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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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.

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

Kafka maintains message order within partitions using keys for routing.

Request-Response asks for a result, while Events notify that something has happened.

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

You can replay Kafka events safely by separating live and replay processes.

Leader election helps distributed systems select one coordinator from many nodes.

Microservices can communicate using five key patterns based on their needs.

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.