# Example Trace: Kubernetes Anomaly

A complete abductive reasoning trace demonstrating how Peircean Abduction handles a technical anomaly.

## The Observation

```
Query: "Our Kubernetes cluster CPU usage dropped 40% but response latency increased 200%. What's happening?"
```

---

## Phase 1: Observation Analysis

```json
{
  "fact": "CPU usage dropped 40% while response latency increased 200%",
  "surprise_level": "anomalous",
  "surprise_score": 0.92,
  "expected_state": "CPU and latency should correlate positively (more load -> higher latency)",
  "surprise_source": "Negative correlation between CPU and latency violates fundamental performance expectations"
}
```

**Peirce's First Premise**: "The surprising fact is observed: CPU usage decreased significantly (40%) while response latency increased significantly (200%), exhibiting an anomalous negative correlation."

---

## Phase 2: Hypothesis Generation

### H1: Pod Scheduling Failure

**Statement**: Kubernetes scheduler is failing to place pods, causing requests to queue

**Explanation**: If pods are stuck in Pending state, requests have nowhere to go. The running pods show low CPU because they're processing what they can, but the queue grows, causing latency.

**Prior Probability**: 0.28

**Assumptions**:
- Scheduler is the bottleneck
- Pods are in Pending state
- No scheduler errors being logged

**Testable Predictions**:
- `kubectl get pods` will show pods in Pending state
- Scheduler logs will show resource constraints
- Node resource requests will exceed capacity

---

### H2: Database Connection Pool Exhaustion

**Statement**: Application is blocked waiting for database connections

**Explanation**: All connections in the pool are in use. Threads wait for connections, consuming no CPU but adding latency. The application appears "idle" while actually being blocked.

**Prior Probability**: 0.24

**Assumptions**:
- Fixed connection pool size
- Database is shared resource
- No connection pool monitoring

**Testable Predictions**:
- Connection pool metrics show 100% utilization
- Database shows open connections at limit
- Thread dumps show threads waiting for connections

---

### H3: Network Partition

**Statement**: Network partition is isolating high-CPU pods from the load balancer

**Explanation**: Active, high-CPU pods are unreachable. Traffic routes to remaining pods that are either idle or slow. Overall CPU drops because the busy pods aren't counted.

**Prior Probability**: 0.18

**Assumptions**:
- Network can partition within cluster
- Health checks aren't detecting the issue
- Load balancer is not aware of partition

**Testable Predictions**:
- Some pods unreachable via network test
- Load balancer logs show backend failures
- Network policy changes recently deployed

---

### H4: Memory Pressure / GC Storms

**Statement**: Memory pressure is triggering aggressive garbage collection

**Explanation**: JVM or runtime is in constant GC, pausing application threads. CPU appears low (GC is efficient), but all work is blocked during GC pauses, causing latency.

**Prior Probability**: 0.15

**Assumptions**:
- Application uses managed runtime (JVM, .NET, Go)
- Memory limits are too restrictive
- GC overhead not monitored

**Testable Predictions**:
- GC logs show high pause times
- Memory utilization near limit
- GC CPU time high relative to application CPU

---

### H5: Autoscaler Removed Pods

**Statement**: HPA scaled down pods due to low CPU, causing remaining pods to be overloaded

**Explanation**: Horizontal Pod Autoscaler saw low CPU (perhaps due to I/O wait) and removed pods. Remaining pods can't handle the load, causing queuing and latency.

**Prior Probability**: 0.15

**Assumptions**:
- HPA configured for CPU-based scaling
- Scale-down threshold too aggressive
- I/O-bound workload misidentified as low-CPU

**Testable Predictions**:
- HPA events show recent scale-down
- Pod count lower than usual
- Replica count at minimum

---

## Phase 3: Evaluation (IBE Criteria)

| Criterion | H1: Scheduling | H2: DB Pool | H3: Network | H4: GC | H5: HPA |
|-----------|----------------|-------------|-------------|--------|---------|
| Explanatory Scope | 0.90 | 0.80 | 0.70 | 0.60 | 0.95 |
| Explanatory Power | 0.85 | 0.90 | 0.70 | 0.75 | 0.85 |
| Parsimony | 0.80 | 0.85 | 0.60 | 0.70 | 0.90 |
| Testability | 0.95 | 0.90 | 0.80 | 0.85 | 0.95 |
| Consilience | 0.70 | 0.80 | 0.60 | 0.70 | 0.80 |
| Analogy | 0.75 | 0.85 | 0.50 | 0.80 | 0.85 |
| Fertility | 0.70 | 0.75 | 0.60 | 0.65 | 0.80 |
| **COMPOSITE** | **0.81** | **0.84** | **0.64** | **0.72** | **0.87** |

---

## Selection: Inference to the Best Explanation

**Selected Hypothesis**: H5 - Autoscaler Removed Pods

**Composite Score**: 0.87

**Rationale**:
This hypothesis best explains the paradoxical observation because:

1. **Explanatory Scope (0.95)**: It explains both phenomena—low CPU (fewer pods) AND high latency (remaining pods overloaded)—in a single coherent mechanism.

2. **Parsimony (0.90)**: Requires only one assumption: HPA is configured for CPU-based scaling. No additional mechanisms needed.

3. **Testability (0.95)**: Can be verified immediately with `kubectl get hpa` and `kubectl get events`.

4. **Consilience (0.80)**: Fits known pattern of HPA misconfigurations in I/O-bound workloads.

5. **Analogy (0.85)**: Well-documented failure mode in Kubernetes community.

**Confidence**: 0.87 (High)

---

## Recommended Actions

1. **Immediate**: Check HPA configuration and recent scaling events
   ```bash
   kubectl get hpa
   kubectl describe hpa <name>
   kubectl get events --sort-by='.lastTimestamp' | grep -i scale
   ```

2. **Verify**: Confirm pod count is at minimum
   ```bash
   kubectl get pods | wc -l
   kubectl get deployment <name> -o jsonpath='{.status.replicas}'
   ```

3. **Remediate**: If confirmed, adjust HPA settings
   - Increase `minReplicas`
   - Add latency-based scaling metric
   - Increase `scaleDownStabilizationWindowSeconds`

4. **Monitor**: Set up alerts for aggressive scale-down events

---

## Alternative Path

If H5 is falsified (HPA shows no recent scale-down):

**Next hypothesis to test**: H2 (Database Connection Pool)

**Rationale**: Second-highest score (0.84), and the mechanism is independent of H5. If pods are at normal count, connection pool exhaustion becomes more likely.

**Test**:
```bash
# Check connection pool metrics
kubectl exec -it <pod> -- curl localhost:8080/actuator/metrics | grep pool
# Check database connections
psql -c "SELECT count(*) FROM pg_stat_activity;"
```

---

## Peirce's Conclusion

> "Hence, there is reason to suspect that the HPA removed pods due to low CPU metrics, leaving remaining pods unable to handle the full request load, which caused the observed increase in latency."

This is a *hypothesis*, not a certainty. The recommended actions are designed to test and verify before taking corrective action.
