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A Kafka partition is an ordered, append-only sequence of records inside a Kafka topic. Topics are divided into partitions so Kafka can distribute data across brokers, process records in parallel, and scale producers and consumers beyond a single machine.

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 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.

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.

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.