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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.

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

Alerting helps notify the right people about production issues quickly.

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

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

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.

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.

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

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

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.