How Kafka Is Tested

Kevin Feasel



Colin McCabe walks us through the process of a change in Apache Kafka:

The Kafka community has a culture of deep and extensive code review that tries to proactively find correctness and performance issues. Code review is, of course, a pretty common practice in software engineering but it is often cursory check of style and high-level design. We’ve found a deeper investment of time in code review really pays off.

The failures in distributed systems often have to do with error conditions, often in combinations and states that can be difficult to trigger in a targeted test. There is simply no substitute for a deeply paranoid individual going through new code line-by-line and spending significant time trying to think of everything that could go wrong. This often helps to find the kind of rare problem that can be hard to trigger in a test.

Testing data processing engines is difficult, particularly distributed systems where things like network partitions and transient errors are hard to reproduce in a test environment.

Related Posts

Last-Click Attribution With Databricks Delta

Caryl Yuhas and Denny Lee give us an example of building a last-click digital marketing attribution model with Databricks Delta: The first thing we will need to do is to establish the impression and conversion data streams.   The impression data stream provides us a real-time view of the attributes associated with those customers who were served the […]

Read More

Working With Kafka At Scale

Tony Mancill has some tips for working with large-scale Kafka clusters: Unless you have architectural needs that require you to do otherwise, use random partitioning when writing to topics. When you’re operating at scale, uneven data rates among partitions can be difficult to manage. There are three main reasons for this: First, consumers of the “hot” […]

Read More


September 2017
« Aug Oct »