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SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

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.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

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.

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

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

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

DNS helps microservices find each other, but it has limitations that may require a service registry.

Graceful degradation allows apps to function partially during service failures.

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.

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

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.

Correlation IDs help track requests in microservices for easier debugging.

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.

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

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