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Context

Repocheckout-apiBranchv2.15.0Deploy#ed.Incident#kafka-coP2Resolved

Incident verdict

Failure chain detected from production logs and cited evidence lines.

Generated from real production signals. Logs, Slack context, and monitoring traces are correlated before RCA and fix guidance.

ProdRescue AIIncident Report
01 / 11

Kafka Consumer Lag Incident — Root Cause Analysis Example — Incident Report

May 2, 2026 · Prepared for: [Your Organization]

Severity

P2

Service outage

35 min

Peak error rate

lag +2.1M msgs

Users impacted

~120K event delay window

Status

Resolved

ConfidentialMay 2, 2026
ProdRescue AIIncident Report
02 / 11

Executive Summary

On August 19, 2025, the analytics-ingest consumer group on Kafka cluster prod-east lagged by over 2.1 million messages. Producer throughput held steady at ~85k msgs/sec while aggregate consumer fetch rate dropped after deployment v2.8.0 introduced synchronous GeoIP enrichment in the hot path. Partitions (48) could not absorb burst replay after a broker rolling restart. Resolution combined partition reassignment, temporary scale-out of consumers (12 → 28 instances), and rollback of enrichment to async side-channel processing. MTTR from incident declaration to lag under SLA: 35 minutes.

Confidential02 / 11
ProdRescue AIIncident Report
03 / 11

Timeline

  • 12:04:18 UTC — Lag alert: consumer group analytics-ingest > 500k behind (threshold 200k)
  • 12:05:40 UTC — On-call confirms all brokers healthy; producer rate normal; issue isolated to consumer fleet
  • 12:08:12 UTC — Deploy v2.8.0 identified in change window; p99 process time per batch 12ms → 180ms
  • 12:11:00 UTC — Broker rolling restart from prior change completed; replay surge hits consumers
  • 12:15:33 UTC — Decision: scale consumers horizontally + disable sync GeoIP in hot path (feature flag)
  • 12:22:45 UTC — Rollback feature; async enrichment worker enabled; consumer instances scaled to 28
  • 12:31:04 UTC — Lag falling; under 200k at 12:36; SLO restored
  • 12:39:00 UTC — Post-incident: partition count increase scheduled; incident closed
Confidential03 / 11
ProdRescue AIIncident Report
04 / 11

Root Cause Analysis

Primary root cause: v2.8.0 added synchronous third-party GeoIP calls inside the poll loop, increasing per-record latency ~15× under load. With fixed partition count and max poll interval pressure, consumers could not keep pace with producers after a replay window from broker restarts.

Contributing factors: (1) Partition under-provisioned for peak (48 partitions vs. sustained 85k msg/s), (2) no back-pressure to slow producers for this topic, (3) missing load test for v2.8.0 on full production traffic shape.

Confidential04 / 11
ProdRescue AIIncident Report
05 / 11

Impact

  • Duration: 35 minutes to restore lag SLO; 47 minutes total elevated lag visibility
  • Data freshness: Analytics dashboards delayed up to 9 minutes behind real time
  • Severity: P2 (degraded, not total pipeline outage)
Confidential05 / 11
ProdRescue AIIncident Report
06 / 11

Action Items

PriorityActionOwnerDueStatus
P1Increase partitions to 96 + rebalance plan@streaming2025-08-22Open
P1GeoIP only async path; contract tests on poll latency@backend2025-08-21Open
P2Producer back-pressure when lag > 100k@platform2025-08-28Open
Confidential06 / 11
ProdRescue AIIncident Report
07 / 11

Detection, Response & Resolution

Detection via lag monitor (12:04). Response: ruled out broker failure, profiled consumer, tied to deploy. Resolution: feature rollback + scale-out + rebalance.

Confidential07 / 11
ProdRescue AIIncident Report
08 / 11

5 Whys Analysis

  1. Why did dashboards show stale data? → Consumer lag exceeded safe threshold.
  2. Why did lag grow? → Consumers processed fewer msgs/sec than producers emitted.
  3. Why fewer? → Each poll loop spent excessive time in GeoIP enrichment.
  4. Why was GeoIP in the hot path? → v2.8.0 incorrectly marked enrichment as required inline for “accuracy.”
  5. Why shipped?ROOT CAUSE: No consumer lag SLO gate in CI, no soak test at peak TPS, partition capacity not reviewed before deploy.
Confidential08 / 11
ProdRescue AIIncident Report
09 / 11

Prevention Checklist

  • Max poll interval + process latency alarms per consumer group
  • Partition sizing reviewed quarterly vs peak producer rate
  • Async enrichment default; blocking calls forbidden in poll path (lint)
  • Load test: replay + rolling restart simulation before streaming deploys
Confidential09 / 11
ProdRescue AIIncident Report
10 / 11

Evidence & Log Samples

2025-08-19T12:06:02.441Z WARN analytics-ingest-7b2kf lag=1247831 partitions=[0-47] max.poll.interval.ms approaching
{"topic":"events.raw","consumer":"analytics-ingest","partition":12,"lag_offets":482910,"process_ms_p99":176}
[kafka-coordinator] Heartbeat failed: consumer processing exceeded max.poll.interval.ms (300000ms) — rebalance triggered
Confidential10 / 11
ProdRescue AIIncident Report
11 / 11

Lessons Learned

Hot-path network I/O in Kafka consumers starves fetch loops. Treat partition count and consumer processing SLA as one capacity model.

Confidential11 / 11

How this report maps to the real product workflow

This page is a real-format example so teams can evaluate the full flow before login: input signals, evidence-backed RCA, Ask ProdRescue follow-up, and optional GitHub actions by plan.

1) Inputs & context

All plans can paste logs directly. With Slack connected (Pro / Team), pull threads or channels from war rooms and keep that context in one evidence-backed report.

2) Evidence-backed RCA

Timeline, root cause, impact, and action items are generated with citations tied to real log lines (e.g. [1], [6], [8]) so teams can verify every claim.

3) Ask ProdRescue

On report pages, users can ask follow-up questions like "why this happened", "show the evidence", or "suggest a fix" and get incident-context answers grounded in report data.

4) GitHub actions (plan-aware)

Team: connect repo, import commits, run manual deploy analysis, add webhook automation, and submit suggested fixes for review on GitHub (no auto-merge).

Free/PAYG/Pro: incident clarityTeam: automation + suggested fixes

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Get answers. Find the fix.

Incident-aware
What is the root cause?What changed before the incident?What evidence supports this?Suggest a fixWhat should we do next?

Suggested Fix (preview)

- payment.Amount
+ if payment == nil {
+   return ErrInvalidPayment
+ }
+ amount := payment.Amount

Change preview

fix(incident-kafka-co): apply suggested remediation

Team plan can publish the change for review on GitHub. No auto-merge.

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