Category: Partitioning

Brenda Bentz lays out some of the benefits of table partitioning in SQL Server:

Table partitioning in MS SQL Server is an advanced option that breaks a table into logically smaller chunk (partition) and then stores each chuck to a multiple filegroups.  Each chuck can be accessed and maintained separately. Partitioning is not visible to users; it behaves like one logical table to the end user. Partitioning was introduced in SQL 2005 as an Enterprise edition feature but starting with SQL 2016 SP1 it is available on all the editions.

Table partitioning can work to improve performance in specific circumstances, though you’ll definitely want to do a lot of testing before rolling it out. H/T Amanda White.

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Raul Gonzalez shows us five things you can do with partitioning in SQL Server:

Once we have rebuilt that old data to minimise its footprint and moved it to a cheaper storage tier, if we know no one will have to modify it, it’d be a good idea to make it READ_ONLY.

By making the data READ_ONLY, we can not only prevent accidental deletion or modification, but also reduce the workload required to maintain it, because as we’ve seen before, we can action index maintenance only on the READ_WRITE parts (partitions) of the data where fragmentation might still happen.

Read on for the rest of the tips and note that none of these are directly of the “Make your queries faster” variety, though a couple can have positive performance implications.

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Michael Bourgon explains what the WAIT_AT_LOW_PRIORITY option does with table partitioning and that it is available in Standard Edition:

But how about WAIT_AT_LOW_PRIORITY?  That was introduced in 2014 to make table partitioning deal better with That-Dude-From-Accounting-Who-Kicks-Off-A-Massive-Query-On-Friday-at-5pm, which causes partitioning to hang on Saturday when you’re trying to add and remove partitions.

Read on for a demo.

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Kendra Little explains that there are a couple of models available for partitioned table management:

I recently received a terrific question about table partitioning:

I’m writing up a proposal for my company to start partitioning a 2.5TB table. The idea/need is to control the size of the table while saving the old data. The old data will be moved to an archive database on a different server where the BI guys work with it.

In none of the videos articles I’ve seen is the explanation of how the rolling partition works on a long term daily basis.

1. Are the partitions reused, like in a ROUND ROBIN fashion?
2. Or, do you add new partitions each day with new filegroups, drop the oldest partition off – this would be FIFO?

Lots of folks assume the answer here is always #2, simply because there’s a bunch of sample code out there for it.

But option #1 can be simpler to manage when it fits your data retention technique!

Click through to learn more about reusable partitioning.

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Andrew Pruski demonstrates a gotcha when switching partitions between tables:

When working with partitioning the SWITCH operation has to be my favourite. The ability to move a large amount of data from one table to another as a META DATA ONLY operation is absolutely fantastic.

What’s also cool is that we can switch data into a non-partitioned table. Makes life a bit easier not having to manage two sets of partitions!

However, there is a bit of a gotcha when doing this. Let’s run through a quick demo.

Read on for more.

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Derik Hammer shows an example of “old-style” partitioning across servers:

SQL Server has a feature for partitioning tables and indexes. Partitioning can be implemented at many levels, however. You can create multiple tables for one logical data set, you can split the set into multiple databases, and you can even split it among different servers. Sharding is another term. It refers to partitioning data to horizontally scale out compute resources and storage.

There are different methods of handling sharding. Each of them need a central point of connection to handle querying the data on the other shards. This is typically called the control node. The method I am to discuss today is one where linked servers are used to connect the various shards.

This is useful for something like offloading old invoices which you rarely need to a separate server.  Derik also shows that the optimizer can, if it knows your partitioning rules, avoid unnecessary cross-server hits.

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Marlon Ribunal has a script to split partitioned tables automatically:

So, let’s pretend it’s the month of April 2017 and this is the partition currently populated. Based on the query above, aside from the current partition bucket, we also have another available bucket month for May.

Say we want to maintain 3 available buckets at any given time. The next available bucket is May, so that means we need 2 more partitions to cover for June and July.

Read on for more, including some scripts that you can automate.

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