I couldn't save this to docs/postmortems/2026-02-10-checkout-api-500-errors.md because I have no file-write tool in this session. The full document is below, ready to paste into that path.
Owners and due dates are placeholders. The skill requires a named person for each action, and I only know Maya's name. Due dates are proposed, so please confirm them.
Post-Mortem: Checkout API 500 Errors
Date: 2026-02-10
Severity: P2
Duration: 1 hour 15 minutes (14:05 UTC – 15:20 UTC, detection to resolution)
Incident Commander: @Maya
Status: Resolved
Summary
After the v2.41 deploy at 13:58 UTC, the checkout API began returning 500 errors. A new query in the release loaded full result sets into memory, which caused pods to run out of memory and crash-loop. At peak, about 12% of requests failed, against a 0.1% baseline. Rolling back to the previous version at 15:00 restored service, and recovery was confirmed at 15:20. No data was lost and no SLA was breached.
Impact
| Dimension | Value |
|---|---|
| Affected services | Checkout API |
| User-facing impact | Errors (HTTP 500) on a portion of requests |
| Users affected | Not yet quantified (about 12% of requests failed at peak) |
| Peak error rate | ~12% vs 0.1% baseline |
| Data loss | None |
| SLA breach | No |
Duration note: The impact window began at the 13:58 deploy. Detection to resolution was 1h15m. Deploy to resolution was about 1h22m.
Timeline
All times UTC, 2026-02-10.
| Time | Event |
|---|---|
| 13:58 | v2.41 deployed |
| 14:05 | Alert fired for elevated checkout API errors |
| 14:10 | Maya paged and began coordinating as incident commander |
| 14:35 | Root cause identified: a new query loaded full result sets into memory, causing pod OOM crash-loops |
| 15:00 | Rollback of v2.41 performed |
| 15:20 | Recovery confirmed |
Gaps to verify from logs, alerts and Slack:
- When the first 500s appeared. The alert fired 7 minutes after the deploy, but the first symptom may have been earlier.
- What happened between 14:35 and 15:00. Root cause was known, but the rollback took 25 minutes. This could have been decision time, the rollback process, or the deploy pipeline.
- Whether any customer or internal communication was sent, and when.
- Whether the incident was formally declared, and at what time.
Root Cause
The release shipped a query that loaded entire result sets into memory instead of bounding or streaming them. For accounts with large result sets, memory use exceeded pod limits, and the pods crashed and restarted repeatedly. This produced the 500s.
The underlying gap is that the pre-release process had no way to catch this. Load tests covered only median accounts, so memory behavior for large accounts was never exercised before production.
5 Whys
- Why did users see 500s? The checkout API pods were crash-looping.
- Why were they crash-looping? They exceeded their memory limits.
- Why was memory exceeded? The new query loaded full result sets into memory.
- Why wasn't this caught before deploy? Load tests covered only median accounts.
- Why? There were no test fixtures or scenarios for large accounts, so a memory-heavy query path looked fine under test.
Contributing Factors
- Memory alert threshold at 90%. Pods can reach OOM before a 90% alert gives useful lead time, so the memory alert did not give an early signal.
- No runbook for OOM pods. Responders had no documented path for diagnosing memory crash-loops or deciding on a rollback.
- Load tests covered only median accounts. This is the same gap as the root cause, and it also meant the release had no large-account validation.
What Went Well
- The alert fired 7 minutes after the deploy.
- The on-call was paged within 5 minutes of the alert.
- Root cause was identified about 30 minutes after the page.
- Rollback resolved the issue, and recovery was explicitly confirmed.
- No data was lost and no SLA was breached.
What Could Have Gone Better
- 25 minutes passed between identifying root cause and starting the rollback.
- The release had no staged rollout or canary that might have limited exposure to the failing path.
- Memory pressure was not visible early, because the alert threshold left little warning.
- Responders had no runbook to speed up diagnosis and the rollback decision.
Action Items
| # | Action | Owner | Due Date | Priority |
|---|---|---|---|---|
| 1 | Add load-test fixtures for high-volume accounts and include them in the pre-release load test for the checkout API | @TBD | 2026-03-10 | High |
| 2 | Fix the v2.41 query to paginate or stream results with a bounded memory footprint, and add a test asserting a memory ceiling | @TBD | 2026-02-20 | High |
| 3 | Lower the checkout API pod memory alert threshold from 90% to a lower value (proposed 75%), and add an alert on OOMKilled and restart count | @TBD | 2026-02-24 | High |
| 4 | Write a runbook for OOM and crash-looping pods, including diagnosis steps and rollback criteria | @TBD | 2026-03-03 | Medium |
| 5 | Review the 14:35–15:00 gap and document the fastest rollback path. Consider a rollback-first guideline for post-deploy regressions | @Maya | 2026-03-03 | Medium |
| 6 | Evaluate canary or staged rollout for checkout API deploys | @TBD | 2026-03-31 | Medium |
Lessons Learned
- Tests built around typical accounts can hide failures that only appear at the tail. Memory-bound code paths need large-data fixtures.
- An alert threshold that is only reached when a pod is about to die gives little warning. OOM kills and restart counts are better early signals.
- Once the cause is known, rollback speed matters as much as diagnosis speed. A runbook and clear rollback criteria would shorten the gap between the two.
Next steps:
- Fill in owners for the TBD items.
- Check the flagged timeline gaps against logs and Slack, especially 14:35–15:00 and any customer communication.
- Add a user-count estimate if one is available.
Real run in an isolated folder with only this skill installed. Only the Skill and Read tools were enabled, so nothing was fetched from the web and no file was written. The example is fictional. The incident is invented. The model could not save the document to its usual path and printed it inline instead.
Was es macht
Hilft beim Schreiben des Post-Mortems nach einem Ausfall oder einer schweren Beeinträchtigung. Es sammelt die Eckdaten des Vorfalls, rekonstruiert eine genaue Zeitachse und markiert Lücken, führt eine 5-Whys-Analyse durch, die Ursache und beitragende Faktoren trennt, hilft bei der Quantifizierung der Auswirkungen (Dauer von Erkennung bis Behebung, Spitzenfehlerrate, Anteil des Traffics, geschäftliche Folgen) und macht aus jeder Ursache eine konkrete Maßnahme mit benanntem Verantwortlichen und Datum. Die Tonregel lautet schuldfrei: Systeme versagen, nicht Menschen.
So funktioniert es
- Das Modell sammelt vor dem Schreiben Titel, Zeiten, Schweregrad, betroffene Dienste und eine grobe Zeitachse.
- Es arbeitet mit Ihnen Zeitachse, Ursache, beitragende Faktoren und Auswirkungen durch.
- Es erstellt ein vollständiges Dokument: Zusammenfassung, Auswirkungstabelle, Zeitachse, Ursache, beitragende Faktoren, was gut lief, was besser laufen könnte, Maßnahmen und Lehren.
Geeignet für
Produktionsausfälle, für Nutzer sichtbare Fehler, Datenverlust, SLA-Verletzungen und Beinahe-Vorfälle, idealerweise innerhalb von 48-72 Stunden geschrieben.
Reine Anweisungen: keine Skripte, kein Netzwerkzugriff, kein Konto. Im letzten Schritt speichert der Skill das Dokument unter `docs/postmortems/YYYY-MM-DD-<slug>.md` in Ihrem Projekt; sagen Sie dem Modell, wohin, oder lassen Sie es direkt im Chat ausgeben. Post-Mortems enthalten oft interne Systemdetails, Zahlen zu Kundenauswirkungen und Namen, achten Sie daher darauf, was Sie teilen und wer die Datei lesen kann. Vom Modell eingetragene Verantwortliche und Termine sind Platzhalter, bis Ihr Team sie bestätigt. Es ersetzt nicht Ihren formalen Incident- oder Compliance-Prozess.