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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.

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

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

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

Kafka maintains message order within partitions using keys for routing.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

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

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

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

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

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

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

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

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 multiple consumers to process messages in parallel.

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