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

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

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Graceful degradation allows apps to function partially during service failures.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

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

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

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

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

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

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.