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

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

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

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

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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

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.

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

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

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

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

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

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

Kafka consumers resume processing from the last committed offset after a crash.