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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.

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

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

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

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.

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

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.

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

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

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

Graceful degradation allows apps to function partially during service failures.

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

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

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

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

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.

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

Kafka can help ensure messages are processed once, but requires careful design.

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.

Raft handles leader failure by allowing followers to elect a new leader through voting.