AI, what a load of hyped-up bollocks, right? Yet here I am, legit writing a blog about it and not for the clickbait but…gasp…because it’s actually useful.
Used correctly, it’s just like any other tool on your desktop. It helps you get stuff done quicker, better—or both.
As a newcomer to Apache Flink one of the first things I did was join the Slack community (which is vendor-neutral and controlled by the Flink PMC). At the moment I’m pretty much in full-time lurker mode, soaking up the kind of questions that people have and how they’re using Flink.
One question that caught my eye was from Marco Villalobos, in which he asked about the Flink JDBC driver and a SQLDataException he was getting with a particular datatype. Now, unfortunately, I have no idea about the answer to this question—but the idea of a JDBC driver through which Flink SQL could be run sounded like a fascinating path to follow after previously looking at the SQL Client.
Sometimes you might want to access Apache Kafka that’s running on your local machine from another device not on the same network. I’m not sure I can think of a production use-case, but there are a dozen examples for sandbox, demo, and playground environments.
In this post we’ll see how you can use ngrok to, in their words, Put localhost on the internet. And specifically, your local Kafka broker on the internet.
When I started my journey learning Apache Flink one of the things that several people expressed an interest in hearing more about was PyFlink. This appeals to me too, because whilst Java is just something I don’t know and feels beyond me to try and learn, Python is something that I know enough of to at least hack my way around it. I’ve previously had fun with PySpark, and whilst Flink SQL will probably be one of my main focusses, I also want to get a feel for PyFlink.
The first step to using PyFlink is installing it - which should be simple, right?
So far I’ve plotted out a bit of a map for my exploration of Apache Flink, looked at what Flink is, and run my first Flink application. Being an absolutely abysmal coder—but knowing a thing or two about SQL—I figure that Flink SQL is where my focus is going to lie (I’m also intrigued by PyFlink, but that’s for another day…).
🎉 I just ran my first Apache Flink cluster and application on it 🎉
A brief diversion from my journey learning Apache Flink to document an interesting zsh oddity that briefly tripped me up:
cd: string not in pwd: flink-1.17.1
My journey with Apache Flink begins with an overview of what Flink actually is.
What better place to start than the Apache Flink website itself:
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
Like a fortunate child on Christmas Day, I’ve got a brand new toy! A brand new—to me—open-source technology to unwrap, learn, and perhaps even aspire to master elements of within.
I joined Decodable two weeks ago, and since Decodable is built on top of Apache Flink it seems like a great time to learn it. After six years learning Apache Kafka and hearing about this “Flink” thing but—for better or worse—never investigating it, I now have the perfect opportunity to do so.
