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