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/guides/ANALYTICS_QUICKSTART.md
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docs/guides/ANALYTICS_QUICKSTART.md
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# 🚀 RPA Analytics - Quick Start Guide
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## Overview
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Le système RPA Analytics fournit une suite complète d'outils pour analyser, monitorer et optimiser vos workflows RPA.
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## Installation
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Les dépendances analytics sont déjà incluses dans `requirements.txt`. Aucune installation supplémentaire n'est nécessaire.
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## Démarrage Rapide
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### 1. Initialisation du Système
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```python
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from core.analytics.analytics_system import get_analytics_system
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# Initialiser le système analytics
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analytics = get_analytics_system()
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# Démarrer le monitoring des ressources
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analytics.start_resource_monitoring(interval_seconds=60)
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```
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### 2. Collecter des Métriques
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```python
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from core.analytics.collection.metrics_collector import ExecutionMetrics
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from datetime import datetime, timedelta
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# Enregistrer une exécution de workflow
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execution = ExecutionMetrics(
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execution_id="exec_001",
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workflow_id="my_workflow",
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started_at=datetime.now(),
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completed_at=datetime.now() + timedelta(seconds=30),
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duration=30.0,
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status="success"
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)
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analytics.metrics_collector.record_execution(execution)
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analytics.metrics_collector.flush() # Persister immédiatement
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```
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### 3. Analyser les Performances
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```python
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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=24)
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# Analyser les performances
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perf_stats = analytics.performance_analyzer.analyze_performance(
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workflow_id="my_workflow",
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start_time=start_time,
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end_time=end_time
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)
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print(f"Average Duration: {perf_stats.avg_duration:.2f}s")
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print(f"Success Rate: {perf_stats.success_rate:.1f}%")
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print(f"P95 Duration: {perf_stats.p95_duration:.2f}s")
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# Identifier les bottlenecks
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bottlenecks = analytics.performance_analyzer.identify_bottlenecks(
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workflow_id="my_workflow",
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start_time=start_time,
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end_time=end_time
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)
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for bottleneck in bottlenecks:
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print(f"Bottleneck: {bottleneck.node_id} - {bottleneck.avg_duration:.2f}s")
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```
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### 4. Détecter les Anomalies
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```python
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# Détecter les anomalies
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anomalies = analytics.anomaly_detector.detect_anomalies(
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workflow_id="my_workflow",
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start_time=start_time,
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end_time=end_time
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)
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for anomaly in anomalies:
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print(f"Anomaly: {anomaly.metric_name}")
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print(f" Severity: {anomaly.severity:.2f}")
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print(f" Expected: {anomaly.expected_value:.2f}")
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print(f" Actual: {anomaly.actual_value:.2f}")
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```
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### 5. Générer des Insights
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```python
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# Générer des insights automatiques
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insights = analytics.insight_generator.generate_insights(
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start_time=start_time,
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end_time=end_time
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)
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for insight in insights:
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print(f"\n{insight.title}")
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print(f" {insight.description}")
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print(f" Priority: {insight.priority_score:.2f}")
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print(f" Impact: {insight.expected_impact}")
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```
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### 6. Calculer les Taux de Succès
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```python
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# Calculer le taux de succès
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success_stats = analytics.success_rate_calculator.calculate_success_rate(
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workflow_id="my_workflow",
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time_window_hours=24
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)
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print(f"Success Rate: {success_stats.success_rate:.1f}%")
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print(f"Reliability Score: {success_stats.reliability_score:.1f}")
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print(f"Total Executions: {success_stats.total_executions}")
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# Catégoriser les échecs
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for category, count in success_stats.failure_categories.items():
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print(f" {category}: {count}")
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# Classement de fiabilité
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rankings = analytics.success_rate_calculator.rank_workflows_by_reliability(
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time_window_hours=168 # 1 semaine
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)
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for ranking in rankings[:5]:
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print(f"{ranking.rank}. {ranking.workflow_id}")
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print(f" Reliability: {ranking.reliability_score:.1f}")
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print(f" Success Rate: {ranking.success_rate:.1f}%")
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```
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### 7. Tracking en Temps Réel
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```python
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# Démarrer le tracking d'une exécution
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analytics.realtime_analytics.track_execution(
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execution_id="live_exec",
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workflow_id="my_workflow",
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total_steps=10
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)
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# Mettre à jour la progression
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analytics.realtime_analytics.update_progress(
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execution_id="live_exec",
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current_step=5,
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current_node_id="step_5"
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)
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# Obtenir les métriques live
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live_metrics = analytics.realtime_analytics.get_live_metrics("live_exec")
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print(f"Progress: {live_metrics['progress_percent']:.1f}%")
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print(f"ETA: {live_metrics['estimated_completion']}")
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# Compléter l'exécution
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analytics.realtime_analytics.complete_execution(
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execution_id="live_exec",
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status="success"
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)
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```
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### 8. Générer des Rapports
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```python
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from core.analytics.reporting.report_generator import ReportConfig
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# Configurer le rapport
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config = ReportConfig(
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title="Weekly Performance Report",
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metric_types=['execution', 'step'],
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start_time=start_time,
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end_time=end_time,
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workflow_ids=['my_workflow'],
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include_charts=True,
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include_insights=True,
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format='html' # ou 'json', 'csv', 'pdf'
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)
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# Générer et exporter
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report_data = analytics.report_generator.generate_report(config)
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html_path = analytics.report_generator.export_html(report_data)
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print(f"Report generated: {html_path}")
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# Programmer un rapport récurrent
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from core.analytics.reporting.report_generator import ScheduledReport
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scheduled = ScheduledReport(
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report_id="weekly_report",
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config=config,
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schedule_cron="0 9 * * 1", # Tous les lundis à 9h
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delivery_method="email",
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delivery_config={"to": "admin@example.com"}
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)
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analytics.report_generator.schedule_report(scheduled)
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```
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### 9. Créer des Dashboards
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```python
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# Créer un dashboard depuis un template
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dashboard = analytics.dashboard_manager.create_dashboard(
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name="Performance Dashboard",
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description="Monitor workflow performance",
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owner="admin",
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template_id="performance" # Utilise le template prédéfini
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)
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# Ajouter un widget personnalisé
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widget = analytics.dashboard_manager.add_widget(
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dashboard_id=dashboard.dashboard_id,
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widget_type="chart",
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title="Success Rate Trend",
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config={
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'chart_type': 'line',
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'metric': 'success_rate',
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'time_range': '7d'
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},
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position={'x': 0, 'y': 0, 'width': 6, 'height': 4}
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)
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# Partager le dashboard
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analytics.dashboard_manager.share_dashboard(
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dashboard_id=dashboard.dashboard_id,
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username="user@example.com"
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)
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# Rendre public
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analytics.dashboard_manager.make_public(
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dashboard_id=dashboard.dashboard_id,
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is_public=True
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)
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```
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### 10. Utiliser l'API REST
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```python
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from flask import Flask
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app = Flask(__name__)
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# Enregistrer le blueprint analytics
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app.register_blueprint(analytics.api.get_blueprint())
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# Démarrer le serveur
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app.run(host='0.0.0.0', port=5000)
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```
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Endpoints disponibles:
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- `GET /api/analytics/metrics` - Récupérer les métriques
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- `GET /api/analytics/performance` - Analyse de performance
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- `GET /api/analytics/anomalies` - Anomalies détectées
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- `GET /api/analytics/insights` - Insights générés
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- `GET /api/analytics/success-rate` - Taux de succès
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- `POST /api/analytics/reports` - Générer un rapport
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- `GET /api/analytics/dashboards` - Lister les dashboards
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### 11. Gestion de la Rétention
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```python
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from core.analytics.storage.archive_storage import RetentionPolicy
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# Ajouter une politique de rétention
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policy = RetentionPolicy(
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metric_type='execution',
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hot_retention_days=30, # Garder 30 jours en base active
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archive_retention_days=365, # Garder 1 an en archive
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compression_enabled=True
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)
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analytics.retention_engine.add_policy(policy)
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# Appliquer les politiques
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results = analytics.apply_retention_policies(dry_run=False)
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print(f"Archived: {results['archived']}")
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print(f"Deleted: {results['deleted']}")
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```
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## Demo Script
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Exécutez le script de démonstration pour voir toutes les fonctionnalités :
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```bash
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python demo_analytics.py
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```
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## Intégration avec ExecutionLoop
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Pour intégrer automatiquement avec vos workflows :
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```python
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from core.execution.execution_loop import ExecutionLoop
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from core.analytics.analytics_system import get_analytics_system
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# Dans votre ExecutionLoop
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analytics = get_analytics_system()
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# Avant l'exécution
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execution_id = str(uuid.uuid4())
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analytics.realtime_analytics.track_execution(
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execution_id=execution_id,
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workflow_id=workflow.workflow_id,
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total_steps=len(workflow.nodes)
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)
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# Après chaque step
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analytics.realtime_analytics.update_progress(
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execution_id=execution_id,
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current_step=current_step,
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current_node_id=node.node_id
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)
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# Après l'exécution
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from core.analytics.collection.metrics_collector import ExecutionMetrics
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execution_metrics = ExecutionMetrics(
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execution_id=execution_id,
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workflow_id=workflow.workflow_id,
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started_at=start_time,
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completed_at=end_time,
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duration=(end_time - start_time).total_seconds(),
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status="success" if success else "failed",
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error_message=error_msg if not success else None
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)
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analytics.metrics_collector.record_execution(execution_metrics)
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```
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## Statistiques Système
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```python
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# Obtenir les statistiques du système
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stats = analytics.get_system_stats()
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print(f"Metrics Count: {stats['storage']['metrics_count']}")
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print(f"Database Size: {stats['storage']['database_size']} bytes")
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print(f"Archive Stats: {stats['archive']}")
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print(f"Dashboards: {stats['dashboards']['total']}")
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```
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## Arrêt Propre
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```python
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# Arrêter le système proprement
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analytics.shutdown()
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```
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## Bonnes Pratiques
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1. **Flush régulier**: Appelez `flush()` après avoir enregistré des métriques importantes
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2. **Monitoring des ressources**: Activez le monitoring pour suivre l'utilisation CPU/RAM/GPU
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3. **Rétention**: Configurez des politiques de rétention adaptées à vos besoins
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4. **Dashboards**: Utilisez les templates pour démarrer rapidement
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5. **Rapports**: Programmez des rapports récurrents pour un suivi régulier
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6. **Anomalies**: Surveillez les anomalies pour détecter les problèmes tôt
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7. **Insights**: Agissez sur les insights pour optimiser vos workflows
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## Troubleshooting
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### Problème: Métriques non enregistrées
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**Solution**: Appelez `analytics.metrics_collector.flush()` pour forcer la persistance
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### Problème: Base de données trop volumineuse
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**Solution**: Configurez et appliquez des politiques de rétention
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### Problème: Rapports PDF ne se génèrent pas
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**Solution**: Installez `reportlab`: `pip install reportlab`
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### Problème: Monitoring des ressources ne démarre pas
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**Solution**: Vérifiez que `psutil` est installé: `pip install psutil`
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## Support
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Pour plus d'informations, consultez:
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- Documentation complète: `docs/analytics/`
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- Exemples: `examples/analytics/`
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- Tests: `tests/unit/test_analytics*.py`
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## Prochaines Étapes
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1. Intégrez avec vos workflows existants
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2. Créez des dashboards personnalisés
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3. Configurez des rapports automatiques
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4. Optimisez basé sur les insights
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5. Surveillez les anomalies en temps réel
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