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Availability is about access; reliability is about correct performance.

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

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

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

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

Observability helps engineers understand system behavior to debug production issues effectively.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Choosing the right boundaries for microservices is crucial for effective architecture.

Alerting helps notify the right people about production issues quickly.

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

Consistency models determine what data reads in distributed systems can see.

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.