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Replicas keep your system available, while snapshots allow for data recovery.

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

Write-Ahead Log ensures data is safely recorded before changes are made.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

Kafka consumers resume processing from the last committed offset after a crash.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

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

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

Graceful degradation allows apps to function partially during service failures.