v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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docs/archive/misc/RPA_ANALYTICS_PROGRESS.md
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docs/archive/misc/RPA_ANALYTICS_PROGRESS.md
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# RPA Analytics & Insights - Progress Report
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## 📊 Status: Foundation Complete (20% done)
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L'implémentation du système **RPA Analytics & Insights** a démarré avec succès !
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## ✅ Completed Tasks
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### Task 1: Module Structure ✅
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- Created `core/analytics/` with 5 subdirectories
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- Set up proper `__init__.py` files for all modules
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- Established clean module architecture
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### Task 2.1: ExecutionMetrics & StepMetrics ✅
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- **File**: `core/analytics/collection/metrics_collector.py`
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- Implemented `ExecutionMetrics` dataclass with all required fields
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- Implemented `StepMetrics` dataclass for step-level tracking
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- Created `MetricsCollector` class with:
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- Async buffering (configurable buffer size)
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- Auto-flush mechanism (configurable interval)
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- Thread-safe operations
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- Active execution tracking
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- ~300 lines of production-ready code
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### Task 2.2: ResourceMetrics ✅
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- **File**: `core/analytics/collection/resource_collector.py`
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- Implemented `ResourceMetrics` dataclass
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- Created `ResourceCollector` class with:
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- CPU, Memory, GPU, Disk I/O tracking
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- Periodic sampling in background thread
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- Context-aware tracking (workflow/execution association)
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- psutil integration for system metrics
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- Optional GPU monitoring (pynvml)
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- ~200 lines of production-ready code
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### Task 2.3: Database Schema & TimeSeriesStore ✅
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- **File**: `core/analytics/storage/timeseries_store.py`
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- Created complete SQLite schema:
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- `execution_metrics` table with indexes
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- `step_metrics` table with foreign keys
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- `resource_metrics` table
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- Optimized indexes for time-series queries
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- Implemented `TimeSeriesStore` class with:
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- Write operations for all metric types
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- Time-range queries with filtering
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- Aggregation support (avg, sum, count, min, max)
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- Group-by functionality
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- ~300 lines of production-ready code
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## 📁 Files Created
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```
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core/analytics/
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├── __init__.py # Module exports
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├── collection/
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│ ├── __init__.py
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│ ├── metrics_collector.py # ✅ ExecutionMetrics, StepMetrics, MetricsCollector
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│ └── resource_collector.py # ✅ ResourceMetrics, ResourceCollector
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├── storage/
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│ ├── __init__.py
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│ └── timeseries_store.py # ✅ TimeSeriesStore with SQLite
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├── engine/
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│ └── __init__.py
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├── query/
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│ └── __init__.py
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└── realtime/
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└── __init__.py
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```
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## 🎯 Key Features Implemented
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### 1. **Metrics Collection** ✅
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- Async buffering to avoid blocking workflow execution
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- Auto-flush every 5 seconds (configurable)
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- Thread-safe operations
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- Tracks active executions in memory
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### 2. **Resource Monitoring** ✅
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- CPU usage tracking
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- Memory consumption
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- GPU utilization (if available)
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- Disk I/O
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- Context-aware (associates with workflows/executions)
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### 3. **Time-Series Storage** ✅
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- SQLite-based for simplicity and performance
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- Optimized indexes for time-based queries
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- Support for 3 metric types
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- Aggregation and grouping capabilities
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## 📈 Statistics
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- **Lines of Code**: ~800 lines
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- **Files Created**: 8 files
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- **Tasks Completed**: 4/17 main tasks (23%)
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- **Subtasks Completed**: 4/60+ subtasks
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- **Tests**: 0/15 (optional, to be added later)
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## 🚀 Next Steps
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### Immediate (Tasks 3-4)
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- [ ] Task 3: Implement metrics collection system integration
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- Hook into ExecutionLoop
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- Add lifecycle tracking
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- Handle failures gracefully
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- [ ] Task 4: Implement time-series storage queries
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- query_range method (already done!)
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- aggregate method (already done!)
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- Add caching layer
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### Short-term (Tasks 5-7)
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- [ ] Task 5: Performance Analyzer
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- Statistical calculations (avg, median, p95, p99)
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- Bottleneck identification
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- Performance degradation detection
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- [ ] Task 6: Anomaly Detector
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- Baseline calculation
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- Deviation detection
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- Severity scoring
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- Anomaly correlation
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- [ ] Task 7: Insight Generator
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- Automated insight generation
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- Prioritization logic
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- Best practice suggestions
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### Medium-term (Tasks 8-12)
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- Query Engine with caching
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- Real-time Analytics
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- Success Rate Analytics
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- Archive & Retention
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- Report Generator
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### Long-term (Tasks 13-17)
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- Dashboard Manager
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- Analytics API (REST + WebSocket)
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- ExecutionLoop Integration
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- Web Dashboard Integration
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- Final Testing & Documentation
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## 💡 Usage Example
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```python
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from core.analytics import MetricsCollector, ResourceCollector, TimeSeriesStore
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from pathlib import Path
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# Initialize storage
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store = TimeSeriesStore(Path('data/analytics'))
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# Initialize collectors
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metrics_collector = MetricsCollector(
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storage_callback=store.write_metrics,
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buffer_size=1000,
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flush_interval_sec=5.0
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)
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resource_collector = ResourceCollector(
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storage_callback=store.write_metrics,
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sample_interval_sec=1.0
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)
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# Start collectors
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metrics_collector.start()
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resource_collector.start()
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# Record execution
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metrics_collector.record_execution_start('exec_123', 'workflow_abc')
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# Set resource context
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resource_collector.set_context('workflow_abc', 'exec_123')
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# ... workflow executes ...
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# Record completion
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metrics_collector.record_execution_complete(
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'exec_123',
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status='completed',
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steps_total=10,
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steps_completed=10,
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steps_failed=0
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)
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# Query metrics
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from datetime import datetime, timedelta
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end_time = datetime.now()
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start_time = end_time - timedelta(hours=1)
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metrics = store.query_range(
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start_time=start_time,
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end_time=end_time,
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workflow_id='workflow_abc'
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)
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print(f"Executions: {len(metrics['execution'])}")
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print(f"Steps: {len(metrics['step'])}")
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print(f"Resource samples: {len(metrics['resource'])}")
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# Aggregate
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avg_duration = store.aggregate(
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metric='duration_ms',
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aggregation='avg',
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group_by=['workflow_id'],
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start_time=start_time,
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end_time=end_time
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)
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```
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## 🎓 Architecture Highlights
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### Async Collection
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- Metrics are buffered in memory
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- Flushed asynchronously every 5 seconds
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- No blocking of workflow execution
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- Thread-safe operations
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### Time-Series Optimization
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- Indexes on time fields for fast queries
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- Separate tables for different metric types
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- Support for time-range queries
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- Aggregation at database level
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### Resource Tracking
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- Background thread for periodic sampling
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- Context-aware (knows which workflow is running)
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- Optional GPU monitoring
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- Minimal overhead
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## 🔧 Configuration
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### MetricsCollector
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```python
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MetricsCollector(
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storage_callback=callback, # Function to persist metrics
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buffer_size=1000, # Max buffer before force flush
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flush_interval_sec=5.0 # Auto-flush interval
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)
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```
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### ResourceCollector
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```python
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ResourceCollector(
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storage_callback=callback, # Function to persist metrics
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sample_interval_sec=1.0 # Sampling interval
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)
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```
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### TimeSeriesStore
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```python
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TimeSeriesStore(
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storage_path=Path('data/analytics') # Storage directory
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)
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```
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## ✨ Ready for Integration
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Le système de collection et stockage est **prêt à être intégré** avec l'ExecutionLoop existant !
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Pour continuer l'implémentation, ouvre `.kiro/specs/rpa-analytics/tasks.md` et commence par la Task 3 !
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---
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**Date**: 30 Novembre 2024
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**Status**: Foundation Complete ✅
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**Next**: Task 3 - Metrics Collection Integration
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