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

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

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

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.

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

Kafka uses replication and in-sync replicas to ensure data is always available.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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