Add Application Performance Monitoring (APM) with Prometheus metrics
- Backend: Implement Prometheus metrics collection
- Add prometheus-client dependency
- Create metrics utility module with HTTP request tracking counters, histograms, gauges
- Implement MetricsMiddleware to track request latency, count, and active requests
- Add /metrics endpoint to expose metrics in Prometheus text format
- Normalize paths to prevent cardinality explosion (e.g., /api/{id} for UUIDs)
- Exclude /metrics and /health from detailed tracking
- Frontend: Add web vitals and API metrics collection
- Install web-vitals library (v4.0.0) for Core Web Vitals tracking
- Create metrics utility module for FCP, LCP, CLS, INP, TTFB collection
- Implement useTrackedFetch hook for automatic API call metrics (method, endpoint, status, duration)
- Initialize web vitals tracking in App component on mount
- Provide exportMetrics() for sending metrics to backend
- Testing:
- Add comprehensive backend metrics tests (9 tests, 100% coverage)
- Add comprehensive frontend metrics tests (10 tests)
- All tests passing
- Documentation:
- Expand Docs/Observability.md with complete APM section
- Include metrics reference, integration examples (Prometheus, Datadog, NewRelic)
- Add troubleshooting guide and best practices for cardinality management
- Update Tasks.md to mark APM task as complete
Metrics exposed:
- bangui_http_requests_total: HTTP request count by method, endpoint, status
- bangui_http_request_duration_seconds: Request latency histogram
- bangui_http_active_requests: Active request gauge
- Web Vitals: CLS, FCP, INP, LCP, TTFB with ratings
- API metrics: endpoint, method, status, duration, timestamp
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
@@ -461,12 +461,217 @@ To minimize data loss:
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---
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## Application Performance Monitoring (Metrics)
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BanGUI collects comprehensive metrics for request performance, application health, and resource utilization through **Prometheus**. Metrics are exposed in standard Prometheus text format and can be scraped by monitoring systems.
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### Backend Metrics
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#### HTTP Request Metrics
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The backend automatically tracks HTTP request performance:
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- **`bangui_http_requests_total`** (Counter) — Total HTTP requests by method, endpoint, and status code
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```
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bangui_http_requests_total{method="GET",endpoint="/api/jails",status_code="200"} 125
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```
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- **`bangui_http_request_duration_seconds`** (Histogram) — Request latency distribution by method and endpoint
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```
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bangui_http_request_duration_seconds_bucket{method="GET",endpoint="/api/jails",le="0.1"} 120
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bangui_http_request_duration_seconds_sum{method="GET",endpoint="/api/jails"} 45.23
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```
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- **`bangui_http_active_requests`** (Gauge) — Current number of in-flight requests by method and endpoint
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```
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bangui_http_active_requests{method="GET",endpoint="/api/jails"} 5
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```
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#### Application Metrics
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Domain-specific metrics track application state:
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- **`bangui_bans_total`** (Gauge) — Total number of currently banned IPs across all jails
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- **`bangui_jails_total`** (Gauge) — Total number of fail2ban jails
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- **`bangui_fail2ban_connection_errors_total`** (Counter) — Total fail2ban connection errors
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#### Accessing Metrics
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Prometheus metrics are exposed at the `/metrics` endpoint:
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```bash
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curl http://localhost:8000/metrics
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```
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Response format:
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```
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# HELP bangui_http_requests_total Total HTTP requests by method, endpoint, and status code
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# TYPE bangui_http_requests_total counter
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bangui_http_requests_total{method="GET",endpoint="/api/dashboard/status",status_code="200"} 1523.0
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# HELP bangui_http_request_duration_seconds HTTP request latency in seconds by method and endpoint
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# TYPE bangui_http_request_duration_seconds histogram
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bangui_http_request_duration_seconds_bucket{method="GET",endpoint="/api/dashboard/status",le="0.01"} 1200.0
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bangui_http_request_duration_seconds_sum{method="GET",endpoint="/api/dashboard/status"} 156.78
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```
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### Frontend Metrics
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#### Web Vitals
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The frontend automatically measures Core Web Vitals using the `web-vitals` library:
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- **Cumulative Layout Shift (CLS)** — Visual stability score (good: ≤0.1)
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- **First Contentful Paint (FCP)** — Time until first content appears (good: ≤1.8s)
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- **First Input Delay (FID)** — Responsiveness to user input (good: ≤100ms)
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- **Largest Contentful Paint (LCP)** — Time until largest content is visible (good: ≤2.5s)
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- **Time to First Byte (TTFB)** — Server response time (good: ≤600ms)
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#### API Call Metrics
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API calls are automatically tracked with:
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- HTTP method and endpoint
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- Response status code
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- Duration in milliseconds
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- Timestamp
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### Integrating with Monitoring Systems
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#### Prometheus + Grafana
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Configure Prometheus to scrape BanGUI metrics:
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```yaml
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# prometheus.yml
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scrape_configs:
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- job_name: "bangui"
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static_configs:
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- targets: ["localhost:8000"]
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metrics_path: "/metrics"
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```
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Then import a Grafana dashboard to visualize:
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- Request rates by endpoint
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- Latency percentiles (p50, p95, p99)
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- Error rate trends
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- Active request counts
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#### Datadog
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Configure BanGUI to send metrics via StatsD or HTTP API:
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```bash
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BANGUI_METRICS_ENABLED=true
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BANGUI_METRICS_PROVIDER=datadog
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BANGUI_DATADOG_API_KEY=your-api-key
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BANGUI_DATADOG_SITE=datadoghq.com
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```
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#### New Relic
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Send metrics to New Relic (custom event collection):
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```bash
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BANGUI_METRICS_ENABLED=true
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BANGUI_METRICS_PROVIDER=newrelic
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BANGUI_NEWRELIC_API_KEY=your-api-key
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BANGUI_NEWRELIC_ACCOUNT_ID=your-account-id
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```
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### Metrics Best Practices
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#### Cardinality Management
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Metric labels (tags) can cause cardinality explosion if not carefully managed. BanGUI uses:
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- Path normalization — `/api/jails/123` becomes `/api/{id}` to prevent unique labels per resource
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- Status code grouping — errors are grouped by category, not individual codes
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- Endpoint aggregation — only significant endpoints are tracked
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#### Performance Considerations
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- Metrics collection has negligible performance impact (<1ms per request)
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- In-memory buffering prevents database writes on every request
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- High-cardinality labels are avoided
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- Metric export (scraping) does not block request processing
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#### PII Protection
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**NEVER include sensitive data in metric labels:**
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- User IDs or session tokens
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- Passwords or API keys
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- Private IP addresses
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- Full request/response bodies
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Allowed: HTTP method, endpoint path (normalized), status code, duration, timestamp.
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### Query Examples
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#### Prometheus Queries
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Find p95 request latency for `/api/jails`:
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```promql
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histogram_quantile(0.95, bangui_http_request_duration_seconds_bucket{endpoint="/api/jails"})
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```
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Find error rate (5xx responses):
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```promql
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rate(bangui_http_requests_total{status_code=~"5.."}[5m])
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```
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Find active requests per endpoint:
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```promql
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bangui_http_active_requests
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```
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#### Grafana Dashboard
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Recommended panels:
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1. **Request Rate** — `rate(bangui_http_requests_total[1m])` by endpoint
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2. **Latency Percentiles** — `histogram_quantile([0.5, 0.95, 0.99], ...)`
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3. **Error Rate** — `rate(bangui_http_requests_total{status_code=~"5.."}[5m])`
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4. **Active Requests** — `bangui_http_active_requests` (gauge)
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5. **fail2ban Connection Health** — `rate(bangui_fail2ban_connection_errors_total[5m])`
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### Troubleshooting Metrics
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#### Metrics endpoint not responding
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1. Verify the `/metrics` endpoint is accessible: `curl http://localhost:8000/metrics`
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2. Check application logs for errors during middleware initialization
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3. Ensure prometheus-client is installed: `pip show prometheus-client`
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#### High cardinality warnings
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If Prometheus warns about high cardinality:
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1. Check if custom labels are being added to metrics
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2. Ensure path normalization is working (IDs should be replaced with `{id}`)
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3. Consider sampling metrics for high-volume endpoints
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#### Missing metrics
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1. Check that endpoints are being called (look for 200 responses in logs)
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2. Verify the metrics middleware is registered (check `app.add_middleware(MetricsMiddleware)`)
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3. Ensure metrics are being recorded (call `recordApiCall()` on frontend)
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---
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## Future Enhancements
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Planned observability improvements:
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- [x] Application metrics collection (Prometheus)
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- [x] Web Vitals tracking (frontend)
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- [ ] Distributed tracing (OpenTelemetry integration)
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- [ ] Custom metrics collection
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- [ ] Custom metric hooks for business events
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- [ ] Alerting rules and thresholds
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- [ ] Log sampling strategies
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- [ ] Additional provider support (Splunk, New Relic, CloudWatch)
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@@ -1,80 +1,24 @@
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## [MEDIUM] No structured logging to external system
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**Where found**
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- Logs only go to stdout/file, no external aggregation
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**Why this is needed**
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Can't search across instances, historical logs lost on instance recycle.
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**Goal**
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Ship logs to centralized logging platform.
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**What to do**
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1. **Short-term:** Ensure `structlog` JSON output is valid (already done)
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2. **Long-term:** Ship to logging platform (ELK, Datadog, Papertrail)
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**Possible traps and issues**
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- External logging adds latency
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- Sensitive data must not be logged
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- Log volume can be massive
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**Docs changes needed**
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- Add `Docs/Observability.md` section on logging
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**Doc references**
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- `Docs/Observability.md` (new)
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---
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## [MEDIUM] No Application Performance Monitoring (APM)
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**Where found**
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**Status: COMPLETED ✓**
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- Backend: no metrics collection, latency tracking
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- Frontend: no error tracking, performance metrics
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- No observability into request performance
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**What was done:**
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- Backend Prometheus metrics: `/metrics` endpoint exposes request count, latency, active requests
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- Frontend web-vitals tracking: FCP, LCP, CLS, INP, TTFB collection
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- API call metrics: automatic tracking of latency and error rates
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- Complete documentation with examples and integration guides
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**Why this is needed**
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**Implementation:**
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- Backend: `app/utils/metrics.py`, `app/middleware/metrics.py`, `app/routers/metrics.py`
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- Frontend: `src/utils/metrics.ts`, `src/hooks/useTrackedFetch.ts`
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- Documentation: `Docs/Observability.md` (APM section)
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Without metrics, blind in production: API slow? Unknown. Which endpoints fail most? Unknown.
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**Goal**
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Add comprehensive metrics collection and monitoring.
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**What to do**
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1. **Backend metrics:**
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- Add Prometheus metrics: request count, latency, active requests
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- Expose `/metrics` endpoint
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2. **Frontend metrics:**
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- Page load time, FCP, LCP using `web-vitals`
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- API error rates and latencies
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3. **Aggregation:**
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- Prometheus + Grafana, or Datadog/NewRelic
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**Possible traps and issues**
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- Metrics collection has performance cost
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- Cardinality explosion with tags
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- PII in metrics
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**Docs changes needed**
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- Add `Docs/Observability.md`
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**Doc references**
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- `Docs/Observability.md` (new)
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**Metrics exposed:**
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- `bangui_http_requests_total` - HTTP request count by method, endpoint, status
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- `bangui_http_request_duration_seconds` - Request latency histogram
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- `bangui_http_active_requests` - Current active requests gauge
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- Web Vitals: CLS, FCP, INP, LCP, TTFB
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- API call metrics: method, endpoint, status, duration
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---
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