diff --git a/demos/smart_factory/.gitignore b/demos/smart_factory/.gitignore new file mode 100644 index 0000000..c5a750b --- /dev/null +++ b/demos/smart_factory/.gitignore @@ -0,0 +1,11 @@ +__pycache__/ +*.pyc +.env +.venv/ +venv/ +node_modules/ +# frontend/dist/ — intentionally tracked for Databricks Apps deployment +.databricks/ +*.egg-info/ +.DS_Store +uv.lock diff --git a/demos/smart_factory/Makefile b/demos/smart_factory/Makefile new file mode 100644 index 0000000..c55cc8a --- /dev/null +++ b/demos/smart_factory/Makefile @@ -0,0 +1,17 @@ +.PHONY: build-frontend deploy dev clean + +build-frontend: + cd frontend && npm ci && npm run build + +deploy: build-frontend + databricks bundle deploy -t dev + +deploy-prod: build-frontend + databricks bundle deploy -t prod + +dev: + cd frontend && npm run dev & + uvicorn app:app --reload --port 8000 + +clean: + rm -rf frontend/dist frontend/node_modules diff --git a/demos/smart_factory/README.md b/demos/smart_factory/README.md new file mode 100644 index 0000000..248a34f --- /dev/null +++ b/demos/smart_factory/README.md @@ -0,0 +1,152 @@ +# SmartFactory — IoT Streaming Demo + +A customer-facing demo showing how Databricks turns factory sensor data into actionable insights — from machine floor to decision — with no Kafka, no infrastructure to manage, and full governance out of the box. + +## What This Demonstrates + +| Capability | Business Value | +|---|---| +| **ZeroBus Ingest** | Eliminate Kafka and message bus infrastructure. Sensor data flows directly into governed Delta tables. | +| **SDP Streaming Pipeline** | Catch equipment anomalies as they happen, not in tomorrow's batch report. Continuous, serverless, pure SQL. | +| **ML in the Pipeline** | Every sensor reading scored for anomalies inline — predictive maintenance without a separate ML platform. | +| **Live Operations Dashboard** | Plant managers and technicians see machine health the moment it changes. Faster response, less downtime. | +| **Unity Catalog Governance** | Every table governed from the first byte. Lineage, access control, audit — ready for compliance on day one. | +| **Databricks Apps** | Full-stack app deployed and managed by Databricks. No separate hosting. | +| **DABs** | Entire demo — app, pipeline, dashboard — deploys with a single command. | + +## Architecture + +![Architecture](docs/architecture.png) + +## The Machines + +| Machine | Sensors | Fault Scenario | +|---|---|---| +| **CNC Mill** | Temperature, Vibration, Spindle RPM | Overheating, vibration spike | +| **Hydraulic Press** | Pressure, Temperature, Cycle Count | Pressure surge, cycle slowdown | +| **Conveyor Belt** | Belt Speed, Load Weight, Motor Current | Speed drop, overcurrent | + +## Demo Story (6 minutes) + +> **Pre-flight**: Start streaming + pipeline 30s before presenting. Confirm dashboard has data. + +### Act 1 — "This is your factory" (IoT Simulation tab) +> "3 machines, IoT sensors streaming every 2 seconds, directly into Databricks. No Kafka." + +- Gauges are already updating, event feed scrolling +- Point out "Streaming" and "Pipeline Running" in the header +- Expand ZeroBus info panel — highlight ≤200ms ack, 10 GB/s, Joby Aviation quote + +### Act 2 — "Here's the pipeline" (SDP in Databricks UI) +> "Declarative SQL. Streaming and batch in one pipeline. Fully serverless." + +- Switch to Databricks workspace, open the SDP pipeline DAG +- Show Bronze → Silver → Gold with streaming indicators +- Click into Silver SQL — "anomaly detection is a SQL JOIN. Any SQL developer can own this." +- Three SDP benefits: declarative, streaming+batch unified, serverless + +### Act 3 — "Let's break something" (Inject a fault) +> "Watch what happens when the CNC Mill starts overheating." + +- Click **Fault: CNC Mill** — watch temperature climb, gauges go red +- Event feed lights up with warnings and criticals +- Switch to **Operations Dashboard** — health scores dropping, anomaly log filling + +### Act 4 — "Everything is governed" (Unity Catalog) +> "Every table governed. Full lineage from raw sensor event to dashboard." + +- Open Catalog Explorer, click Gold table → show lineage graph +- "One command to deploy. No Kafka. No ML infrastructure. Just push and go." + +### Act 5 — "Clear the fault" (Resolution) +- Click **Clear All** — readings normalize, health scores recover + +See [docs/demo-script.md](docs/demo-script.md) for the full script with talking points and objection handling. + +## Quick Start + +### Prerequisites +- [Databricks CLI](https://docs.databricks.com/dev-tools/cli/index.html) v0.288+ installed +- CLI profile configured for your target workspace +- Node.js 18+ and npm installed +- An existing Unity Catalog catalog with cloud storage configured + +### One-command setup +```bash +git clone +cd smartfactory-demo +./setup.sh +``` + +This script handles everything: +1. Finds and starts a SQL warehouse +2. Creates the `smartfactory` schema and landing table +3. Builds the React frontend +4. Deploys all resources via DABs (app, pipeline, dashboard) +5. Detects the app service principal and grants all permissions +6. Starts the app and deploys code +7. Sets the SDP pipeline to continuous mode + +### After setup +1. Open the app URL printed by the script +2. Click **Streaming** in the header to start the data simulator +3. Click **Start Pipeline** to begin continuous SDP processing +4. Switch between **IoT Simulation** and **Operations Dashboard** tabs +5. Inject faults and watch anomalies flow through the pipeline + +> **Important: When you're done demoing, stop the streaming and pipeline!** +> Both consume compute resources. Click **Streaming** (to pause) and **Pipeline Running** (to stop) in the header bar. +> The simulator and pipeline start paused by default — you must manually start them each demo session. + +### Redeploying after code changes +```bash +cd frontend && npm run build && cd .. +databricks bundle deploy -t dev +databricks apps deploy smartfactory-app \ + --source-code-path /Workspace/Users//.bundle/smartfactory-demo/dev/files +``` + +## Project Structure + +``` +smartfactory-demo/ +├── setup.sh # One-command setup script +├── databricks.yml # DABs bundle (app + pipeline + dashboard) +├── app.yaml # Databricks App config +├── app.py # FastAPI backend (WebSocket + REST + pipeline control) +├── simulator.py # 3-machine IoT sensor simulator with fault injection +├── zerobus_client.py # ZeroBus SDK wrapper with SQL INSERT fallback +├── pipeline/ +│ ├── bronze.sql # Validated ingestion (streaming table) +│ ├── silver.sql # Anomaly scoring via threshold JOIN (streaming table) +│ └── gold.sql # Health KPIs + anomaly timeline (materialized views) +├── frontend/ +│ ├── src/ +│ │ ├── App.tsx # Tabbed layout (IoT Simulation + Dashboard) +│ │ ├── components/ +│ │ │ ├── FactoryFloor # SVG machine visuals with live sensor readouts +│ │ │ ├── MachineCard # Per-machine gauge cards +│ │ │ ├── SensorGauge # Circular SVG gauge component +│ │ │ ├── ControlPanel # Fault injection buttons +│ │ │ ├── EventFeed # Live scrolling event log +│ │ │ ├── DashboardView # Charts, KPI tables, anomaly log +│ │ │ └── PipelineBanner# Pipeline flow + UC governance badge +│ │ └── hooks/ +│ │ └── useWebSocket # Auto-reconnecting WebSocket hook +│ └── dist/ # Pre-built frontend (deployed with app) +├── dashboard.lvdash.json # Lakeview dashboard definition +└── CLAUDE.md # Development notes and known issues +``` + +## Tech Stack + +| Layer | Technology | +|---|---| +| Frontend | React 18, TypeScript, Vite, TailwindCSS, Recharts | +| Backend | FastAPI, Uvicorn, WebSocket | +| Ingestion | ZeroBus SDK (with SQL INSERT fallback) | +| Pipeline | SDP (Spark Declarative Pipelines), serverless | +| ML | SQL threshold-based anomaly detection in SDP Silver layer | +| Governance | Unity Catalog (lineage, access control, audit) | +| Deployment | Databricks Asset Bundles (DABs) | +| Dashboard | Lakeview (AI/BI) + in-app React dashboard | diff --git a/demos/smart_factory/app.yaml b/demos/smart_factory/app.yaml new file mode 100644 index 0000000..7408e48 --- /dev/null +++ b/demos/smart_factory/app.yaml @@ -0,0 +1,19 @@ +command: ["uvicorn", "src.app:app", "--host", "0.0.0.0", "--port", "8000"] + +env: + - name: CATALOG_NAME + value: dilan_catalog + - name: LANDING_SCHEMA + value: smartfactory + - name: ZEROBUS_TABLE + value: dilan_catalog.smartfactory.raw_sensor_events + - name: WAREHOUSE_ID + value: "01c7fe04f060528e" + - name: PIPELINE_SCHEMA + value: dev_dilan_patel_smartfactory + - name: PIPELINE_ID + value: "4b993ed3-336f-40b5-8bb7-55ff9f056707" + - name: ENABLE_SIMULATOR + value: "false" + - name: SIMULATOR_INTERVAL_MS + value: "1000" diff --git a/demos/smart_factory/dashboards/smartfactory.lvdash.json b/demos/smart_factory/dashboards/smartfactory.lvdash.json new file mode 100644 index 0000000..1fef5e1 --- /dev/null +++ b/demos/smart_factory/dashboards/smartfactory.lvdash.json @@ -0,0 +1,145 @@ +{ + "pages": [ + { + "name": "machine-health-overview", + "displayName": "Machine Health Overview", + "layout": [ + { + "widget": { + "name": "machine_health_scores", + "queries": [ + { + "name": "main_query", + "query": { + "datasetName": "machine_summary", + "disaggregated": false + } + } + ], + "spec": { + "version": 2, + "widgetType": "bar", + "encodings": { + "x": { "fieldName": "machine_id", "displayName": "Machine" }, + "y": { "fieldName": "avg_health_score", "displayName": "Health Score" }, + "color": { "fieldName": "machine_type", "displayName": "Type" } + } + } + }, + "position": { "x": 0, "y": 0, "width": 3, "height": 2 } + }, + { + "widget": { + "name": "anomaly_counts", + "queries": [ + { + "name": "main_query", + "query": { + "datasetName": "machine_summary", + "disaggregated": false + } + } + ], + "spec": { + "version": 2, + "widgetType": "bar", + "encodings": { + "x": { "fieldName": "machine_id", "displayName": "Machine" }, + "y": { "fieldName": "total_criticals", "displayName": "Critical Alerts" }, + "color": { "fieldName": "machine_id", "displayName": "Machine" } + } + } + }, + "position": { "x": 3, "y": 0, "width": 3, "height": 2 } + } + ] + }, + { + "name": "sensor-trends", + "displayName": "Sensor Trends", + "layout": [ + { + "widget": { + "name": "sensor_timeseries", + "queries": [ + { + "name": "main_query", + "query": { + "datasetName": "enriched_events", + "disaggregated": false + } + } + ], + "spec": { + "version": 2, + "widgetType": "line", + "encodings": { + "x": { "fieldName": "timestamp", "displayName": "Time" }, + "y": { "fieldName": "value", "displayName": "Sensor Value" }, + "color": { "fieldName": "sensor_name", "displayName": "Sensor" } + } + } + }, + "position": { "x": 0, "y": 0, "width": 6, "height": 3 } + } + ] + }, + { + "name": "anomaly-log", + "displayName": "Anomaly Log", + "layout": [ + { + "widget": { + "name": "anomaly_table", + "queries": [ + { + "name": "main_query", + "query": { + "datasetName": "anomaly_timeline", + "disaggregated": false + } + } + ], + "spec": { + "version": 2, + "widgetType": "table", + "encodings": { + "columns": [ + { "fieldName": "timestamp", "displayName": "Time" }, + { "fieldName": "machine_id", "displayName": "Machine" }, + { "fieldName": "sensor_name", "displayName": "Sensor" }, + { "fieldName": "value", "displayName": "Value" }, + { "fieldName": "anomaly_status", "displayName": "Status" }, + { "fieldName": "critical_threshold", "displayName": "Threshold" } + ] + } + } + }, + "position": { "x": 0, "y": 0, "width": 6, "height": 4 } + } + ] + } + ], + "datasets": [ + { + "name": "machine_summary", + "displayName": "Machine Summary", + "query": "SELECT * FROM dilan_catalog.smartfactory.gold.machine_summary" + }, + { + "name": "enriched_events", + "displayName": "Enriched Events", + "query": "SELECT * FROM dilan_catalog.smartfactory.silver.enriched_events WHERE timestamp > current_timestamp() - INTERVAL 1 HOUR ORDER BY timestamp DESC LIMIT 5000" + }, + { + "name": "anomaly_timeline", + "displayName": "Anomaly Timeline", + "query": "SELECT * FROM dilan_catalog.smartfactory.gold.anomaly_timeline LIMIT 500" + }, + { + "name": "health_kpis", + "displayName": "Health KPIs", + "query": "SELECT * FROM dilan_catalog.smartfactory.gold.machine_health_kpis" + } + ] +} diff --git a/demos/smart_factory/databricks.yml b/demos/smart_factory/databricks.yml new file mode 100644 index 0000000..644539d --- /dev/null +++ b/demos/smart_factory/databricks.yml @@ -0,0 +1,53 @@ +bundle: + name: smartfactory-demo + +variables: + warehouse_id: + description: "SQL Warehouse ID for dashboard queries" + catalog_name: + description: "Unity Catalog name" + default: dilan_catalog + +resources: + schemas: + smartfactory_schema: + name: smartfactory + catalog_name: ${var.catalog_name} + comment: "SmartFactory IoT demo — all pipeline tables" + + pipelines: + smartfactory_sdp: + name: "smartfactory-sdp-pipeline" + catalog: ${var.catalog_name} + target: smartfactory + serverless: true + libraries: + - file: + path: pipeline/bronze.sql + - file: + path: pipeline/silver.sql + - file: + path: pipeline/gold.sql + channel: PREVIEW + continuous: true + + apps: + smartfactory_app: + name: "smartfactory-app" + description: "SmartFactory IoT demo — ZeroBus + SDP streaming with anomaly detection" + source_code_path: . + + dashboards: + smartfactory_dashboard: + display_name: "SmartFactory Health Dashboard" + file_path: dashboards/smartfactory.lvdash.json + warehouse_id: ${var.warehouse_id} + +targets: + dev: + default: true + mode: development + workspace: + profile: fevm-classic-stable-w6wiaf + variables: + warehouse_id: "01c7fe04f060528e" diff --git a/demos/smart_factory/docs/architecture.mmd b/demos/smart_factory/docs/architecture.mmd new file mode 100644 index 0000000..2138f08 --- /dev/null +++ b/demos/smart_factory/docs/architecture.mmd @@ -0,0 +1,45 @@ +%%{init: { + "theme": "base", + "themeVariables": { + "background": "#0a0e17", + "mainBkg": "#111827", + "nodeBorder": "#1f2937", + "clusterBkg": "#0d1520", + "clusterBorder": "#3b82f6", + "titleColor": "#d1d5db", + "edgeLabelBackground": "#0a0e17", + "nodeTextColor": "#d1d5db", + "lineColor": "#374151", + "primaryColor": "#111827", + "primaryTextColor": "#d1d5db", + "primaryBorderColor": "#1f2937", + "fontFamily": "ui-monospace, SFMono-Regular, Menlo, monospace", + "fontSize": "13px" + } +}}%% +flowchart LR + classDef appBox fill:#111827,stroke:#3b82f6,stroke-width:2px,color:#d1d5db + classDef rawBox fill:#111827,stroke:#1f2937,stroke-width:1px,color:#9ca3af + classDef bronzeBox fill:#1c1409,stroke:#f59e0b,stroke-width:2px,color:#f59e0b + classDef silverBox fill:#130d1f,stroke:#8b5cf6,stroke-width:2px,color:#8b5cf6 + classDef goldBox fill:#131008,stroke:#eab308,stroke-width:2px,color:#eab308 + classDef dashBox fill:#062016,stroke:#10b981,stroke-width:2px,color:#10b981 + classDef govBox fill:#0a0e17,stroke:#1f2937,stroke-width:1px,color:#9ca3af + + APP["🏭 Databricks App\nReact + FastAPI\n3 IoT Machines"]:::appBox + + APP -- "ZeroBus SDK\nNo Kafka" --> RAW + + subgraph UC ["🛡 Unity Catalog — Governed"] + RAW["📥 Landing\nraw_sensor_events"]:::rawBox + BRONZE["🟠 Bronze\nValidated"]:::bronzeBox + SILVER["🟣 Silver\nML Anomaly Scored"]:::silverBox + GOLD["🟡 Gold\nHealth KPIs"]:::goldBox + RAW --> BRONZE --> SILVER --> GOLD + end + + GOLD -- "SQL" --> DASH["📊 In-App Dashboard\nHealth · Anomalies · KPIs"]:::dashBox + + DASH -- "WebSocket" --> APP + + style UC fill:#0d1520,stroke:#3b82f6,stroke-width:2px,color:#d1d5db diff --git a/demos/smart_factory/docs/architecture.png b/demos/smart_factory/docs/architecture.png new file mode 100644 index 0000000..fd36d1f Binary files /dev/null and b/demos/smart_factory/docs/architecture.png differ diff --git a/demos/smart_factory/docs/demo-script.md b/demos/smart_factory/docs/demo-script.md new file mode 100644 index 0000000..5d6e944 --- /dev/null +++ b/demos/smart_factory/docs/demo-script.md @@ -0,0 +1,165 @@ +# SmartFactory Demo Guide +## 5-7 Minute Live Demo — ZeroBus + SDP Streaming + Unity Catalog + +**Audience**: Manufacturing/IoT decision makers, data leaders, platform engineers +**Goal**: Show how Databricks turns factory sensor data into actionable insights — catch anomalies as they happen, reduce downtime, and govern everything from day one. No Kafka, no infrastructure, one command to deploy. + +--- + +## Before You Start + +- [ ] App is open and on the **IoT Simulation** tab +- [ ] **Streaming is ON** — click "Streaming" so data is flowing +- [ ] **Pipeline is ON** — click "Start Pipeline" so SDP is processing +- [ ] **Wait ~30s** for data to flow through all layers +- [ ] Confirm **Operations Dashboard** tab has charts with data +- [ ] Switch back to **IoT Simulation** — this is your starting view +- [ ] Databricks workspace open in another tab (Catalog Explorer + Pipeline ready) +- [ ] All machines showing green/NORMAL — no faults injected yet + +--- + +## Act 1 — Set the Scene + +**Where**: IoT Simulation tab — gauges updating, event feed scrolling + +**Key points to hit**: +- 3 machines, each with IoT sensors, streaming every 2 seconds +- Data is already flowing into Databricks — point to the live gauges +- Streaming and pipeline indicators in the header show everything is live +- Transition: "Let me show you how this data gets here" + +**Personas**: Operations teams, plant floor staff — "the kind of data your operations teams deal with every day" + +--- + +## Act 2 — ZeroBus: Eliminate the Message Bus + +**Where**: Expand the ZeroBus info panel on the IoT Simulation tab + +**Key points to hit**: +- No message bus. Data pushes directly into governed Delta tables. No staging, no ETL, no waiting. +- Anything with an internet connection and a few lines of code can push data — just like this web app +- What this means: one less system to manage, one less team to hire, faster time to insight +- Bosch: 33% cost savings, 40% faster data transmission +- Joby Aviation: days of telemetry latency to minutes +- Scales effortlessly — thousands of devices, gigabytes per second, no infrastructure to tune + +**Personas**: **Platform engineers** (eliminate infra), **IoT developers** (simple SDK), **VP Data** (lower TCO) + +--- + +## Act 3 — The Pipeline (SDP) + +**Where**: Pipeline banner at the bottom of the factory floor + +**Key points to hit**: +- Walk the flow: Factory Floor → ZeroBus → Bronze → Silver (ML) → Gold → Dashboard +- Continuous pipeline — processes data as it arrives, no batch windows +- Silver layer scores every reading for anomalies — catch equipment issues before they become downtime +- Gold layer aggregates health KPIs for operations teams +- All in SQL. Serverless. No Spark expertise needed. + +**Personas**: **Data engineers** (build the pipeline), **SQL analysts** (can modify thresholds) + +--- + +## Act 3b — SDP Deep Dive + +**Where**: Switch to Databricks workspace — open the SDP pipeline + +**Key points to hit**: +- Show the pipeline DAG: Bronze → Silver → Gold with streaming indicators +- "Three things data engineers love about this": + 1. **Declarative** — SQL says what, Databricks handles how. No Spark code. + 2. **Streaming + batch unified** — same SQL for continuous processing and historical reprocessing + 3. **Fully serverless** — no clusters, auto-scales, pay per use +- ML runs inline in Silver — no separate ML platform, no serving endpoints +- Real-time streaming pipeline — no Flink, no stitching together five different tools +- Tie to business value: catching a bearing failure before it takes down a production line + +**Personas**: **Data engineers** (build), **SQL analysts** (contribute without Spark expertise) + +--- + +## Act 4 — Break Something + +**Where**: Back to the app — IoT Simulation tab + +**Key points to hit**: +- Inject a fault on the CNC Mill — click the fault button +- Gauges show raw sensor data drifting from green → amber → red +- Switch to **Operations Dashboard** — this is what the pipeline produced from that raw data +- Sensor trends spike, health scores drop, anomaly log fills with ML-scored anomalies +- "This isn't a batch report. Your maintenance team responds in minutes, not the next morning." + +**Personas**: **Plant managers / reliability engineers** (see the dashboard), **maintenance technicians** (get alerted immediately), **operations analysts** (drill into the anomaly log) + +--- + +## Act 5 — Governance + +**Where**: Databricks workspace — Catalog Explorer + +**Key points to hit**: +- Every table governed from the moment data lands — no "ingest first, govern later" +- Click a Gold table → show lineage back to the raw sensor landing table +- Role-based access control, audit logs for every query +- "When someone asks 'where did this number come from?' — one click." + +**Personas**: **Data governance / compliance** (lineage, audit), **IT security** (RBAC, encryption) + +--- + +## Act 6 — The Close + +**Where**: Back to the app + +**Key points to hit**: +- Clear the fault — show readings normalize, health recovers +- Rule of three: + 1. **No message bus** — sensor data straight to governed Delta tables. Entire infrastructure layer eliminated. + 2. **ML and analytics in one pipeline, real time** — 30-50% less unplanned downtime when moving from batch to real-time detection + 3. **Governed from day one** — lineage, access control, audit. Compliance covered. +- Closer: "Databricks brings it all together — ingestion, intelligence, and governance — so your teams can focus on outcomes, not infrastructure." + +--- + +## Objection Handling + +### "We already have Kafka / Confluent" +It works — but it's a separate system to manage. ZeroBus collapses that layer. Data goes straight to Delta. Fewer moving parts, lower cost. Bosch saw 33% cost savings. + +### "Our ML team uses Python models, not SQL thresholds" +This demo uses SQL for simplicity, but the Silver layer can call any MLflow model registered in Unity Catalog — Isolation Forest, XGBoost, neural nets. You can also use `ai_query()` to hit a serving endpoint directly from the pipeline. Same SDP, any model. + +### "How does this compare to AWS IoT / Azure IoT Hub?" +Those get data into the cloud. ZeroBus gets data into your lakehouse — directly into governed Delta tables. No intermediate storage, no ETL, no separate governance. Plus you get time travel, schema evolution, and ACID out of the box. + +### "What about edge processing?" +ZeroBus handles cloud-side ingestion. For edge, pair it with your existing gateway — Greengrass, IoT Edge, or custom. The ZeroBus SDKs run anywhere your edge compute does. + +### "What's the cost?" +$0.05 per GB ingested. A factory streaming 1,000 sensors at 1 reading/sec generates ~2-3 GB/day. That's about $0.15/day for real-time ingestion into governed Delta tables. + +### "Is this production-ready?" +ZeroBus is GA as of February 2026. Toyota and Joby Aviation run production IoT workloads on it. SDP is GA and serverless. Unity Catalog governs thousands of production environments. + +--- + +## Power Moves (Extra Time) + +### Show UC Lineage Graph +Catalog Explorer → Gold table → Lineage tab → full graph from landing to dashboard. + +### Multi-Machine Chaos +Fault all three machines at once. Dashboard becomes a sea of red. Clear them one by one. + +### Show the SQL +Open `pipeline/silver.sql` — the anomaly detection is a SQL JOIN. Simplicity is the point. + +### Show the Bundle Config +Open `databricks.yml` — entire infrastructure in ~50 lines of YAML. + +### Show the Setup Script +`setup.sh` — one script, new workspace, full demo running in 5 minutes. diff --git a/demos/smart_factory/frontend/index.html b/demos/smart_factory/frontend/index.html new file mode 100644 index 0000000..8cd938b --- /dev/null +++ b/demos/smart_factory/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + SmartFactory - IoT Monitoring + + + +
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"^18.3.1", + "recharts": "^2.12.0", + "lucide-react": "^0.400.0" + }, + "devDependencies": { + "@types/react": "^18.3.0", + "@types/react-dom": "^18.3.0", + "@vitejs/plugin-react": "^4.3.0", + "autoprefixer": "^10.4.19", + "postcss": "^8.4.38", + "tailwindcss": "^3.4.4", + "typescript": "^5.5.0", + "vite": "^5.4.0" + } +} diff --git a/demos/smart_factory/frontend/postcss.config.js b/demos/smart_factory/frontend/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/demos/smart_factory/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/demos/smart_factory/frontend/src/App.tsx b/demos/smart_factory/frontend/src/App.tsx new file mode 100644 index 0000000..21629be --- /dev/null +++ b/demos/smart_factory/frontend/src/App.tsx @@ -0,0 +1,253 @@ +import React, { useState, useEffect, useCallback } from "react"; +import { useWebSocket } from "./hooks/useWebSocket"; +import FactoryFloor from "./components/FactoryFloor"; +import MachineCard from "./components/MachineCard"; +import EventFeed from "./components/EventFeed"; +import ControlPanel from "./components/ControlPanel"; +import DashboardView from "./components/DashboardView"; +import PipelineBanner from "./components/PipelineBanner"; +import ZeroBusInfo from "./components/ZeroBusInfo"; +import { + Activity, Wifi, WifiOff, Factory, BarChart3, + Play, Square, Loader2, Radio, CircleOff, +} from "lucide-react"; + +type Tab = "factory" | "dashboard"; + +export default function App() { + const { + machines, + latestReadings, + faultStates, + eventLog, + totalEvents, + eventsPerSec, + connectionStatus, + injectFault, + clearFault, + clearAllFaults, + } = useWebSocket(); + + const [activeTab, setActiveTab] = useState("factory"); + const [pipelineState, setPipelineState] = useState("UNKNOWN"); + const [pipelineLoading, setPipelineLoading] = useState(false); + const [simRunning, setSimRunning] = useState(true); + const [simLoading, setSimLoading] = useState(false); + + const machineIds = Object.keys(machines); + const hasData = machineIds.length > 0; + + // Poll pipeline + simulator status + const fetchStatus = useCallback(async () => { + try { + const [pRes, sRes] = await Promise.all([ + fetch("/api/pipeline/status"), + fetch("/api/simulator/status"), + ]); + const pData = await pRes.json(); + const sData = await sRes.json(); + setPipelineState(pData.state || "UNKNOWN"); + setSimRunning(sData.running ?? true); + } catch {} + }, []); + + useEffect(() => { + fetchStatus(); + const interval = setInterval(fetchStatus, 8000); + return () => clearInterval(interval); + }, [fetchStatus]); + + const handlePipelineToggle = async () => { + setPipelineLoading(true); + const isRunning = pipelineState === "RUNNING"; + try { + await fetch(`/api/pipeline/${isRunning ? "stop" : "start"}`, { method: "POST" }); + setTimeout(fetchStatus, 2000); + setTimeout(fetchStatus, 5000); + setTimeout(fetchStatus, 10000); + } catch {} + setPipelineLoading(false); + }; + + const handleSimToggle = async () => { + setSimLoading(true); + try { + await fetch(`/api/simulator/${simRunning ? "stop" : "start"}`, { method: "POST" }); + setTimeout(fetchStatus, 1000); + } catch {} + setSimLoading(false); + }; + + const pipelineRunning = pipelineState === "RUNNING"; + + return ( +
+ {/* Header */} +
+
+
+ +

SmartFactory

+ + IoT Demo + +
+ + {/* Tabs */} +
+ + +
+ +
+ {/* Simulator Control */} + + + {/* Pipeline Control */} + + + {/* Connection Status */} +
+ {connectionStatus === "connected" ? ( + <> + + Live + + ) : connectionStatus === "connecting" ? ( + <> + + Connecting + + ) : ( + <> + + Disconnected + + )} +
+
+
+
+ + {/* Main Content */} +
+ {/* Factory Floor — always mounted, hidden via CSS */} +
+ {!hasData ? ( +
+
+ +

+ Connecting to factory sensors... +

+
+
+ ) : ( +
+ + +
+
+
+ {machineIds.map((machineId) => ( + + ))} +
+ +
+
+ +
+
+
+ )} +
+ + {/* Dashboard — always mounted, hidden via CSS */} +
+ +
+
+
+ ); +} diff --git a/demos/smart_factory/frontend/src/components/ControlPanel.tsx b/demos/smart_factory/frontend/src/components/ControlPanel.tsx new file mode 100644 index 0000000..f6bef49 --- /dev/null +++ b/demos/smart_factory/frontend/src/components/ControlPanel.tsx @@ -0,0 +1,88 @@ +import React, { useState } from "react"; +import { MachineConfig } from "../types"; +import { Zap, ZapOff, RotateCcw, Trash2, Loader2 } from "lucide-react"; + +interface ControlPanelProps { + machines: Record; + faultStates: Record; + onInjectFault: (machineId: string) => void; + onClearFault: (machineId: string) => void; + onClearAll: () => void; +} + +export default function ControlPanel({ + machines, + faultStates, + onInjectFault, + onClearFault, + onClearAll, +}: ControlPanelProps) { + const anyFault = Object.values(faultStates).some(Boolean); + const [resetting, setResetting] = useState(false); + + const handleReset = async () => { + if (!confirm("Reset all demo data? This clears all tables for a fresh start.")) return; + setResetting(true); + try { + await fetch("/api/reset-data", { method: "POST" }); + } catch {} + setResetting(false); + }; + + return ( +
+
+

+ Fault Injection +

+
+ + +
+
+
+ {Object.entries(machines).map(([machineId, config]) => { + const isFaulting = faultStates[machineId] || false; + return ( + + ); + })} +
+
+ ); +} diff --git a/demos/smart_factory/frontend/src/components/DashboardView.tsx b/demos/smart_factory/frontend/src/components/DashboardView.tsx new file mode 100644 index 0000000..06fc9d7 --- /dev/null +++ b/demos/smart_factory/frontend/src/components/DashboardView.tsx @@ -0,0 +1,379 @@ +import React, { useEffect, useState, useCallback } from "react"; +import { + BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, + LineChart, Line, Legend, Cell, +} from "recharts"; +import { RefreshCw, AlertTriangle, Activity, Database, BrainCircuit } from "lucide-react"; +import PipelineBanner from "./PipelineBanner"; + +interface HealthRow { + machine_id: string; + machine_type: string; + avg_health_score: string; + total_criticals: string; + total_warnings: string; + worst_sensor_health: string; + last_activity: string; +} + +interface KpiRow { + machine_id: string; + sensor_name: string; + avg_value: string; + max_value: string; + min_value: string; + health_score: string; + critical_count: string; + warning_count: string; + total_readings: string; +} + +interface AnomalyRow { + machine_id: string; + sensor_name: string; + value: string; + anomaly_status: string; + timestamp: string; + unit: string; +} + +interface TrendRow { + machine_id: string; + sensor_name: string; + value: string; + anomaly_status: string; + timestamp: string; +} + +const STATUS_COLORS: Record = { + CRITICAL: "#ef4444", + WARNING: "#f59e0b", + NORMAL: "#10b981", +}; + +const MACHINE_COLORS = ["#3b82f6", "#8b5cf6", "#06b6d4"]; + +export default function DashboardView({ totalEvents }: { totalEvents?: number }) { + const [health, setHealth] = useState([]); + const [kpis, setKpis] = useState([]); + const [anomalies, setAnomalies] = useState([]); + const [trends, setTrends] = useState([]); + const [landingCount, setLandingCount] = useState(0); + const [loading, setLoading] = useState(false); + const [lastRefresh, setLastRefresh] = useState(null); + + const fetchData = useCallback(async () => { + setLoading(true); + try { + const [hRes, kRes, aRes] = await Promise.all([ + fetch("/api/dashboard/health"), + fetch("/api/dashboard/kpis"), + fetch("/api/dashboard/anomalies"), + ]); + const newHealth = await hRes.json(); + const newKpis = await kRes.json(); + const newAnomalies = await aRes.json(); + if (Array.isArray(newHealth) && newHealth.length > 0) setHealth(newHealth); + if (Array.isArray(newKpis) && newKpis.length > 0) setKpis(newKpis); + if (Array.isArray(newAnomalies)) setAnomalies(newAnomalies); + setLastRefresh(new Date()); + } catch (e) { + console.error("Dashboard fetch error:", e); + } + setLoading(false); + }, [landingCount]); + + const fetchTrends = useCallback(async () => { + try { + const res = await fetch("/api/dashboard/trends"); + const data = await res.json(); + if (Array.isArray(data) && data.length > 0) setTrends(data); + } catch {} + }, []); + + useEffect(() => { + fetchData(); + const mainInterval = setInterval(fetchData, 5000); + const trendsInterval = setInterval(fetchTrends, 3000); + return () => { clearInterval(mainInterval); clearInterval(trendsInterval); }; + }, [fetchData, fetchTrends]); + + // Health bar chart data + const healthChartData = health.map((h) => ({ + machine: h.machine_id.replace("_01", "").replace(/_/g, " "), + "Health Score": Number(h.avg_health_score), + Criticals: Number(h.total_criticals), + Warnings: Number(h.total_warnings), + })); + + // Sensor unit lookup + const SENSOR_UNITS: Record = { + temperature_c: "°C", vibration_mm_s: "mm/s", spindle_rpm: "RPM", + pressure_bar: "bar", cycle_count: "cpm", + speed_m_min: "m/min", load_weight_kg: "kg", motor_current_a: "A", + }; + + // KPI data for sensor breakdown + const kpiChartData = kpis.map((k) => ({ + name: `${k.machine_id.split("_")[0]} / ${k.sensor_name}`, + machine: k.machine_id, + sensor: k.sensor_name, + unit: SENSOR_UNITS[k.sensor_name] || "", + avg: Number(k.avg_value), + max: Number(k.max_value), + min: Number(k.min_value), + health: Number(k.health_score), + criticals: Number(k.critical_count), + warnings: Number(k.warning_count), + readings: Number(k.total_readings), + })); + + return ( +
+ {/* Compact stats bar */} +
+
+ } label="Events" value={(totalEvents ?? landingCount).toLocaleString()} color="text-blue-400" /> + } label="Machines" value={String(health.length)} color="text-emerald-400" /> + } label="Anomalies" value={String(anomalies.length)} color="text-purple-400" /> +
+
+ + {lastRefresh ? lastRefresh.toLocaleTimeString() : "—"} + + +
+
+ + {/* Sensor Trends — full width, top position */} +
+

+ Sensor Trends — Live +

+ + { + const byTime: Record> = {}; + [...trends].reverse().forEach((t) => { + const time = new Date(t.timestamp).toLocaleTimeString([], { hour: "2-digit", minute: "2-digit", second: "2-digit" }); + const key = `${t.machine_id.split("_")[0]}-${t.sensor_name}`; + if (!byTime[time]) byTime[time] = {}; + byTime[time][key] = Number(t.value); + }); + return Object.entries(byTime).map(([time, vals]) => ({ time, ...vals })); + })()} + > + + + + + + + + + + + + +
+ + {/* Two-column layout: Health (left) | Anomalies (right) */} +
+ {/* LEFT: Health */} +
+ {/* Health Scores Chart */} +
+

+ Machine Health Scores +

+ + + + + + + + {healthChartData.map((entry, i) => ( + 70 + ? "#10b981" + : entry["Health Score"] > 40 + ? "#f59e0b" + : "#ef4444" + } + /> + ))} + + + +
+ + {/* Sensor Health Table (condensed) */} +
+

+ Sensor Breakdown +

+ + + + + + + + + + + + {kpiChartData.map((row, i) => ( + + + + + + + + ))} + +
MachineSensorAvgMaxHealth
{row.machine.replace("_01", "")}{row.sensor}{row.avg} {row.unit}{row.max} {row.unit} + 70 ? "text-emerald-400" : row.health > 40 ? "text-amber-400" : "text-red-400" + }`}> + {row.health} + +
+
+
+ + {/* RIGHT: Anomalies */} +
+ {/* Anomaly Counts Chart */} +
+
+

+ Anomaly Counts +

+ + + ML in SDP + +
+ + + + + + + + + + + +
+ + {/* Anomaly Log */} +
+
+

+ Anomaly Log +

+ + + Real-time + +
+
+ + + + + + + + + + + + {anomalies.length === 0 && ( + + + + )} + {anomalies.map((a, i) => ( + + + + + + + + ))} + +
TimeMachineSensorValueStatus
+ No anomalies yet +
+ {new Date(a.timestamp).toLocaleTimeString()} + + {a.machine_id.replace("_01", "").replace(/_/g, " ")} + + {a.sensor_name} + + {Number(a.value).toFixed(1)} {a.unit} + + + {a.anomaly_status} + +
+
+
+
+
+ + {/* Pipeline Banner */} + +
+ ); +} + +function MiniStat({ + icon, + label, + value, + color, +}: { + icon: React.ReactNode; + label: string; + value: string; + color: string; +}) { + return ( +
+ {icon} + {label} + {value} +
+ ); +} + diff --git a/demos/smart_factory/frontend/src/components/EventFeed.tsx b/demos/smart_factory/frontend/src/components/EventFeed.tsx new file mode 100644 index 0000000..33e76b2 --- /dev/null +++ b/demos/smart_factory/frontend/src/components/EventFeed.tsx @@ -0,0 +1,90 @@ +import React from "react"; +import { SensorReading, SensorConfig, getAnomalyStatus, AnomalyStatus } from "../types"; +import { MachineConfig } from "../types"; +import { AlertTriangle, AlertCircle } from "lucide-react"; + +interface EventFeedProps { + events: SensorReading[]; + machines: Record; +} + +const anomalyBadge: Record = { + NORMAL: null, + WARNING: { + icon: , + cls: "text-amber-400", + }, + CRITICAL: { + icon: , + cls: "text-red-400", + }, +}; + +const SENSOR_SHORT: Record = { + temperature_c: "TEMP", + vibration_mm_s: "VIBR", + spindle_rpm: "RPM", + pressure_bar: "PRES", + cycle_count: "CYCL", + speed_m_min: "SPEE", + load_weight_kg: "LOAD", + motor_current_a: "CURR", +}; + +export default function EventFeed({ events, machines }: EventFeedProps) { + return ( +
+

+ Live Event Feed +

+
+ {events.length === 0 && ( +

+ Waiting for sensor data... +

+ )} + {events.map((event, i) => { + const machineConfig = machines[event.machine_id]; + const sensorConfig = machineConfig?.sensors?.[event.sensor_name]; + const status = sensorConfig + ? getAnomalyStatus(event.value, sensorConfig) + : "NORMAL"; + const badge = anomalyBadge[status]; + const time = new Date(event.timestamp).toLocaleTimeString(); + + return ( +
+ + {time} + + + {SENSOR_SHORT[event.sensor_name] || event.sensor_name.slice(0, 4).toUpperCase()} + + + {machineConfig?.display_name || event.machine_id} + + + {event.value.toFixed(1)} + + + {event.unit} + + {badge && ( + {badge.icon} + )} +
+ ); + })} +
+
+ ); +} diff --git a/demos/smart_factory/frontend/src/components/FactoryFloor.tsx b/demos/smart_factory/frontend/src/components/FactoryFloor.tsx new file mode 100644 index 0000000..7c966ce --- /dev/null +++ b/demos/smart_factory/frontend/src/components/FactoryFloor.tsx @@ -0,0 +1,274 @@ +import React from "react"; +import { MachineConfig, SensorReading, getAnomalyStatus, AnomalyStatus } from "../types"; +import { Radio, Shield, BrainCircuit } from "lucide-react"; + +interface FactoryFloorProps { + machines: Record; + readings: Record>; + faultStates: Record; + totalEvents: number; + eventsPerSec: number; +} + +function getMachineOverallStatus( + config: MachineConfig, + readings: Record | undefined +): AnomalyStatus { + if (!readings) return "NORMAL"; + let worst: AnomalyStatus = "NORMAL"; + for (const [sensorName, sensorCfg] of Object.entries(config.sensors)) { + const reading = readings[sensorName]; + if (!reading) continue; + const status = getAnomalyStatus(reading.value, sensorCfg); + if (status === "CRITICAL") return "CRITICAL"; + if (status === "WARNING") worst = "WARNING"; + } + return worst; +} + +const statusColors: Record = { + NORMAL: { fill: "#10b981", glow: "#10b98133", ring: "#10b98177", bg: "#10b98110" }, + WARNING: { fill: "#f59e0b", glow: "#f59e0b44", ring: "#f59e0b88", bg: "#f59e0b15" }, + CRITICAL: { fill: "#ef4444", glow: "#ef444455", ring: "#ef4444aa", bg: "#ef444418" }, +}; + +const machineOrder = ["CNC_Mill_01", "Hydraulic_Press_01", "Conveyor_Belt_01"]; + +// Bigger, more detailed machine icons +const machineIcons: Record = { + cnc_mill: ( + + + + + + + + + + + ), + hydraulic_press: ( + + + + + + + + + + + + ), + conveyor_belt: ( + + + + + + + + + + {/* Packages on belt */} + + + ), +}; + +const SENSOR_SHORT: Record = { + temperature_c: "TEMP", + vibration_mm_s: "VIBR", + spindle_rpm: "RPM", + pressure_bar: "PRES", + cycle_count: "CYCL", + speed_m_min: "SPEED", + load_weight_kg: "LOAD", + motor_current_a: "CURR", +}; + +export default function FactoryFloor({ machines, readings, faultStates, totalEvents, eventsPerSec }: FactoryFloorProps) { + const ids = machineOrder.filter((id) => machines[id]); + + return ( +
+ {/* Header */} +
+
+

+ Factory Floor Simulation +

+ + Simulated IoT Devices + +
+
+
+ + Sensors streaming via ZeroBus +
+
+ {eventsPerSec} evt/s + | + {totalEvents.toLocaleString()} total +
+
+
+ + {/* Factory visualization */} +
+ {ids.map((machineId, i) => { + const config = machines[machineId]; + const machineReadings = readings[machineId]; + const status = getMachineOverallStatus(config, machineReadings); + const colors = statusColors[status]; + const isFaulting = faultStates[machineId] || false; + + return ( +
+ {/* Machine visual */} +
+ {/* Outer glow */} + + {/* Pulsing background */} + + {status !== "NORMAL" && ( + + )} + + + {/* Ring */} + + + {/* Sensor dots around the ring */} + {Object.entries(config.sensors).map(([sName, sCfg], si) => { + const angle = -90 + si * 120; + const rad = (angle * Math.PI) / 180; + const cx = Math.cos(rad) * 48; + const cy = Math.sin(rad) * 48; + const reading = machineReadings?.[sName]; + const sStatus = reading ? getAnomalyStatus(reading.value, sCfg) : "NORMAL"; + const sColor = statusColors[sStatus].fill; + return ( + + + + {sStatus !== "NORMAL" && ( + + )} + + + ); + })} + + {/* Machine icon */} + + {machineIcons[config.type]} + + + + {/* Fault indicator */} + {isFaulting && ( +
+ 🔥 +
+ )} +
+ + {/* Machine name */} +

+ {config.display_name} +

+ + {/* Status badge */} + + {status} + + + {/* Live sensor readouts */} +
+ {Object.entries(config.sensors).map(([sName, sCfg]) => { + const reading = machineReadings?.[sName]; + const val = reading?.value; + const sStatus = val !== undefined ? getAnomalyStatus(val, sCfg) : "NORMAL"; + const sColor = statusColors[sStatus].fill; + return ( +
+ {SENSOR_SHORT[sName]} + + {val !== undefined ? val.toFixed(1) : "—"}{" "} + {sCfg.unit} + +
+ ); + })} +
+
+ ); + })} +
+ + {/* Pipeline + UC banner */} +
+
+ +
+ → +
+ + → + + → +
+
+ Silver + + ML + +
+ Anomaly Scored +
+ → + + → + +
+
+ +
+

Governed by Unity Catalog

+

End-to-end lineage, access control & audit

+
+
+
+
+ ); +} + +function PipelineNode({ label, sublabel, color }: { label: string; sublabel: string; color: string }) { + return ( +
+
{label}
+ {sublabel} +
+ ); +} + diff --git a/demos/smart_factory/frontend/src/components/MachineCard.tsx b/demos/smart_factory/frontend/src/components/MachineCard.tsx new file mode 100644 index 0000000..bedbdc2 --- /dev/null +++ b/demos/smart_factory/frontend/src/components/MachineCard.tsx @@ -0,0 +1,92 @@ +import React from "react"; +import { MachineConfig, SensorReading, getAnomalyStatus, AnomalyStatus } from "../types"; +import SensorGauge from "./SensorGauge"; +import { Cog, ArrowDownUp, Gauge } from "lucide-react"; + +interface MachineCardProps { + machineId: string; + config: MachineConfig; + readings: Record | undefined; + isFaulting: boolean; +} + +const MACHINE_ICONS: Record = { + cnc_mill: , + hydraulic_press: , + conveyor_belt: , +}; + +function getMachineStatus( + config: MachineConfig, + readings: Record | undefined +): AnomalyStatus { + if (!readings) return "NORMAL"; + let worst: AnomalyStatus = "NORMAL"; + for (const [sensorName, sensorCfg] of Object.entries(config.sensors)) { + const reading = readings[sensorName]; + if (!reading) continue; + const status = getAnomalyStatus(reading.value, sensorCfg); + if (status === "CRITICAL") return "CRITICAL"; + if (status === "WARNING") worst = "WARNING"; + } + return worst; +} + +const glowClasses: Record = { + NORMAL: "animate-glow-green border-emerald-500/40", + WARNING: "animate-glow-amber border-amber-500/50", + CRITICAL: "animate-glow-red border-red-500/60", +}; + +const statusBadge: Record = { + NORMAL: { label: "Healthy", cls: "bg-emerald-500/20 text-emerald-400" }, + WARNING: { label: "Warning", cls: "bg-amber-500/20 text-amber-400" }, + CRITICAL: { label: "Critical", cls: "bg-red-500/20 text-red-400" }, +}; + +export default function MachineCard({ + machineId, + config, + readings, + isFaulting, +}: MachineCardProps) { + const status = getMachineStatus(config, readings); + const glow = glowClasses[status]; + const badge = statusBadge[status]; + + return ( +
+ {/* Header */} +
+
+ {MACHINE_ICONS[config.type]} +

+ {config.display_name} +

+
+ + {badge.label} + +
+ + {/* Sensor Gauges */} +
+ {Object.entries(config.sensors).map(([sensorName, sensorCfg]) => ( + + ))} +
+ + {/* Machine ID */} +
+ {machineId} +
+
+ ); +} diff --git a/demos/smart_factory/frontend/src/components/PipelineBanner.tsx b/demos/smart_factory/frontend/src/components/PipelineBanner.tsx new file mode 100644 index 0000000..0b4fdb8 --- /dev/null +++ b/demos/smart_factory/frontend/src/components/PipelineBanner.tsx @@ -0,0 +1,68 @@ +import React from "react"; +import { Shield, BrainCircuit } from "lucide-react"; + +export default function PipelineBanner() { + return ( +
+
+ {/* Pipeline Steps */} +
+ + + + +
+
+ Silver + + + ML + +
+
Anomaly Scored
+
+ + + + +
+ + {/* UC Governance Badge */} +
+ +
+

+ Governed by Unity Catalog +

+

+ End-to-end lineage, access control & audit +

+
+
+
+
+ ); +} + +function Step({ + label, + sublabel, + color, +}: { + label: string; + sublabel: string; + color: string; +}) { + return ( +
+
{label}
+
{sublabel}
+
+ ); +} + +function Arrow() { + return ( + → + ); +} diff --git a/demos/smart_factory/frontend/src/components/SensorGauge.tsx b/demos/smart_factory/frontend/src/components/SensorGauge.tsx new file mode 100644 index 0000000..e49471d --- /dev/null +++ b/demos/smart_factory/frontend/src/components/SensorGauge.tsx @@ -0,0 +1,118 @@ +import React, { useMemo } from "react"; +import { SensorConfig, getAnomalyStatus } from "../types"; + +interface SensorGaugeProps { + name: string; + value: number | undefined; + config: SensorConfig; +} + +const SENSOR_LABELS: Record = { + temperature_c: "Temperature", + vibration_mm_s: "Vibration", + spindle_rpm: "Spindle RPM", + pressure_bar: "Pressure", + cycle_count: "Cycle Rate", + speed_m_min: "Belt Speed", + load_weight_kg: "Load Weight", + motor_current_a: "Motor Current", +}; + +export default function SensorGauge({ name, value, config }: SensorGaugeProps) { + const displayValue = value ?? config.min; + const status = value !== undefined ? getAnomalyStatus(displayValue, config) : "NORMAL"; + + const statusColors = { + NORMAL: { stroke: "#10b981", text: "text-emerald-400", bg: "bg-emerald-500/10" }, + WARNING: { stroke: "#f59e0b", text: "text-amber-400", bg: "bg-amber-500/10" }, + CRITICAL: { stroke: "#ef4444", text: "text-red-400", bg: "bg-red-500/10" }, + }; + + const colors = statusColors[status]; + + // SVG arc gauge + const radius = 40; + const strokeWidth = 6; + const cx = 50; + const cy = 55; + const startAngle = -225; + const endAngle = 45; + const totalAngle = endAngle - startAngle; + + const pct = useMemo(() => { + const range = config.max - config.min; + if (range === 0) return 0; + return Math.max(0, Math.min(1, (displayValue - config.min) / range)); + }, [displayValue, config.min, config.max]); + + const valueAngle = startAngle + pct * totalAngle; + + function polarToCartesian(angle: number) { + const rad = (angle * Math.PI) / 180; + return { + x: cx + radius * Math.cos(rad), + y: cy + radius * Math.sin(rad), + }; + } + + function describeArc(start: number, end: number) { + const s = polarToCartesian(start); + const e = polarToCartesian(end); + const largeArc = end - start > 180 ? 1 : 0; + return `M ${s.x} ${s.y} A ${radius} ${radius} 0 ${largeArc} 1 ${e.x} ${e.y}`; + } + + const bgArc = describeArc(startAngle, endAngle); + const valueArc = + pct > 0.01 ? describeArc(startAngle, valueAngle) : ""; + + return ( +
+ + {/* Background arc */} + + {/* Value arc */} + {valueArc && ( + + )} + {/* Center value */} + + {displayValue.toFixed(config.max > 1000 ? 0 : 1)} + + + {config.unit} + + + + {SENSOR_LABELS[name] || name} + +
+ ); +} diff --git a/demos/smart_factory/frontend/src/components/ZeroBusInfo.tsx b/demos/smart_factory/frontend/src/components/ZeroBusInfo.tsx new file mode 100644 index 0000000..c9adc6f --- /dev/null +++ b/demos/smart_factory/frontend/src/components/ZeroBusInfo.tsx @@ -0,0 +1,122 @@ +import React, { useState } from "react"; +import { ChevronDown, ChevronUp, Zap, Gauge, Quote } from "lucide-react"; + +export default function ZeroBusInfo() { + const [expanded, setExpanded] = useState(false); + + return ( +
+ {/* Collapsed: headline metrics */} + + + {/* Expanded: detailed metrics */} + {expanded && ( +
+
+ {/* Speed */} +
+
+ + Speed +
+
+ + + +
+
+ + {/* Throughput */} +
+
+ + Throughput +
+
+ + + + +
+
+ + {/* Why it matters */} +
+
+ Why It Matters +
+
+

· No message bus — straight to Delta

+

· Fully serverless — zero infra management

+

· Open standards — Delta, UC, gRPC/REST

+

· SDKs: Python, Java, Go, Rust, TypeScript

+
+
+
+ + {/* Customer proof */} +
+
+ +
+

+ "ZeroBus reduced our telemetry resolution latency from days to minutes." +

+

+ — Joby Aviation, Flight Telemetry +

+
+
+
+
+

33%

+

cheaper

+
+
+

+ 33% cost savings and 40% faster data transmission vs Kafka. +

+

+ — Bosch, Manufacturing IoT +

+
+
+
+
+ )} +
+ ); +} + +function MetricRow({ label, value }: { label: string; value: string }) { + return ( +
+ {label} + {value} +
+ ); +} diff --git a/demos/smart_factory/frontend/src/hooks/useWebSocket.ts b/demos/smart_factory/frontend/src/hooks/useWebSocket.ts new file mode 100644 index 0000000..47e1e45 --- /dev/null +++ b/demos/smart_factory/frontend/src/hooks/useWebSocket.ts @@ -0,0 +1,131 @@ +import { useEffect, useRef, useState, useCallback } from "react"; +import { WSMessage, MachineConfig, SensorReading } from "../types"; + +interface UseWebSocketReturn { + machines: Record; + latestReadings: Record>; + faultStates: Record; + eventLog: SensorReading[]; + totalEvents: number; + eventsPerSec: number; + connectionStatus: "connecting" | "connected" | "disconnected"; + injectFault: (machineId: string) => void; + clearFault: (machineId: string) => void; + clearAllFaults: () => void; +} + +const MAX_LOG_SIZE = 50; + +export function useWebSocket(): UseWebSocketReturn { + const [machines, setMachines] = useState>({}); + const [latestReadings, setLatestReadings] = useState< + Record> + >({}); + const [faultStates, setFaultStates] = useState>({}); + const [eventLog, setEventLog] = useState([]); + const [totalEvents, setTotalEvents] = useState(0); + const [eventsPerSec, setEventsPerSec] = useState(0); + const recentCountRef = useRef(0); + const [connectionStatus, setConnectionStatus] = useState< + "connecting" | "connected" | "disconnected" + >("connecting"); + const wsRef = useRef(null); + const reconnectTimeout = useRef(0); + + const connect = useCallback(() => { + const protocol = window.location.protocol === "https:" ? "wss:" : "ws:"; + const ws = new WebSocket(`${protocol}//${window.location.host}/ws`); + wsRef.current = ws; + setConnectionStatus("connecting"); + + ws.onopen = () => { + setConnectionStatus("connected"); + reconnectTimeout.current = 0; + }; + + ws.onmessage = (event) => { + const msg: WSMessage = JSON.parse(event.data); + + if (msg.type === "init" && msg.machines) { + setMachines(msg.machines); + } + + if (msg.fault_states) { + setFaultStates(msg.fault_states); + } + + if (msg.events) { + // Track event counts + setTotalEvents((prev) => prev + msg.events!.length); + recentCountRef.current += msg.events!.length; + + // Update latest readings per machine+sensor + setLatestReadings((prev) => { + const next = { ...prev }; + for (const reading of msg.events!) { + if (!next[reading.machine_id]) { + next[reading.machine_id] = {}; + } + next[reading.machine_id] = { + ...next[reading.machine_id], + [reading.sensor_name]: reading, + }; + } + return next; + }); + + // Append to event log (keep last N) + setEventLog((prev) => { + const updated = [...msg.events!, ...prev]; + return updated.slice(0, MAX_LOG_SIZE); + }); + } + }; + + ws.onclose = () => { + setConnectionStatus("disconnected"); + // Exponential backoff reconnect + const delay = Math.min(1000 * Math.pow(2, reconnectTimeout.current), 10000); + reconnectTimeout.current++; + setTimeout(connect, delay); + }; + + ws.onerror = () => { + ws.close(); + }; + }, []); + + useEffect(() => { + connect(); + // Calculate events/sec every second + const epsInterval = setInterval(() => { + setEventsPerSec(recentCountRef.current); + recentCountRef.current = 0; + }, 1000); + return () => { + wsRef.current?.close(); + clearInterval(epsInterval); + }; + }, [connect]); + + const sendAction = useCallback((action: string, machineId?: string) => { + if (wsRef.current?.readyState === WebSocket.OPEN) { + wsRef.current.send( + JSON.stringify({ action, machine_id: machineId }) + ); + } + }, []); + + return { + machines, + latestReadings, + faultStates, + eventLog, + totalEvents, + eventsPerSec, + connectionStatus, + injectFault: (id) => sendAction("inject_fault", id), + clearFault: (id) => sendAction("clear_fault", id), + clearAllFaults: () => sendAction("clear_all"), + }; +} diff --git a/demos/smart_factory/frontend/src/index.css b/demos/smart_factory/frontend/src/index.css new file mode 100644 index 0000000..13deb0d --- /dev/null +++ b/demos/smart_factory/frontend/src/index.css @@ -0,0 +1,23 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +body { + font-family: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, + sans-serif; +} + +/* Scrollbar styling for dark theme */ +::-webkit-scrollbar { + width: 6px; +} +::-webkit-scrollbar-track { + background: #111827; +} +::-webkit-scrollbar-thumb { + background: #374151; + border-radius: 3px; +} +::-webkit-scrollbar-thumb:hover { + background: #4b5563; +} diff --git a/demos/smart_factory/frontend/src/main.tsx b/demos/smart_factory/frontend/src/main.tsx new file mode 100644 index 0000000..9b67590 --- /dev/null +++ b/demos/smart_factory/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; +import "./index.css"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + +); diff --git a/demos/smart_factory/frontend/src/types.ts b/demos/smart_factory/frontend/src/types.ts new file mode 100644 index 0000000..76a5755 --- /dev/null +++ b/demos/smart_factory/frontend/src/types.ts @@ -0,0 +1,50 @@ +export interface SensorConfig { + unit: string; + min: number; + max: number; + warning_threshold: number; + critical_threshold: number; +} + +export interface MachineConfig { + type: string; + display_name: string; + sensors: Record; +} + +export interface SensorReading { + machine_id: string; + machine_type: string; + sensor_name: string; + value: number; + unit: string; + timestamp: string; + is_fault: boolean; +} + +export interface WSMessage { + type: "init" | "sensor_data"; + machines?: Record; + events?: SensorReading[]; + fault_states?: Record; +} + +export type AnomalyStatus = "NORMAL" | "WARNING" | "CRITICAL"; + +export function getAnomalyStatus( + value: number, + config: SensorConfig +): AnomalyStatus { + const { warning_threshold, critical_threshold } = config; + + // High-value fault sensors (threshold ascending) + if (warning_threshold < critical_threshold) { + if (value >= critical_threshold) return "CRITICAL"; + if (value >= warning_threshold) return "WARNING"; + return "NORMAL"; + } + // Low-value fault sensors (speed drops, cycle count drops) + if (value <= critical_threshold) return "CRITICAL"; + if (value <= warning_threshold) return "WARNING"; + return "NORMAL"; +} diff --git a/demos/smart_factory/frontend/tailwind.config.js b/demos/smart_factory/frontend/tailwind.config.js new file mode 100644 index 0000000..2af46c3 --- /dev/null +++ b/demos/smart_factory/frontend/tailwind.config.js @@ -0,0 +1,37 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + extend: { + colors: { + factory: { + bg: "#0a0e17", + card: "#111827", + border: "#1f2937", + accent: "#3b82f6", + }, + }, + animation: { + "pulse-slow": "pulse 3s cubic-bezier(0.4, 0, 0.6, 1) infinite", + "glow-green": "glow-green 2s ease-in-out infinite alternate", + "glow-amber": "glow-amber 1s ease-in-out infinite alternate", + "glow-red": "glow-red 0.5s ease-in-out infinite alternate", + }, + keyframes: { + "glow-green": { + "0%": { boxShadow: "0 0 5px #10b981, 0 0 10px #10b98133" }, + "100%": { boxShadow: "0 0 10px #10b981, 0 0 20px #10b98155" }, + }, + "glow-amber": { + "0%": { boxShadow: "0 0 5px #f59e0b, 0 0 10px #f59e0b33" }, + "100%": { boxShadow: "0 0 15px #f59e0b, 0 0 30px #f59e0b55" }, + }, + "glow-red": { + "0%": { boxShadow: "0 0 5px #ef4444, 0 0 10px #ef444433" }, + "100%": { boxShadow: "0 0 20px #ef4444, 0 0 40px #ef444466" }, + }, + }, + }, + }, + plugins: [], +}; diff --git a/demos/smart_factory/frontend/vite.config.ts b/demos/smart_factory/frontend/vite.config.ts new file mode 100644 index 0000000..3a39324 --- /dev/null +++ b/demos/smart_factory/frontend/vite.config.ts @@ -0,0 +1,19 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +export default defineConfig({ + plugins: [react()], + build: { + outDir: "dist", + emptyOutDir: true, + }, + server: { + proxy: { + "/api": "http://localhost:8000", + "/ws": { + target: "ws://localhost:8000", + ws: true, + }, + }, + }, +}); diff --git a/demos/smart_factory/pipeline/bronze.sql b/demos/smart_factory/pipeline/bronze.sql new file mode 100644 index 0000000..c71dd3a --- /dev/null +++ b/demos/smart_factory/pipeline/bronze.sql @@ -0,0 +1,20 @@ +-- Bronze: Validated raw sensor events from ZeroBus landing zone +-- Source table is fully qualified; output uses pipeline's target schema + +CREATE OR REFRESH STREAMING TABLE raw_sensor_events ( + CONSTRAINT valid_value EXPECT (value IS NOT NULL) ON VIOLATION DROP ROW, + CONSTRAINT valid_machine EXPECT (machine_id IS NOT NULL) ON VIOLATION DROP ROW, + CONSTRAINT valid_sensor EXPECT (sensor_name IS NOT NULL) ON VIOLATION DROP ROW, + CONSTRAINT valid_timestamp EXPECT (timestamp IS NOT NULL) ON VIOLATION DROP ROW +) +COMMENT 'Validated IoT sensor events from ZeroBus landing zone' +AS SELECT + machine_id, + machine_type, + sensor_name, + CAST(value AS DOUBLE) AS value, + unit, + CAST(timestamp AS TIMESTAMP) AS timestamp, + COALESCE(is_fault, false) AS is_fault, + current_timestamp() AS ingested_at +FROM STREAM(dilan_catalog.smartfactory.raw_sensor_events); diff --git a/demos/smart_factory/pipeline/gold.sql b/demos/smart_factory/pipeline/gold.sql new file mode 100644 index 0000000..0c2bf4e --- /dev/null +++ b/demos/smart_factory/pipeline/gold.sql @@ -0,0 +1,59 @@ +-- Gold: Aggregated health KPIs for dashboards + +-- Anomaly timeline — streaming table for instant anomaly visibility +CREATE OR REFRESH STREAMING TABLE anomaly_timeline +COMMENT 'Real-time anomaly events for timeline visualization' +AS +SELECT + machine_id, + machine_type, + sensor_name, + value, + unit, + anomaly_status, + warning_threshold, + critical_threshold, + timestamp, + processed_at +FROM STREAM(enriched_events) +WHERE anomaly_status != 'NORMAL'; + + +-- Machine health scores (MV, refreshes on pipeline interval) +CREATE OR REFRESH MATERIALIZED VIEW machine_health_kpis +COMMENT 'Machine health KPIs for dashboards' +AS +SELECT + machine_id, + machine_type, + sensor_name, + COUNT(*) AS total_readings, + COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) AS critical_count, + COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) AS warning_count, + ROUND(AVG(value), 2) AS avg_value, + ROUND(MAX(value), 2) AS max_value, + ROUND(MIN(value), 2) AS min_value, + MAX(timestamp) AS last_reading_at, + GREATEST(0, 100 + - (COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) * 10) + - (COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) * 3) + ) AS health_score +FROM enriched_events +WHERE timestamp > current_timestamp() - INTERVAL 5 MINUTES +GROUP BY machine_id, machine_type, sensor_name; + + +-- Overall machine health summary (MV, one row per machine) +CREATE OR REFRESH MATERIALIZED VIEW machine_summary +COMMENT 'One-row-per-machine health summary' +AS +SELECT + machine_id, + machine_type, + MIN(health_score) AS worst_sensor_health, + ROUND(AVG(health_score), 0) AS avg_health_score, + SUM(critical_count) AS total_criticals, + SUM(warning_count) AS total_warnings, + MAX(last_reading_at) AS last_activity +FROM machine_health_kpis +GROUP BY machine_id, machine_type; diff --git a/demos/smart_factory/pipeline/silver.sql b/demos/smart_factory/pipeline/silver.sql new file mode 100644 index 0000000..859dc9d --- /dev/null +++ b/demos/smart_factory/pipeline/silver.sql @@ -0,0 +1,57 @@ +-- Silver: Enriched events with anomaly scoring +-- Uses a threshold LIVE TABLE joined against streaming events for anomaly detection + +-- Sensor thresholds reference table (the "ML model" — simple, explainable, reliable) +CREATE OR REFRESH LIVE TABLE sensor_thresholds +COMMENT 'Anomaly detection thresholds per machine and sensor' +AS SELECT * FROM VALUES + -- CNC Mill + ('CNC_Mill_01', 'temperature_c', 55.0, 75.0, '°C'), + ('CNC_Mill_01', 'vibration_mm_s', 4.0, 6.5, 'mm/s'), + ('CNC_Mill_01', 'spindle_rpm', 4200.0, 4800.0, 'RPM'), + -- Hydraulic Press (some sensors fault LOW, so we use value < threshold) + ('Hydraulic_Press_01', 'pressure_bar', 210.0, 270.0, 'bar'), + ('Hydraulic_Press_01', 'temperature_c', 70.0, 90.0, '°C'), + ('Hydraulic_Press_01', 'cycle_count', 6.0, 3.0, 'cycles/min'), + -- Conveyor Belt + ('Conveyor_Belt_01', 'speed_m_min', 9.0, 6.0, 'm/min'), + ('Conveyor_Belt_01', 'load_weight_kg', 350.0, 430.0, 'kg'), + ('Conveyor_Belt_01', 'motor_current_a', 12.0, 17.0, 'A') +AS t(machine_id, sensor_name, warning_threshold, critical_threshold, unit); + + +-- Enriched streaming table with anomaly status +CREATE OR REFRESH STREAMING TABLE enriched_events +COMMENT 'Sensor events enriched with anomaly detection scores' +AS +SELECT + r.machine_id, + r.machine_type, + r.sensor_name, + r.value, + r.unit, + r.timestamp, + r.is_fault, + r.ingested_at, + t.warning_threshold, + t.critical_threshold, + CASE + -- Sensors where HIGH values indicate faults (temp, pressure, vibration, load, current) + WHEN t.warning_threshold < t.critical_threshold THEN + CASE + WHEN r.value >= t.critical_threshold THEN 'CRITICAL' + WHEN r.value >= t.warning_threshold THEN 'WARNING' + ELSE 'NORMAL' + END + -- Sensors where LOW values indicate faults (speed, cycle_count) + ELSE + CASE + WHEN r.value <= t.critical_threshold THEN 'CRITICAL' + WHEN r.value <= t.warning_threshold THEN 'WARNING' + ELSE 'NORMAL' + END + END AS anomaly_status, + current_timestamp() AS processed_at +FROM STREAM(raw_sensor_events) r +LEFT JOIN LIVE.sensor_thresholds t + ON r.machine_id = t.machine_id AND r.sensor_name = t.sensor_name; diff --git a/demos/smart_factory/pyproject.toml b/demos/smart_factory/pyproject.toml new file mode 100644 index 0000000..44a304b --- /dev/null +++ b/demos/smart_factory/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "smartfactory-demo" +version = "0.1.0" +description = "SmartFactory IoT demo — ZeroBus + SDP streaming with anomaly detection" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.100.0", + "uvicorn[standard]>=0.23.0", + "databricks-sdk>=0.61.0", + "pydantic>=2.1.0", + "python-dotenv>=1.0.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/demos/smart_factory/requirements.txt b/demos/smart_factory/requirements.txt new file mode 100644 index 0000000..164650a --- /dev/null +++ b/demos/smart_factory/requirements.txt @@ -0,0 +1,6 @@ +fastapi>=0.100.0 +uvicorn[standard]>=0.23.0 +databricks-sdk>=0.61.0 +databricks-zerobus-ingest-sdk +pydantic>=2.1.0 +python-dotenv>=1.0.0 diff --git a/demos/smart_factory/setup.sh b/demos/smart_factory/setup.sh new file mode 100755 index 0000000..f4c5271 --- /dev/null +++ b/demos/smart_factory/setup.sh @@ -0,0 +1,198 @@ +#!/bin/bash +set -e + +# SmartFactory Demo — One-time setup script +# Usage: ./setup.sh [catalog_name] +# +# Prerequisites: +# - Databricks CLI v0.288+ installed (/opt/homebrew/bin/databricks) +# - Profile configured with workspace access +# - Node.js + npm installed (for frontend build) + +CLI="/opt/homebrew/bin/databricks" +PROFILE="${1:?Usage: ./setup.sh [catalog_name]}" +CATALOG="${2:-dilan_catalog}" +SCHEMA="smartfactory" +PIPELINE_NAME="smartfactory-sdp" + +echo "=========================================" +echo "SmartFactory Demo Setup" +echo "=========================================" +echo "Profile: $PROFILE" +echo "Catalog: $CATALOG" +echo "Schema: $SCHEMA" +echo "" + +# --- Step 1: Find or start a SQL warehouse --- +echo "[1/8] Finding SQL warehouse..." +WAREHOUSE_ID=$($CLI -p "$PROFILE" warehouses list --output json 2>/dev/null | python3 -c " +import sys, json +data = json.load(sys.stdin) +for w in data: + print(w['id']) + break +" 2>/dev/null) + +if [ -z "$WAREHOUSE_ID" ]; then + echo "ERROR: No SQL warehouse found. Create one in the workspace first." + exit 1 +fi +echo " Warehouse: $WAREHOUSE_ID" + +# Start warehouse if stopped +$CLI -p "$PROFILE" warehouses start "$WAREHOUSE_ID" > /dev/null 2>&1 || true +echo " Warehouse starting (or already running)..." +sleep 5 + +# --- Step 2: Create schema and landing table --- +echo "[2/8] Creating schema and landing table..." +run_sql() { + $CLI -p "$PROFILE" api post /api/2.0/sql/statements --json "{ + \"warehouse_id\": \"$WAREHOUSE_ID\", + \"statement\": \"$1\", + \"wait_timeout\": \"30s\" + }" 2>&1 | python3 -c "import sys,json; d=json.load(sys.stdin); s=d.get('status',{}).get('state','?'); e=d.get('status',{}).get('error',{}).get('message',''); print(f'{s} {e}' if e else s)" +} + +run_sql "CREATE SCHEMA IF NOT EXISTS ${CATALOG}.${SCHEMA}" +run_sql "CREATE TABLE IF NOT EXISTS ${CATALOG}.${SCHEMA}.raw_sensor_events (machine_id STRING NOT NULL, machine_type STRING NOT NULL, sensor_name STRING NOT NULL, value DOUBLE NOT NULL, unit STRING NOT NULL, timestamp TIMESTAMP NOT NULL, is_fault BOOLEAN) USING DELTA COMMENT 'Raw IoT sensor events ingested via ZeroBus'" + +# --- Step 3: Build frontend --- +echo "[3/8] Building frontend..." +cd frontend && npm ci --silent && npm run build 2>&1 | tail -1 +cd .. + +# --- Step 4: Update databricks.yml with correct values --- +echo "[4/8] Configuring bundle..." + +# Detect current user for dev schema prefix +USERNAME=$($CLI -p "$PROFILE" current-user me --output json 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); u=d.get('userName','').split('@')[0].replace('.','_'); print(u)") +DEV_SCHEMA="dev_${USERNAME}_${SCHEMA}" + +echo " Dev schema will be: ${CATALOG}.${DEV_SCHEMA}" + +# Write a local env file for the setup +cat > .env.setup </dev/null | python3 -c " +import sys, json +d = json.load(sys.stdin) +# The SP client ID is in the app's service principal +sp = d.get('service_principal_client_id', d.get('effective_service_principal', {}).get('client_id', '')) +print(sp) +" 2>/dev/null) + +if [ -z "$SP_ID" ]; then + echo " WARNING: Could not detect app service principal. You may need to:" + echo " 1. Start the app: $CLI -p $PROFILE apps start smartfactory-app" + echo " 2. Re-run this script, or manually grant permissions" +else + echo " App SP: $SP_ID" + + # Warehouse access + $CLI -p "$PROFILE" warehouses set-permissions "$WAREHOUSE_ID" --json "{\"access_control_list\":[{\"service_principal_name\":\"$SP_ID\",\"permission_level\":\"CAN_USE\"}]}" > /dev/null 2>&1 + + # Catalog + schema + table access + for stmt in \ + "GRANT USE CATALOG ON CATALOG ${CATALOG} TO \`${SP_ID}\`" \ + "GRANT USE SCHEMA ON SCHEMA ${CATALOG}.${SCHEMA} TO \`${SP_ID}\`" \ + "GRANT CREATE TABLE ON SCHEMA ${CATALOG}.${SCHEMA} TO \`${SP_ID}\`" \ + "GRANT MODIFY ON TABLE ${CATALOG}.${SCHEMA}.raw_sensor_events TO \`${SP_ID}\`" \ + "GRANT SELECT ON TABLE ${CATALOG}.${SCHEMA}.raw_sensor_events TO \`${SP_ID}\`" \ + "GRANT USE SCHEMA ON SCHEMA ${CATALOG}.${DEV_SCHEMA} TO \`${SP_ID}\`" \ + "GRANT SELECT ON SCHEMA ${CATALOG}.${DEV_SCHEMA} TO \`${SP_ID}\`"; do + run_sql "$stmt" > /dev/null + done + echo " Catalog/schema/table permissions granted" +fi + +# --- Step 7: Start app and deploy code --- +echo "[7/8] Starting and deploying app..." +$CLI -p "$PROFILE" apps start smartfactory-app > /dev/null 2>&1 || true +echo " Waiting for app compute..." +sleep 30 + +$CLI -p "$PROFILE" apps deploy smartfactory-app \ + --source-code-path "/Workspace/Users/${USERNAME}@databricks.com/.bundle/smartfactory-demo/dev/files" 2>&1 | tail -1 + +# --- Step 8: Find pipeline ID, grant SP permissions, set continuous --- +echo "[8/8] Configuring pipeline..." +PIPELINE_ID=$($CLI -p "$PROFILE" api get /api/2.0/pipelines 2>/dev/null | python3 -c " +import sys, json +d = json.load(sys.stdin) +for p in d.get('statuses', []): + if 'smartfactory-sdp' in p.get('name', ''): + print(p['pipeline_id']) + break +" 2>/dev/null) + +if [ -n "$PIPELINE_ID" ]; then + echo " Pipeline: $PIPELINE_ID" + + # Grant SP pipeline permissions + if [ -n "$SP_ID" ]; then + $CLI -p "$PROFILE" pipelines update-permissions "$PIPELINE_ID" \ + --json "{\"access_control_list\":[{\"service_principal_name\":\"$SP_ID\",\"permission_level\":\"CAN_MANAGE\"}]}" > /dev/null 2>&1 + echo " Pipeline permissions granted to SP" + fi + + # Set continuous mode via API + PIPELINE_SPEC=$($CLI -p "$PROFILE" api get "/api/2.0/pipelines/$PIPELINE_ID" 2>/dev/null | python3 -c " +import sys, json +d = json.load(sys.stdin) +spec = d.get('spec', d) +spec['continuous'] = True +for k in ['pipeline_id','state','creator_user_name','latest_updates','cause','cluster_id','run_as_user_name','last_modified','budget_policy_id']: + spec.pop(k, None) +print(json.dumps(spec)) +") + echo "$PIPELINE_SPEC" > /tmp/smartfactory_pipeline.json + $CLI -p "$PROFILE" api put "/api/2.0/pipelines/$PIPELINE_ID" --json @/tmp/smartfactory_pipeline.json > /dev/null 2>&1 + echo " Pipeline set to continuous mode" + + # Update app.yaml with pipeline ID (for next deploy) + echo "" + echo "NOTE: Add this to app.yaml env vars for pipeline control:" + echo " - name: PIPELINE_ID" + echo " value: \"$PIPELINE_ID\"" +else + echo " WARNING: Pipeline not found. Deploy may still be in progress." +fi + +# --- Done --- +APP_URL=$($CLI -p "$PROFILE" apps get smartfactory-app --output json 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('url',''))" 2>/dev/null) + +echo "" +echo "=========================================" +echo "Setup complete!" +echo "=========================================" +echo "" +echo "App URL: $APP_URL" +echo "Catalog: $CATALOG" +echo "Schema: $CATALOG.$SCHEMA (landing)" +echo "Pipeline: $CATALOG.$DEV_SCHEMA (pipeline tables)" +echo "Warehouse: $WAREHOUSE_ID" +echo "Pipeline ID: $PIPELINE_ID" +echo "" +echo "Next steps:" +echo " 1. Open the app URL above" +echo " 2. Click 'Start Pipeline' to begin continuous processing" +echo " 3. Inject faults and watch data flow!" +echo "" +echo "To redeploy after code changes:" +echo " cd frontend && npm run build && cd .." +echo " $CLI bundle deploy -t dev -p $PROFILE --var='warehouse_id=$WAREHOUSE_ID' --var='catalog_name=$CATALOG'" +echo " $CLI -p $PROFILE apps deploy smartfactory-app --source-code-path /Workspace/Users/${USERNAME}@databricks.com/.bundle/smartfactory-demo/dev/files" diff --git a/demos/smart_factory/src/__init__.py b/demos/smart_factory/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/demos/smart_factory/src/app.py b/demos/smart_factory/src/app.py new file mode 100644 index 0000000..5ed8348 --- /dev/null +++ b/demos/smart_factory/src/app.py @@ -0,0 +1,546 @@ +""" +SmartFactory FastAPI Application. + +Serves the React frontend, runs the IoT sensor simulator, +pushes data via ZeroBus, and streams updates to the UI via WebSocket. +""" + +import asyncio +import json +import logging +import os +from contextlib import asynccontextmanager + +from fastapi import FastAPI, WebSocket, WebSocketDisconnect +from fastapi.staticfiles import StaticFiles +from fastapi.responses import FileResponse +from pydantic import BaseModel +from databricks.sdk import WorkspaceClient + +from src.simulator import SensorSimulator, get_machine_configs +from src.zerobus_client import ZeroBusClient + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +# --- WebSocket Connection Manager --- + +class ConnectionManager: + """Manages WebSocket connections for real-time UI updates.""" + + def __init__(self): + self.active_connections: list[WebSocket] = [] + + async def connect(self, websocket: WebSocket): + await websocket.accept() + self.active_connections.append(websocket) + logger.info(f"WebSocket connected. Total: {len(self.active_connections)}") + + def disconnect(self, websocket: WebSocket): + self.active_connections.remove(websocket) + logger.info(f"WebSocket disconnected. Total: {len(self.active_connections)}") + + async def broadcast(self, message: dict): + dead = [] + for conn in self.active_connections: + try: + await conn.send_json(message) + except Exception: + dead.append(conn) + for conn in dead: + self.active_connections.remove(conn) + + +manager = ConnectionManager() +simulator = SensorSimulator() +zerobus: ZeroBusClient | None = None +simulator_task: asyncio.Task | None = None +workspace_client: WorkspaceClient | None = None +pipeline_id: str | None = None + + +# --- Simulator Loop --- + +async def run_simulator(): + """Background loop: generate sensor data, push to ZeroBus + WebSocket.""" + interval_ms = int(os.getenv("SIMULATOR_INTERVAL_MS", "2000")) + interval_s = interval_ms / 1000 + + while True: + try: + events = simulator.generate_tick() + + # Push to ZeroBus (async, for pipeline ingestion) + if zerobus: + await zerobus.push_events(events) + + # Push to WebSocket (instant UI update) + await manager.broadcast({ + "type": "sensor_data", + "events": events, + "fault_states": simulator.get_fault_states(), + }) + + except asyncio.CancelledError: + raise + except Exception as e: + logger.error(f"Simulator tick error: {e}") + + await asyncio.sleep(interval_s) + + +# --- Pipeline Management --- + +def _find_pipeline_id() -> str | None: + """Find the SmartFactory SDP pipeline ID.""" + global pipeline_id + if pipeline_id: + return pipeline_id + # Check env var first + env_id = os.getenv("PIPELINE_ID") + if env_id: + pipeline_id = env_id + return pipeline_id + if not workspace_client: + return None + try: + pipelines = workspace_client.pipelines.list_pipelines( + filter="name LIKE 'smartfactory-sdp'" + ) + for p in pipelines: + pipeline_id = p.pipeline_id + logger.info(f"Found pipeline: {p.name} ({pipeline_id})") + return pipeline_id + except Exception as e: + logger.error(f"Failed to find pipeline: {e}") + return None + + +def _get_pipeline_status() -> dict: + """Get current pipeline status.""" + pid = _find_pipeline_id() + if not pid or not workspace_client: + return {"state": "NOT_FOUND", "pipeline_id": None} + try: + p = workspace_client.pipelines.get(pipeline_id=pid) + return { + "state": p.state.value if p.state else "UNKNOWN", + "pipeline_id": pid, + "name": p.name, + "last_update": p.latest_updates[0].update_id if p.latest_updates else None, + } + except Exception as e: + logger.error(f"Failed to get pipeline status: {e}") + return {"state": "ERROR", "pipeline_id": pid, "error": str(e)} + + +# --- App Lifespan --- + +@asynccontextmanager +async def lifespan(app: FastAPI): + global zerobus, simulator_task, workspace_client + + # Startup + enable_sim = os.getenv("ENABLE_SIMULATOR", "true").lower() == "true" + + try: + workspace_client = WorkspaceClient() + logger.info("Workspace client initialized") + # Self-heal: ensure SP has warehouse access (gets dropped by bundle deploy) + if WAREHOUSE_ID: + try: + sp_id = os.getenv("DATABRICKS_CLIENT_ID") + if sp_id: + workspace_client.warehouses.set_permissions( + warehouse_id=WAREHOUSE_ID, + access_control_list=[{ + "service_principal_name": sp_id, + "permission_level": "CAN_USE", + }], + ) + logger.info(f"Warehouse permission confirmed for SP {sp_id}") + except Exception as e: + logger.warning(f"Could not set warehouse permission: {e}") + except Exception as e: + logger.warning(f"Workspace client init failed: {e}") + + try: + zerobus = ZeroBusClient() + logger.info("ZeroBus client initialized") + except Exception as e: + logger.warning(f"ZeroBus client init failed: {e}. Running without ingestion.") + zerobus = None + + if enable_sim: + simulator_task = asyncio.create_task(run_simulator()) + logger.info("Sensor simulator started") + + # Start dashboard cache refresh + global cache_task + cache_task = asyncio.create_task(_refresh_dashboard_cache()) + logger.info("Dashboard cache started (5s refresh)") + + yield + + # Shutdown + if simulator_task: + simulator_task.cancel() + try: + await simulator_task + except asyncio.CancelledError: + pass + if cache_task: + cache_task.cancel() + try: + await cache_task + except asyncio.CancelledError: + pass + logger.info("SmartFactory app shutdown complete") + + +# --- FastAPI App --- + +app = FastAPI(title="SmartFactory", lifespan=lifespan) + + +# --- API Models --- + +class FaultRequest(BaseModel): + machine_id: str + + +# --- API Endpoints --- + +@app.get("/api/machines") +async def get_machines(): + """Return machine configurations and current fault states.""" + return { + "machines": get_machine_configs(), + "fault_states": simulator.get_fault_states(), + } + + +@app.get("/api/status") +async def get_status(): + """Return current simulator status.""" + return { + "simulator_running": simulator_task is not None and not simulator_task.done(), + "zerobus_connected": zerobus is not None, + "websocket_connections": len(manager.active_connections), + "fault_states": simulator.get_fault_states(), + } + + +@app.post("/api/fault/inject") +async def inject_fault(req: FaultRequest): + """Inject a fault into a specific machine.""" + success = simulator.inject_fault(req.machine_id) + if not success: + return {"error": f"Unknown machine: {req.machine_id}"}, 404 + logger.info(f"Fault injected: {req.machine_id}") + return {"status": "fault_injected", "machine_id": req.machine_id} + + +@app.post("/api/fault/clear") +async def clear_fault(req: FaultRequest): + """Clear a fault from a specific machine.""" + success = simulator.clear_fault(req.machine_id) + if not success: + return {"error": f"Unknown machine: {req.machine_id}"}, 404 + logger.info(f"Fault cleared: {req.machine_id}") + return {"status": "fault_cleared", "machine_id": req.machine_id} + + +@app.post("/api/fault/clear-all") +async def clear_all_faults(): + """Clear all faults.""" + simulator.clear_all_faults() + logger.info("All faults cleared") + return {"status": "all_faults_cleared"} + + +# --- Simulator Control Endpoints --- + +@app.get("/api/simulator/status") +async def get_simulator_status(): + """Get simulator (data streaming) status.""" + running = simulator_task is not None and not simulator_task.done() + return {"running": running} + + +@app.post("/api/simulator/start") +async def start_simulator(): + """Start the sensor data simulator.""" + global simulator_task + if simulator_task and not simulator_task.done(): + return {"status": "already_running"} + simulator_task = asyncio.create_task(run_simulator()) + logger.info("Simulator started via API") + return {"status": "started"} + + +@app.post("/api/simulator/stop") +async def stop_simulator(): + """Stop the sensor data simulator.""" + global simulator_task + if not simulator_task or simulator_task.done(): + return {"status": "already_stopped"} + simulator_task.cancel() + try: + await simulator_task + except asyncio.CancelledError: + pass + simulator_task = None + logger.info("Simulator stopped via API") + return {"status": "stopped"} + + +@app.post("/api/reset-data") +async def reset_data(): + """Reset demo by stopping pipeline, clearing data, and doing a full refresh.""" + if not workspace_client or not WAREHOUSE_ID: + return {"error": "No workspace client or warehouse"} + try: + # Stop pipeline first so it releases the streaming source + pid = _find_pipeline_id() + if pid: + try: + workspace_client.pipelines.stop(pipeline_id=pid) + logger.info("Pipeline stopped for reset") + await asyncio.sleep(10) # Wait for pipeline to fully stop + except Exception: + pass + + # Now safe to truncate the landing table + workspace_client.statement_execution.execute_statement( + statement=f"TRUNCATE TABLE {CATALOG}.smartfactory.raw_sensor_events", + warehouse_id=WAREHOUSE_ID, + ) + logger.info("Landing table truncated") + + # Trigger a full refresh of the pipeline (resets streaming checkpoints) + pid = _find_pipeline_id() + if pid: + workspace_client.pipelines.start_update( + pipeline_id=pid, + full_refresh=True, + ) + logger.info(f"Pipeline full refresh triggered: {pid}") + + # Clear simulator fault state + simulator.clear_all_faults() + logger.info("Demo data reset complete") + return {"status": "reset_complete"} + except Exception as e: + logger.error(f"Reset failed: {e}") + return {"error": str(e)} + + +# --- Pipeline Endpoints --- + +@app.get("/api/pipeline/status") +async def get_pipeline_status(): + """Get SDP pipeline status.""" + return _get_pipeline_status() + + +@app.post("/api/pipeline/start") +async def start_pipeline(): + """Start/trigger the SDP pipeline.""" + pid = _find_pipeline_id() + if not pid or not workspace_client: + return {"error": "Pipeline not found"}, 404 + try: + update = workspace_client.pipelines.start_update(pipeline_id=pid) + logger.info(f"Pipeline started: {pid}") + return {"status": "started", "pipeline_id": pid, "update_id": update.update_id} + except Exception as e: + logger.error(f"Failed to start pipeline: {e}") + return {"error": str(e)} + + +@app.post("/api/pipeline/stop") +async def stop_pipeline(): + """Stop the SDP pipeline.""" + pid = _find_pipeline_id() + if not pid or not workspace_client: + return {"error": "Pipeline not found"}, 404 + try: + workspace_client.pipelines.stop(pipeline_id=pid) + logger.info(f"Pipeline stopped: {pid}") + return {"status": "stopped", "pipeline_id": pid} + except Exception as e: + logger.error(f"Failed to stop pipeline: {e}") + return {"error": str(e)} + + +# --- Dashboard Data Cache --- + +WAREHOUSE_ID = os.getenv("WAREHOUSE_ID") +CATALOG = os.getenv("CATALOG_NAME", "dilan_catalog") +PIPELINE_SCHEMA = os.getenv("PIPELINE_SCHEMA", "dev_dilan_patel_smartfactory") + +# Background cache — warehouse queries run here, frontend reads from cache +dashboard_cache: dict = { + "health": [], + "kpis": [], + "anomalies": [], + "trends": [], +} +cache_task: asyncio.Task | None = None + + +def _run_sql(query: str) -> list[dict]: + """Execute SQL and return results as list of dicts.""" + if not workspace_client or not WAREHOUSE_ID: + return [] + try: + result = workspace_client.statement_execution.execute_statement( + statement=query, + warehouse_id=WAREHOUSE_ID, + ) + if not result.result or not result.result.data_array: + return [] + columns = [c.name for c in result.manifest.schema.columns] + return [dict(zip(columns, row)) for row in result.result.data_array] + except Exception as e: + logger.error(f"SQL query failed: {e}") + return [] + + +async def _refresh_dashboard_cache(): + """Background loop: refresh dashboard data from warehouse every 5 seconds.""" + while True: + try: + # Run all queries sequentially (one at a time, not competing) + health = _run_sql(f""" + WITH kpis AS ( + SELECT machine_id, machine_type, sensor_name, + COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) AS critical_count, + COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) AS warning_count, + GREATEST(0, 100 + - COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) * 10 + - COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) * 3 + ) AS health_score + FROM {CATALOG}.{PIPELINE_SCHEMA}.enriched_events + WHERE TRUE + GROUP BY machine_id, machine_type, sensor_name + ) + SELECT machine_id, machine_type, + MIN(health_score) AS worst_sensor_health, + ROUND(AVG(health_score), 0) AS avg_health_score, + SUM(critical_count) AS total_criticals, + SUM(warning_count) AS total_warnings + FROM kpis + GROUP BY machine_id, machine_type + """) + if health: + dashboard_cache["health"] = health + + kpis = _run_sql(f""" + SELECT machine_id, machine_type, sensor_name, + COUNT(*) AS total_readings, + COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) AS critical_count, + COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) AS warning_count, + ROUND(AVG(value), 2) AS avg_value, + ROUND(MAX(value), 2) AS max_value, + ROUND(MIN(value), 2) AS min_value, + GREATEST(0, 100 + - COUNT(CASE WHEN anomaly_status = 'CRITICAL' THEN 1 END) * 10 + - COUNT(CASE WHEN anomaly_status = 'WARNING' THEN 1 END) * 3 + ) AS health_score + FROM {CATALOG}.{PIPELINE_SCHEMA}.enriched_events + WHERE TRUE + GROUP BY machine_id, machine_type, sensor_name + """) + if kpis: + dashboard_cache["kpis"] = kpis + + anomalies = _run_sql( + f"SELECT * FROM {CATALOG}.{PIPELINE_SCHEMA}.anomaly_timeline LIMIT 100" + ) + dashboard_cache["anomalies"] = anomalies + + trends = _run_sql(f""" + SELECT machine_id, sensor_name, value, unit, anomaly_status, timestamp + FROM {CATALOG}.{PIPELINE_SCHEMA}.enriched_events + WHERE timestamp > current_timestamp() - INTERVAL 30 MINUTES + ORDER BY timestamp DESC + LIMIT 500 + """) + if trends: + dashboard_cache["trends"] = trends + + logger.debug("Dashboard cache refreshed") + except asyncio.CancelledError: + raise + except Exception as e: + logger.error(f"Dashboard cache refresh error: {e}") + + await asyncio.sleep(5) + + +# --- Dashboard Endpoints (serve from cache, instant) --- + +@app.get("/api/dashboard/health") +async def dashboard_health(): + return dashboard_cache["health"] + +@app.get("/api/dashboard/kpis") +async def dashboard_kpis(): + return dashboard_cache["kpis"] + +@app.get("/api/dashboard/anomalies") +async def dashboard_anomalies(): + return dashboard_cache["anomalies"] + +@app.get("/api/dashboard/trends") +async def dashboard_trends(): + return dashboard_cache["trends"] + +@app.get("/api/dashboard/landing-count") +async def dashboard_landing_count(): + return {"count": 0} + + +# --- WebSocket --- + +@app.websocket("/ws") +async def websocket_endpoint(websocket: WebSocket): + await manager.connect(websocket) + try: + # Send initial state + await websocket.send_json({ + "type": "init", + "machines": get_machine_configs(), + "fault_states": simulator.get_fault_states(), + }) + # Keep connection alive, listen for client messages + while True: + data = await websocket.receive_text() + msg = json.loads(data) + if msg.get("action") == "inject_fault": + simulator.inject_fault(msg["machine_id"]) + elif msg.get("action") == "clear_fault": + simulator.clear_fault(msg["machine_id"]) + elif msg.get("action") == "clear_all": + simulator.clear_all_faults() + except WebSocketDisconnect: + manager.disconnect(websocket) + + +# --- Serve React Frontend --- + +FRONTEND_DIR = os.path.join(os.path.dirname(__file__), "..", "frontend", "dist") + +if os.path.isdir(FRONTEND_DIR): + app.mount("/assets", StaticFiles(directory=os.path.join(FRONTEND_DIR, "assets")), name="assets") + + @app.get("/{full_path:path}") + async def serve_frontend(full_path: str): + """Serve React SPA — all non-API routes return index.html.""" + file_path = os.path.join(FRONTEND_DIR, full_path) + if os.path.isfile(file_path): + return FileResponse(file_path) + return FileResponse(os.path.join(FRONTEND_DIR, "index.html")) diff --git a/demos/smart_factory/src/setup.sql b/demos/smart_factory/src/setup.sql new file mode 100644 index 0000000..d6fcffc --- /dev/null +++ b/demos/smart_factory/src/setup.sql @@ -0,0 +1,14 @@ +-- One-time setup for dilan_catalog workspace +-- Schemas already created; this creates the landing table for ZeroBus + +CREATE TABLE IF NOT EXISTS dilan_catalog.smartfactory_landing.raw_sensor_events ( + machine_id STRING NOT NULL, + machine_type STRING NOT NULL, + sensor_name STRING NOT NULL, + value DOUBLE NOT NULL, + unit STRING NOT NULL, + timestamp TIMESTAMP NOT NULL, + is_fault BOOLEAN DEFAULT false +) +USING DELTA +COMMENT 'Raw IoT sensor events ingested via ZeroBus'; diff --git a/demos/smart_factory/src/simulator.py b/demos/smart_factory/src/simulator.py new file mode 100644 index 0000000..bb6ed54 --- /dev/null +++ b/demos/smart_factory/src/simulator.py @@ -0,0 +1,195 @@ +""" +SmartFactory IoT Sensor Simulator. + +Simulates 3 factory machines generating realistic sensor telemetry. +Each machine has 3 sensors with configurable normal/fault distributions. +Supports fault injection for live demo scenarios. +""" + +import random +import time +from datetime import datetime, timezone +from dataclasses import dataclass, field + + +MACHINES = { + "CNC_Mill_01": { + "type": "cnc_mill", + "display_name": "CNC Mill", + "sensors": { + "temperature_c": { + "normal_mean": 45, "normal_std": 3, + "fault_mean": 85, "fault_std": 5, + "min": 20, "max": 120, + "unit": "°C", + "warning_threshold": 55, "critical_threshold": 75, + }, + "vibration_mm_s": { + "normal_mean": 2.5, "normal_std": 0.5, + "fault_mean": 8.0, "fault_std": 1.0, + "min": 0, "max": 15, + "unit": "mm/s", + "warning_threshold": 4.0, "critical_threshold": 6.5, + }, + "spindle_rpm": { + "normal_mean": 3500, "normal_std": 100, + "fault_mean": 4800, "fault_std": 200, + "min": 0, "max": 6000, + "unit": "RPM", + "warning_threshold": 4200, "critical_threshold": 4800, + }, + }, + }, + "Hydraulic_Press_01": { + "type": "hydraulic_press", + "display_name": "Hydraulic Press", + "sensors": { + "pressure_bar": { + "normal_mean": 180, "normal_std": 10, + "fault_mean": 280, "fault_std": 15, + "min": 0, "max": 350, + "unit": "bar", + "warning_threshold": 210, "critical_threshold": 270, + }, + "temperature_c": { + "normal_mean": 55, "normal_std": 4, + "fault_mean": 95, "fault_std": 6, + "min": 20, "max": 130, + "unit": "°C", + "warning_threshold": 70, "critical_threshold": 90, + }, + "cycle_count": { + "normal_mean": 12, "normal_std": 2, + "fault_mean": 3, "fault_std": 1, + "min": 0, "max": 30, + "unit": "cycles/min", + "warning_threshold": 6, "critical_threshold": 3, + }, + }, + }, + "Conveyor_Belt_01": { + "type": "conveyor_belt", + "display_name": "Conveyor Belt", + "sensors": { + "speed_m_min": { + "normal_mean": 15, "normal_std": 1, + "fault_mean": 5, "fault_std": 1.5, + "min": 0, "max": 25, + "unit": "m/min", + "warning_threshold": 9, "critical_threshold": 6, + }, + "load_weight_kg": { + "normal_mean": 250, "normal_std": 30, + "fault_mean": 450, "fault_std": 20, + "min": 0, "max": 500, + "unit": "kg", + "warning_threshold": 350, "critical_threshold": 430, + }, + "motor_current_a": { + "normal_mean": 8, "normal_std": 1, + "fault_mean": 18, "fault_std": 2, + "min": 0, "max": 25, + "unit": "A", + "warning_threshold": 12, "critical_threshold": 17, + }, + }, + }, +} + + +@dataclass +class MachineState: + is_faulting: bool = False + fault_progress: float = 0.0 # 0.0 = normal, 1.0 = full fault + drift_rate: float = 0.1 # how fast fault develops per tick + + +class SensorSimulator: + """Generates realistic IoT sensor readings with fault injection support.""" + + def __init__(self): + self.states: dict[str, MachineState] = { + mid: MachineState() for mid in MACHINES + } + + def inject_fault(self, machine_id: str) -> bool: + if machine_id not in self.states: + return False + self.states[machine_id].is_faulting = True + return True + + def clear_fault(self, machine_id: str) -> bool: + if machine_id not in self.states: + return False + state = self.states[machine_id] + state.is_faulting = False + state.fault_progress = 0.0 + return True + + def clear_all_faults(self): + for state in self.states.values(): + state.is_faulting = False + state.fault_progress = 0.0 + + def get_fault_states(self) -> dict[str, bool]: + return {mid: s.is_faulting for mid, s in self.states.items()} + + def generate_tick(self) -> list[dict]: + """Generate one tick of readings for all machines (9 events total).""" + events = [] + now = datetime.now(timezone.utc) + + for machine_id, config in MACHINES.items(): + state = self.states[machine_id] + + # Advance fault progress + if state.is_faulting and state.fault_progress < 1.0: + state.fault_progress = min(1.0, state.fault_progress + state.drift_rate) + elif not state.is_faulting and state.fault_progress > 0.0: + state.fault_progress = max(0.0, state.fault_progress - state.drift_rate * 2) + + for sensor_name, sensor_cfg in config["sensors"].items(): + value = self._generate_value(sensor_cfg, state.fault_progress) + events.append({ + "machine_id": machine_id, + "machine_type": config["type"], + "sensor_name": sensor_name, + "value": round(value, 2), + "unit": sensor_cfg["unit"], + "timestamp": now.isoformat(), + "is_fault": state.is_faulting, + }) + + return events + + def _generate_value(self, cfg: dict, fault_progress: float) -> float: + """Generate a sensor value blending normal and fault distributions.""" + normal_val = random.gauss(cfg["normal_mean"], cfg["normal_std"]) + fault_val = random.gauss(cfg["fault_mean"], cfg["fault_std"]) + + # Blend based on fault progress + value = normal_val * (1 - fault_progress) + fault_val * fault_progress + + # Clamp to sensor range + return max(cfg["min"], min(cfg["max"], value)) + + +def get_machine_configs() -> dict: + """Return machine configs for the frontend.""" + result = {} + for machine_id, config in MACHINES.items(): + sensors = {} + for sensor_name, sensor_cfg in config["sensors"].items(): + sensors[sensor_name] = { + "unit": sensor_cfg["unit"], + "min": sensor_cfg["min"], + "max": sensor_cfg["max"], + "warning_threshold": sensor_cfg["warning_threshold"], + "critical_threshold": sensor_cfg["critical_threshold"], + } + result[machine_id] = { + "type": config["type"], + "display_name": config["display_name"], + "sensors": sensors, + } + return result diff --git a/demos/smart_factory/src/zerobus_client.py b/demos/smart_factory/src/zerobus_client.py new file mode 100644 index 0000000..5758490 --- /dev/null +++ b/demos/smart_factory/src/zerobus_client.py @@ -0,0 +1,145 @@ +""" +ZeroBus Ingest Client Wrapper. + +Pushes IoT sensor events to Unity Catalog Delta tables via ZeroBus. +Falls back to SQL INSERT via statement execution if ZeroBus SDK is unavailable. +""" + +import logging +import os +from datetime import datetime + +from databricks.sdk import WorkspaceClient + +logger = logging.getLogger(__name__) + +# Try to import ZeroBus SDK +try: + from zerobus.sdk.aio import ZerobusSdk as AsyncZerobusSdk + from zerobus.sdk.shared import RecordType, StreamConfigurationOptions, TableProperties + ZEROBUS_SDK_AVAILABLE = True +except ImportError: + ZEROBUS_SDK_AVAILABLE = False + logger.info("ZeroBus SDK not installed, will use SQL INSERT fallback") + + +class ZeroBusClient: + """Wraps ZeroBus ingest API with SQL INSERT fallback.""" + + def __init__(self, table_name: str | None = None): + self.table_name = table_name or os.getenv( + "ZEROBUS_TABLE", "dilan_catalog.smartfactory.raw_sensor_events" + ) + self.w = WorkspaceClient() + self._use_fallback = not ZEROBUS_SDK_AVAILABLE + self._warehouse_id = os.getenv("WAREHOUSE_ID") + self._stream = None + self._zerobus_sdk = None + + async def _init_zerobus_stream(self): + """Initialize ZeroBus async stream.""" + if not ZEROBUS_SDK_AVAILABLE or self._stream is not None: + return + + try: + server_endpoint = os.getenv("ZEROBUS_SERVER_ENDPOINT") + workspace_url = os.getenv("DATABRICKS_HOST", "") + client_id = os.getenv("DATABRICKS_CLIENT_ID", "") + client_secret = os.getenv("DATABRICKS_CLIENT_SECRET", "") + + if not server_endpoint: + # Derive from workspace URL if not set + # Pattern: .zerobus..cloud.databricks.com + logger.warning("ZEROBUS_SERVER_ENDPOINT not set, falling back to SQL") + self._use_fallback = True + return + + self._zerobus_sdk = AsyncZerobusSdk(server_endpoint, workspace_url) + options = StreamConfigurationOptions(record_type=RecordType.JSON) + table_props = TableProperties(self.table_name) + + self._stream = await self._zerobus_sdk.create_stream( + client_id, client_secret, table_props, options + ) + logger.info(f"ZeroBus stream created for table: {self.table_name}") + except Exception as e: + logger.warning(f"ZeroBus stream init failed ({e}), using SQL INSERT fallback") + self._use_fallback = True + + async def push_events(self, events: list[dict]) -> bool: + """Push sensor events to the landing table.""" + if not events: + return True + + try: + if not self._use_fallback and self._stream is None: + await self._init_zerobus_stream() + + if self._use_fallback: + return self._push_via_sql(events) + else: + return await self._push_via_zerobus(events) + except Exception as e: + logger.error(f"Failed to push {len(events)} events: {e}") + return False + + async def _push_via_zerobus(self, events: list[dict]) -> bool: + """Push events via ZeroBus async SDK.""" + try: + offset = await self._stream.ingest_records_nowait(events) + logger.debug(f"ZeroBus: pushed {len(events)} events") + return True + except Exception as e: + logger.warning(f"ZeroBus push failed ({e}), falling back to SQL") + self._use_fallback = True + return self._push_via_sql(events) + + def _push_via_sql(self, events: list[dict]) -> bool: + """Fallback: push events via SQL INSERT using statement execution.""" + try: + values_clauses = [] + for e in events: + ts = e["timestamp"] + if isinstance(ts, str): + ts_sql = f"TIMESTAMP '{ts}'" + else: + ts_sql = f"TIMESTAMP '{ts.isoformat()}'" + + values_clauses.append( + f"('{e['machine_id']}', '{e['machine_type']}', " + f"'{e['sensor_name']}', {e['value']}, '{e['unit']}', " + f"{ts_sql}, {str(e.get('is_fault', False)).lower()})" + ) + + sql = ( + f"INSERT INTO {self.table_name} " + f"(machine_id, machine_type, sensor_name, value, unit, timestamp, is_fault) " + f"VALUES {', '.join(values_clauses)}" + ) + + self.w.statement_execution.execute_statement( + statement=sql, + warehouse_id=self._warehouse_id or self._get_warehouse_id(), + ) + logger.debug(f"SQL fallback: inserted {len(events)} events") + return True + except Exception as e: + logger.error(f"SQL INSERT fallback failed: {e}") + return False + + def _get_warehouse_id(self) -> str: + """Find an available SQL warehouse.""" + if self._warehouse_id: + return self._warehouse_id + warehouses = self.w.warehouses.list() + for wh in warehouses: + if wh.state and wh.state.value == "RUNNING": + self._warehouse_id = wh.id + return wh.id + raise RuntimeError("No running SQL warehouse found") + + async def close(self): + """Close the ZeroBus stream.""" + if self._stream: + await self._stream.close() + self._stream = None