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Curated SQL Posts

Using WorkloadTools to Analyze a Workload

Published 2019-03-01 by Kevin Feasel

Gianluca Sartori shares an example of configuring and using the WorkloadTools SQL workload analyzer:

Now that the analysis database contains the performance data, you can use WorkloadViewer to visualize it and draw your conclusions.

WorkloadViewer is a GUI tool that reads performance data from the analysis database and gives a graphical representation using charts and grids. It accepts a number of command line arguments that allow to automate its behavior, but it can be also opened without specifying any arguments: in this case, WorkloadViewer will present a form to fill the missing information.

WorkloadViewer can be used to visualize information about a single benchmark (analysis mode) or two benchmarks (comparison mode). In this case, you just need to work with a single benchmark, so it is enough to enter the connection info on the left, including the schema name where the tables are. When using Windows Authentication, you can leave UserName and Password blank.

Gianluca has a full demo from the beginning of data capture to analysis.

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Benefits of Inline Indexes

Published 2019-03-01 by Kevin Feasel

Phil Factor covers a topic fairly close to my heart:

Usually, the added features of the CREATE TABLE syntax in new releases of SQL Server are esoteric, and unless you are dealing with memory-optimized tables or other esoteric stuff, they aren’t of great interest. However, the Inline INDEX for both a table and column index has just crept in quietly with SQL Server 2014 (12.x). This was interesting because the SQL Server team back-fitted it to all tables rather than just in-memory OLTP tables for which it was, at the time, found necessary. The new syntax was introduced which allows you to create certain index types inline with the table definition. These could be at column level, concerning just that column, or at the table level, with indexes containing several columns.

Why interesting? This affects multi-statement table functions, user-defined table types, table-valued parameters as well as table variables. It was considered a game-change for table variables because, for a start, it allowed non-unique indexes or explicit clustered indexes to be declared on columns for the first time because you can create indexes on table variables as part of the table definition. Of more significance were the table-level indexes that allowed you to specify multi-column indexes. Previous releases had allowed multi-column primary or unique constraints, but not explicitly named indexes. You still cannot declare an index after the table is created, which is a shame as there are good reasons for being able to do so after a table is stocked with data. Any sort of large import of data into a table that is over-indexed or prematurely-indexed is doomed to crawl rather than to run. I’ll show this later on in this article.

Click through for an analysis of inline indexes themselves as well as how they fit on table variables—something I tend not to do much.

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Capturing Queries With XEvent Profiler

Published 2019-03-01 by Kevin Feasel

Erin Stellato explains how to use the XEvent Profiler in SSMS 17.3 and later:

It’s worth pointing out that neither the Standard or TSQL session writes out to a file. In fact, there’s no target for either event session (if you didn’t know that you can create an event session without a target, now you know). If you want to save this data for further analysis, you need to do one of the following:

1. Stop the data feed and save the output to a file via the Extended Events menu (Export to | XEL File…)
2. Stop the data feed and save the output to a table in a database via the Extended Events menu (Export to | Table…)
3. Alter the event session and add the event_file as a target.

Read the whole thing.

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Which Power BI Visuals Send Data Externally

Published 2019-03-01 by Kevin Feasel

Meagan Longoria does a bit of investigation into data privacy and Power BI visuals:

One thing that makes understanding data privacy in custom visuals easier is the designation of a certified custom visual. One of the requirements for certification is ” Does not access external services or resources, including but not limited to, no HTTP/S or WebSocket requests go out of Power BI to any services.”

You can find the list of currently certified custom visuals on this page. Custom visuals are also identified in the marketplace by a blue star with a check mark.

Read on for some good investigative analysis.

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Robust Regressions in R

Published 2019-02-28 by Kevin Feasel

Michael Grogan shows how you can find and re-weigh outliers when performing regressions:

A useful way of dealing with outliers is by running a robust regression, or a regression that adjusts the weights assigned to each observation in order to reduce the skew resulting from the outliers.

In this particular example, we will build a regression to analyse internet usage in megabytes across different observations. You will see that we have several outliers in this dataset. Specifically, we have three incidences where internet consumption is vastly higher than other observations in the dataset.

Let’s see how we can use a robust regression to mitigate for these outliers.

Click through for a demonstration.

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Disable Priority Boost

Published 2019-02-28 by Kevin Feasel

Randolph West wants you to disable priority boost if you have it turned on:

It turns out that on one of the benchmarks, Microsoft was able to achieve higher throughput by setting the SQL Server process to HIGH_PRIORITY_CLASS, and the thread priority level over and above that to THREAD_PRIORITY_HIGHEST. While it was extremely helpful to beat artificial benchmarks, it came at the cost of giving SQL Server higher execution context on the CPU than almost every other process on Windows.

And that’s the only good use of priority boost ever. If you’ve never heard of priority boost, just keep on ignoring it.

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Storing SQL Server Helm Charts in GitHub

Published 2019-02-28 by Kevin Feasel

Andrew Pruski shows how we can use GitHub to store Helm charts and access them easily:

In a previous post I ran through how to create a custom SQL Server Helm chart.

Now that the chart has been created, we need somewhere to store it.

We could keep it locally but what if we wanted to use our own Helm chart repository? That way we wouldn’t have to worry about deleting the chart on our local machine.

I use Github to store all my code to guard against accidentally deleting it (I’ve done that more than once) so why not use Github to store my Helm charts?

Cluster configurations are still code, and code belongs in source control.

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Automating Azure Storage To Move Between Tiers

Published 2019-02-28 by Kevin Feasel

Ryan Adams built a process to save money on storage costs for a customer’s test environment:

One of the best things about Azure, and the cloud in general, is we can automate most anything, and we are going to look at how to automate Azure VM Storage.  This allows us to come up with some outside-of-the-box solutions.  I had a customer with a road block that we were able to work around by automating some things with their Azure Virtual Machines.

Their challenge was that they wanted to move their test and development environments to Azure, but the storage cost was prohibitive.  They needed premium storage to mimic their production environment, but it was not financially viable for test and development so they were going to keep it all on premises.  During our conversations I learned that they only test between 8am and 5pm, Monday through Friday.  My suggestion was that we put their databases on cheaper storage during off times and only premium when they are actively using it.

This doesn’t look like a one-hour task but if you’re in need of some cost savings on storage in non-production environments, check out Ryan’s scripts.

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Microsoft Data Platform Bug Reporting Links

Published 2019-02-28 by Kevin Feasel

Brent Ozar has put together a compendium of where you should go if you want to file bug reports or feature requests for different products in the Microsoft data platform space:

Azure Data Studio – open an issue in the Github repo. While you open an issue, Github helps by searching the existing issues as you’re typing, so you’ll find out if there’s already a similar existing issue.

Click through for all of the links. I personally just yell skyward in the hopes that they hear me and fix my problems. It doesn’t work very often so I don’t recommend it as a strategy.

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