Data Partitioning – When to partition the data

Last week we were discussing why do we want to partition the data. I hope at this point we all agreed that data partitioning could help us with the large table. Which leads to the question – when the table becomes “big enough” for partitioning.

This is kind of theoretical discussion. I heard a lot of different opinions varied from hundreds MBs to TBs. We can spend a lot of time discussing that and will never end up with the right answer. Or, better say, all answers would be the right ones. Obviously your mileage may vary – a lot of things depend on your system, environment and hardware. But my personal take is very simple. If I expect my system to collect large amount of data, the right time (for me) to partition the data is:

Even if Data Partitioning introduces some overhead on development and testing there are two particular reasons why I’d suggest to do that. Most importantly - data partitioning changes execution plans. As result, when you finally implemented it you’d need to re-test entire system. And this could be very expensive and time consuming. Not even mention the possibility of another round of performance tuning. It’s much easier to deal with potential issues during initial development and testing especially when you don’t have a lot of data and impact of sub-optimal execution plans is smaller.

Second reason is that it’s very hard (if even possible) to partition the data keeping the system online. In most part of the cases we are going to deal with exclusive schema modification locks that prevent any access to the table even in read uncommitted transaction isolation level. And if you think how long physical data movement of 100s GBs or TBs of data could take.. Well, you get an idea.

Again, everything depends on the system and requirements. There is no need to spend time and efforts on data partitioning if you don’t expect to have a lot of data. But remember that situation and requirements can change. There is always the possibility that project would be more successful than we expect it to be :)

 Next: Scaling Out by separation operational and historical data

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>