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

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

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

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

RPC allows one service to call a function in another service over the network.

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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

Read-after-write consistency ensures users see their latest updates immediately.

Jitter helps spread out client retries to avoid traffic spikes.

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

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