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Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

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

Alerting helps notify the right people about production issues quickly.

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

Leader election helps distributed systems select one coordinator from many nodes.

Distributed locks ensure only one server performs a task, avoiding duplication.

Availability is about access; reliability is about correct performance.

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

Choosing the right distributed lock depends on your system's needs and existing tools.