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Author: Kevin Feasel

Disabling All Triggers on a Database

Published 2019-08-22 by Kevin Feasel

Jess Pomfret is on a mission:

Sometimes it’s best not to ask why. However, if for some reason you have a number of triggers on tables within a database that you would like to temporarily disable, read on.

I came across a situation recently while automating a process to refresh test environments where this exact scenario came up.  As part of the process several scripts were run to obfuscate production data. While these ran all the UPDATE triggers were firing. Not only were the triggers adding a significant amount of time to the process, they were also updating dates and other values that we’d prefer kept their original values.

Jess has a tiny bit of Powershell code which does this work for you.

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Locking Azure Resources

Published 2019-08-21 by Kevin Feasel

Stuart Moore shows us how we can lock Azure resources to prevent accidental mistakes:

A resource deletion may not sound like too much of a big thing if you’re deploying Infrastructure as code, hey we’ll just terraform apply again and it’ll pop backup.

In theory that’s a great idea, just with one big problem. The new resource isn’t the old resource!

For an example, an Azure SQL Database server is a unique resource. If you delete one you lose any backups you’ve taken as they’re hosted on the server. Spinning up a new one isn’t going to get them back!

Stuart shows us how to take these locks. Because I have my doubts that everyone has all of their infrastructure prepped as terraform scripts, Stuart’s point is even more relevant.

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From Excel to R: Three Examples

Published 2019-08-21 by Kevin Feasel

Abdul Majed Raja has a few examples of things which are easy to do in Excel and how you can do them in R:

Create a difference variable between the current value and the next value
This is also known as lead and lag – especially in a time series dataset this varaible becomes very important in feature engineering. In Excel, This is simply done by creating a new formula field and subtracting the next cell with the current cell or the current cell with the previous cell and dragging the cell formula to the last cell.

These things aren’t as easy to do as in Excel—it’s hard to get simpler than “push a button” or “click and drag your mouse”—but they are useful to know in R. H/T R-bloggers

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T-SQL Tuesday 117 Roundup

Published 2019-08-21 by Kevin Feasel

Steve Jones has the roundup for this month’s T-SQL Tuesday:

The summary from my fourth T-SQL Tuesday hosting for #117. This time I was scrambling a bit, but since I’ve worked with a few customers in the last year that use MOT tables, I thought this might be a good topic.

Either everyone is on vacation or not many people think about them. Or maybe they aren’t interesting. In any case, here’s the roundup.

I’d like to say it’s because everyone’s on vacation, but I think In-Memory OLTP is an underutilized technology. Granted, there are reasons why it’s not used as much as it should be—early versions were too limiting and could have weird consequences on your servers—but if you’re on SQL Server 2017, it’s worth another look.

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Enabling Database-Level Change Tracking

Published 2019-08-21 by Kevin Feasel

Tim Weigel continues a series on change tracking:

If you don’t provide a retention period, SQL Server’s default is 2 days. Auto-cleanup defaults to ON unless you tell it otherwise.

Easy!

The table level commands aren’t any more complicated. Before we get started, please note that change tracking requires a primary key on the table you want to track. This is reasonable – you need some kind of unique identifier to tell you which row has changed.

Read on for the scripts and further explanation.

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Blank Rows and DAX

Published 2019-08-21 by Kevin Feasel

Alberto Ferrari explains how different DAX functions treat blank rows differently:

DAX offers two functions to retrieve the list of values of a column: VALUES and DISTINCT. The difference between the two is subtle. To understand it better, we first need to introduce the concept of the blank row. The blank row is a special row added to a table, in case the table is on the one-side of a strong relationship and the relationship is invalid. Let us elaborate on this.

Click through for the detailed explanation, along with plenty of examples.

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Calculated Columns and Memory Usage in Analysis Services

Published 2019-08-21 by Kevin Feasel

Teo Lachev troubleshoots a customer issue:

Scenario: A client reports a memory spike during processing. They have a Tabular semantic model deployed to Azure Analysis Services. They fully process the model daily. The model normally takes less than 50 GB RAM but during processing, it spikes five times and Azure Analysis Services terminates the processing task complaining that it “reached the maximum allowable memory in our pricing tier”. Normally, fully processing the model should take about twice the memory but five times?

Teo gives us the explanation for this problem as well as a recommendation on how to fix it.

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Waiting on Stats Refreshes

Published 2019-08-21 by Kevin Feasel

Erik Darling looks at a new wait type:

I mean ever really wondered just how long a query of yours waited on stats to automatically update before running?

Have you ever been absolutely, positively sure that those gosh dang darn stats updates were putting your query on the trolley to Slowsville?

Your wishes will be 100% granted in SQL Server 2019.

This is a wait type that I’d consider useful but hopefully uncommon.

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Requirements for DevOps Success

Published 2019-08-21 by Kevin Feasel

Grant Fritchey lays out what you need to succeed at DevOps:

The project managers and others flip. Delivery is slipping. The amount of code being written has changed. Stuff is happening that wasn’t on the schedule. The implementation of DevOps is shut down quickly.

You have to get buy-in from management before you attempt to implement DevOps or it will fail. They have to understand what you’re doing, why you’re doing it, and the measurable benefits it will bring.

Click through for some good thoughts, none of which is “use this software.”

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Calculating AUC in R

Published 2019-08-20 by Kevin Feasel

Andrew Treadway shows how you can calculate Area Under the Curve in R:

AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For a detailed explanation of AUC, see this link.

Since AUC is widely used, being able to get a confidence interval around this metric is valuable to both better demonstrate a model’s performance, as well as to better compare two or more models. For example, if model A has an AUC higher than model B, but the 95% confidence interval around each AUC value overlaps, then the models may not be statistically different in performance. We can get a confidence interval around AUC using R’s pROC package, which uses bootstrapping to calculate the interval.

There are plenty of ways to calculate this useful metric, but this is definitely one of the easier methods. H/T R-bloggers

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