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Message queues allow services to communicate asynchronously, improving scalability and reliability.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Backpressure helps manage message flow in Kafka during traffic spikes.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka uses replication and in-sync replicas to ensure data is always available.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

You can replay Kafka events safely by separating live and replay processes.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Microservices can communicate using five key patterns based on their needs.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Kafka's `seek()` lets you replay only specific messages, saving time.

Kafka can help ensure messages are processed once, but requires careful design.

Kafka maintains message order within partitions using keys for routing.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Apache Kafka is a system for managing large volumes of events efficiently.

Older offsets can overwrite newer ones in Kafka if not handled properly.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka commit strategies determine how offsets are managed during message processing.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.