Any concept.
clear in 60 seconds.

A timeout indicates a deadline was exceeded, not necessarily a failure.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Partial failure means some services fail while others keep running, impacting system reliability.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Retry storms occur when too many retries overload a failing service.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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

Crash failures stop a service, while network failures disrupt communication.

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful degradation allows apps to function partially during service failures.

The CAP Theorem explains the trade-offs in distributed systems during network failures.