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

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

Classifying failures and using controlled retries in Kafka prevents outages.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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.

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

Alerting helps notify the right people about production issues quickly.

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

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

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

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

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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

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

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

Availability is about access; reliability is about correct performance.

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

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

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

RPC allows one service to call a function in another service over the network.

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

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Write-Ahead Log ensures data is safely recorded before changes are made.

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

Microservices can communicate using five key patterns based on their needs.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

FAANG interviewers evaluate problem understanding, architecture, scalability, and communication skills.

Functional requirements define what a system does, while non-functional requirements define how it performs.

Kafka commit strategies determine how offsets are managed during message processing.

Logs, metrics, and traces help engineers debug issues in software systems.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

Jitter helps spread out client retries to avoid traffic spikes.