The book

Origin, context, the problem Kafka solves, and why it isn't just a queue.

A technical comparison between Kafka, RabbitMQ, AWS SQS, AWS SNS, and Google Pub/Sub.

Producer, Broker, Cluster, Topic, Partition, Offset, Consumer, and Consumer Group: the pieces that make up Kafka.

Why partitions exist, how the key determines ordering, and why Kafka doesn't guarantee global order.

Replication Factor, Leader, Follower, ISR, and what happens when a broker goes down.

How a Consumer Group splits partitions among consumers, what happens when that split changes, and the real cost of a rebalance.

How a consumer marks how far it has read, the difference between auto commit and manual commit, and the risks of each approach.

How long Kafka keeps an event, and how to use that retention to reprocess history with replay.

How to tell a transient error from a permanent one, and why Kafka has no native DLQ like SQS does.

At Most Once, At Least Once, and Exactly Once: what each guarantee actually promises, and where exactly-once stops applying.

Why consumers need to be idempotent, and how to implement it with eventId, a uniqueness constraint, and atomicity.

The dual write problem between a database and Kafka, and how the transactional outbox solves it with guaranteed consistency.

KafkaTemplate in practice: topic, key, value, serialization, callbacks, headers, and correlationId.

@KafkaListener in practice: groupId, concurrency, Acknowledgment, deserialization, and exception handling with retry and DLQ.

Consumer lag, throughput, structured logs, correlationId, metrics, and tracing: how to know a Kafka pipeline is healthy.