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Author: Kevin Feasel

Pivoting Performance Counter Data

Published 2019-03-21 by Kevin Feasel

Dave Bland shows how you can build a dynamic pivot to see performance counter data over a stretch of time:

The next step is to write the code to capture the counter values and insert the data it the temporary table created above.  Because we need to capture the values over a period of time, the WAITFOR DELAY is used.  In this case the delay is 10 seconds, although you can change this to suit your needs. Of course, don’t forget to increment the counter variable. You will need to determine what counters you would like to capture.  Notice in the WHERE clause, we are looking for an instance_name of ” or ‘_total’.  This will allow the code to only capture one row for each counter.  The number 10 is the number of times we want to capture the counter values.  If you want to capture the data more frequently, simpley modify the number of seconds in the WAITFOR DELAY.  Here is link to my post in this topic, WAITFOR.

Dynamic pivoting in SQL is unnecessarily difficult, especially compared to languages like R.

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Cleaning Up After Yourself in Azure Data Factory

Published 2019-03-21 by Kevin Feasel

Rayis Imayev shows how you can automatically delete old files in Azure Data Factory:

File management may not be at the top of my list of priorities during data integration projects. I assume that once I learn enough about sourcing data systems and target destination platform, I’m ready to design and build a data integration solution between two or more connecting points. Then, a historical file management process becomes a necessity or a need to log and remove some of the incorrectly loaded data files. Basically, a step in my data integration process to remove (or clean) such files would be helpful. 

Click through to see how to do this.

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Reading From a Secondary with Azure Data Studio

Published 2019-03-21 by Kevin Feasel

Mohammad Darab shows how you can access a readable secondary using Azure Data Studio:

Recently, I had to use Azure Data Studio to access a application intent read only secondary replica. I had to use Azure Data Studio because I was using a Mac. I usually use SSMS on my Windows machines. If you want to connect with the “applicationintent=readonly” property via SQL Server Management Studio, you do so by typing it out in the “Additional Connection Parameters” as shown in the screenshot below:

Since I am fairly new to Azure Data Studio I was fumbling my way around to find the equivalent setting. And I finally found it…

Read on to see how you can set this in Azure Data Studio.

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Building a DMV Diagnostic Queries Notebook

Published 2019-03-21 by Kevin Feasel

Gianluca Sartori shows how you can use dbatools and Powershell to build a Jupyter notebook in Azure Data Studio for Glenn Berry’s DMV scripts:

For presentations, it is fairly obvious what the use case is: you can prepare notebooks to show in your presentations, with code and results combined in a convenient way. It helps when you have to establish a workflow in your demos that the attendees can repeat at home when they download the demos for your presentation.

For troubleshooting scenarios, the interesting feature is the ability to include results inside a Notebook file, so that you can create an empty Notebook, send it to your client and make them run the queries and send it back to you with the results populated. For this particular usage scenario, the first thing that came to my mind is running the diagnostic queries by Glenn Berry in a Notebook.

Obviously, I don’t want to create such a Notebook manually by adding all the code cells one by one. Fortunately, PowerShell is my friend and can do the heavy lifting for me.

This type of scenario is one of the best ones I see for database administrators: consistent, documented troubleshooting guides. Oh, and you can save results off if you need to review them later. This has the potential to be a killer feature for Azure Data Studio.

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String or Binary Data and Associated Bug

Published 2019-03-21 by Kevin Feasel

Brent Ozar looks at the “String or binary data would be truncated” improvement:

Don’t leave this trace flag enabled.
There’s at least one bug with it as of today on SQL Server 2017 CU13: table variables will throw errors saying their contents are being truncated even when no data is going into them. Andrew Pruski reports:

Special shout out to three of my co-workers on finding that issue. I had nothing to do with it but will take credit nonetheless.

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Interactive ggplot Plots with plotly

Published 2019-03-20 by Kevin Feasel

Laura Ellis takes us through ggplotly:

As someone very interested in storytelling, ggplot2 is easily my data visualization tool of choice. It is like the Swiss army knife for data visualization. One of my favorite features is the ability to pack a graph chock-full of dimensions. This ability is incredibly handy during the data exploration phases. However, sometimes I find myself wanting to look at trends without all the noise. Specifically, I often want to look at very dense scatterplots for outliers. Ggplot2 is great at this, but when we’ve isolated the points we want to understand, we can’t easily examine all possible dimensions right in the static charts.

Enter plotly. The plotly package and ggploty function do an excellent job at taking our high quality ggplot2 graphs and making them interactive.

Read on for several quality, interactive visuals.

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Goodbye, gather and spread; Hello pivot_long and pivot_wide

Published 2019-03-20 by Kevin Feasel

John Mount covers a change in tidyr which mimics Mount and Nina Zumel’s pivot_to_rowrecs and unpivot_to_blocks functions in the cdata package:

If you want to work in the above way we suggest giving our cdatapackage a try. We named the functions pivot_to_rowrecs and unpivot_to_blocks. The idea was: by emphasizing the record structure one might eventually internalize what the transforms are doing. On the way to that we have a lot of documentation and tutorials.

This is your regular reminder that the Tidyverse is very useful, but it is not the entirety of R.

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Near-Zero Downtime Identity Column Changes

Published 2019-03-20 by Kevin Feasel

I’m getting close to the end of my series on near-zero downtime deployments. This latest post involves identity column changes:

There are some tables where you create an identity value and expect to cycle through data. An example for this might be a queue table, where the data isn’t expected to live permanently but it is helpful to have a monotonically increasing function to determine order (just watch out for those wrap-arounds and you’re fine). An example of reseeding is below:

DBCC CHECKIDENT('dbo.MyTable', RESEED, 1);

This operation needs to take a LCK_M_SCH_M lock, otherwise known as a schema modification lock. Any transactions which are writing to the table will block your transaction but so will any readers unless you have Read Committed Snapshot Isolation turned on or the reader is in the READ UNCOMMITTED or SNAPSHOT transaction isolation level.

If you are using RCSI and don’t have extremely long-running transactions, this is an in-and-out operation, so even though there’s a little bit of blocking, it’s minimal.

Not all changes are this easy, though.

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The State of SQL Server Monitoring Survey

Published 2019-03-20 by Kevin Feasel

Kendra Little would like a few minutes of your time:

Calling all Database Administrators, Developers, Analysts, Consultants, and Managers: Redgate has a survey open asking how you monitor your SQL Servers.

Take the survey before April 5, 2019.

Your time is valuable. The survey will take 5 – 10 minutes to complete. That’s not a ton of time, but it’s a noticeable part of your day, and there should be something in it for you. Here’s why it’s worthwhile to take the survey.

Read the whole thing and take that survey.

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Exporting SQL Server Tables to Excel with Powershell

Published 2019-03-20 by Kevin Feasel

Aaron Nelson shows how you can export the tables in a SQL Server database to Excel, using a warehouse as an example:

Obviously, you have to have the module installed, and a copy of AdventureWorksDW2017 db restored to a SQL Server.  After that,  all you have to do is loop through the tables, ‘query’ them with the Read-SqlTableData cmdlet, and pipe the results to the Export-Excel cmdlet.

I did some trial and error with this yesterday.  I settled on exporting all of the Dimension tables to separate Worksheets within the same Excel file, and exporting all of the Fact tables to their own individual files (since they tend to be much larger).

Click through for Aaron’s script.

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