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Curated SQL Posts

Power BI Report Performance: Number of Visuals on a Page Edition

Published 2020-09-21 by Kevin Feasel

Chris Webb looks at some extreme scenarios:

You may have read the title of this post and guessed that I’m going to talk about reducing the number of visuals that display data from your dataset as a way of improving performance, but that’s not the case. In this blog post I want to show how visuals that do not display any data from your dataset can have a significant impact on report performance. Before we carry on I suggest you read the series of posts I wrote late last year on measuring the performance of reports in the browser using Chrome/Edge DevTools (part 1 is here, part 2 is here, part 3 is here) because I’ll be using techniques described in these posts in my testing.

Click through for an interesting demo.

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Baselining SQL Server with Grafana

Published 2020-09-21 by Kevin Feasel

Ajay Dwivedi has an interesting project:

Are you tired of not knowing what is usual workload of your server? Do you want to know what is speed of your server?

If you are a developer or DBA who manages Microsoft SQL Servers, it becomes important to understand the current load vs usual load when SQL Server is slow.

Recently, for this reason, I have been working on my own automation to capture performance counters that would help me get an idea of CPU/Memory/IO/Network issues. Baselining also lets me know if the Server load has increased gradually over time.

I have created GitHub repository ‘SqlServer-Baselining-Grafana’ that contains all the scripts that will help anyone to set up the baseline on individual SQL Server instances, and then visualize the collected data using Grafana through one Inventory server with Linked Server for individual SQL Server instances.

Baselining is one of those concepts we often talk about but just as often have trouble implementing.

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Correlation and Predictive Power Score in Python

Published 2020-09-18 by Kevin Feasel

Abhinav Choudhary looks at two methods for understanding the relationship between variables:

dataframes while working in python which is supported by the pandas library. Pandas come with a function corr() which can be used in order to find relation amongst the various columns of the data frame. 
Syntax :DataFrame.corr() 
Returns:dataframe with value between -1 and 1 
For details and parameter about the function check out Link 
Let’s try this in action. 

Read on to see how it works, how to visualize results, and where Predictive Power Score can be a better option.

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The Power of AUC

Published 2020-09-18 by Kevin Feasel

John Mount takes a deeper look at Area Under the Curve:

I am finishing up a work-note that has some really neat implications as to why working with AUC is more powerful than one might think.

I think I am far enough along to share the consequences here. This started as some, now reappraised, thoughts on the fallacy of thinking knowing the AUC (area under the curve) means you know the shape of the ROC plot (receiver operating characteristic plot]. I now think for many practical applications the AUC number carries a lot more information about the ROC shape than one might expect.

Read on for the explanation.

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Connecting to an API with Username and Password in Power Query

Published 2020-09-18 by Kevin Feasel

Gilbert Quevauvilliers has a challenge:

In the blog post I am going to show you the steps that I took to get data from the XE.COM API which uses a username and password to log into the API

You might be thinking that I could put in the username and password when I used the Web Connector.

My challenge is that I wanted to create a function that I could pass through multiple currencies to the API. And in order to do that I wanted to store the details within the function.

Read on to see how Gilbert solves this.

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Working with Keys and Certificates using dbatools

Published 2020-09-18 by Kevin Feasel

Mikey Bronowski concludes a series on using dbatools to replace SQL Server Management Studio functionality:

There are multiple security-related objects that are not easily accessible via SQL Server Management Studio. The first one would be Service Master Key, if exists, can be seen under the master database. Luckily, dbatools can help us to take a backup.

Click through for the details. If you’ve missed any of Mikey’s posts, here is the full list of posts.

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Optimize for Unknown with Inline Table-Valued Functions

Published 2020-09-18 by Kevin Feasel

Koen Verbeeck hits on a strange case:

Turns out SQL Server used a plan with a hash join in the fast query, and a nested loop in the slow query. Due to SQL Server also wildly using incorrect estimates, the nested loops performs really poorly. Quite similar to parameter sniffing with stored procedures. Erik Darling has written a great article about it: Inline Table Valued Functions: Parameter Snorting.

The thing is, in contrast to scalar functions or multi-statement table-valued functions, the iTVF should have better performance because it will be expanded into the calling query. This way, SQL Server can use “more correct” estimates and create a plan for each different parameter. Well, today was not that day.

Read on for details on how Koen performed troubleshooting and the solution.

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Understanding the Size of a Power BI File

Published 2020-09-18 by Kevin Feasel

Lazaros Viastikopoulos explains that finding the size of a Power BI file isn’t quite as easy as you’d think:

As you can see from the above screenshot, the size of the PBIX file is 1.2 MB (1,238 KB) by looking at the size in file explorer. Also, when we look at all the files we are ingesting into Power BI, the total size comes to 2.8 MB. However, when we ingest data into Power BI which is processed to memory, it can be bigger than the PBIX file size due to some compression that occurs and various other elements which again, are not displayed through the disk.

So, when we are getting various errors notifying us that our Power BI file is too large and we see that it is under the 1GB restriction on disk, it can leave us scratching our head. But, as I mentioned above due to compression and various other elements, once data is processed to memory it can be larger then what we see on disk, but still below the total size of the data source we are ingesting due to the compression.

Click through for information on two tools you can use to determine the in-use size.

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