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

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

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

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

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

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

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

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

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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