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Author: Kevin Feasel

Powershell Core On Ubuntu Using Docker

Published 2019-01-28 by Kevin Feasel

Max Trinidad has an Ubuntu VM running Powershell Core on Docker:

While learning about Docker Container, I notice that is much easier to installed on a Linux system. In Windows, Hyper-V is a requirement to install Docker, and specially if you want to use the “Windows Subsystem in Linux” WSL feature, there’s more setup to complete. So, I’m not using Hyper-V, I’m using VMware Workstation.  To keeping simple, I created an Ubuntu 18.04 VM using VMWare Workstation.

You can find the Docker CE installation instructions in the following link.

If you’re using Ubuntu 18.04. make sure to install Curl, as it isn’t included in the OS.

Click through for instructions on how to set this up and join the three layers club (which is not quite the three commas club but close).

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Emulating PERCENTILE_DISC

Published 2019-01-28 by Kevin Feasel

Lukas Eder points out that there are two ways to use PERCENTILE_DISC() and most database platforms support only one:

Oracle has the most sophisticated implementation, which supports both the ordered set aggregate function, and the window function version:
– Aggregate function: PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY x)
– Window function: PERCENTILE_DISC (0.5) WITHIN GROUP (ORDER BY x) OVER (PARTITION BY y)

But there are ways to calculate PERCENTILE_DISC() using a couple of window functions, so read the whole thing.

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Platform Compatibility and SSDT

Published 2019-01-28 by Kevin Feasel

Ed Elliott walks us through platform compatibility in SQL Server Data Tools:

Sometimes you don’t have the perfect development environment for SQL Server, sometimes you deploy to things like SQL Azure and want to test locally, for various reasons it is possible that you want to deploy to one version of SQL Server but set the project properties to a different version of SQL Server. If you find yourself in this situation you might need to use the parameter AllowIncompatiblePlatform
 which essentially says “I know I said the project is for SQL 2017 but I am deploying to SQL 2014”, anytime you say this you also sign the contract that says “If I am deploying to a lower version then I have made sure I haven’t used any keywords or object types that didn’t exist in that version and also if everything fails then so be it, I live with my choices every day”.

The story is a little complicated, but Ed straightens it out for us.

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SESSION_CONTEXT Is Case-Sensitive

Published 2019-01-28 by Kevin Feasel

Steve Jones notes an issue people may have when trying out the SESSION_CONTEXT() function in SQL Server:

Notice a difference? In the first query, I have SupplierID, but the second is SupplierId, with a lower case “d”. These keys are determined when you use sp_set_session_context, which takes a sysname value for the key. These are going to be case sensitive, as each one is a different identifier.

Click through for the full story. I’m not a fan of case-sensitivity in general and especially not in a system where, by default, most things are case-insensitive.

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Building Credit Scorecards

Published 2019-01-25 by Kevin Feasel

Andre Violante uses SAS to build credit scorecards and analyze credit data:

For this analysis I’m using the SAS Open Source library called SWAT (Scripting Wrapper for Analytics Transfer) to code in Python and execute SAS CAS Action Sets. SWAT acts as a bridge between the python language to CAS Action Sets. CAS Action Sets are synonymous to libraries in Python or packages in R. The one main difference and benefit is that the algorithms within these action sets have been highly parallelized to run on a CAS (Cloud Analytic Services) server. The CAS server is a distributed in-memory engine where I can do all my heavy lifting or computations. The code and Jupyter Notebook are available on GitHub.

Click through for the analysis.

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Disentangling Nested Functions

Published 2019-01-25 by Kevin Feasel

Brent Ozar takes us through a realistic but nasty scenario:

When I do performance tuning for clients, I really pride myself on making as few changes as possible in order to make a tremendous difference. I consider it a failure when I have to tell someone to rewrite a bunch of queries from scratch.
However, there are some cases where I just can’t work around a perfect storm of anti-patterns. To understand why, let’s take an example. I’ve kept the general idea the same, but I’ve rewritten the entire example in the Stack Overflow database to protect the innocent.

I’ve seen cases similar to what Brent has. Developers understand encapsulation and minimizing code repetition, so they naturally want to do that with SQL, but the optimizer eventually gives up and picks a terrible plan. DRY is great for application code and normalization, but unfortunately, it’s not always great for T-SQL.

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Automating Azure SQL Database Log Analytics

Published 2019-01-25 by Kevin Feasel

Joey D’Antoni shares a Powershell script which automates deployment of Log Analytics for Azure SQL Database:

However, I’m working with a customer who is building their own service based on Azure SQL Database, and I have fully automated their database deployment process. I wanted to take this a few steps further and add the SQL Analytics step as part of our deployment. This was harder than I expected it to be—the code samples in the books online post above weren’t working in my environment. And furthermore, once I got it working, I was having failures in my Azure Automation Runbook once I got the code running in the PowerShell ISE (I was having issues using VS Code on my Mac).

Joey takes us through the problems and provides a working script.

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Query Store Bugfixes

Published 2019-01-25 by Kevin Feasel

Erin Stellato has a roundup of Query Store bugfixes as of January 2019:

The latest CUs for SQL Server 2016 and 2017 contain some important Query Store fixes that I thought worth mentioning for those of you on either version or those of you looking to upgrade.  As of this writing, the current CU for SQL Server 2016 SP2 is CU5, and for SQL Server 2017 it is CU13.  Many times we see fixes that make it into a SQL Server 2017 CU ported back to a SQL Server 2016 build.  Interestingly enough, there are some Query Store fixes in 2016 CUs that are not in 2017 CUs.  I don’t know if that’s because the issues do not exist in 2017, or if it’s just that they have been fixed yet in 2017.  I’m planning to update this post if the fixes are added down the read.  So here we go, in descending CU order…

This post is a great reason to keep those SQL Server instances up to date.

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Multi-File Power BI

Published 2019-01-25 by Kevin Feasel

Marc Lelijveld shows us how to break up a single Power BI desktop file into several:

Normally a Power BI Desktop file (PBIX) contains your queries, data model, and reports (visualization). Looking at a multi-file strategy, we split this up into two (or more) files.

The first file only contains the queries and data model. The second file is directly connected to the first one (direct query) and reads the data model. The file it selves includes all report content like visualizations, booksmarks and everything related to that. By working this way, you will be able to build multiple reports based on the same dataset.

Click through for a demonstration.

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