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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallTrace an outage by first confirming who and what is affected, then building a shared timeline from monitoring and change records. Mitigate user impact as soon as a safe, evidence-based action is available; continue investigating afterward. Verify recovery and record the root cause, contributing conditions, and follow-up owners in a blameless postmortem. An alert starts the investigation—it does not prove an outage or explain its cause.
1. Confirm the alert and measure the impact
Start by checking whether the alert corresponds to a real service problem. Identify the affected operation, users, region, and time window. Review service health and service-level objective (SLO) indicators where available, and compare the alert with other signals before treating it as confirmed impact.
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Monitoring supports alerting, diagnosis, visualization, and trend analysis; use it to establish the scope rather than relying on one alert in isolation. Google SRE’s monitoring guidance describes these monitoring roles.
- Which user-visible operations are failing or degraded?
- Are failures limited to a region, dependency, request type, or group of users?
- When did the first symptom appear, and is the affected population growing?
- Which service-health or SLO indicators show the same problem?
2. Create a shared incident timeline
Keep a single incident record or shared channel where responders add observations with timestamps. Capture the alert, first known symptom, deployments and configuration changes, dependency events, mitigation attempts, and checks showing recovery. Separate observations from interpretations so that a plausible explanation does not become accepted as fact merely because it fits the timeline.
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Timing is useful evidence, but it is not proof of causation. Monitoring may reflect an event after a delay, so an apparent lag between a change and a metric shift can mislead responders. Google SRE’s monitoring guidance cautions about delays between actions and their appearance in monitoring.
3. Use metrics and logs for different questions
Metrics give a relatively fast, aggregated view of service health and scale. They are useful for dashboards and alerts: for example, they can show whether error rates rose across a service or whether the problem is concentrated in one region. Logs add event-level detail, request context, and entity identifiers that may be too high-cardinality to use effectively as metric labels.
Use the two together: metrics can narrow down when and where to investigate, while logs can help explain what happened to affected requests. The exact tools and available telemetry vary by system. Google SRE’s monitoring guidance discusses the complementary roles of metrics and logs.
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4. Coordinate the response before investigation branches multiply
Assign an incident lead to maintain the overall picture, communicate status, and coordinate the response. Keep a concise status summary and give responders distinct investigation threads—for example, deployment history, dependency health, or affected request logs—so findings can be compared rather than duplicated.
Use established escalation paths to reach service owners and dependency teams. A clear on-call process, shared incident record, and access to relevant runbooks help responders act on evidence and keep the impact visible while technical work continues. Google SRE’s incident management guide covers incident roles and coordination.
5. Mitigate impact when a safe action is available
Do not wait for a complete root-cause explanation before reducing user harm. If the affected area is understood and a prepared action is safe for the system, responders may roll back a change, shift traffic, restart a component, or use another runbook-defined recovery. The right action depends on the service, its risks, and its operational controls; none of these actions is universally safe.
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Google SRE describes its priority as stopping incident impact first and then finding the root cause, unless the cause is identified early. Keep the investigation moving while mitigation proceeds, and record what was changed and when. Google SRE’s incident-response example illustrates why impact reduction need not wait for a full explanation.
6. Test cause hypotheses against evidence
For each plausible cause, state what evidence would support or weaken it. Compare symptom onset and recovery against logs, metrics, deployment and configuration history, and dependency behavior. If tracing is available, it may help follow a request across services, but telemetry capabilities differ; the cited Google guidance directly supports the metrics-and-logs distinction rather than ranking observability products.
- Hypothesis: A recent deployment caused the failure. Check: Compare the affected operation and onset with the deployment timeline and relevant error details.
- Hypothesis: A dependency is failing. Check: Compare dependency health and failures with the affected requests, and ask its owner to validate the evidence.
- Hypothesis: A configuration or traffic change caused degradation. Check: Confirm the change, its scope, and whether the symptoms respond to a controlled rollback or traffic shift.
A convincing story is not enough. In a Google SRE case study, responders were initially distracted by an apparent image-source issue before finding a corrupt image in a different storage layer. The lesson is to test where the failure actually occurs rather than stopping at the first plausible explanation. The case study describes the investigation.
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7. Verify recovery and continue watching
After mitigation, check the operations users were experiencing—not only whether an alert has cleared. Confirm relevant service-health indicators have recovered, ask the appropriate on-call engineers to validate their areas, and monitor for recurrence. Communicate the incident’s status and close it only when recovery has been checked against the affected service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Record root cause and make the learning actionable
Write a blameless postmortem that distinguishes the trigger from contributing conditions. Include impact, timeline, root cause, detection and response lessons, and corrective actions with owners. Focus on system and process conditions that made the incident possible or prolonged it, rather than treating the last person or change in the timeline as the whole explanation.
Google SRE presents postmortems as a way to identify systemic patterns and guide improvements. Its postmortem-analysis chapter reports the following historical categories; they describe Google’s sample, not the likelihood of causes across the industry:
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| Category | Share reported | Qualification |
|---|---|---|
| Binary push | 37% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Configuration push | 31% | Google SRE sample of thousands of postmortems across 2010–2017 |
| User behavior change | 9% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Processing pipeline | 6% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Service provider change | 5% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Performance decay | 5% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Capacity management | 5% | Google SRE sample of thousands of postmortems across 2010–2017 |
| Hardware | 2% | Google SRE sample of thousands of postmortems across 2010–2017 |
A separate category breakdown in the same Google SRE chapter reports software at 41.35%, development process failure at 20.23%, complex system behaviors at 16.90%, deployment planning at 6.74%, and network failure at 2.75%. The excerpt does not specify a separate period for that breakdown. Neither set of figures should be read as a general industry forecast. Google SRE’s postmortem analysis explains its categories and sample.
Assign each follow-up action an owner and track it to completion; a postmortem that identifies improvements without ownership does not ensure they happen. Google SRE’s postmortem practices explain how structured learning supports improvement. The Google SRE Workbook index links to additional incident-management material.
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