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Month: February 2017

Galaxy Classification With SQL Server

Published 2017-02-16 by Kevin Feasel

David Smith points out a nice Microsoft demo for classifying galaxies using SQL Server:

The SQL Server Blog has since published a step-by-step tutorial on implementing the galaxy classifier in SQL Server (and the code is also available on GitHub). This updated version of the demo uses the new MicrosoftML package in Microsoft R Server 9, and specifically the rxNeuralNet function for deep neural networks. The tutorial recommends using the Azure NC class of virtual machines, to take advantage of the GPU-accelerated capabilities of the function, and provides details on using the SQL Server interfaces to train the neural netowrk and run predictions (classifications) on the image database. For the details, follow the link below.

If you’re going to get into SQL Server R Services at any level of seriousness, I highly recommend R Tools for Visual Studio, as it will make building those external stored procedure calls much easier.

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AT TIME ZONE And Reports

Published 2017-02-16 by Kevin Feasel

Rob Farley shows how to use AT TIME ZONE without sacrificing performance:

Because how am I supposed to know whether a particular date was before daylight saving started or after? I might know that an incident occurred at 6:30am in UTC, but is that 4:30pm in Melbourne or 5:30pm? Obviously I can consider which month it’s in, because I know that Melbourne observes daylight saving time from the first Sunday in October to the first Sunday in April, but then if there are customers in Brisbane, and Auckland, and Los Angeles, and Phoenix, and various places within Indiana, things get a lot more complicated.

To get around this, there were very few time zones in which SLAs could be defined for that company. It was just considered too hard to cater for more than that. A report could then be customised to say “Consider that on a particular date the time zone changed from X to Y”. It felt messy, but it worked. There was no need for anything to look up the Windows registry, and it basically just worked.

But these days, I would’ve done it differently.

Now, I would’ve used AT TIME ZONE.

Read on for the scenario.

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Instant File Initialization In DMVs

Published 2017-02-16 by Kevin Feasel

Rodney Landrum shows off a couple new columns in SQL Server 2016 SP1 DMVs:

Microsoft announced many new features in SQL Server 2016 SP1 and the fanfare was mostly centered around the Enterprise features now available in SQL Server 2016 Standard Edition.  Many may have missed some hidden gems in the announcement.  Two of these are columns added to the existing DMVs, sys.dm_server_services and sys.dm_os_sys_info. The columns provide information for two specific features that previously had to be gathered by opening gpedit.msc and/or scrolling through SQL error logs. I am referring to Lock Pages in Memory and Instant File Initialization (enabled via Perform Volume Maintenance Tasks privilege).

It is now possible to simply query the DMVs to determine if these are being used for the running SQL Server instance.

Click through for the details.

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Session Context

Published 2017-02-16 by Kevin Feasel

Ewald Cress looks at SESSION_CONTEXT() as a replacement for CONTEXT_INFO():

SESSION_CONTEXT() brings two major innovations. Firstly, it replaces a 128-byte scalar payload with a key-value structure that can accommodate 256kB of data. You can really go to town filling this thing up.

The second change is less glamorous, but possibly more significant: it is possible to set an entry to read-only, meaning that it can safely be used for the kind of contextual payload you don’t want tampered with. This makes me happy, not because I currently have a great need for it, but because it neatly ties in with things I have been thinking about a lot lately.

Read on for more.

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Calculated Dimensions

Published 2017-02-15 by Kevin Feasel

Ginger Grant shows how calculated dimensions can solve the classic role-playing dimension problem in Analysis Services Tabular:

Working with role playing dimensions, which are found when you have say multiple dates in a table and you want to relate them back to a single date table, have always been problematic in SQL Server Analysis Services Tabular. Tabular models only allow one active relationship to a single column at a time. The picture on the left shows how tabular models represent a role playing dimension, and the model on the right is the recommended method for how to model the relationships in Analysis Services Tabular as then users can filter the data on a number of different date tables.

The big downside to this is one has to import the date table into the model multiple times, meaning the same data is imported again and again. At least that was the case until SQL Server 2016 was released. This weeks TSQL topic Fixing Old Problems with Shiny New Toys is really good reason to describe a better way of handling this problem.

Read on for how to implement calculated dimensions.

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Not All Shiny Toys Are Good

Published 2017-02-15 by Kevin Feasel

Wayne Sheffield rains on our parade:

There are other issues with the MERGE statement. It has bugs… some of which can cause database corruption.

Here we have a Shiny New Toy (feature), supposed to make life easier, yet it causes problems. Until it can perform better (and the bugs are eliminated), I just don’t use it.

Beware the Shiny New Toys.

Wayne makes a great point.  Not all new things are good, even when they’re potentially quite useful.  I love shiny new toys a lot, but part of being a database administrator is protecting data, and part of that means being able to trust your tools.  Sometimes the tools work really well right out of the gate, and sometimes (like in the case of MERGE) they don’t.

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Single-Query Wait Stats

Published 2017-02-15 by Kevin Feasel

Robert Davis shows off a new wait stats-related feature in 2016 SP1:

Paul’s process gives you info on every instance of a wait the query experienced and it’s very easy to aggregate those results to see the top waits and their total effect on a query. Quite often though, you don’t need a lot of detail. You don’t need to know every wait, just the top several. If you are already generating the actual query plan to have a in-depth look at the plan, wouldn’t it be nice if the query wait stats were already in there for you?

Now they are. In SQL Server 2016 (I’m told it came in SP1, but I don’t have a non-SP1 instance to verify that), the actual execution plan includes the top waits for the query execution in the plan. You can see them by clicking on the left-most (first) operator in the plan and viewing the Properties (shortcut F4). It will list the top waits right there in the properties dialog for you.

Getting single-query wait stats in the execution plan makes life so much simpler.

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Change Detection Temporal Tables

Published 2017-02-15 by Kevin Feasel

Adam Machanic shows how to find net changes using temporal tables:

For now, consider the following set of propositions, given that we’re asking at time Y for all changes since a prior time X.

  • INSERT: The key did not exist at time X but does exist at time Y.
  • DELETE: The key existed at time X but does not exist at time Y.
  • UPDATE: The key existed at both time X and at time Y, and at least one change occurred between time X and time Y.

Given these assumptions, we can begin work on a temporal queries that return the necessary rows. Solving for these conditions will require all rows that were active as of time X, rows that were (or are) active at time Y, and for the final case, all rows that were active in-between times X and Y. Since this is a range-based scenario, our best Temporal predication option will be either FROM or BETWEEN. The difference between these two is subtle: FROM uses an open interval (non-inclusive at both endpoints), whereas BETWEEN uses a half-open interval, inclusive on the end date. Given the choice in the scenario, BETWEEN makes more sense, as we can take advantage of the inclusive endpoint to avoid dropping a badly-timed row. But more on that in a moment.

Adam put a lot of thought into edge cases, making this a must-read.

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