# Prometheus Metrics Exporter - Implementation Summary
## Overview
Implemented a complete Prometheus metrics exporter for Session-Buddy Phase 4, enabling monitoring systems (Prometheus, Grafana) to scrape and visualize skill metrics in real-time.
## File Created
**`/Users/les/Projects/session-buddy/session_buddy/realtime/metrics_exporter.py`**
## Features Implemented
### 1. Prometheus Metrics
All metrics follow Prometheus best practices with proper labeling and documentation:
#### **Counter: skill_invocations_total**
- Tracks total number of skill invocations
- Labels: `skill_name`, `workflow_phase`, `completed`
- Monotonically increasing count
#### **Histogram: skill_duration_seconds**
- Tracks skill execution duration distribution
- Labels: `skill_name`, `workflow_phase`
- Buckets: [0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 30.0, 60.0, 300.0] seconds
- Provides sum, count, and bucket counts
#### **Gauge: skill_completion_rate**
- Tracks current skill completion rate
- Labels: `skill_name`
- Values: 0.0 to 1.0
#### **Gauge: active_sessions_total**
- Tracks number of currently active sessions
- No labels (single global value)
#### **Counter: anomalies_detected_total**
- Tracks detected anomalies
- Labels: `anomaly_type`, `skill_name`
- Anomaly types: "drop", "spike", "pattern_shift"
### 2. PrometheusExporter Class
```python
class PrometheusExporter:
"""Export skills metrics to Prometheus format."""
def __init__(self, port: int = 9090) -> None:
"""Initialize Prometheus exporter."""
def start(self) -> None:
"""Start Prometheus metrics HTTP server."""
def record_invocation(
self,
skill_name: str,
workflow_phase: str | None,
completed: bool,
duration_seconds: float | None,
) -> None:
"""Record a skill invocation."""
def update_completion_rate(self, skill_name: str, rate: float) -> None:
"""Update skill completion rate gauge."""
def record_anomaly(self, anomaly_type: str, skill_name: str) -> None:
"""Record detected anomaly."""
def update_active_sessions(self, count: int) -> None:
"""Update active sessions gauge."""
def is_running(self) -> bool:
"""Check if exporter is currently running."""
```
### 3. Key Features
- **Thread-safe**: All metrics can be updated concurrently from multiple threads/agents
- **Idempotent start()**: Safe to call multiple times
- **Input validation**: Port range validation, rate bounds checking
- **Debug logging**: Comprehensive logging for monitoring
- **Prometheus compliant**: Proper metric types, labels, and HELP/TYPE metadata
## Usage Examples
### Basic Usage
```python
from session_buddy.realtime import PrometheusExporter
# Create and start exporter
exporter = PrometheusExporter(port=9090)
exporter.start()
# Record skill invocation
exporter.record_invocation(
skill_name="pytest-run",
workflow_phase="execution",
completed=True,
duration_seconds=45.2
)
# Update completion rate
exporter.update_completion_rate("pytest-run", 0.92)
# Record anomaly
exporter.record_anomaly("performance_drop", "pytest-run")
# Update active sessions
exporter.update_active_sessions(5)
# Metrics available at: http://localhost:9090/metrics
```
### Integration with SkillsStorage
```python
from session_buddy.realtime import PrometheusExporter
from session_buddy.storage import SkillsStorage
# Initialize
exporter = PrometheusExporter(port=9090)
exporter.start()
storage = SkillsStorage(db_path=".session-buddy/skills.db")
# Record invocation when skill called
def track_skill_invocation(skill_name: str, completed: bool, duration: float):
# Store in database
storage.record_invocation(
skill_name=skill_name,
workflow_phase="execution",
completed=completed,
duration_seconds=duration,
session_id="session-123",
workflow_path=None
)
# Export to Prometheus
exporter.record_invocation(
skill_name=skill_name,
workflow_phase="execution",
completed=completed,
duration_seconds=duration
)
```
### Integration with WebSocket Server
```python
from session_buddy.realtime import PrometheusExporter, RealTimeMetricsServer
# Start both servers
exporter = PrometheusExporter(port=9090)
exporter.start()
ws_server = RealTimeMetricsServer(
host="localhost",
port=8765,
db_path=Path(".session-buddy/skills.db")
)
await ws_server.start()
# Periodically update Prometheus metrics from database
async def update_prometheus_metrics():
while True:
# Get top skills from database
top_skills = await asyncio.to_thread(
ws_server.storage.get_top_skills,
limit=10
)
# Update Prometheus gauges
for skill in top_skills:
exporter.update_completion_rate(
skill.skill_name,
skill.completion_rate
)
await asyncio.sleep(60) # Update every minute
```
## Prometheus Output Format
The `/metrics` endpoint produces standard Prometheus format:
```
# HELP skill_invocations_total Total number of skill invocations
# TYPE skill_invocations_total counter
skill_invocations_total{skill_name="pytest-run",workflow_phase="execution",completed="true"} 142.0
skill_invocations_total{skill_name="ruff-check",workflow_phase="setup",completed="true"} 89.0
# HELP skill_duration_seconds Skill execution duration in seconds
# TYPE skill_duration_seconds histogram
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="0.1"} 0.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="0.5"} 2.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="1.0"} 5.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="2.0"} 12.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="5.0"} 45.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="10.0"} 89.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="30.0"} 120.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="60.0"} 138.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="300.0"} 142.0
skill_duration_seconds_bucket{skill_name="pytest-run",workflow_phase="execution",le="+Inf"} 142.0
skill_duration_seconds_sum{skill_name="pytest-run",workflow_phase="execution"} 4532.5
skill_duration_seconds_count{skill_name="pytest-run",workflow_phase="execution"} 142.0
# HELP skill_completion_rate Current skill completion rate
# TYPE skill_completion_rate gauge
skill_completion_rate{skill_name="pytest-run"} 0.92
skill_completion_rate{skill_name="ruff-check"} 0.87
skill_completion_rate{skill_name="mypy-check"} 0.95
# HELP active_sessions_total Number of currently active sessions
# TYPE active_sessions_total gauge
active_sessions_total 3.0
# HELP anomalies_detected_total Total number of anomalies detected
# TYPE anomalies_detected_total counter
anomalies_detected_total{anomaly_type="performance_drop",skill_name="pytest-run"} 5.0
anomalies_detected_total{anomaly_type="spike",skill_name="ruff-check"} 2.0
```
## Testing
### Test Script
A test script is provided at `/Users/les/Projects/session-buddy/test_prometheus_metrics.py`:
```bash
cd /Users/les/Projects/session-buddy
python test_prometheus_metrics.py
```
This will:
1. Start Prometheus exporter on port 9092
2. Record sample metrics
3. Display expected Prometheus output format
4. Keep server running for manual testing
5. Visit http://localhost:9092/metrics to see actual output
### Manual Testing
```bash
# Run the demo
python session_buddy/realtime/metrics_exporter.py
# In another terminal, scrape metrics
curl http://localhost:9090/metrics
```
## Prometheus Configuration
To scrape these metrics, add to your `prometheus.yml`:
```yaml
scrape_configs:
- job_name: 'session-buddy'
scrape_interval: 15s
static_configs:
- targets: ['localhost:9090']
```
## Grafana Dashboard Queries
Example PromQL queries for Grafana dashboards:
### Skill Completion Rate
```promql
skill_completion_rate{skill_name="pytest-run"}
```
### Average Skill Duration (p95)
```promql
histogram_quantile(0.95,
sum(rate(skill_duration_seconds_bucket[5m])) by (skill_name, le)
)
```
### Skills With Most Anomalies
```promql
topk(10, sum(anomalies_detected_total) by (skill_name))
```
### Invocation Rate
```promql
sum(rate(skill_invocations_total[5m])) by (skill_name)
```
### Active Sessions Over Time
```promql
active_sessions_total
```
## Dependencies
**Already in pyproject.toml:**
- `prometheus-client>=0.24.1` (line 37)
No additional dependencies needed!
## Integration Points
### 1. WebSocket Server (`websocket_server.py`)
- Update metrics when broadcasting to clients
- Track active sessions via client connections
### 2. SkillsTracker (future)
- Record invocations as they happen
- Update completion rates periodically
### 3. Anomaly Detection (future)
- Record anomalies when detected
- Track deviation scores
## Architecture Compliance
✅ **Protocol-based design**: Clean interfaces with clear contracts
✅ **Thread-safe**: Safe for concurrent updates
✅ **Error handling**: Input validation and clear error messages
✅ **Logging**: Comprehensive debug logging
✅ **Documentation**: Full docstrings with examples
✅ **Type hints**: Complete type annotations
✅ **Testing**: Example script provided
✅ **No dependencies conflict**: Uses existing prometheus-client
## Next Steps
1. **Integration**: Connect exporter to WebSocket server for periodic metric updates
2. **Dashboard**: Create Grafana dashboard for skill metrics visualization
3. **Alerts**: Set up Prometheus alerts for anomaly detection
4. **Monitoring**: Configure Prometheus to scrape metrics endpoint
5. **Documentation**: Update Session-Buddy docs with monitoring setup instructions
## Files
- **Implementation**: `/Users/les/Projects/session-buddy/session_buddy/realtime/metrics_exporter.py`
- **Test Script**: `/Users/les/Projects/session-buddy/test_prometheus_metrics.py`
- **This Document**: `/Users/les/Projects/session-buddy/PROMETHEUS_EXPORTER_IMPLEMENTATION.md`
## Summary
✅ **Prometheus metrics exporter fully implemented**
✅ **All 5 metric types defined (Counter x2, Histogram, Gauge x2)**
✅ **PrometheusExporter class with complete API**
✅ **Thread-safe and production-ready**
✅ **Test script provided for validation**
✅ **No additional dependencies required**
✅ **Integration examples documented**
The exporter is ready to use and can be integrated into the Session-Buddy workflow for real-time monitoring and alerting.
