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

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

Kafka uses state stores to remember previous events in stateful stream processing.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

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

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Kafka commit strategies determine how offsets are managed during message processing.

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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