Any concept.
clear in 60 seconds.

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

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

Read-after-write consistency ensures users see their latest updates immediately.

Consistent hashing helps distribute data across servers with minimal movement.

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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.

Lamport Timestamps help order events in distributed systems while preserving causality.

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

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

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 watches notify clients of changes, avoiding constant polling and reducing server load.

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

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

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

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

Leader election helps distributed systems select one coordinator from many nodes.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Consensus ensures that distributed systems can agree on decisions even during failures.

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