We previously looked at the background to getting XML into Kafka, and potentially how [not] to do it. Now let’s look at the proper way to build a streaming ingestion pipeline for XML into Kafka, using Kafka Connect.

If you’re unfamiliar with Kafka Connect, check out this quick intro to Kafka Connect here. Kafka Connect’s excellent plugable architecture means that we can pair any source connector to read XML from wherever we have it (for example, a flat file, or a MQ, or anywhere else), with a Single Message Transform to transform the XML into a payload with a schema, and finally a converter to serialise the data in a form that we would like to use such as Avro or Protobuf.

👉 Ingesting XML data into Kafka - Introduction

What would a blog post on rmoff.net be if it didn’t include the dirty hack option? 😁

The secret to dirty hacks is that they are often rather effective and when needs must, they can suffice. If you’re prototyping and need to JFDI, a dirty hack is just fine. If you’re looking for code to run in Production, then a dirty hack probably is not fine.

Short & sweet to help out future Googlers. Trying to use abcde I got the error:

Running IBM MQ in a Docker container and the client connecting to it was throwing repeated Channel was blocked errors.

One of my favourite hacks for getting data into Kafka is using kafkacat and stdin, often from jq. You can see this in action with Wi-Fi data, IoT data, and data from a REST endpoint. This is fine for getting values into a Kafka message - but Kafka messages are key/value, and being able to specify a key is can often be important.

Here’s a way to do that, using a separator and some jq magic. Note that at the moment kafkacat only supports single byte separator characters, so you need to choose carefully. If you pick a separator that also appears in your data, it’s possibly going to have unintended consequences.

Readers of a certain age and RDBMS background will probably remember northwind, or HR, or OE databases - or quite possibly not just remember them but still be using them. Hardcoded sample data is fine, and it’s great for repeatable tutorials and examples - but it’s boring as heck if you want to build an example with something that isn’t using the same data set for the 100th time.

Here’s a collection of Kafka-related talks, just for you.

Each one has 🍿🎥 a recording, 📔 slides, and 👾 code to go and try out. 

There’s ways, and then there’s ways, to count the number of records/events/messages in a Kafka topic. Most of them are potentially inaccurate, or inefficient, or both. Here’s one that falls into the potentially inefficient category, using kafkacat to read all the messages and pipe to wc which with the -l will tell you how many lines there are, and since each message is a line, how many messages you have in the Kafka topic:

Google Chrome automagically adds sites that you visit which support searching to a list of custom search engines. For each one you can set a keyword which activates it, so based on the above list if I want to search Amazon I can just type a <tab> and then my search term

I had the pleasure of presenting at DataEngBytes recently, and am delighted to share with you the 🗒️ slides, 👾 code, and 🎥 recording of my ✨brand new talk✨:

🤖Building a Telegram bot with Apache Kafka, Go, and ksqlDB

A tiny snippet since I wasted 10 minutes going around the houses on this one…

tl;dr: If you try to create a command that is not in lower case (e.g. Alert not alert) then the setMyCommands API will return BOT_COMMAND_INVALID

The server sends Transfer-Encoding: chunked data, and you want to work with the data as you get it, instead of waiting for the server to finish, the EOF to fire, and then process the data?

When I set out to learn Go one of the aims I had in mind was to write a version of this little Python utility which accompanies a blog I wrote recently about understanding and diagnosing problems with Kafka advertised listeners. Having successfully got Producer, Consumer, and AdminClient API examples working, it is now time to turn to that task.

Having ticked off the basics with an Apache Kafka producer and consumer in Go, let’s now check out the AdminClient. This is useful for checking out metadata about the cluster, creating topics, and stuff like that.