Latest Kafka Articles

Kafka topics, partitions, and offsets define how data is organized, scaled, ordered, consumed, and replayed. Most Kafka design mistakes eventually come back to one of these three concepts.

Kafka consumers turn durable event streams into application work. They fetch records from partitions, process them, track progress through offsets, and cooperate through consumer groups to divide a topic across multiple application instances.

Kafka delivery semantics describe what can happen to a record when producers, brokers, consumers, networks, or downstream systems fail. The familiar terms at-most-once , at-least-once , and exactly-once are useful only when the processing boundary is defined precisely.

Kafka fault tolerance is built around partition replication. Instead of storing a partition on only one broker, Kafka keeps multiple replicas across brokers so another replica can take over when the current leader becomes unavailable.

Apache Kafka is a distributed event-streaming platform designed for moving large volumes of records between systems while keeping those records durable, ordered within defined boundaries, and available for later replay.

A Kafka producer does much more than send individual messages to a broker. It chooses partitions, accumulates records into batches, compresses data, retries failed requests, waits for acknowledgements, and can prevent many retry-generated duplicates through idempotent publishing.