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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.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

Availability is about access; reliability is about correct performance.

Cache invalidation is about knowing when to refresh cached data.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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

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