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

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

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

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

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

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

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Retry storms occur when too many retries overload a failing service.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Kafka can help ensure messages are processed once, but requires careful design.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.