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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.

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

Graceful degradation allows apps to function partially during service failures.

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

Availability is about access; reliability is about correct performance.

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

An error budget shows how much downtime is acceptable while meeting reliability goals.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Database replication helps keep data available and allows systems to handle more read requests.

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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