Any concept.
clear in 60 seconds.

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

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

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.

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful degradation allows apps to function partially during service failures.

Vector clocks help track the order and concurrency of events in distributed systems.

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

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

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

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

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

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

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.