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Graceful degradation allows apps to function partially during service failures.

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

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

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

Event time is when an event occurs, while processing time is when it is handled.

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

Physical time tells when events happen, while logical time tells their order.

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

A timeout indicates a deadline was exceeded, not necessarily a failure.

Failover ensures systems remain operational by managing leader changes during failures.

Availability Zones help prevent downtime by isolating failures across multiple locations.

Jitter helps spread out client retries to avoid traffic spikes.