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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

Graceful degradation allows apps to function partially during service failures.

Vector clocks help track the order and concurrency of events in distributed systems.

Synchronous communication waits for a response, while asynchronous continues without waiting.

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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

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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.