Data Partitioning – Why do we want to partition the data?

Even if we spoke about table partitioning a long time ago, I’d like to circle back to this question from the different, architectural standpoint. In the next a few posts we will discuss why we want to partition data and what options do we have to do that.

Data Partitioning is the complex and time consuming process. So as the first step I’d like to explain why we want to go through all the efforts to do that.

Reason #1 – Without partitioning everything is in the same place.

Quite obvious, is not it? And it’s not necessarily bad. One of advantages of when data is in the same place – it makes development and optimization very easy. Execution plans are predictable but that’s all about it. The biggest problem is that when everything is in the same table (or better say partition) it
stored on the same filegroup, same set of files and same disk array. Well, technically speaking we can separate our clustered and non-clustered indexes between different filegroups but at the end it introduces its own set of the issues especially in disaster recovery situations.

The problem here is that in every system we have data that belongs to the different “groups” / types – operational and historical. Duration of the operational period (which is basically customer facing OLTP portion of the system) varies – could be a day, week, month, maybe even year(s) but in the large system
there is always additional historical data and often there is a lot of historical data.

Obviously we care more about operational (customer facing) performance and would like to put operational data to the fastest disk array possible. But when everything is in the same place we don’t have a lot of choices – we either need to buy disk array big enough to store historical data, which could be
terribly expensive and generally speaking is waste of money, or compromise and go with slower disks due budget constraints.

Reason #2 – The same schema, indexes and compression

Operational and historical data usually have different access patterns. As I mentioned above, operational data is usually OLTP-related data. So we have a lot of short optimized queries. Historical data is often used for analysis and reporting – e.g. we are talking about Data Warehouse type access. Different
access patterns require different index structures. Again, when we have everything in the one table we don’t have a lot of choices. We either create additional indexes to support queries against historical data and hurt performance of OLTP part of the system or, alternatively, do not have those indexes and, as result, have bad performance of the queries against historical portion of the data.

Another thing is that in some cases we want to have slightly different schemas for those portions of the data. One of the examples – operational data can have some additional columns used for the processing. There is the good chance that historical data does not necessarily need them and when we have a lot of
records every byte counts.

Lastly, we cannot partially compress our data. Compression could help a lot with historical data which is relatively static – we are increasing performance by reducing number of pages and, as result, amount of IO operations. On the other hand, for operational data compression usually hurts performance because
data is changing quite often.

Reason #3 – Index maintenance

Same thing as above. Generally speaking we don’t need to rebuild or reorganize the indexes for historical data which is static. But there is no way we can do rebuild/reorg on the part of the data.

Reason #4 – Backup strategy

Same thing again. All data is in the same filegroup. We cannot exclude historical portion from the backups. As result, it increases backup time, size of backup file and introduces additional overhead during the process

Reason #5 – Disaster recovery

One of the biggest benefits of Enterprise Edition of SQL Server is piecemeal restore. It allows us to bring system partially online on filegroup by filegroup basis. If we had operational and historical data separated to the different filegroups we could restore operational part first and make system available
to the customers while we are working on historical part. With everything in the same non-partitioned table it’s impossible. We will need to restore everything first before system becomes available and online.

Reason #6 – Statistics

SQL Server needs to estimate number of rows on the every step of the execution plan to make it efficient. In order to do so SQL Server uses statistics and histogram in
particular. Histogram contains some values from the key as well as the information about data distributions in the intervals of the values. The problem is that histogram contains
at most 200 steps/values. So more data we have in the table, bigger intervals are. Approximations are done on each interval and as result our estimations are less accurate.

Another thing that’s worth to mention is that by default SQL Server updates statistics only after 20% of the key values are updated. So if we have 1 million rows in the table, we can insert 200,000 new rows, delete 200,000 rows or update them before statistics update is triggered. Obviously, more rows we have,
less often statistics would be updated.

That list is not completed by any means. But each of those reasons is big enough by itself to start thinking about data partitioning.

Next: When to partition the data

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