Tag Archives: Analysis Services

How to Automate Processing of Azure Analysis Services Models

I’ve been working on a proof of concept for a customer that involved using Azure Analysis Services as a cache for some data in an Azure Data Warehouse instance. One of the things I’ve been working on is scheduling the automatic processing of the Azure AS database. I did find the following documentation on the process, but the screenshots of the Azure portal are out of date and I did find some errors in the instructions. I also found this very extensive project for partition management in Azure AS, but this was a little overkill for my purposes and I was just interested in the very basics.

Read my recap for MS Data Summit here

These previously mentioned resources led me to write this blog post. In this post I’m going to leverage the previously mentioned article and walk through creating an Azure Function App to automatically refresh my Azure Analysis Services model, while correcting a few errors and updating the screenshots.

If you’re new to Azure Analysis Services, take a look at this documentation. For the purposes of this post, I’m going to assume you have a basic understanding of Analysis Services.

Continue reading How to Automate Processing of Azure Analysis Services Models


What’s New in SQL Server Analysis Services 2016?

There’s a load of new features that are included in the release of SQL Server Analysis Services 2016 CTP2. I’m pretty excited about these changes and while these changes have been public for a while now, I’d like to share my thoughts. I’ll say that these features are included in the SSAS 2016 CTP2 release. This release does not include all the enhancements to SSAS 2016 and these enhancements are subject to change. You can read about the enhancements here. Continue reading What’s New in SQL Server Analysis Services 2016?

Three SSAS Best Practices to Follow

There is a lot of work that goes into performance tuning a SQL Server Analysis Services solution for a client. And even though there may be many steps involved in identifying performance and management issues with a large SSAS solution, there are a few things that we can quickly check and implement to give us quick wins for improving the performance of our cube. In this post, I’d like to quickly point out three best practices that we can follow to improve performance and create a more positive experience for our users. These are not meant to be the top three best practices to follow, but rather three (among many) very important best practices you should follow. I believe following these three best practices will make a difference in your solution.

Create Hierarchies with Attribute Relationships

In my opinion, creating natural hierarchies are the single most beneficial thing an SSAS developer can do to improve the performance and usability of a cube. There are several reasons correctly defined user hierarchies are beneficial, but here are a couple of the top reasons.

Increased Query Performance

Calendar HierarchyCreating attribute relationships between attributes that are included in a user defined hierarchy improve the performance of queries using these attributes for a couple different reasons. In this case, we’ll look at the Calendar hierarchy in the Adventure Works Date dimension. The Calendar hierarchy has five levels with the Calendar Year attribute at the top level and the Date attribute at the bottom level of the hierarchy. We can also see the following attribute relationships created to give SSAS an understanding of how the members of these attributes relate to one another. Once created, these relationships give SSAS the ability to understand that a given date member is related to a single month member, a given month member relates to a single quarter, and so on and so forth.

imageThis also means that during processing, special indexes are created that map the relationships between each member of each level in the hierarchy. This means that before a query is written, SSAS also knows to which month, quarter, semester, and year each date aggregates. These indexes are only created if you correctly define the attribute relationships between the attributes in the hierarchy.

Improved User Experience

imageUser defined hierarchies also improve the user’s experience with the dimension because the hierarchy provides a very natural way for the user to navigate through the data in the dimension. A user defined hierarchy does two things for a user: 1) Using the hierarchy obviously presents the data to the user in an understandable and clear way. And 2) the hierarchy organizes the data based on your business requirements. This means that the user doesn’t have to think about how should these attributes be organized or labeled because you’ve done that for them. The user can focus on reading their reports, understanding the data, and making business decisions.

Partition Measure Groups to Separate Volatile and Static Data

imageMeasure groups that are larger than (about) one million records should be separated into multiple partitions. There are several advantages to separating larger measure groups into multiple partitions.

Increased Processing Performance

Partitions in a measure group are processed in parallel. This means that a measure group containing three years worth of data separated into one partition for each month will process faster than a measure group with all three years worth of data in a single partition. Your processing strategy for each measure group may vary depending on the amount of data. For instance, a large telecommunication company may collect hundreds of millions of records per day, dictating a more complex and granular partitioning strategy.

Also, we should consider which partitions contain data that is changing vs. data that is now static. If our businesses will continually log transaction in the current month, we can partition our data by month effectively separating our volatile data from the static data. This means we only need to process one months worth of data in our cube to pick up the latest changes instead of having to processing the whole measure group.

Increased Query Performance

By partitioning our data at the month level, certain queries will also perform better. For instance, imagine a user queries our measure group searching for last month’s sales figures. Because we have partitioned our measure group with each month being loaded into a separate partition, this means that the entire measure group does not have to be scanned. Only the partition containing the data for the month in question needs to be queried.

Create Aggregations for Large Measure Groups

You can think of Aggregations as indexes for SSAS. Aggregations are used to help SSAS find the answer to a user’s query faster. Aggregations are typically used for measure groups that are large and take a considerable amount to query.

Improved Query Performance

Aggregations contain the data of a measure group at a summary level typically higher than the lowest level of data included in the measure group. Aggregations are populated during the processing phase of the partition. You can think of Aggregations as exactly the same as the lowest level of the measure group just at a summary level. This means that when SSAS receives a query that can be answered using an Aggregation, SSAS does not have to spend the additional time required to retrieve the measure group data from the lowest level and roll up the data to the requested level because the Aggregation design already contains the data at the desired level.

Beware Too Many Aggregations

Because Aggregations are built during the processing phase, this means that for every aggregation you define more time is required to build the aggregations. This also means that it is especially important to only build useful aggregations that are necessary to improve performance. By creating aggregations that are not often used, you can degrade query performance and increase processing time with little to no benefit. Like indexes on a SQL Server table, too many aggregations or the wrong aggregations can actually hurt performance, so make sure you test, test, and then test to ensure your aggregations are helping your query performance.

I hope you have found this information useful. By following these three best practices, I really believe you’ll see a generous improvement in the performance of your SSAS cubes.

If you found this information helpful, I’d love to hear from you! Please leave me a comment and let me know what you think. Do you have any ideas on something I left out or should have included? Please let me know! Thank you!