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James Serra's Blog

James is currently a Senior Business Intelligence Architect/Developer and has over 20 years of IT experience. James started his career as a software developer, then became a DBA 12 years ago, and for the last five years he has been working extensively with Business Intelligence using the SQL Server BI stack (SSAS, SSRS, and SSIS). James has been at times a permanent employee, consultant, contractor, and owner of his own business. All these experiences along with continuous learning has helped James to develop many successful data warehouse and BI projects. James has earned the MCITP Business Developer 2008, MCITP Database Administrator 2008, and MCITP Database Developer 2008, and has a Bachelor of Science degree in Computer Engineering. His blog is at .

Formula engine and storage engine in SSAS

In SSAS, to improve query performance, it’s important to understand what happens inside analysis services when a query is run.  Analysis Services is made up of two engines:

Formula Engine (FE) – It is single-threaded.  It processes the MDX queries, determines what data is needed to answer them, then requests that data from the Storage Engine, and then performs all calculations needed for the query.  It does most of the analysis work and tries to keep cells in memory.  Fast clock speeds are the best way to improve a Formula Engine performance.

Storage Engine (SE) – It is multi-threaded.  It handles all reading and writing of data: it fetches the data requested by the Formula Engine when a query is run and aggregates it to the required granularity.  If cells are not in memory, it is the Storage Engine which gets the data from disk.  A performance goal is to minimize Storage Engine use and keep data in memory for the Formula Engine.  Try to use faster storage (SSD) or more disk drives for quicker responses to Storage Engine requests.

So in summary, when you run an MDX query, that query goes first to the Formula Engine where it is parsed; the Formula Engine then requests all of the raw data needed to answer the query from the Storage Engine, performs any calculations on that data that are necessary, and then returns the results in a cellset back to the user.

There are numerous opportunities for performance tuning at all stages of this process, as I will discuss in my next blog post.

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