mirror of
https://github.com/azaion/detections.git
synced 2026-04-23 02:36:32 +00:00
Oleksandr Bezdieniezhnykh
be4cab4fcb
- Added `/detect/video` endpoint for true streaming video detection, allowing inference to start as upload bytes arrive. - Introduced `run_detect_video_stream` method in the inference module to handle video processing from a file-like object. - Updated media hashing to include a new function for computing hashes directly from files with minimal I/O. - Enhanced documentation to reflect changes in video processing and API behavior. Made-with: Cursor
70 lines
2.9 KiB
Markdown
70 lines
2.9 KiB
Markdown
# Observability
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## Logging
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**Current state**: loguru with daily rotated files (`Logs/log_inference_YYYYMMDD.txt`, 30-day retention) + stdout/stderr. Format: `[HH:mm:ss LEVEL] message`.
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**Recommended improvements**:
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| Aspect | Current | Recommended |
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|--------|---------|-------------|
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| Format | Human-readable | Structured JSON to stdout (container-friendly) |
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| Fields | timestamp, level, message | + service, correlation_id, context |
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| PII | Not applicable | No user IDs or tokens in logs |
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| Retention | 30 days (file) | Console in dev; 7 days staging; 30 days production (via log aggregator) |
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**Container logging pattern**: Log to stdout/stderr only; let the container runtime (Docker/K8s) handle log collection and routing. Remove file-based logging in containerized deployments.
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## Metrics
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**Recommended `/metrics` endpoint** (Prometheus-compatible):
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| Metric | Type | Labels | Description |
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|--------|------|--------|-------------|
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| `detections_requests_total` | Counter | method, endpoint, status | Total HTTP requests |
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| `detections_request_duration_seconds` | Histogram | method, endpoint | Request processing time |
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| `detections_inference_duration_seconds` | Histogram | media_type (image/video) | Inference processing time |
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| `detections_active_inferences` | Gauge | — | Currently running inference jobs (0-2) |
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| `detections_sse_clients` | Gauge | — | Connected SSE clients |
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| `detections_engine_status` | Gauge | engine_type | 1=ready, 0=not ready |
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**Collection**: Prometheus scrape at 15s intervals.
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## Distributed Tracing
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**Limited applicability**: The Detections service makes outbound HTTP calls to Loader and Annotations. Trace context propagation is recommended for cross-service correlation.
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| Span | Parent | Description |
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|------|--------|-------------|
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| `detections.detect_image` | Client request | Full image detection flow |
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| `detections.detect_video` | Client request | Full video detection flow |
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| `detections.model_download` | detect_* | Model download from Loader |
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| `detections.post_annotation` | detect_* | Annotation POST to Annotations service |
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**Implementation**: OpenTelemetry Python SDK with OTLP exporter. Sampling: 100% in dev/staging, 10% in production.
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## Alerting
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| Severity | Response Time | Condition |
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|----------|---------------|-----------|
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| Critical | 5 min | Health endpoint returns non-200; container restart loop |
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| High | 30 min | Error rate > 5%; inference duration p95 > 10s |
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| Medium | 4 hours | SSE client count = 0 for extended period; disk > 80% |
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| Low | Next business day | Elevated log warnings; model download retries |
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## Dashboards
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**Operations dashboard**:
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- Service health status
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- Request rate by endpoint
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- Inference duration histogram (p50, p95, p99)
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- Active inference count (0-2 gauge)
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- SSE connected clients
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- Error rate by type
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**Inference dashboard**:
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- Detections per frame/video
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- Model availability status timeline
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- Engine type distribution (ONNX vs TensorRT)
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- Video batch processing rate
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