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

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

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

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

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

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

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

Request-Response asks for a result, while Events notify that something has happened.

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

Classifying failures and using controlled retries in Kafka prevents outages.

Backpressure helps manage message flow in Kafka during traffic spikes.

Synchronous communication waits for a response, while asynchronous continues without waiting.

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

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

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

REST, RPC, and gRPC are different methods for communication in software systems.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.