{"id":30151778,"url":"https://github.com/sculpttechproject/fleetstream","last_synced_at":"2026-05-16T08:33:40.098Z","repository":{"id":301018502,"uuid":"1007211898","full_name":"SculptTechProject/FleetStream","owner":"SculptTechProject","description":"FleetStream is an experimental sensor‑to‑dashboard playground. 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You can start/stop the simulator from the UI and export CSV for analysis.\n\n---\n\n## Features\n\n- **Simulator**: car state (Warsaw seed), road-type logic (urban/rural/highway), red lights, gear/RPM model, fuel, rare fault codes.\n- **Kafka**: JSON events published to a topic (key = `vehicle_id`).\n- **API**: start/stop/status, recent snapshots, violations, CSV export.\n- **Dashboard** (`/dashboard`):\n  - Live map (Leaflet) with **heatmap** (intensity ≈ speed; violations boost)\n  - **Colored trail** by speed (green → blue → yellow → orange → red)\n  - Speed/RPM mini chart, quick stats, **Start/Stop** buttons, **Export CSV**\n- **Optional analytics**: Spark Structured Streaming consumer (Kafka source).\n\n---\n\n## Quick start\n\n### With Docker\n\n```bash\ndocker compose up -d --build\n# API: http://localhost:8000\n# UI:  http://localhost:8000/dashboard\n```\n\n### Local (Python)\n\n```bash\npython -m venv .venv \u0026\u0026 source .venv/bin/activate  # (or .venv\\Scripts\\activate on Windows)\npip install -r requirements.txt\n# For websockets support use uvicorn extra or install a WS lib:\npip install \"uvicorn[standard]\" websockets\nexport KAFKA_BOOTSTRAP_SERVERS=\"localhost:9092\"   # adjust as needed\nuvicorn services.main:app --reload --port 8000\n```\n\n---\n\n## Configuration\n\nEnvironment variables:\n\n\n| Name                      | Default                 | Description                |\n| ------------------------- | ----------------------- | -------------------------- |\n| `VEHICLE_ID`              | `TESTCAR01`             | Car identifier (Kafka key) |\n| `KAFKA_BOOTSTRAP_SERVERS` | `localhost:9092`        | Kafka bootstrap servers    |\n| `KAFKA_TOPIC`             | `vehicle.telemetry.raw` | Topic for raw telemetry    |\n\n---\n\n## REST \u0026 WS API\n\nBase URL: `http://localhost:8000`\n\n### Simulator control\n\n```bash\n# Start at 5 Hz\ncurl -X POST \"http://localhost:8000/start-sim?rate_hz=5\"\n\n# Stop\ncurl -X POST \"http://localhost:8000/stop-sim\"\n\n# Status\ncurl \"http://localhost:8000/status\"\n```\n\n### Telemetry\n\n```bash\n# Latest point\ncurl \"http://localhost:8000/telemetry/latest\"\n\n# Recent N points (default 100, cap 2000)\ncurl \"http://localhost:8000/telemetry/recent?limit=500\"\n\n# Recent violations (speeding / over redline)\ncurl \"http://localhost:8000/telemetry/violations?limit=50\"\n\n# Export CSV\ncurl -L -o telemetry.csv \"http://localhost:8000/telemetry/export.csv?limit=2000\"\n```\n\n### WebSocket\n\n- Endpoint: `ws://localhost:8000/ws`\n- Messages:\n  - `{\"type\":\"snapshot\",\"items\":[ /* latest */ ]}`\n  - `{\"type\":\"event\",\"data\": { /* single telemetry event */ }}`\n\n\u003e If you see `Unsupported upgrade request` in logs, install WS support:\n\u003e `pip install \"uvicorn[standard]\" websockets` and restart the server.\n\n---\n\n## Dashboard\n\nOpen **`http://localhost:8000/dashboard`**.\n\n- **Start / Stop** buttons call the API.\n- **Rate (Hz)** controls simulator emission frequency.\n- **Trail** toggle shows the colored path; **Heatmap** toggle shows density (weighted by speed, violations highlighted).\n- **Export CSV** downloads recent events for analysis.\n\n\u003cimg width=\"1907\" height=\"985\" alt=\"image\" src=\"https://github.com/user-attachments/assets/38e469ce-b3ed-41b6-97a1-7355a787bc44\" /\u003e\n\n\n![1754770129260](images/README/1754770129260.png)\n\n---\n\n## Kafka\n\n- Topic: `${KAFKA_TOPIC}` (default `vehicle.telemetry.raw`)\n- Key: `vehicle_id` (`TESTCAR01` by default)\n- Value: JSON, example:\n\n```json\n{\n  \"vehicle_id\": \"TESTCAR01\",\n  \"timestamp\": \"2025-08-09T12:34:56.789012+00:00\",\n  \"location\": { \"lat\": 52.2304, \"lon\": 21.0122 },\n  \"speed_kmh\": 73.4,\n  \"engine_rpm\": 2140,\n  \"gear\": 4,\n  \"fuel_level_pct\": 92.1,\n  \"fault_codes\": [],\n  \"road_type\": \"urban\",\n  \"speed_limit_kmh\": 50,\n  \"speeding\": true,\n  \"rpm_over_redline\": false\n}\n```\n\n\u003e Fields like `road_type`, `speed_limit_kmh`, `speeding`, `rpm_over_redline` are included in the live API/feed for convenience (derivable on the consumer side as well).\n\n---\n\n## Spark Structured Streaming (optional)\n\nExample approach (Kafka → aggregation → sink):\n\n```python\n# See processing/stream_agg.py for a reference:\n# - reads Kafka 'vehicle.telemetry.raw'\n# - parses JSON schema\n# - aggregates avg speed in 1-min windows\n# - writes to memory/table or parquet sink\n```\n\nRun inside your Spark container or locally (adjust bootstrap servers and sink path).\n\n---\n\n## Dev notes\n\n- Python ≥ 3.11 recommended.\n- For WebSocket support use `uvicorn[standard]` or install `websockets`/`wsproto`.\n- The simulator protects against unreal speeds and uses a simple but realistic gear/RPM model.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsculpttechproject%2Ffleetstream","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsculpttechproject%2Ffleetstream","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsculpttechproject%2Ffleetstream/lists"}