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

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

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Graceful degradation allows apps to function partially during service failures.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

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

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

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

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

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

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

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

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

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

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

Apache Kafka is a system for managing large volumes of events efficiently.