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Graceful degradation allows apps to function partially during service failures.

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

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

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

gRPC allows efficient communication between services using a structured request-response model.

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

Distributed backpressure helps manage the flow of work between services to prevent overload.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

In distributed systems, timestamps don't always reflect the true order of events.

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

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

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

Synchronous communication waits for a response, while asynchronous continues without waiting.

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

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

Load shedding helps systems reject excess requests to maintain performance during high demand.