161 أسطر
3.7 KiB
Markdown
161 أسطر
3.7 KiB
Markdown
# Postmortem Report – Repeated OOMKilled Incident
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**Incident Date:** YYYY-MM-DD
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**Duration:** 45 minutes
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**Service:** Application hosted on Ghaymah Systems
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**Severity:** High
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---
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# 1. Summary
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The application experienced repeated `OOMKilled` events, causing continuous container restarts and making the service unavailable for approximately 45 minutes.
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The issue occurred because the application exceeded its available memory limit. Since the platform automatically restarted the container after each crash, the application entered a crash loop until the memory issue was resolved.
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---
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# 2. Timeline
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| Time | Event |
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|------|-------|
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| 10:00 | New application version deployed |
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| 10:05 | Memory usage started increasing rapidly |
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| 10:10 | First `OOMKilled` event occurred |
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| 10:11 | Platform restarted the container |
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| 10:14 | Container exceeded memory limit again |
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| 10:15 | Second `OOMKilled` event |
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| 10:20 | Multiple restart attempts continued |
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| 10:35 | Engineering team identified abnormal memory consumption |
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| 10:45 | Memory issue resolved and application recovered |
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---
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# 3. Root Cause Analysis
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## Immediate Cause
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The application consumed more memory than the container's configured memory limit, causing the Linux Out-Of-Memory (OOM) Killer to terminate the process.
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## Root Cause
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Possible contributing factors include:
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- Memory leak in the application
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- Large objects remaining in memory
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- Insufficient memory limits for production workload
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- Lack of early monitoring and alerting
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---
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# 4. Impact
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- Service unavailable for 45 minutes
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- Users could not access the application
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- Multiple container restarts
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- Increased error rate and failed requests
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---
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# 5. Recommendations
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## Short-Term
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- Increase container memory limit.
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- Restart affected containers.
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- Verify application memory usage after deployment.
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- Roll back if abnormal memory growth is detected.
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## Long-Term
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- Fix memory leaks.
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- Perform load testing before production deployments.
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- Configure monitoring and alerting for memory usage.
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- Monitor container restart count.
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- Enable automatic scaling.
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- Establish deployment health checks.
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---
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# Auto-Scaling Policy
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## Objective
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Prevent service outages caused by high resource utilization.
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### Scale-Out Rules
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- Add one new instance when:
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- CPU usage > 70% for 5 minutes.
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- Memory usage > 80% for 5 minutes.
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- Average response time > 500 ms.
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### Scale-In Rules
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- Remove one instance when:
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- CPU usage < 30% for 10 minutes.
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- Memory usage < 40% for 10 minutes.
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### Minimum Instances
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- 2 running instances
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### Maximum Instances
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- 10 running instances
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### Health Checks
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- Check `/health` every 30 seconds.
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- Replace unhealthy containers automatically.
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---
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# Early Detection Using Monitoring
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To detect similar issues before they cause downtime, monitor the following metrics:
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## Infrastructure Metrics
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- Memory Usage
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- Memory Limit
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- CPU Usage
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- Container Restarts
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- OOMKilled Events
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- Disk Usage
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## Application Metrics
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- HTTP Response Time
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- Request Rate
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- Error Rate (4xx / 5xx)
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- Active Connections
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## Alerts
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Create alerts when:
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- Memory usage exceeds 80%
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- Container restart count increases
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- OOMKilled event detected
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- Response time exceeds 500 ms
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- Error rate exceeds 5%
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## Monitoring Stack
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Example monitoring solution:
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- Prometheus (metrics collection)
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- Grafana (dashboards)
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- Alertmanager (notifications)
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- Email / Slack / Microsoft Teams notifications
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---
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# Lessons Learned
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- Resource limits should be validated before deployment.
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- Memory consumption should be continuously monitored.
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- Health checks and alerts must be configured for production services.
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- Auto-scaling helps reduce downtime but does not replace fixing application memory leaks.
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- Regular load testing can identify memory-related issues before release. |