Incident Correlation for Production Investigations
Incident correlation is the work of testing whether events around an outage belong to the same failure story. It is not a rule that says “the last deployment caused the incident.” The objective is to turn a noisy timeline into a short, reviewable list of hypotheses with evidence for and against each one.
Start with a precise incident boundary
Before joining data, write down four facts that should not move during the investigation:
- User-visible symptom: for example, elevated checkout errors, not merely “the alert fired.”
- Start and recovery times: include the timezone and whether the time came from a monitor, log, or human report.
- Blast radius: affected service, route, region, tenant, cluster, or deployment ring.
- Known change window: the deploys, configuration edits, infrastructure events, and dependency changes that occurred before the symptom.
This matters because timestamps from CI, Kubernetes, application logs, and monitoring systems may have different precision or clock skew. A timeline without source and scope is a story, not evidence.
Build the evidence table before choosing a cause
For each candidate cause, capture the same fields. This prevents the team from collecting detail for the first plausible theory and treating missing evidence for alternatives as confirmation.
| Candidate | Timing | Scope match | Direct evidence | Disconfirming evidence | Next check |
|---|---|---|---|---|---|
Release v2.18 | 5 minutes before errors | One service | new version on affected pods | unaffected pods on same version | compare request path and config |
| Node disk pressure | 3 minutes before errors | one node | DiskPressure, failed log writes | other nodes healthy | move workload and observe |
| Payment dependency | overlaps incident | all checkout traffic | timeout increase | dependency health check normal | inspect upstream latency |
The table is deliberately simple. Its job is to make confidence explainable. Correlation can increase the priority of a candidate; it cannot prove causation on its own.
Connect events through stable identifiers
Useful joins are usually boring identifiers, not model magic:
- commit SHA → build run → immutable artifact → deployment revision;
- deployment revision → namespace, workload, pod, node, region, and time;
- alert labels → service, route, environment, region, and trace or request identifiers;
- incident channel or ticket → timestamped human decisions and mitigation steps.
If those fields do not exist in the source systems, collect that as a data-quality finding. Do not invent a relationship from text similarity alone. A deploy marker should include at least service, environment, revision, commit or artifact ID, actor, and deployment time.
Test the leading hypothesis safely
Choose the cheapest test that can reduce uncertainty without worsening the incident. Examples include comparing healthy and unhealthy pods with the same release, checking whether the failure follows one node, or validating the affected route against a known-good revision in a safe environment.
Avoid turning an investigation into a production experiment. A rollback, traffic shift, or node drain can be a mitigation, but record it as such. The fact that a mitigation helped is useful evidence; it is not automatically a complete root-cause explanation.
Where OctoLaunch fits
OctoLaunch is designed to assemble source-control, CI/CD, infrastructure, runtime, and incident metadata into an investigation timeline. It should make the links and evidence inspectable, show competing candidates, and keep human approval in front of production actions. It does not replace the source systems or guarantee a root cause.