Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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LIFE Runtime Monitor
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Samlar operativa mätvärden under burn-in
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"""
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import sqlite3
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import json
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import time
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from datetime import datetime, timedelta
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from pathlib import Path
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DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
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LOG_DIR = Path("/home/bernt/.openclaw/workspace/life-weather/logs")
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METRICS_FILE = Path("/home/bernt/.openclaw/workspace/life-weather/metrics.json")
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def collect_metrics():
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"""Samla mätvärden från databasen"""
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conn = sqlite3.connect(DB_PATH)
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c = conn.cursor()
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metrics = {
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"timestamp": datetime.now().isoformat(),
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"pipeline": {},
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"observations": {},
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"system": {}
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}
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# Pipeline-mätvärden
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c.execute("SELECT COUNT(*) FROM weather_observations")
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total_obs = c.fetchone()[0]
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c.execute("""
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SELECT COUNT(*) FROM weather_observations
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WHERE created_at > datetime('now', '-1 hour')
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""")
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obs_last_hour = c.fetchone()[0]
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# Dubbletter
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c.execute("""
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SELECT road_id, observation_type, timestamp, COUNT(*) as cnt
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FROM weather_observations
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GROUP BY road_id, observation_type, timestamp
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HAVING cnt > 1
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""")
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duplicates = len(c.fetchall())
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# Reality Latency
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c.execute("""
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SELECT MAX(created_at) FROM weather_observations
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""")
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last_obs = c.fetchone()[0]
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if last_obs:
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last_time = datetime.fromisoformat(last_obs)
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latency_minutes = (datetime.now() - last_time).total_seconds() / 60
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else:
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latency_minutes = None
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metrics["observations"] = {
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"total": total_obs,
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"last_hour": obs_last_hour,
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"duplicates": duplicates,
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"reality_latency_minutes": round(latency_minutes, 1) if latency_minutes else None
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}
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# System-mätvärden (från loggar)
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log_file = LOG_DIR / "scheduler.log"
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if log_file.exists():
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with open(log_file) as f:
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lines = f.readlines()
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# Räkna fel
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errors = [l for l in lines if "ERROR" in l]
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warnings = [l for l in lines if "WARNING" in l]
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metrics["pipeline"] = {
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"total_runs": len([l for l in lines if "WEATHER JOB STARTAR" in l]),
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"errors": len(errors),
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"warnings": len(warnings)
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}
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conn.close()
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# Spara mätvärden
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if METRICS_FILE.exists():
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with open(METRICS_FILE) as f:
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history = json.load(f)
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else:
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history = []
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history.append(metrics)
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# Behåll senaste 168 timmar (7 dagar)
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cutoff = datetime.now() - timedelta(hours=168)
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history = [h for h in history if datetime.fromisoformat(h["timestamp"]) > cutoff]
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with open(METRICS_FILE, 'w') as f:
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json.dump(history, f, indent=2)
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return metrics
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def print_status():
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"""Skriv ut aktuell status"""
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metrics = collect_metrics()
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print("=" * 60)
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print("LIFE RUNTIME STATUS")
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print("=" * 60)
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print(f"Tid: {metrics['timestamp']}")
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print()
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print("OBSERVATIONER:")
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print(f" Total: {metrics['observations']['total']}")
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print(f" Senaste timmen: {metrics['observations']['last_hour']}")
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print(f" Dubbletter: {metrics['observations']['duplicates']}")
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print(f" Reality Latency: {metrics['observations']['reality_latency_minutes']} min")
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print()
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print("PIPELINE:")
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print(f" Körningar: {metrics['pipeline'].get('total_runs', 0)}")
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print(f" Fel: {metrics['pipeline'].get('errors', 0)}")
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print(f" Varningar: {metrics['pipeline'].get('warnings', 0)}")
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print()
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# Beräkna success rate
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total = metrics['pipeline'].get('total_runs', 0)
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errors = metrics['pipeline'].get('errors', 0)
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if total > 0:
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success_rate = ((total - errors) / total) * 100
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print(f" Success Rate: {success_rate:.1f}%")
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print("=" * 60)
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if __name__ == "__main__":
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print_status()
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