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

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

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

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

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

gRPC allows efficient communication between services using a structured request-response model.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

Kafka uses state stores to remember previous events in stateful stream processing.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

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

Database per Service ensures each microservice owns its data, reducing dependencies.

Jitter helps spread out client retries to avoid traffic spikes.

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Lamport Timestamps help order events in distributed systems while preserving causality.