Database Compression Effects on SSAS Processing

Just recently I was optimising the processing time for a rather large cube on my laptop. The hardware I used was not tremendously powerful (a mobile i5 CPU, 8Gb RAM and one 7200 RPM hard disk) and having a 500M rows fact table proved to be somewhat of a challenge for it.

The set up was fairly typical – a large fact table, with a few dimensions in a typical star schema. The goal was to get it to process as fast as possible.

After ensuring all best-practice advice from Microsoft (and in particular SQL CAT) was followed, I managed to get the processing down from 7381.075 seconds to 5801.536 s (approximately 21% improvement). However, one thing still did not look quite right – the CPU utilisation during the partition processing phase.

Considering my hardware configuration it was easy to deduce that the HDD would be more of a bottleneck than the CPU. There was only one HDD and everything, including the source database and the SSAS files, was on it. During the partition processing reading and writing is happening simultaneously, which is not good for performance. At the same time in my case the CPU utilisation was on around 75% with 25% to spare, which was not great. Ideally, I would like to see my CPU on 100%, or very close to that to ensure that I am utilising all of its power. However, I had no way to add more disks.

Luckily, in SQL Server 2008 (Enterprise Edition) we have the option to compress database tables, or partitions. We do get a performance hit on the CPU when we (de)compress the data, but it takes less space on the disk and thus requires less IOPS to read it. It sounded like a perfect fit for my problem. After estimating the benefits in terms of size row and page compression gave me, I decided to go with the heavier option since even its marginally better (5%) reduction of size over row-level compression was desirable. After waiting patiently for the compression operation to finish, the overall database size did drop to around 30% as predicted.

I run my SSAS processing script again and it completed in 4805.408 seconds – approximately 17% better than the previous run, which is a very significant improvement. The CPU utilisation went up to around 97%, which also meant that my blunder with the page vs. row compression produced, generally, good results.

One thing we must always consider is whether the bottleneck is in the IO. A few months later I replaced my hard disk with a new SSD (OSZ Vertex 2), which increased the IO performance of my laptop dramatically. When I tested SSAS processing tasks I could almost always see my CPU steady at 100% without any compression. If I were to compress my fact tables I would expect to get worse performance because my CPU would struggle to both decompress and process the data at the same time.

In conclusion, you must know what your bottleneck is before you opt for a solution like compression. It comes at a price, but the price may well be worth paying if your system is not well balanced.