Ever dealt with a misbehaving consumer group? Imbalanced broker load? This could be due to your consumer group and partitioning strategy!
Learn about the role of batch.size and linger.ms in data compression.
Learn the basics of what an Apache Kafka cluster is and how they work, from brokers to partitions, how they balance load, and how they handle replication, and leader and replica failures.
The term “event” shows up in a lot of different Apache Kafka® arenas. There’s “event-driven design,” “event sourcing,” “designing events,” and “event streaming.” What is an event, and what is the difference between the role an event has to play in each of these contexts?
If you’ve been working with Kafka Streams and have seen an “unknown magic byte” error, you might be wondering what a magic byte is in the first place, and also, how to resolve the error. This post explains the answers to both questions.
This article summarizes dynamic versus static consumer group membership in Apache Kafka. It shows how the approaches affect rebalancing in heavy state applications and teaches the user how to choose between the methods.
Learn how to avoid confusion by implementing co-partitioning.
Learn what windowing is in Kafka Streams and get comfortable with the differences between the main types.
Learn why configuring consumer Group IDs are a crucial part of designing your consumer application. By the end of this post, you’ll understand the impact they have on three areas: work sharing, new data detection, and data recovery.