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SQL Server Best Practices Article

Published: June 7, 2007

Writers: Denny Lee, Nicholas Dristas, Carl Rabeler

Contributors: Kevin Cox, Craig Utley, Eric Jacobsen, Akshai Mirchandani

Technical Reviewer: Sasha (Alexander) Berger, Dave Wickert

Applies To: SQL Server 2005 SP2

Summary: Following the best practices described in this article will help you improve the performance of OLAP database processing.

On This Page

Introduction
Dimension Processing Best Practices
Cube Processing Best Practices
Parallel Processing Best Practices
Troubleshooting Best Practices
Summary

Introduction

The recommendations in this white paper provide best practices for processing in Microsoft® SQL Server™ 2005 Analysis Services. These performance optimizations are based on real-world implementations with some of our top customers and partners who use SQL Server 2005 Analysis Services Enterprise Edition.

This document presents Analysis Services processing best practices recommendations in the following sections:

Dimension Processing Best Practices

While dimension processing is quite fast in Analysis Services 2005, here are some best practices to help optimize dimension processing. For more information on the inner workings of Analysis Services dimension processing, see the "Processing Engine" section in Analysis Services 2005 Processing Architecture by T.K. Anand.

Add indexes to the underlying table

In most cases, adding indexes to the underlying table improves dimension processing because the SQL statement that is generated is in the form of SELECT DISTINCT AttribA, AttribB FROM DimensionTable. Building good attribute / hierarchy relationships forces the Analysis Services engine to build multiple smaller DISTINCT queries instead of one large and very expensive SELECT DISTINCT AttribA, AttribB, ..., AttribN query. Therefore, additional indexes improve the performance of these DISTINCT queries.

Create a separate table for dimension processing

If you experience slow dimension processing, creating a new table that is used only to process the dimension can improve processing speed. For example, in the case of a snowflake dimension where multiple tables are joined together to define your dimension, consider creating a separate new table specifically for processing the dimension. This is especially important if you have dimensions that are designed on top of disparate databases; this way you can avoid OPENROWSET processing queries.

Use SQL views to implement query binding for dimensions

While query binding for dimensions does not exist within Analysis Services 2005, you can implement it by using a view (instead of tables) for your underlying dimension data source. That way, you can use hints or other SQL tuning techniques to optimize the SQL statement that accesses the dimension tables through your view.

Be careful when you use the ProcessUpdate enumeration

If you use ProcessUpdate and dimension members are being changed or deleted, flexible aggregations will be dropped. These must be recreated by processing the indexes on the affected partitions and objects or by enabling lazy aggregations. Otherwise, there will not be any aggregations for users to query against; this results in slower query performance. Also, if rigid relationships have been defined and a change is detected, an error will be returned, requiring you to perform a full process.

Use the ProcessAdd enumeration if you are only adding new dimension members

The ProcessAdd process enumeration is not available in the processing UI but it is available within XMLA. Introduced in Analysis Services 2005, it is essentially an optimized version of ProcessUpdate in cases where only new members are being added. To use ProcessAdd correctly to improve performance, restrict the dimension table to only the new rows added. For more information on this, including sample code, see the second set of code in Analysis Services 2005 Processing Architecture on the Microsoft Developers Network (MSDN). This processing type allows dimension processing to be much faster as it will never delete or update existing dimension members nor does it drop flexible aggregations.

Upgrade to Analysis Services SP2 because it has dimension process caching

Analysis Services Service Pack (SP2) provides processing improvements through dimension processing caching. To take advantage of this feature, for larger systems add memory so that Analysis Services can use it. (It is not uncommon for Analysis Services to use 10 GB (gigabytes) of memory if it is available.) This prevents it from paging to disk.

Performance improvements when using dimension process caching are typically seen when processing on servers with two or more processors. For example, the following table shows processing times for a 4-GB RAM, four-processor server that is processing 32 partitions in parallel. Both the Analysis Services and SQL Server databases are on the same server.

Service Pack

Process time (hh:mm:ss.sss)

SP1

00:13:28.020

SP2

00:12:29.966

Cube Processing Best Practices

To ensure fast and effective cube processing, it is important to remember that cube processing is performed in Analysis Services by executing Analysis Services-generated SQL statements against the underlying relational database. Because of this, it is possible (when necessary) to tune the underlying SQL statements and/or update statistics. Adding indexes to the underlying tables may improve performance as well, but test this first as table scanning is often a faster approach for cube processing.

Use integer keys if at all possible

When working with any version of Analysis Services, ensure that the key columns are integer. This is important for both SQL and non-SQL RDBMS systems because of the SQL statement generated by Analysis Services when it is processing a cube, measure group, or partition. The resulting query plan and execution of this SQL statement will be more efficient if the joins are between integer keys.

Performance improvements are even more apparent with Analysis Services SP2 due to its dimension processing caching feature. These improvements also optimize in the case of two composite integer keys of sizes 1, 2, 4, and 8 bytes. While string keys provide limited improvement, this is not as evident as when using integer keys.

Following is the SQL query that is generated by Analysis Services when processing the [Internet Sales] measure group in the [Adventure Works DW] OLAP database.

SELECT 
     [dbo_FactInternetSales].[CustomerKey] AS 
     [dbo_FactInternetSalesCustomerKey0_0],
     [dbo_FactInternetSales].[PromotionKey] AS 
     [dbo_FactInternetSalesPromotionKey0_1],
     [dbo_FactInternetSales].[SalesTerritoryKey] AS 
     [dbo_FactInternetSalesSalesTerritoryKey0_2],
     [dbo_FactInternetSales].[OrderDateKey] AS 
     [dbo_FactInternetSalesOrderDateKey0_3],
     [dbo_FactInternetSales].[ShipDateKey] AS 
     [dbo_FactInternetSalesShipDateKey0_4],
     [dbo_FactInternetSales].[DueDateKey] AS 
     [dbo_FactInternetSalesDueDateKey0_5],
     [dbo_FactInternetSales].[ProductKey] AS 
     [dbo_FactInternetSalesProductKey0_6],
     [dbo_FactInternetSales].[CurrencyKey] AS 
     [dbo_FactInternetSalesCurrencyKey0_7]
FROM  (
     SELECT 
          [dbo].[FactInternetSales].[ProductKey],
          [dbo].[FactInternetSales].[OrderDateKey],
          [dbo].[FactInternetSales].[DueDateKey],
          [dbo].[FactInternetSales].[ShipDateKey], 
          [dbo].[FactInternetSales].[CustomerKey], 
          [dbo].[FactInternetSales].[PromotionKey],
          [dbo].[FactInternetSales].[CurrencyKey],
          [dbo].[FactInternetSales].[SalesTerritoryKey],
          [dbo].[FactInternetSales].[SalesOrderNumber],
          [dbo].[FactInternetSales].[SalesOrderLineNumber],
          [dbo].[FactInternetSales].[RevisionNumber],
          [dbo].[FactInternetSales].[OrderQuantity],
          [dbo].[FactInternetSales].[UnitPrice],
          [dbo].[FactInternetSales].[ExtendedAmount],
          [dbo].[FactInternetSales].[UnitPriceDiscountPct],
          [dbo].[FactInternetSales].[DiscountAmount],
          [dbo].[FactInternetSales].[ProductStandardCost],
          [dbo].[FactInternetSales].[TotalProductCost],
          [dbo].[FactInternetSales].[SalesAmount],
          [dbo].[FactInternetSales].[TaxAmt],
          [dbo].[FactInternetSales].[Freight],
          [dbo].[FactInternetSales].[CarrierTrackingNumber],
          [dbo].[FactInternetSales].[CustomerPONumber]
     FROM [dbo].[FactInternetSales]
     WHERE OrderDateKey >= '915' AND OrderDateKey <= '1280'
)
AS [dbo_FactInternetSales]
ORDER BY [dbo_FactInternetSales].[CustomerKey]
ASC

The SQL statement, and particularly the query plan, that are generated by Analysis Services are different when the underlying fact table for the [Internet Sales] Measure Group uses varchar keys as shown in Figure 1.

Cc966525.ASPBPR01(en-us,TechNet.10).gif

Figure 1   Estimated query plan between varchar-key vs. integer-key fact tables

As you can see from the estimated query plan in Figure 1, the varchar-key fact table (Query 2) has a higher cost associated with the table scan than does the integer-key fact table (Query 1). Specifically, there are higher subtree costs associated with both the Sort and Table Scan tasks for the varchar-key fact table.

Task

Total Subtree Cost

Total Subtree Cost

 

Query 1 (integer keys)

Query 2 (varchar keys)

Sort

3.19582

3.375974

Table Scan

0.9134235

1.247498

Use query binding to optimize processing

You can improve cube processing performance by tuning the underlying SQL statements executed. For this reason, it is important to use query binding instead of table binding when you build your measure group partitions (see the SQL statements in Figures 2 and 3). Looking at the Adventure Works OLAP cube in Microsoft Visual Studio®, notice the Partitions tab as shown in Figure 2.

Figure 2   Adventure Works Partitions tab

Figure 2   Adventure Works Partitions tab

Clicking on the partition source opens the Partition Source dialog box as shown in Figure 3. As you can see, by using the query binding binding type, the data source is a developer-created SQL statement.

Cc966525.ASPBPR03(en-us,TechNet.10).gif

Figure 3   Partition Source dialog box

By using this binding type option, you can optimize the SQL query that is sent to the relational data source.

Partition measure groups if you have a lot of data

It is generally a good idea to map the measure group partitions to either separate SQL table partitions by using partition keys or onto a separate SQL table. Use partitioning wherever possible; this limits the amount of reprocessing that needs to be done based on your extraction, transformation, and loading (ETL). For more information, see "Partition Design Best Practices" in OLAP Design Best Practices for Analysis Services 2005 on Microsoft TechNet.

Use ProcessData and ProcessIndex instead of ProcessFull

The ProcessFull enumeration type executes the ProcessData and ProcessIndexes jobs. For more information on these process enumeration types, see the "Dimension-Processing Commands" and "Partition-Processing Commands" sections in the SQL Server Analysis Services 2005 Performance Guide.

Breakdown of processing enumerations

While testing, the Microsoft SQL Server Development Customer Advisory Team (SQLCAT) noticed that the processing against the relational database completes faster and Analysis Services consumes fewer memory resources when the ProcessData and ProcessIndexes enumerations are used as compared to using ProcessFull. Memory considerations are especially important on 32-bit systems because of their lower memory consumption. As you can see from the following table, there are noticeable improvements in processing time.

Process enumeration

Processing time

Two-step process

00:12:23

   ProcessData

00:10:18

   ProcessIndexes

00:02:05

ProcessFull

00:13:34

To change the processing in the XMLA script, you need only alter the <Process><Type> </Type></Process> attribute as shown in the following code example. XML namespaces have been removed from the code for clarity.

<Batch xmlns="...">
  <Parallel>
    <Process xmlns:xsd="..." xmlns:xsi="..." 
        xmlns:ddl2="..." xmlns:ddl2_2="...">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
      </Object>
      <Type>ProcessData</Type> --- or ProcessIndexes
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
  </Parallel>
</Batch>

For more information on the available processing enumerations, see SQL Server  Books Online, particularly the Type Element topic.

CPU usage patterns are different between the enumerations

The apparent reason for this difference is that the CPU usage patterns between the two different actions are quite different. See the performance monitor output in the next three figures (the % Processor Time is highlighted in the figures). As you can see, not only is there faster processing time by using ProcessData and ProcessIndexes, but there are also differences in CPU utilization as well (ProcessFull: 64.021, ProcessData: 47.847, ProcessIndexes: 54.353).

Cc966525.ASPBPR04(en-us,TechNet.10).gif

Figure 4   CPU usage pattern: ProcessFull, average 64.021

Cc966525.ASPBPR05(en-us,TechNet.10).gif

Figure 5   CPU usage pattern: ProcessData, average 47.847

Cc966525.ASPBPR06(en-us,TechNet.10).gif

Figure 6   CPU usage pattern: ProcessIndexes, average 54.353

As you can see from Figures 5 and 6, the ProcessData and ProcessIndexes enumeration behave differently from ProcessFull. For some customers, this difference resulted in less processing time against their relational data source.

To investigate the scalability of your system, process using ProcessData with one partition and MaxParallel=1. Then measure two partitions with MaxParallel=2, ..., and so forth. Measure and compare the impact (memory utilized, CPU usage, disk I/O, and so on) to determine optimal performance.

Parallel Processing Best Practices

Here are a number of techniques for optimizing parallel processing.

Do not always use the UI default for parallel processing

By default, when Analysis Services processes the selected cube, measure group, or partitions, the server decides how to process the object(s) in parallel. However, letting the server decide how many threads to process in parallel may not always be optimal. For example, for the [Adventure Works DW] sample OLAP database on a 4-GB RAM, four-processor server running Analysis Services SP2, the comparable processing times in seconds are:

Parallel processing setting

Process time (ss.sss)

Letting the server decide

50.990

Manually setting parallel to four tasks

47.347

To change the parallel processing setting from within the UI, from the Analysis Services Processing dialog box, click the Change Settings button to open up the Change Settings dialog box shown in Figure 7. From the Maximum parallel tasks list, select an appropriate value for the number of parallel tasks. The next section tells you how to determine the appropriate value for your system.

Cc966525.ASPBPR07(en-us,TechNet.10).gif

Figure 7   Analysis Services Change Settings dialog box

Set the appropriate values for parallel processing

Generally, the appropriate number of parallel tasks is 1.5 – 2 times the number of CPUs; exceeding this value may cause competition for resources. Always test the system to see how well parallelism will work for you. Since you have limited resources, a plausible option could also be to use less parallelism instead of more.

The ascmd.exe command-line utility is a handy tool for testing performance characteristics to determine the appropriate values for parallel processing. This utility is similar to the sqlcmd.exe utility that you use to execute SQL statements in SQL Server. The ascmd.exe utility executes XMLA commands against an Analysis Services server.

The ascmd.exe utility is included in SQL Server Samples, available at SQL Server 2005 Samples and Sample Databases on the Microsoft Download Center. After you install the samples, you can find ascmd.exe in the c:\Program Files\Microsoft SQL Server\90\Samples\Analysis Services\Administrator\ascmd folder. For more information on the ascmd command-line utility, see the Readme For Ascmd Command-line Utility Sample on MSDN.

Following is an example XMLA script to process the [Adventure Works DW] cube; XMLA name spaces have been removed for clarity.

<Batch xmlns="...">
  <Parallel MaxParallel="8">
    <Process xmlns:xsd="..." xmlns:xsi="..." 
        xmlns:ddl2="..." xmlns:ddl2_2="...">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
  </Parallel>
</Batch>

By using ascmd.exe and XMLA, you can adjust the XMLA MaxParallel parameter to try out different values. (The XMLA example set this to a value of 8; the previous tip, Do not always let the UI default for parallel processing, shows how to set this in the UI.) This setting specifies the maximum number of threads on which to run commands in parallel as in the Change Settings dialog box (accessed through the processing UI) in Figure 7.

Using the ascmd.exe utility and four different XMLA scripts, we were able to quickly automate and test four different parallel processing settings. Following are the results of this test. As you can see, for the particular machine configuration used in this test, it was beneficial to set the parallelism to 8 instead of letting the server decide.

MaxParallel settings

SQL/OLAP on

same server

SQL/OLAP on

different servers

Let the server decide (default)

00:12:29.966

00:07:43.060

Parallel = 4

00:17:05.000

00:08:07.953

Parallel = 6

00:12:57.023

00:07:37.023

Parallel = 8

00:11:28.983

00:07:31.933

Use the XMLA <Parallel> nodes to group processing tasks

In the processing tasks defined within your XMLA, create logical <Parallel></Parallel> sets to group processing tasks together so that the server uses the maximum resources. At the same time, get into the habit of processing the individual partitions (versus the entire measure group) using the <PartitionID></PartitionID> node to process less data at any one time.

Within the <Batch></Batch> element, if you set the Transaction attribute to true, all commands within the batch will be considered as a single transaction. If this attribute is set to false, every single processing command will be committed upon successful completion. This latter option is beneficial if you are processing different measure groups and/or objects in parallel.  As you can see in the following example, the XMLA is processing the [Fact Sales Summary] measure group and its individual partitions in parallel.

<Batch xmlns="..." Transaction="true">
  <Parallel>
    <Process xmlns:xsd="..." xmlns:xsi="..." ...="">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
        <MeasureGroupID>Fact Sales Summary</MeasureGroupID>
        <PartitionID>Total_Sales_2001</PartitionID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
    <Process xmlns:xsd="..." xmlns:xsi="..." ...="">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
        <MeasureGroupID>Fact Sales Summary</MeasureGroupID>
        <PartitionID>Total_Sales_2002</PartitionID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
    <Process xmlns:xsd="..." xmlns:xsi="..." ...="">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
        <MeasureGroupID>Fact Sales Summary</MeasureGroupID>
        <PartitionID>Total_Sales_2003</PartitionID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
    <Process xmlns:xsd="..." xmlns:xsi="..." ...="">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
        <MeasureGroupID>Fact Sales Summary</MeasureGroupID>
        <PartitionID>Total_Sales_2004</PartitionID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
  </Parallel>
</Batch>

For the sake of clarity, the xml namespaces in this example were removed. Note that the Transaction attribute has been set to true, meaning that the processing of the four [Fact Sales Summary] partitions will be committed as a single transaction. On a single-processor desktop computer, you may notice that the processing of this measure group will take 11s (seconds) when processing as a single measure group and 6s when processing as four individual partitions in parallel.

Troubleshooting Best Practices

These tips for troubleshooting processing will help you to understand your processing bottlenecks.

Profile your SQL statements to tune them

To help with tuning, you can use SQL Profiler to capture the SQL statements issued by Analysis Services so that you can perform your regular SQL tuning efforts (such as updating statistics, adding indexes, and so on). For example, processing the [Sales Summary] measure group in the [AdventureWorksDW] OLAP DB provides the SQL Profiler trace in Figure 8.

Cc966525.ASPBPR08(en-us,TechNet.10).gif

Figure 8   SQL Server Profiler trace for AdventureWorks processing

By selecting one of many the SQL statements issued, you can then run a query plan execution. In the case of the SQL statement selected in Figure 8, the total subtree cost was 2.285195.

Cc966525.ASPBPR09(en-us,TechNet.10).gif

Figure 9   Total subtree cost of 2.285195

In this particular case, the Sales Summary measure group is partitioned by the Order Date; the underlying SQL has a WHERE clause using the OrderDateKey. Adding a clustered index on the FactResellerSales table, OrderDateKey column would improve the performance of cube processing. The new query plan for the same SQL statement now has a total subtree cost of 1.142899 (Figure 10).

Cc966525.ASPBPR10(en-us,TechNet.10).gif

Figure 10   Total subtree cost 1.142899 with clustered indexes

Create a system-wide trace file

If you are having problems determining what is causing your processing bottlenecks, keep a system-wide processing trace file. Use it to determine which processing queries are running slower (or faster) than expected, as well as if they were to be executed at all. You can obtain this trace file by using SQL Profiler to record the information as described in the previous best practice, Profile your SQL statements to tune them.

Another handy way to do this is to run the following XMLA script (which is processing the Adventure Works cube), which outputs the trace to the c:\OLAP_Processing.trc file.

<Batch xmlns="http://schemas.microsoft.com/analysisservices/2003/engine" 
xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/">
  <Create mlns="http://schemas.microsoft.com/analysisservices/2003/engine">
    <ObjectDefinition>
      <Trace>
        <ID>Analysis Services Processing Trace</ID>
        <Name>Analysis Services Processing Trace</Name>
        <LogFileName>C:\OLAP_Processing.trc</LogFileName>
        <LogFileAppend>1</LogFileAppend>
        <AutoRestart>1</AutoRestart>
        <LogFileSize>50000</LogFileSize>
        <LogFileRollover>1</LogFileRollover>
        <Events>
          <Event>
            <EventID>5</EventID>
            <Columns>
              <ColumnID>7</ColumnID>
              <ColumnID>15</ColumnID>
              <ColumnID>39</ColumnID>
              <ColumnID>8</ColumnID>
              <ColumnID>12</ColumnID>
              <ColumnID>28</ColumnID>
              <ColumnID>32</ColumnID>
              <ColumnID>40</ColumnID>
              <ColumnID>1</ColumnID>
              <ColumnID>13</ColumnID>
              <ColumnID>25</ColumnID>
              <ColumnID>33</ColumnID>
              <ColumnID>41</ColumnID>
              <ColumnID>2</ColumnID>
              <ColumnID>14</ColumnID>
              <ColumnID>42</ColumnID>
              <ColumnID>3</ColumnID>
              <ColumnID>11</ColumnID>
              <ColumnID>43</ColumnID>
            </Columns>
          </Event>
          <Event>
            <EventID>6</EventID>
            <Columns>
              <ColumnID>7</ColumnID>
              <ColumnID>15</ColumnID>
              <ColumnID>23</ColumnID>
              <ColumnID>39</ColumnID>
              <ColumnID>8</ColumnID>
              <ColumnID>24</ColumnID>
              <ColumnID>32</ColumnID>
              <ColumnID>40</ColumnID>
              <ColumnID>1</ColumnID>
              <ColumnID>9</ColumnID>
              <ColumnID>25</ColumnID>
              <ColumnID>33</ColumnID>
              <ColumnID>41</ColumnID>
              <ColumnID>2</ColumnID>
              <ColumnID>6</ColumnID>
              <ColumnID>10</ColumnID>
              <ColumnID>14</ColumnID>
              <ColumnID>22</ColumnID>
              <ColumnID>42</ColumnID>
              <ColumnID>3</ColumnID>
              <ColumnID>11</ColumnID>
              <ColumnID>43</ColumnID>
              <ColumnID>4</ColumnID>
              <ColumnID>12</ColumnID>
              <ColumnID>28</ColumnID>
              <ColumnID>5</ColumnID>
              <ColumnID>13</ColumnID>
            </Columns>
          </Event>
          <Event>
            <EventID>8</EventID>
            <Columns>
              <ColumnID>7</ColumnID>
              <ColumnID>15</ColumnID>
              <ColumnID>39</ColumnID>
              <ColumnID>8</ColumnID>
              <ColumnID>24</ColumnID>
              <ColumnID>1</ColumnID>
              <ColumnID>5</ColumnID>
              <ColumnID>9</ColumnID>
              <ColumnID>13</ColumnID>
              <ColumnID>25</ColumnID>
              <ColumnID>41</ColumnID>
              <ColumnID>2</ColumnID>
              <ColumnID>10</ColumnID>
              <ColumnID>14</ColumnID>
              <ColumnID>22</ColumnID>
              <ColumnID>42</ColumnID>
              <ColumnID>3</ColumnID>
              <ColumnID>11</ColumnID>
              <ColumnID>43</ColumnID>
              <ColumnID>4</ColumnID>
              <ColumnID>12</ColumnID>
              <ColumnID>28</ColumnID>
            </Columns>
          </Event>
        </Events>
      </Trace>
    </ObjectDefinition>
  </Create>
  <Parallel>
    <Process xmlns:xsd="http://www.w3.org/2001/XMLSchema" 
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
xmlns:ddl2="http://schemas.microsoft.com/analysisservices/2003/engine/2" 
xmlns:ddl2_2="http://schemas.microsoft.com/analysisservices/2003/engine/2/2">
      <Object>
        <DatabaseID>Adventure Works DW</DatabaseID>
        <CubeID>Adventure Works DW</CubeID>
      </Object>
      <Type>ProcessFull</Type>
      <WriteBackTableCreation>UseExisting</WriteBackTableCreation>
    </Process>
  </Parallel>
</Batch>
 
<Delete xmlns="http://schemas.microsoft.com/analysisservices/2003/engine"
xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/">
  <Object>
    <TraceID>Analysis Services Processing Trace</TraceID>
  </Object>
</Delete>

Upgrade to Analysis Services SP2 for NUMA support

A big improvement to processing with SP2 is Analysis Services native support of non-uniform memory access (NUMA) hardware. For more information on NUMA, see Understanding Non-uniform Memory Access in SQL Server Books Online.

Prevent long-running queries from interfering with processing

Long-running queries that are executed concurrently with processing can prevent processing from completing. In order to commit the processing activity, Analysis Services takes an exclusive lock on the database. But, a long running-query will block this lock request and processing must wait for the queries to finish before processing can complete. If you cannot create an exclusive processing window, consider architecting a querying/processing Analysis Services server architecture as in Figure 11. The Analysis Services processing server exclusively processes data; upon completion, it synchronizes with the Analysis Services querying server, which solely renders MDX queries.

Figure 11   Querying / Processing AS server architecture

Figure 11   Querying / Processing AS server architecture

Yes, a SAN helps

Similar to SQL Server processing, Analysis Services processing executes faster on a Storage Area Network (SAN). Ensuring that the SAN (spindles, LUNs, and so on) and HBA (drivers, throughput, and so on) are properly configured, enables enterprise Analysis Services projects to process faster because they are not slowed down by suboptimal disk activity. For more information, see SQL Server I/O Predeployment Best Practices on TechNet.

Summary

Following the best practices described in this article will help you improve the performance of OLAP database processing.

For more information

Analysis Services 2005 Processing Architecture white paper on MSDN

OLAP Design Best Practices for Analysis Services 2005 white paper on TechNet

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