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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.

Graceful degradation allows apps to function partially during service failures.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Failover ensures systems remain operational by managing leader changes during failures.

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

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.

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

Load shedding helps systems reject excess requests to maintain performance during high demand.