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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Late-arriving events can be dropped, updated, or routed based on system needs.

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

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

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

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

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