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

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

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

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

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

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

Observability helps engineers understand system behavior to debug production issues effectively.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

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