Analyzing Your Dump Files

I’m blogging about this, because A) It’s something really awesome that the SQL Server team built and B) it seems to have terrible SEO, because it took me like three google searches to find the page. With the introduction of SQL Server Management Studio 17, the Tiger team at Microsoft built a plugin that allows you to debug and resolve memory dumps you may have encountered during otherwise normal operations. This is really awesome, as it is something that usually requires a support case with CSS.

For those of you wearing aluminum hats, this does require you do upload the dump file to Azure, where it is analyzed for free (as in beer) on Microsoft’s software. You can even choose your region if you have data provenance concerns. And according to this blog post the memory dumps are stored in accorded with Microsoft’s Privacy Policy.

You will need SSMS 17 for this, as well as to install the plug in, which you can get here.

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After that you can quickly get feedback on your dumps. Microsoft have even built an API, so if you want to built something automated to upload your dump files using Python or PowerShell you can.

The Self-Tuning Database?

There was a lot of talk on Twitter over the weekend, about automation, and the future of the DBA role. I’ve spoken frequently on the topic, and even though the PASS Summit program committee has had limited interest in my automation sessions, they have been amongst my most popular talks at other large conferences. Automating mundane tasks makes you a better DBA, and escalating the level of those tasks allows you to focus on activities that really help your business like better data quality, and watching cat videos on YouTube.

But Tuning is Hard

Performance tuning has always been something of a black art. I remember that Oracle called 9i the self-tuning database, because we no longer had to manually calculate how many blocks (pages) of memory where allocated to each pool. That was a joke—and those databases still required a lot of manual effort for tuning. However, that was then, and we’re in the future now. We’ll call the future August. Stay with me here, I’m going to talk about some theories I have.

So It’s August 2017

Let’s say you were a vendor of a major RDBMS, who also happened to own a major hyperscale cloud, and you had invested heavily in collecting query metadata in the last two releases of your RDBMS. Let us also accept the theory that the best way to get an optimal execution plan, is to generate as many potential execution plans as possible. Most databases attempt a handful of plans, before picking the best available plan—this is always a compromise as generating execution plans involves a lot of math, and is very expensive from a CPU perspective. Let us also portend that as owner of the hyperscale cloud, you also have a lot of available processing power, and you’ve had your users opt-in to reviewing their metadata for performance purposes. Additionally, you’ve taken care of all of the really mundane tasks like automating backups, high availability, consistency checks, etc. So now it’s on to bigger fish.

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Still With Me?

Ok, so we have all the tools in place to build our self-tuning database, so let’s think about what we would need to do. Let’s take a somewhat standard heuristic I like to use in query tuning—if a query takes more than 30ms to execute or is executed more than 1000/times in a day, we should pay attention to it for tuning purposes. That’s a really big filter—so we’ve already narrowed down the focus of our tuning engine (and we have this information in our runtime engine, which we’ll call the Query Boutique). We have also had our users opt-in to use using their metadata to help improve performance.

So we identify our problem queries in your database. We then export the statistics from your database, into our backed tuning system. We look for (and attempt to apply) any missing indexes to the structure, to evaluate the benefit of a missing index. We then attempt to generate all of the execution plans (yes, all of them—this processing is asynchronous, and doesn’t need to be real time). We could even collect read/write statistics on given objects and apply a weighted value to a given index. We could then take all of this data and run it through our back end machine learning service, to ensure that our query self tuner algorithm was accurate, and to help improve our overall process.

We could then feed this data back into the production system as a pinned execution plan. Since we are tracking the runtime statistics, if the performance of the plan drops off, or we noticed that the statistics changed, we could force out our new execution plan, and start the whole process over again.

So there, I didn’t write a line of code, but I laid out the architecture for a self-tuning database. (I thought about all of this on a bike ride, I’m sure Conor could do a lot better than me Smile)  I’m sure this would take years and many versions to come into effect. Or not at all. To be successful in this changing world of data, you need to stay ahead of the curve, learn about clouds work, how to script, how to automate, and how to add value.

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