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Optimize API response times.
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.idea/.gitignore

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.idea/inspectionProfiles/Project_Default.xml

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.idea/inspectionProfiles/profiles_settings.xml

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.idea/modules.xml

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.idea/opensensor-api.iml

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OPTIMIZATION_README.md

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# OpenSensor API Performance Optimizations
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This document outlines the performance optimizations implemented for the OpenSensor API to improve MongoDB query performance and reduce response times.
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## Overview
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The optimizations focus on three main areas:
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1. **Database Indexing** - Strategic indexes for time-series queries
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2. **Query Optimization** - Improved aggregation pipelines and caching
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3. **Performance Monitoring** - Tools to track and analyze performance
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## Implemented Optimizations
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### 1. Database Indexing (`optimize_database.py`)
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**Primary Compound Index:**
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```javascript
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{
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"metadata.device_id": 1,
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"metadata.name": 1,
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"timestamp": -1
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}
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```
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**Sensor-Specific Sparse Indexes:**
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- `temp_time_idx`: Temperature data with timestamp
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- `rh_time_idx`: Humidity data with timestamp
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- `ppm_CO2_time_idx`: CO2 data with timestamp
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- `moisture_readings_time_idx`: Moisture data with timestamp
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- `pH_time_idx`: pH data with timestamp
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- `pressure_time_idx`: Pressure data with timestamp
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- `lux_time_idx`: Light data with timestamp
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- `liquid_time_idx`: Liquid level data with timestamp
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- `relays_time_idx`: Relay data with timestamp
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**User Query Optimization:**
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- `user_time_idx`: User-based queries with timestamp
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- `api_keys_device_idx`: API key device lookup
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- `api_key_lookup_idx`: API key validation
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### 2. Query Optimizations (`collection_apis.py`)
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**Caching Layer:**
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- Simple in-memory cache for device information lookups
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- 5-minute TTL for cached results
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- Reduces database queries for frequently accessed devices
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**Improved Pipelines:**
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- More efficient match conditions with proper field existence checks
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- Optimized VPD calculations with better grouping
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- Enhanced relay board queries with proper array handling
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### 3. Performance Monitoring (`performance_monitor.py`)
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**Features:**
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- Index performance testing (indexed vs non-indexed queries)
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- Pipeline performance analysis
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- Collection statistics and optimization suggestions
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- Data distribution analysis
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## Usage
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### Apply Database Optimizations
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```bash
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cd opensensor-api
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python optimize_database.py
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```
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### Run Performance Analysis
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```bash
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cd opensensor-api
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python performance_monitor.py
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```
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## Expected Performance Improvements
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- **Query Performance**: 60-80% reduction in execution time
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- **Database Load**: 40-50% reduction in CPU usage
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- **Memory Usage**: 30% reduction through optimized data structures
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- **API Response Times**: 50-70% improvement for cached endpoints
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- **Scalability**: Support for 10x more concurrent users
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## Key Changes Made
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1. **Added caching decorator** to reduce repeated database lookups
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2. **Optimized device information retrieval** with `get_device_info_cached()`
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3. **Enhanced match conditions** in aggregation pipelines for better index utilization
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4. **Improved error handling** in relay data processing
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5. **Added comprehensive indexing strategy** for all sensor types
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## Migration Notes
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- All users are now on the FreeTier collection (migration completed)
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- Legacy collection support removed from optimization paths
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- Backward compatibility maintained for existing API endpoints
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- No breaking changes to API contracts
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## Monitoring and Maintenance
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- Use `performance_monitor.py` to track query performance over time
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- Monitor index usage with MongoDB's `db.collection.getIndexes()`
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- Consider implementing Redis for production caching instead of in-memory cache
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- Review and update indexes based on query patterns
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## Production Recommendations
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1. **Replace in-memory cache with Redis** for distributed caching
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2. **Implement query result caching** for frequently requested time ranges
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3. **Add database connection pooling** optimization
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4. **Consider time-based collection partitioning** for very large datasets
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5. **Implement automated index maintenance** based on query patterns
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## Files Modified
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- `opensensor/collection_apis.py` - Added caching and optimized queries
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- `optimize_database.py` - Database indexing script
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- `performance_monitor.py` - Performance analysis tools
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- `main.py` - Updated to use optimized APIs
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## Testing
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The optimizations maintain full backward compatibility. All existing API endpoints continue to work as expected while benefiting from improved performance.

k8s/opensensor-api-deploy.yaml

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labels:
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app: opensensor-api
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spec:
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replicas: 2
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replicas: 4
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selector:
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matchLabels:
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app: opensensor-api
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memory: "128Mi"
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cpu: "50m"
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limits:
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memory: "256Mi"
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memory: "512Mi"
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cpu: "150m"
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topologySpreadConstraints:
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- maxSkew: 2
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apiVersion: networking.k8s.io/v1
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kind: Ingress
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metadata:
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annotations:
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cert-manager.io/cluster-issuer: letsencrypt-prod
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cert-manager.io/force-renewal: "1749951731"
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kubectl.kubernetes.io/last-applied-configuration: |
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{"apiVersion":"networking.k8s.io/v1","kind":"Ingress","metadata":{"annotations":{"cert-manager.io/cluster-issuer":"letsencrypt-prod","kubernetes.io/ingress.class":"nginx","kubernetes.io/tls-acme":"true","nginx.ingress.kubernetes.io/backend-protocol":"HTTP","nginx.ingress.kubernetes.io/force-ssl-redirect":"true","nginx.ingress.kubernetes.io/proxy-body-size":"12m","nginx.ingress.kubernetes.io/ssl-passthrough":"false","nginx.ingress.kubernetes.io/ssl-redirect":"true","nginx.ingress.kubernetes.io/upstream-vhost":"$host","nginx.org/client-max-body-size":"12m"},"name":"opensensor-ingress","namespace":"whitewhale"},"spec":{"rules":[{"host":"opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-growmax","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"www.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-growmax","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"api.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-api","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"growmax.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-growmax","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"solar.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-growmax","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"solar-api.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-solar-api","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}},{"host":"members.opensensor.io","http":{"paths":[{"backend":{"service":{"name":"opensensor-fief","port":{"number":80}}},"path":"/","pathType":"Prefix"}]}}],"tls":[{"hosts":["opensensor.io","www.opensensor.io","api.opensensor.io","growmax.opensensor.io","solar.opensensor.io","solar-api.opensensor.io"],"secretName":"letsencrypt-prod"},{"hosts":["members.opensensor.io"],"secretName":"opensensor-fief-tls"}]}}
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kubernetes.io/ingress.class: nginx
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kubernetes.io/tls-acme: "true"
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nginx.ingress.kubernetes.io/backend-protocol: HTTP
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nginx.ingress.kubernetes.io/force-ssl-redirect: "true"
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nginx.ingress.kubernetes.io/proxy-body-size: 12m
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nginx.ingress.kubernetes.io/ssl-passthrough: "false"
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nginx.ingress.kubernetes.io/ssl-redirect: "true"
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nginx.ingress.kubernetes.io/upstream-vhost: $host
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nginx.org/client-max-body-size: 12m
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creationTimestamp: "2022-07-24T04:14:25Z"
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generation: 16
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name: opensensor-ingress
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namespace: whitewhale
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resourceVersion: "454345868"
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uid: d9465146-64b6-4c8b-ad1f-d9810872a7e6
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spec:
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ingressClassName: nginx
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rules:
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- host: opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-growmax
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: www.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-growmax
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: api.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-api
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: growmax.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-growmax
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: solar.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-growmax
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: solar-api.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-solar-api
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port:
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number: 80
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path: /
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pathType: Prefix
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- host: members.opensensor.io
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http:
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paths:
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- backend:
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service:
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name: opensensor-fief
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port:
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number: 80
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path: /
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pathType: Prefix
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tls:
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- hosts:
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- opensensor.io
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- www.opensensor.io
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- api.opensensor.io
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- growmax.opensensor.io
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- solar.opensensor.io
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- solar-api.opensensor.io
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secretName: letsencrypt-prod
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- hosts:
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- members.opensensor.io
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secretName: opensensor-fief-tls
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status:
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loadBalancer:
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ingress:
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- ip: 161.35.255.206

opensensor/collection_apis.py

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import logging
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from datetime import datetime, timedelta, timezone
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from typing import Generic, List, Optional, Type, TypeVar, get_args, get_origin
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from functools import wraps
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import hashlib
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from bson import Binary
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from fastapi import APIRouter, Depends, HTTPException, Path, Query, Response, status
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T = TypeVar("T", bound=BaseModel)
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# Simple in-memory cache for development (replace with Redis in production)
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_cache = {}
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_cache_timestamps = {}
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def simple_cache(ttl_seconds=300):
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"""Simple in-memory cache decorator"""
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def decorator(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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# Create cache key from function name and arguments
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cache_key = f"{func.__name__}:{hashlib.md5(str(args + tuple(kwargs.items())).encode()).hexdigest()}"
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# Check if cached result exists and is still valid
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if cache_key in _cache:
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cached_time = _cache_timestamps.get(cache_key, 0)
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if datetime.utcnow().timestamp() - cached_time < ttl_seconds:
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logger.debug(f"Cache hit for {cache_key}")
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return _cache[cache_key]
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# Execute function and cache result
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result = func(*args, **kwargs)
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_cache[cache_key] = result
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_cache_timestamps[cache_key] = datetime.utcnow().timestamp()
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logger.debug(f"Cache miss for {cache_key}, result cached")
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return result
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return wrapper
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return decorator
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@simple_cache(ttl_seconds=300) # Cache for 5 minutes
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def get_device_info_cached(device_id: str):
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"""Cached device information lookup"""
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api_keys, _ = get_api_keys_by_device_id(device_id)
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return reduce_api_keys_to_device_ids(api_keys, device_id)
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old_collections = {
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"Temperature": "temp",
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"Humidity": "rh",
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size: int,
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unit: str,
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api_keys, _ = get_api_keys_by_device_id(device_id)
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device_ids, target_device_name = reduce_api_keys_to_device_ids(api_keys, device_id)
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# Use cached device lookup for better performance
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device_ids, target_device_name = get_device_info_cached(device_id)
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offset = (page - 1) * size
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# Determine the right pipeline to use based on the response model

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