Voice of the DBA

Author: way0utwest

Pro SQL Server on Linux- More Installs

As part of my learning goals for 2018, I wanted to work through various books. This is part of my series on Pro SQL Server on Linux from Bob Ward.

After my last post, I decided to try and install the SQL Server 2019 preview version and see how that worked. I followed the instructions from Books Online.

Adding a new Repo

At first, I used curl to get the new repo file and then run the install. When looking at the install (sudo yum install –y mssql-server), I wondered if this would know I wanted 2019 instead of 2017. At this point, my system has two repos, so how does it know?

No idea, but it worked.

Or did it?

I think I upgraded my instance, which is fine. This is testing, so it works fine for me.

January 14, 2019
Sorting Values in a Column
Another post for me that is simple and hopefully serves as an example for people trying to get blogging as #SQLNewBloggers.

This was a post that caught my eye, since I’d just written a piece on STRING_SPLIT(). Someone had this data:
```
DECLARE @Names VARCHAR(8000) = 'Steve, Grant, Kathi, Kendra';
```
They wanted this returned as a string that was sorted, so that the values would be:

Grant, Kathi, Kendra, Steve

String manipulation isn’t the strength of SQL Server, but we can do this with STRING_SPLIT(). If we use STRING_SPLIT(), we get a list of values. We want to remove the spaces after the comma, so we use REPLACE to remove that.
```
SELECT *

FROM STRING_SPLIT(REPLACE(@Names, ' ', ''), ',')

ORDER BY value;
```
We can then aggregate these back together in a variable assignment, adding the comma for each row.
```
SELECT @newtext = @newtext + Value + ', '

FROM STRING_SPLIT(REPLACE(@Names, ' ', ''), ',')

ORDER BY value;
```
This gives us:

SQLNewBlogger

A quick T-SQL application of some skills I learned. Can you do something similar? Maybe order dates or numbers that are in a string in the wrong order?

That should be a 10-15 minute post.
January 10, 2019
Badly Trained AI

Most of us know that data is being used to make more and more decisions inside of all kinds of organizations from retail giants to banks to sports teams. We are constantly asked, or see reports, of data driven decisions. We often need to show some data that supports and explains the rationale for making some choice. As our populace becomes more data savvy, I expect this trend to continue.

AI (Artificial Intelligence), and the related Machine Learning (ML), are becoming more and more widely used. From mobile phones to autos to trading systems, we regularly see new “AI capabilities” being added to products and services. No business or industry seems immune, and I’m sure many of you are seeing AI being incorporated or feeling pressure to start using some AI in your work. As you work with AI, or start to, you’ll quickly realize the importance of data in your efforts.

This is true for the cleanliness of data, but perhaps even more important in the tagging of data sets. As Amazon learned, building an AI or ML system, is hard. They scrapped one system that was being used to rate resumes and help their recruiters sort through the volume of applications they received. Why? Because of bias.

Apparently the system would downgrade women’s resumes for various reasons. To me, this is a perfect example of a principle I’ve had throughout my career: garbage in garbage out. In this case it’s not necessarily bad data that was the problem, but bad tagging of what was a good and bad resume, probably from the internal prejudices of a few people.

There will be more dangers as we use ML and AI technologies in our work. It won’t be enough that we clean the raw data for training, but also that we clean and properly manage the tagging of what data sets represent the results we are looking for. Like in much of our software, it’s easy for us to only consider the happy path, to only tag those items we think are good results. That is useful, but we might also be unconsciously tagging other results as bad, which appears to have happened to Amazon.

We can build systems that do a better, more rational job than most humans, but we need extraordinary care to ensure our training data lacks bias. Unfortunately, most people both think they’re not biased and are unwilling to spend extra resources to deeply examine the data. Two things that worry me about the future of our AI/ML systems that will inform us.

Steve Jones

January 10, 2019
Pro SQL Server on Linux–Install
As part of my learning goals for 2018, I wanted to work through various books. This is part of my series on Pro SQL Server on Linux from Bob Ward.

I had an Ubuntu VM setup at home, but it started to flake and eventually stopped responding as a desktop. Since I’m not a Linux expert, I wasn’t sure what to do and really didn’t think it was worth debugging a broken Linux OS. Instead, I moved on.

In the book, Bob uses Red Hat Linux, so I downloaded RHEL and installed it, after going through the process of joining the Red Hat Developer network. If I don’t do this, I only get a 30 day trial, which I didn’t want. I get the process, and understand them making money.

For a lab, it was a pain. This made me want to go back and just get Ubuntu.

In following the install instructions, I started with getting the Microsoft registry set up. I got an error when downloading the repo file.

From this link, I added my user with
```
usermod –aG wheel sjones
```
Had to change the network to bridged in VMWare. Not sure why, but this is how I have a few VMs set, so I just matched this.

Subscription

I needed to attach my subscription. I started by registering, which was fun. I had to go back to the Red Hat portal, log off and back on.

Then dependency errors. Isn’t yum supposed to resolve this?

The key is that I wasn’t getting updates. I had to run
```
subscription-manager attach –auto
```
which linked my system to the update system. I can see how Red Hat makes money. This feels as onerous as Windows for management and product key matching. In any case, this allowed the install to proceed:

Eventually this completes, which is a welcome sight.

One note, when trying to get the dependencies, I removed the –y option for the install. Once things worked, I had to answer “y” to a lot of questions, so you’ll want that enabled.

Configuration

The next step is to configure your instance. I started this, but it didn’t quite work.

If you look, you’ll see the failure message is that we need 2000MB (2GB) of memory. The VM has 2048, but that’s not enough. I reset this to 3072MB and the config worked.

Validating the Install

From the book, the first step is to check the service.

The the process.

That’s good, so I followed instructions to install tools. This ran off the screen, so I didn’t bother capturing a screenshot, but it worked according to instructions.

Next, connect to SQL Server and check the version.

Tada. Well, not that exciting. The process was more annoying than I expected or remembered, but most of that appears to be RHEL more than SQL Server.

However, this was a nice test and project, with no GUI on the system. Looking forward to doing more with this across the month.
January 9, 2019