In this post, I’ll show how to bring posts from open social media networks (Bluesky and Mastodon) into Kafka using Kafka Connect source connectors.
My goal is to be able to populate a Kafka topic with status updates posted to social media.
Rather than to try and do this with the full firehose of all status updates, this is done with status updates that match a search term or hashtag.
For example, the screenshot above is a Kafka topic with posts from Bluesky that mention the term “xbox”.
In this post, I want to share an example of handling bespoke structured messages with the Kafka Connect MQ Source Connector.
The MQ Source Connector gets data from MQ messages and produces it as events on Kafka topics. The default record builder makes a copy of the data as-is. For example, this can mean taking a JMS TextMessage from MQ and producing a string to Kafka. Or it can mean taking a JMS BytesMessage from MQ and producing a byte array to Kafka.
In my last post, I showed an example of using the XML record builder, to read XML documents from MQ and turn them into structured Kafka Connect records. From this point, I could choose the format I want the data to be produced to Kafka in (e.g. JSON or Avro) by choosing an appropriate value converter (e.g. org.apache.kafka.connect.json.JsonConverter or io.apicurio.registry.utils.converter.AvroConverter).
But what if your MQ messages have a custom structure, but you still want Kafka Connect to be able to parse your messages and output them to Kafka in any format of your choice?
In that case, you need to use a record builder that can correctly parse your MQ messages. In this post, I’ll explain what that means, show you how to create one, and share a sample you can use to get started.
In this post, I’ll walk through a demo of using IBM Event Processing to create an Apache Flink job that calculates summaries of messages from IBM MQ queues.
This is a high-level overview of the demo:
I’ve added instructions for how you can create a demo like this for yourself to my demos repo on Github.
The rest of this post is a walkthrough and explanation of how it all works.
In this post, I’ll share a demo I gave today to explain some of the processing nodes in the palette of IBM Event Processing.
I’ve found that demonstrations of Event Processing are easier to understand when I don’t need to explain the stream of events I’m processing in the first place. This means I’m always looking for interesting real-world event streams that are widely understood, as they can make for the most effective demos.
With this in mind, today I tried explaining a few of the Event Processing nodes by using them with a live stream of events representing pages that are being created and edited in the English Wikipedia.
Click on the image for a higher-resolution screenshot
Each event contains:
Every edit on Wikipedia results in an event on the Kafka topic, so there are typically a few events a second. It’s not a super-high-throughput topic in Kafka terms, but there are enough events to try out interesting ideas.
Click on the image for a higher-resolution screenshot
Here are a few of the demos I gave today.
This is by no means an exhaustive list of what you could do with this data, but it was enough to let me show what the most commonly-used tools in the palette can do.
In this post, I want to share a quick demo of using Event Processing to process social media posts.
A fun surprise from Nintendo today: they’ve introduced a new product! “Alarmo” is a game-themed alarm clock, with some interesting gesture recognition features.
I was (unsurprisingly!) tempted…
But that got me wondering how the rest of the Internet was reacting.
In this post, I want to share a (super-simple!) demo for how to look at this – using IBM Event Processing to create an Apache Flink job that looks at the sentiment of social media posts about this unusual new product.
MATCH_RECOGNIZEI play a lot of video games. That includes a lot of modern games, but I also still love going back to the retro games of my childhood. There are a lot of fun things from that era of video games that I love.
For example, cheat codes. You’d press a specific sequence of buttons on the game controller at a specific time to unlock some “secret” bit of content – like special abilities, special resources, or levels.
Some of these are so ingrained in me now that my fingers just know how to enter them without thinking. The level select cheat for Sonic the Hedgehog is the best example of this: press UP, DOWN, LEFT, RIGHT, START + A during the title screen to access a level select mode that would let you jump immediately to any part of the game.
level select cheat code for Sonic the Hedgehog
With this in the back of my head, it’s perhaps no surprise that when I needed to explain pattern recognition in Apache Flink, the metaphor I thought of first was how games of yesteryear could recognize certain button press sequences.
If you think of each button press on the game controller as an event, then recognizing a cheat code is just a pattern of events to recognize.
And once I thought of the metaphor – I had to build it. 🙂
There is more detail on how I built this in the git repository, but this is the overall idea for what I’ve made.
In this post, I share a few examples for how to run Event Gateways for Event Endpoint Management.
When we talk about Event Endpoint Management, we often draw logical diagrams like this, with Kafka client applications able to produce and consume events to back-end Kafka clusters via an Event Gateway.
When it comes to start planning a deployment, we need to make decisions about the best way to create that logical Event Gateway layer. This typically includes running multiple gateways, but there are many different ways to do this, depending on your requirements for scaling and availability.
For this post, I want to show two approaches for running two Event Gateways, as a way of illustrating the kind of topologies that are possible.
In this post, I want to share examples of how watsonx can enhance the event processing flows you create using IBM Event Processing.
I’ll start by describing how Event Processing and watsonx complement each other.
Then I’ll share a couple of simple examples of what this looks like in action.
Finally, I’ll walkthrough how I built the example flows to show you how you can try doing something like this for yourself, and share tips for how to create flows like this.