statements.  They look like this:

  1: SELECT
  2:   {   [Entity].[Entity].[All]
  3:     ,[Entity].[Entity].&[School A]
  4:     ,[Entity].[Entity].&[School B]
  5:     //…about 400 more
  6:   } DIMENSION PROPERTIES MEMBER_TYPE on 0
  7: FROM [MetricsDatamart]
  8: 

and this:
  1: SELECT
  2:   {
  3:    IIF([Entity].[Entity].[All].Parent IS NULL,[Entity].[Entity].[All],[Entity].[Entity].[All].Parent)
  4:   ,IIF([Entity].[Entity].&[School A].Parent IS NULL,[Entity].[Entity].&[School A],[Entity].[Entity].&[School A].Parent)
  5:     // … about 400 more
  6:  } DIMENSION PROPERTIES MEMBER_TYPE on 0
  7: FROM [MetricsDatamart]
  8: 

These are fairly long statements, and they are expensive to run.  SSAS was having to queue dozens of these statements just to parse them when under load.  They are fired for each page load, even though our data is static (i.e., no caching is occurring).  All those inline IIF’s did horrible, terrible things to SSAS.  SSAS was getting very backed up, which caused IIS to queue, which caused extremely long waits on the front end.


To fix the problem, I switched that filter to use a SQL Server RDBMS source.  The data was static, so no big deal there.  I did find that using more than 1 tabular source gave me an error in PPS… I wasn’t able to resolve that on our timeline, and I was able to fix the performance issue anyway.


Final Results


After that one change, the performance of the whole system skyrocketed.  With our heaviest test case, I was able to run 1,500 concurrent users. The WFE processor was at ~70% utilization, averaging 300 requests per second with no contention or queuing. The database server was at ~20 utilization, no issues. Responses were < 1 second, with an average response time of ~.3 seconds. User experience excellent. Even when the response times would increase, page load times were still reasonable (under 10 seconds for pages with more 15 objects).


Next Steps


We got everything pretty much humming for this phase of the project… but we’re building a much larger version for our client in the very near future.  Here are some thoughts on making our solution bigger, faster, better.


SharePoint


We can tune SharePoint quite a bit to increase performance.  This includes lightening some of the templates and base pages, optimizing images and web pages, and making sure all the user supplied content is fairly lean (i.e., not using a 300kb file produced by Word when a 20kb html file could be used).  We can use some of the caching features available in MOSS to improve the performance on the web servers.


PerformancePoint Filters


OK, I saw our filters as a bit of a bottleneck.  For a large installation, I think performance could be significantly improved by building a custom filter using the Monitor SDK.  Some of the key features would be enabling caching and perhaps using XMLA discover queries instead of some of the MDX.  I’d also make the ordering of the elements a little more configurable, and perhaps try and implement a way to create a ‘global filter’ (I find I use a single filter on many different Dashboards… and I’d like to reduce the maintenance/development time/improve consistency.


Servers


Scaling out the web servers and scaling up the SQL Servers looks like a fairly effective strategy here.  Coupled with some custom components and a well thought out design, this looks like an effective strategy with fairly predictable performance characteristics.  We could easily double our capacity right now by snagging two extra WFEs.  To scale by a factor of 10, we’d probably need an extra SQL Server and some minor changes to our strategy.


Key Takeaways


Our testing went well, and we were able to exceed the performance we were expecting.  Here are a few key takeaways from this exercise.



  1. Plan on hunting down some performance bottlenecks.  In a PPS deployment, they can be located on any tier, and it may take a little time to find and fix them.  We had to make adjustments to basically every technology in this instance.

  2. You’re going to want to tune your servers.  It took a little over a week for me to go through that exercise, but I had help, a lab, and experience with all the various components. You’re mileage may vary.  We identified and corrected a number of significant performance issues… everything from tweaking IIS and SQL Server to ensuring the correct Cumulative Update was applied.  Make only small changes for each test iteration, and analyze carefully.  And document the changes you make.

  3. The components you use, and your design, will have extreme ramifications on performance.  If your dashboards heavily use PAS views vs. Excel Services vs. Analytic Charts, your performance characteristics will change dramatically.  Carefully consider the ramifications of the different technologies, and look at where the work is being done in your configuration.

  4. Overall, scalability and performance was good, and everything was pretty much in line with what I saw in the Performance Tuning and Capacity Planning for PerformancePoint Monitoring Server.  Keep in mind that your implementation will probably be be somewhat different than the PPS Team’s test installation, so keep that in mind in your planning.

Happy PerformancePointing…


David