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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.

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

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.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Classifying failures and using controlled retries in Kafka prevents outages.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

Kafka's parallel processing depends on partitions, not just consumers or threads.

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

Kafka consumer lag shows how far behind consumers are from new messages.