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Graceful degradation allows apps to function partially during service failures.

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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

Consistency models determine what data reads in distributed systems can see.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

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

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Kafka uses replication and in-sync replicas to ensure data is always available.

Leader election helps distributed systems select one coordinator from many nodes.