rmoff’s random ramblings

✨ Kafka, ksqlDB, and other random geekery 🤓

Detecting and Analysing SSH Attacks with ksqlDB

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. Create and configure the Syslog connector To start with, create a source connector:

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Copy MongoDB collections from remote to local instance

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.

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Kafka Connect - Request timed out

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

HTTP/1.1 500 Internal Server Error
Date: Fri, 29 Nov 2019 14:33:53 GMT
Content-Type: application/json
Content-Length: 48
Server: Jetty(9.4.18.v20190429)

{"error_code":500,"message":"Request timed out"}

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Using tcpdump With Docker

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:

root@ksqldb-server:/# tcpdump
bash: tcpdump: command not found

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Common mistakes made when configuring multiple Kafka Connect workers

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: It can scale It is fault-tolerant It can be run on a single node sandbox or a multi-node production environment It is the same configuration method however you run it I usually find that Standalone is appropriate when:

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Streaming data from SQL Server to Kafka to Snowflake ❄️ with Kafka Connect

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.

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Running Dockerised Kafka Connect worker on GCP

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.

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Debezium & MySQL v8 : Public Key Retrieval Is Not Allowed

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

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Using Kafka Connect and Debezium with Confluent Cloud

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.

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Skipping bad records with the Kafka Connect JDBC sink connector

The Kafka Connect framework provides generic error handling and dead-letter queue capabilities which are available for problems with [de]serialisation and Single Message Transforms. When it comes to errors that a connector may encounter doing the actual pull or put of data from the source/target system, it’s down to the connector itself to implement logic around that. For example, the Elasticsearch sink connector provides configuration (behavior.on.malformed.documents) that can be set so that a single bad record won’t halt the pipeline. Others, such as the JDBC Sink connector, don’t provide this yet. That means that if you hit this problem, you need to manually unblock it yourself. One way is to manually move the offset of the consumer on past the bad message.

TL;DR : You can use kafka-consumer-groups --reset-offsets --to-offset <x> to manually move the connector past a bad message

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Kafka Connect and Elasticsearch

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.

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Copying data between Kafka clusters with Kafkacat

kafkacat gives you Kafka super powers 😎

I’ve written before about kafkacat and what a great tool it is for doing lots of useful things as a developer with Kafka. I used it too in a recent demo that I built in which data needed manipulating in a way that I couldn’t easily elsewhere. Today I want share a very simple but powerful use for kafkacat as both a consumer and producer: copying data from one Kafka cluster to another. In this instance it’s getting data from Confluent Cloud down to a local cluster.

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Kafka Summit GoldenGate bridge run/walk

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 :)

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Staying sane on the road as a Developer Advocate

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.

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Where I’ll be on the road for the remainder of 2019

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.

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Reset Kafka Connect Source Connector Offsets

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.

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Starting a Kafka Connect sink connector at the end of a topic

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:

[2019-08-05 23:31:35,405] INFO ConsumerConfig values:
        allow.auto.create.topics = true
        auto.commit.interval.ms = 5000
        auto.offset.reset = earliest
…

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Resetting a Consumer Group in Kafka

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.

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Migrating Alfred Clipboard to New Laptop

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:

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So how DO you make those cool diagrams? July 2019 update

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: How do you make those cool diagrams? 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.

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Robin Moffatt

Robin Moffatt is a Principal DevEx Engineer at LakeFS. He likes writing about himself in the third person, eating good breakfasts, and drinking good beer.