Any concept.
clear in 60 seconds.

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

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

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

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

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

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

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

matchIndex tracks what each follower has, while commitIndex shows what is committed.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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.

Timeouts in distributed systems indicate suspicion, not confirmed failure.