What metrics prove Kafka Exactly-Once Semantics is healthy in prod?

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Quick interview answer

Prove Kafka Exactly-Once Semantics is healthy with latency (p50/p95/p99), error/saturation rates, and queue/event-loop lag — not just CPU. Context: Idempotent producer + transactional writes (read-process-write in one transaction) give EOS within Kafka. End-to-end EOS needs idempotent sinks or outbox pattern.

Detailed answer

Production health signals for Kafka Exactly-Once Semantics: • Latency: p50/p95/p99 of the critical path that uses Kafka Exactly-Once Semantics. • Errors: failure rate, timeouts, and retry storms. • Saturation: queue depth, event-loop delay, thread/pool utilization. • Business SLIs: request success and user-visible freshness where relevant. Trade-off lens: Pros: No duplicate downstream messages within pipeline. Cons: Higher latency; transactional overhead; not magic for external DB writes. Explain thresholds + alerts, then how you triage. Parent context: Idempotent producer + transactional writes (read-process-write in one transaction) give EOS within Kafka. End-to-end EOS needs idempotent sinks or outbox pattern.

Full explanation

Good answers name measurable signals and what “bad” looks like for Kafka Exactly-Once Semantics. Idempotent producer + transactional writes (read-process-write in one transaction) give EOS within Kafka. End-to-end EOS needs idempotent sinks or outbox pattern.

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Parent context — Kafka Exactly-Once Semantics

Idempotent producer + transactional writes (read-process-write in one transaction) give EOS within Kafka. End-to-end EOS needs idempotent sinks or outbox pattern.

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