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

Kafka's parallel processing depends on partitions, not just consumers or threads.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Paxos is a protocol that helps distributed systems agree on one value despite failures.

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

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

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

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

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