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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

Cross-region database replication keeps databases synchronized across different geographical locations.

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.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

Availability Zones help prevent downtime by isolating failures across multiple locations.

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

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