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Contents:
  1. MDX with Microsoft SQL Server 2016 Analysis Services Cookbook - Third Edition
  2. Microsoft Power BI, Analysis Services, MDX, DAX, M, Power Pivot and Power Query
  3. MDX with Microsoft SQL Server 2008 R2 Analysis Services Cookbook
  4. OLAP – Creating Cubes with SQL Server Analysis Services

The memory Perfmon counters that I mention in this post only apply to Vertipaq. If the VertiPaq engine holds all the data in memory, then what does a cache mean? It is true that VertiPaq data is stored in memory assuming you have enough memory, more on this later but VertiPaq compresses data in each column before it stores it. The cache holds uncompressed data so if a query can be answered from the cache, it will have a better performance since it will require less CPU.

In the case that VertiPaq data does not fit in memory, you have the option of enabling SSAS to page data to disk which is the default behavior.

MDX with Microsoft SQL Server 2016 Analysis Services Cookbook - Third Edition

It is important that you monitor how much data you are paging to disk. At a high level, once overall system memory reaches a certain limit Low Memory Limit , a cleaner thread wakes up and starts cleaning the least frequently used data sets and calculations out of memory. As the memory pressure increase the cleaner gets more aggressive. They have the same default values as in multidimensional.

Since VertiPaq is a memory based engine, there has been some changes in the way the cleaner thread runs so that it does not interact badly with the Vertipaq data. Here is what the above settings mean:.

Microsoft Power BI, Analysis Services, MDX, DAX, M, Power Pivot and Power Query

By default it is set to 1 which means paging is allowed. If the VertiPaq data needs more memory, an out of memory error is thrown.


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You can try this on a development server. For example if you have 32 GB of memory on a server and your database is taking 16 GB of memory, if you set VertiPaqPagingPolicy to 0 and try to process the database, you will most likely get an out of memory error.

MDX with Microsoft SQL Server 2008 R2 Analysis Services Cookbook

You can even try to submit a large query to the database while it is being processed to get the error faster! If you want to make it even worse, drop the VertiPaqMemoryLimit to a much smaller number only on a development box! The VertiPaqMemoryLimit is no longer a memory limit but it is the maximum amount of VertiPaq memory that is visible to the cleaner process.

Implementing SSAS Security

In other words, this limit is only used for calculating price of memory by the cleaner to decide how aggressively it needs to clean memory. With the default memory settings, this translates to the following numbers:. These are things such as sessions, connections, global variables and other non-data settings that SSAS needs for its internal operations. This counter shows the total amount of memory used by SSAS.

Not all of this memory is visible to the cleaner. This is possible since paging to disk is allowed. How much memory does the cleaner thread see now? Below you can see a rough estimate of how I think this would look like if you graphed it. The lighter blue line shows the memory visible to the cleaner for the purpose of calculating the price of memory. Disclaimer: I have not actually had this scenario recorded on a real server! Here is a screenshot of Perfmon from my test server where I intentionally dropped the memory limits very low to simulate a memory pressure situation:.

Notice how the price of memory is but nothing has been shrunk yet. When you look at your server, if you find Cleaner Current Price to be constantly high, you should look into adding more memory to your server or reviewing your databases design to see if you can reduce their size. For example, eliminate columns that are not actually being used and optimize your design.

OLAP – Creating Cubes with SQL Server Analysis Services

Think about reducing cardinality in a column if you can: Split DataTime columns into a Date column and a Time column. I once left a datetime column in a model and very soon it became the largest consumer of memory in my model. It is great that you can see all these counters in Perfmon, but how do you save this to a SQL Server table over time? You can use Perfmon Data Collectors to do this.

Perfmon Data collectors can collect counter values over time using a predefined schedule and stop criteria. You can set the sampling interval here. If you are troubleshooting a problem then you should choose a shorter sampling interval. Follow the default setting from the next couple of screens. We will change this data collectors target to a SQL Server table shortly so you can ignore the disk folder in the next screen. If you were to write the results to a comma separated file, this is where it would write it to. Fortunately we can write the results directly to a SQL Serve table.


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  4. In order to do this, first we need to create an ODBC data source. This is very easy to do! I kept the default settings for the connection properties so that Windows NT authentication is used. In the last screen, you get to choose the database that the results will be written into. In this case, I chose an empty database called Test. Follow the default setting for the rest of the screens and do a Test connection at the end to make sure your ODBC data source was created properly. As soon as you do this, a Data Source Name drop-down becomes active.

    From the drop down, choose the ODBC data source you just created.

    Account Options

    In this case, I chose Test. There is one last step you need to do before you can start the Data Collector set. This time right click on the Data Collector Set not the Data Collector itself and get the properties. If should look like this:. This is an important step. Once you have this set up, you can right click on the data collector set and choose start.

    A green arrow shows next to it that indicates it started successfully. At this point, the data collector is writing to your SQL database based on the interval you gave it. If you check the database, you will see three tables are created for you. XEvents traces have a much smaller footprint than SQL Server Profiler traces so they are suitable for monitoring production boxes.

    You can read this blog from Adam Saxton about Extended Events. Here I will only explain settings that are related to our goal here to collect query statistics. The only event that we need is called Query End. This is true for both multidimensional and tabular instances. This event will provide statistics such as query duration, cpu time, information about the user who ran the query, the database it was run against, etc. You can use a development version of SSAS to generate sample scripts and then edit the server name.

    Right click on Sessions and choose New Session. Give the session a name. The list by default filters all the events that have Query End in their name. Click on Data Storage. Here you have to choose at least one Target which is where the results will be stored.