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

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Kafka maintains message order within partitions using keys for routing.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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.

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

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

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

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

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

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

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

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

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

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

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

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