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Graceful degradation allows apps to function partially during service failures.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

A timeout indicates a deadline was exceeded, not necessarily a failure.

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Cache invalidation is about knowing when to refresh cached data.

Jitter helps spread out client retries to avoid traffic spikes.

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

Availability is about access; reliability is about correct performance.

Classifying failures and using controlled retries in Kafka prevents outages.

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

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.

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

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Cache stampede occurs when many requests hit the database due to a cache miss.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

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

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Cache penetration occurs when requests for non-existent data overload the database.

Poison messages in Kafka can cause infinite retries, risking system stability.

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

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

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

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