COMING SOON – New Content

Published on: 2020-07-16

Watch this week’s video on YouTube.

Hey everyone. Long time no chat. I wanted to write this quick post to let you know why there haven’t been any videos in a while. If you are interested in technical content, skip this post and come back next week for a post on filtering data from heaps.

First off, thanks to everyone who reached out to see if things were ok. Things are great! Well, as good as they can be anyway. Since my last post/video a few things have changed in my life:

  • I got a new job building out a new data science team
  • My wife and I had our second child, which has been very exciting
  • COVID19 has turned the world upside down

Combine all of that with America’s racial atrocities and injustices being put in the spotlight and well, writing new posts and filming videos just wasn’t a priority.

And while the world still sometimes feels like a terrible place, and I’m still not getting adequate sleep with a 4 month old in the house, I’ve been wanting to get back to writing blog posts and making videos because they bring me, and I know many of you, enjoyment.

With that said, going forward I am going to make a few changes to make things more sustainable:

  • For over 3 years, I wrote posts and filmed videos every week. Most of the time this was manageable, but sometimes it caused me stress to meet deadlines for something that I’m doing for fun. So while I still plan to update this blog regularly, I plan on doing it in a way that doesn’t self-impose stress.
  • You may have noticed the logo change to Data with Bert. SQL has always been just a small slice of my technical life and I’ve wanted to incorporate the rest of my data-related projects into my website. Things like what I’m currently building with Arduinos, and how I’m using the cloud to store data in non-relational services. Writing about these other projects never felt right given the site had “SQL” in the name, so that’s what brought about the change. I am still going to write primarily about SQL, but I’m basically giving myself permission to widen the topic area to other data-related projects. They will all still have a heavy data problem solving focus, so if you’ve enjoyed the posts up to this point, I’m sure you will find the future posts relevant as well.

Anyway, thanks for reading :).

Thanks for reading. You might also enjoy following me on Twitter.

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Using Python And NetworkX To Build A Twitter Follower Recommendation Engine

Published on: 2020-01-07

Watch this week’s video on YouTube.

This week, I want to share my process for analyzing Twitter. Specifically, I want to find who all of my friends follow on Twitter that I don’t currently follow. Essentially, I want to build a Twitter follower recommendation engine.

Let’s start with the theory behind the problem I’m trying to solve. On Twitter, I follow a bunch of people. Around 450 at the time of filming this video.

Not quite 450…but you get the idea

These are people I’ve either met in person and want to keep in touch with or am interested in following – or both!

Now I don’t attend many conferences and events, so finding more people to follow that way is slow going; I probably only meet a handful of new people every year.

However, the second category of people, the interesting ones out there on the internet, is almost limitless. The problem is finding them in a virtual sea of bots, marketing-only accounts, and complainers.

Twitter does have a “Who to Follow” page, and while I’m sure it has some great suggestions, I don’t necessarily trust all of Twitter’s recommendations. It’s kind of like trusting a site like Yelp for restaurant reviews when the local McDonalds rates better than your favorite burger place. I’m sure that particular McDonalds is beautiful by fast food standards, but I’m looking for more personalized recommendations.

So that leads me to this project: I decided to find better personalized recommendations on Twitter by looking at who my friends are following that I am not.

Getting the Data

To start, I needed a list of the people I follow on Twitter, and then a list of who they follow. The proper way to do this is to use the official Twitter API, so I wrote some Python code to do exactly that. Twitter calls the people you follow “Friends”, which is funny since I usually don’t consider one-way relationships friendships.

Screenshots of code are lame – go to GitHub to see the actual code.

I won’t go through the code in detail (you can download it from GitHub if you’d like to do that) but essentially it uses the friends/list endpoint to download all of my Friends. I then went through each of those Friends and found all of their Friends.

In total, this ended up being around 125,000 people.

As a quick side note, the Twitter API is terrible. Using it is fine, but the rate limiting is horrendous. I was basically capped at downloading 12,000 people per hour, which meant it took about a day to download all of the data I needed.

Graph Analysis with NetworkX

Once I had the data downloaded, it was time to find relationships between my friends and the people they follow. For this, I decided to use an open source Python library called NetworkX. NetworkX helps perform complex network analysis, which is perfect for what I was trying to do.

NetworkX uses a graph structure to help with its analysis. A graph is made up of of nodes and edges. In our case, the Twitter users are our nodes, and our edges are the relationships. The first thing I did was load all of the people I follow and created a directional edge (aka. an arrow) to indicate the one-way relationship from me to them. Next, I looped over all of those people’s friends, adding additional nodes and edges.

At this point, my graph had 125,000 nodes which was way too many to draw quickly on my computer, and way too many to model with Lego minifigs. I figured not ALL of this data would be useful and I needed a way to filter it.

I started by filtering out any of my friends’ friends who happen to already be my friends – this wouldn’t be useful since I already follow these people.

Next, I decided to keep only the top 50 most followed people in that second tier of Twitter users. This would limit the processing needed while still giving me a list of the most followed Twitter users that my friends follow that I don’t currently follow. Once again, all of this code is available on my GitHub.

At this point, I was able to use NetworkX’s drawing capabilities to beautifully render the network of users and relationships. Sure, I could have just listed out the top users that I don’t follow, but there is something special about being able to create a visual network of those relationships.

Click through to see a more legible version

So that’s it. It was fun using a graph library to find new people to follow on Twitter using the people I currently follow as a proxy for finding relevant users instead of Twitter’s black box algorithms.

Thanks for reading. You might also enjoy following me on Twitter.

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SQL Server Converts Numbers to Asterisks

Published on: 2019-12-17

Watch this week’s episode on YouTube.

Pop quiz: What will be the output of the below three statements?

DECLARE @val_varchar varchar(3) = '100';
DECLARE @val_bigint bigint = 100;
DECLARE @val_tinyint tinyint = 100;

PRINT('varchar to varchar:');
PRINT(CAST(@val_varchar AS VARCHAR(2)));

PRINT('bigint to varchar:');
PRINT(CAST(@val_bigint AS VARCHAR(2)));

PRINT('tinyint to varchar:');
PRINT(CAST(@val_tinyint AS VARCHAR(2)));

As you might have guessed, the first conversion truncates the value, leaving the answer as “10”:

varchar to varchar

The second response is also somewhat intuitive – a number can’t be converted into string that has fewer places than original digits:

bigint to varchar

However, the final answer returns:

tinyint to varchar

NOTE: if we change all of the conversions to VARCHAR(3) above, all three results return the string value ‘100’

What the ****?

Why does SQL Server sometimes error when converting a number into a string, but other times succeeds and returns an asterisk?

I don’t know.

The best (and logical) answer I could find online is from Robert Sheldon, who attributes it to poor error handling practices, “…before error handling got a more reputable foothold.”

My understanding is that the asterisk was originally the default truncation value. Later, when the SQL Server development team was adding the bigint datatype, they decided throwing an exception is a better way to handle truncation in errors. However, probably for backwards compatibility, they never went back to change the behavior of the asterisks for pre-existing datatypes.

The documentation is clear about this behavior as well: any int, smallint, or tiny int when converted to char or varchar with fewer characters will result in an asterisk.

This becomes a problem if you create strings out of tinyints, smallints, and ints and suddenly start receiving values larger than you originally expected:

DECLARE @Age int = 75;
PRINT(CAST(@Age AS VARCHAR(2)) + ' years old.');

SET @Age = 100;
PRINT(CAST(@Age AS VARCHAR(2)) + ' years old.');
* years old

Conversions are hard

So is SQL Server to blame for this issue? Sure. But I can empathize: breaking changes are not something people typically want to add to a product.

I’ve written before how implicit conversions can lead to funny results in SQL Server. This asterisk scenario is another case of SQL Server having to make a judgement call about handling an impossible situation you created for it.

While it’s easy to complain about whether SQL Server handles the conversion the correct way or not, at the end of the day the responsibility falls on you to prevent these types of impossible calculations from being requested of SQL Server in the first place.

Thanks for reading. You might also enjoy following me on Twitter.

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