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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

gRPC allows efficient communication between services using a structured request-response model.

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

Crash failures stop a service, while network failures disrupt communication.

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

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

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

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

Physical time tells when events happen, while logical time tells their order.

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

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

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

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