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Category: In-Memory OLTP

How Do Natively Compiled UDFs Perform?

Published 2016-01-14 by Kevin Feasel

Gail Shaw investigates natively compiled user-defined functions in SQL Server 2016:

When I saw that, the first question that came to mind is whether natively compiling a scalar function reduces the overhead when calling that function within another query. I’m not talking about data-accessing scalar UDFs, since natively compiled functions can only access in-memory tables, but functions that do simple manipulation of the parameters passed in. String formatting, for example, or date manipulation.

While not as harmful as data-accessing scalar UDFs, there’s still overhead as these are not inline functions, they’re called for each row in the resultset (as a look at the Stored Procedure Completed XE event would show), and the call to the function takes time. Admittedly not a lot of time, but when it’s on each row of a large resultset the total can be noticeable.

Read the whole thing and check out Gail’s method and conclusions.

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Running Out Of In-Memory Disk Space

Published 2015-12-25 by Kevin Feasel

Jack Li shows us what happens when we run out of disk space for checkpoint files on a memory-optimized table:

The question is what happens if the disk that host the In-Memory checkpoint files runs out of disk space?  So I decided to do some testing and document the symptoms and recovery steps here in case you run into such issue.  With our Azure, test was really easy.  All I had to do was to spawn a VM and attach a very small disk to simulate out of disk space condition.

If your disk runs out of space, you will see various errors below though your database stays online

It looks like you can work your way out of a full drive scenario.  Hopefully, however, you won’t get into this scenario too often.

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In-Memory Analytics

Published 2015-12-10 by Kevin Feasel

Sunil Agarwal introduces us to In-Memory Analytics, forthcoming in SQL Server 2016:

SQL Server 2016 has significant advancements over SQL Server 2014 for In-Memory analytics. Some highlights are functionality (e.g. ability to create traditional nonclustered index to enforce PK/FK), performance (e.g. addition of new BatchMode operators, Aggregate pushdown), Online index defragmentation, and supportability (e.g. new DMVs, Perfmon counters and XEvents).

His post talks a little bit about in-memory, but focuses more on clustered columnstore indexes.  I like that columnstore indexes are getting V3 improvements, and I think they’ll be even more useful.  Whether the “in-memory” part becomes useful is a different question; I personally have seen a very limited adoption of In-Memory OLTP (and a few huge bugs for the people brave enough to try it).

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