There are many ways that Apache Kafka has been deployed in the field. In our Kafka Summit 2021 presentation, we took a brief overview of many different configurations that have been observed to date. In this blog series, we will discuss each of these deployments and the deployment choices made along with how they impact reliability.
There are two big gaps in the Apache Kafka project when we think of operating a cluster. The first is monitoring the cluster efficiently and the second is managing failures and changes in the cluster. There are no solutions for these inside the Kafka project but there are many good 3rd party tools for both problems. Cruise Control is one of the earliest open source tools to provide a solution for the failure management problem but lately for the monitoring problem as well.
In March 2021, a 200,000 tonne ship got stuck in the Suez Canal, and the global shipping industry suddenly caught the world’s attention. It made us realize ships play an important role in our daily lives. Really important in fact; 90% of the things we consume arrive by ship. Take a look at this map. By visualizing vessel routes over time, the pattern creates a map of the earth. Note the lack of vessels travelling close to the coast of Somalia where piracy is common.
Suppose that you work for the infosec department of a government agency in charge of tax collection. You recently noticed that some tax fraud incident records went missing from a certain Apache Kafka topic. You panic. It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data. But for Kafka in particular, this can prove challenging.
Suppose that you work for a government tax agency. You recently noticed that some tax fraud incident records have been leaked on the darknet. This information is held in a Kafka Topic. The incident response team wants to know who has accessed this data over the last six months. You panic. It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data to respond to this kind of situation.
We live in a dynamic world. It is safe to say that companies aim to speed up time-to-market and out-innovate their competition with Kafka, but at the same time struggle with some limitations. These can range from compliance-related setbacks for regulations such as GDPR, CCPA and HIPAA, to self-service slip-ups that could see a whole Kafka cluster going down. Even something as seemingly innocuous as configuring and creating a Kafka Topic can lead to operational U-turns, slowdowns and even downtime.
In our world of streaming applications, developers are forever climbing a steep learning curve to stay successful with technologies such as Apache Kafka. There is no end to the debt and the detail you need to manage when it comes to Kafka - and particularly since it doesn’t come with guardrails to help you out, the stakes for making mistakes are high.
If you’ve been following Apache Kafka over the past few years, you’ll know a very important milestone has now been reached. The company that was spun out of LinkedIn to propel this open source technology to the future is now going Public. Well done Confluent, congratulations on your IPO!
