Follow-up · depth 2
What metrics prove Consumer Groups and Rebalance is healthy in prod?
What metrics prove Consumer Groups and Rebalance is healthy in prod?
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Prove Consumer Groups and Rebalance is healthy with latency (p50/p95/p99), error/saturation rates, and queue/event-loop lag — not just CPU. Context: Use Consumer Groups and Rebalance as the core idea. Example shape: 6 partitions, 3 consumers → each gets 2 partitions; add 4th consumer triggers rebalance..
Detailed answer
Production health signals for Consumer Groups and Rebalance: • Latency: p50/p95/p99 of the critical path that uses Consumer Groups and Rebalance. • 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: Elastic scale-out; at-least-once default with offset commit. Cons: Rebalance pauses consumption; sticky assign reduces churn. Explain thresholds + alerts, then how you triage. Parent context: Use Consumer Groups and Rebalance as the core idea. Example shape: 6 partitions, 3 consumers → each gets 2 partitions; add 4th consumer triggers rebalance..
Full explanation
Good answers name measurable signals and what “bad” looks like for Consumer Groups and Rebalance. Each partition consumed by one consumer in group. Rebalance on member join/leave — can cause duplicate processing if offsets not committed carefully.
Follow-up questions
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Parent context — Consumer Groups and Rebalance
Use Consumer Groups and Rebalance as the core idea. Example shape: 6 partitions, 3 consumers → each gets 2 partitions; add 4th consumer triggers rebalance..
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