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First things First One of the caveats of using a clustered columnstore index, is that it must include all of the columns in the table, so the syntax is very simple.

Loading the Data I decided to do a pretty basic test—I took the data from my uncompressed Big Transaction and did an insert into as select.

So with a fresh VM with SQL 2014 CTP2 installed on my laptop I dove right in.

In order to really test any sort of compression technology, you need to work with a large volume of data.

The performance can be shown in the chart below What’s interesting about this data as that the proportion of CPU time to total time is roughly the equivalent percentage in each case, and the update of the columnstore (8M rows) is roughly the same as inserting 31 M rows into the page compressed table, and vice versa. Even though we can update columnstore indexes now, they are probably still best used for insert only workloads.

Anything that requires frequent, quick updates isn’t a great candidate for a columnstore index.

Interestingly, the update was much faster (just under 4x)—that’s mainly because columnstore processes an update as both an insert followed by a delete.I had been holding off on writing this blog post with SQL 2014 CTP1, since updateable columnstores were definite a work in progress.I did get several questions about the updatable columnstores during my presentation at the recent SQL PASS summit.This operation is fairly frequent in ETL pipelines and this change allows it to create directly the highly efficient compressed format.The page compression algorithm isn’t actually applied here, since I’m not doing an insert into.(tablock), so these rows aren’t actually being page compressed as they are being loaded, but even with row compression being applied the performance is lower than with the columnstore.

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