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

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

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

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

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

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

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

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

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

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

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

Paxos is a protocol that helps distributed systems agree on one value despite failures.

Vector clocks help track the order and concurrency of events in distributed systems.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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.

Quorum and majority help distributed systems maintain data consistency and availability.

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

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

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

RPC allows one service to call a function in another service over the network.

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

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

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

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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