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The Token Bucket algorithm helps manage how many requests a server can handle.

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

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

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

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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

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

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

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

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

Raft and Paxos are protocols that help computers agree on values in distributed systems.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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