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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.

Graceful degradation allows apps to function partially during service failures.

Alerting helps notify the right people about production issues quickly.

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

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

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.

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.

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.

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

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.

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

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