I’ve written previously about ingesting Syslog into Kafka and using KSQL to analyse it. I want to revisit the subject since it’s nearly two years since I wrote about it and some things have changed since then.

ksqlDB now includes the ability to define connectors from within it, which makes setting things up loads easier.

You can find the full rig to run this on GitHub.

This is revisiting the blog I wrote a while back, which showed using mongodump and mongorestore to copy a MongoDB database from one machine (a Unifi CloudKey) to another. This time instead of a manual lift and shift, I wanted a simple way to automate the update of the target with changes made on the source.

The source is as before, Unifi’s CloudKey, which runs MongoDB to store its data about the network - devices, access points, events, and so on.

A short & sweet blog post to help people Googling for this error, and me next time I encounter it.

The scenario: trying to create a connector in Kafka Connect (running in distributed mode, one worker) failed with the curl response

I was doing some troubleshooting between two services recently and wanting to poke around to see what was happening in the REST calls between them. Normally I’d reach for tcpdump to do this but imagine my horror when I saw:

Kafka Connect can be deployed in two modes: Standalone or Distributed. You can learn more about them in my Kafka Summit London 2019 talk.

I usually recommend Distributed for several reasons:

I usually find that Standalone is appropriate when:

Snowflake is the data warehouse built for the cloud, so let’s get all ☁️ cloudy and stream some data from Kafka running in Confluent Cloud to Snowflake!

What I’m showing also works just as well for an on-premises Kafka cluster. I’m using SQL Server as an example data source, with Debezium to capture and stream and changes from it into Kafka.

I’m assuming that you’ve signed up for Confluent Cloud and Snowflake and are the proud owner of credentials for both. I’m going to use a demo rig based on Docker to provision SQL Server and a Kafka Connect worker, but you can use your own setup if you want.

I talk and write about Kafka and Confluent Platform a lot, and more and more of the demos that I’m building are around Confluent Cloud. This means that I don’t have to run or manage my own Kafka brokers, Zookeeper, Schema Registry, KSQL servers, etc which makes things a ton easier. Whilst there are managed connectors on Confluent Cloud (S3 etc), I need to run my own Kafka Connect worker for those connectors not yet provided. An example is the MQTT source connector that I use in this demo. Up until now I’d either run this worker locally, or manually build a cloud VM. Locally is fine, as it’s all Docker, easily spun up in a single docker-compose up -d command. I wanted something that would keep running whilst my laptop was off, but that was as close to my local build as possible—enter GCP and its functionality to run a container on a VM automagically.

You can see the full script here. The rest of this article just walks through the how and why.

I started hitting problems when trying Debezium against MySQL v8. When creating the connector:

This is based on using Confluent Cloud to provide your managed Kafka and Schema Registry. All that you run yourself is the Kafka Connect worker.

Optionally, you can use this Docker Compose to run the worker and a sample MySQL database.

I use the Elastic stack for a lot of my talks and demos because it complements Kafka brilliantly. A few things have changed in recent releases and this blog is a quick note on some of the errors that you might hit and how to resolve them. It was inspired by a lot of the comments and discussion here and here.

Coming to Kafka Summit in San Francisco next week? Inspired by similar events at Oracle OpenWorld in past years, I’m proposing an unofficial run (or walk) across the GoldenGate bridge on the morning of Tuesday 1st October. We should be up and out and back in plenty of time to still attend the morning keynotes. Some people will run, some may prefer to walk, it’s open to everyone :)

I’ve been a full-time Developer Advocate for nearly 1.5 years now, and have learnt lots along the way. The stuff I’ve learnt about being an advocate I’ve written about elsewhere (here/here/here); today I want to write about something that’s just as important: staying sane and looking after yourself whilst on the road. This is also tangentially related to another of my favourite posts that I’ve written: Travelling for Work, with Kids at Home.

I’ve had a relaxing couple of weeks off work over the summer, and came back today to realise that I’ve got a fair bit of conference and meetup travel to wrap my head around for the next few months :)

If you’re interested in where I’ll be and want to come and say hi, hear about Kafka—or just grab a coffee or beer, herewith my itinerary as it currently stands.

Kafka Connect in distributed mode uses Kafka itself to persist the offsets of any source connectors. This is a great way to do things as it means that you can easily add more workers, rebuild existing ones, etc without having to worry about where the state is persisted. I personally always recommend using distributed mode, even if just for a single worker instance - it just makes things easier, and more standard. Watch my talk online here to understand more about this. If you want to reset the offset of a source connector then you can do so by very carefully modifying the data in the Kafka topic itself.

When you create a sink connector in Kafka Connect, by default it will start reading from the beginning of the topic and stream all of the existing—and new—data to the target. The setting that controls this behaviour is auto.offset.reset, and you can see its value in the worker log when the connector runs:

I’ve been using Replicator as a powerful way to copy data from my Kafka rig at home onto my laptop’s Kafka environment. It means that when I’m on the road I can continue to work with the same set of data and develop pipelines etc. With a VPN back home I can even keep them in sync directly if I want to.

I hit a problem the other day where Replicator was running, but I had no data in my target topics on my laptop. After a bit of head-scratching I realised that my local Kafka environment had been rebuilt (I use Docker Compose so complete rebuilds to start from scratch are easy), hence no data in the topic. But, even after restarting the Replicator Kafka Connect worker, I still had no data loaded into the empty topics. What was going on? Well Replicator acts as a consumer from the source Kafka cluster (on my home server), and so far as that Kafka cluster was concerned, Replicator had already read the messages. It thought that because even though I’d rebuilt everything on my laptop, Replicator was using the same connector name as before, and the connector name is used as the Consumer group name - which is how the source Kafka cluster keeps track of the offsets. So my "new" Kafka environment was going back to the source, which viewed it as the existing "old" one, which had already received the messages.

Alfred is one of my favourite productivity tools. One of its best features is the clipboard history, which when I moved laptops and it didn’t transfer I realised quite how much I rely on this functionality in my day-to-day work.

Whilst Alfred has the options to syncronise its preferences across machines, it seems that it doesn’t synchronise the clipboard database. To get it to work I did the following:

I write and speak lots about Kafka, and get a fair few questions from this. The most common question is actually nothing to do with Kafka, but instead:

I wrote about this originally last year but since then have evolved my approach. I’ve now pretty much ditched Paper, in favour of Concepts. It was recommended to me after I published the previous post.

I still use the Apple Pencil + iPad, but Concepts is vector-based, supports layers, grids, guidelines…is generally more powerful. Concepts is definitely worth the cost (and learning curve). It doesn’t have quite the same 'hand drawn' feel to it, but it’s close enough for what I want whilst not spending my life re-drawing bitmaps for every permutation of a diagram’s lifecycle :)