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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.

Backpressure helps manage message flow in Kafka during traffic spikes.

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 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.

Classifying failures and using controlled retries in Kafka prevents outages.

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