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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

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

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

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