1 AI Ops - Anomaly Detection
Types of Anomalies in API Monitoring
1. Response Time Anomalies
- When an API suddenly becomes slower than usual.
- Example: The average response time is 200ms, but suddenly it spikes to 2 seconds.
2. Error Rate Anomalies
- When the number of failed API calls increases unexpectedly.
- Example: If an API usually has a 1% error rate but jumps to 20%, it’s an anomaly.
3. Traffic Volume Anomalies
- Sudden increase or decrease in API request traffic.
- Example: If an API normally gets 1,000 requests per minute, but suddenly gets 10,000, it might indicate a DDoS attack or bug.
4. Data Pattern Anomalies
- Unexpected behavior in API responses.
- Example: A user details API suddenly returns empty data for valid users.
Why Detect Anomalies?
✅ Identify performance issues early before they affect users.
✅ Prevent system failures by acting on unusual trends.
✅ Enhance security by detecting suspicious activities.
✅ Improve reliability of the distributed platform.
Machine Learning-Based Anomaly Detection
Isolation Forest (IForest)
- Works by randomly splitting the dataset and identifying points that get isolated quickly.
- Best for detecting sudden spikes or drops in API performance.