#!/usr/bin/env python3 """ Watcher Database Test Data Population Script Füllt die SQLite-Datenbank mit realistischen Testdaten für lokale Entwicklung """ import sqlite3 import random from datetime import datetime, timedelta import os # Datenbankpfad DB_PATH = "Watcher\persistence\watcher.db" # Prüfe ob Datenbank existiert if not os.path.exists(DB_PATH): print(f"❌ Datenbank nicht gefunden: {DB_PATH}") print("Bitte stelle sicher, dass die Anwendung einmal gestartet wurde, um die Datenbank zu erstellen.") exit(1) print(f"📊 Verbinde mit Datenbank: {DB_PATH}") conn = sqlite3.connect(DB_PATH) cursor = conn.cursor() # Lösche vorhandene Daten (optional - auskommentieren wenn bestehende Daten behalten werden sollen) print("\n🗑️ Lösche vorhandene Testdaten...") cursor.execute("DELETE FROM ContainerMetrics") cursor.execute("DELETE FROM Metrics") cursor.execute("DELETE FROM LogEvents") cursor.execute("DELETE FROM Containers") cursor.execute("DELETE FROM Images") cursor.execute("DELETE FROM Tags") cursor.execute("DELETE FROM Servers") # Users werden NICHT gelöscht conn.commit() print("✅ Alte Daten gelöscht (Users bleiben erhalten)\n") # ============================================================================ # 2. SERVERS - Test-Server erstellen # ============================================================================ print("\n🖥️ Erstelle Server...") servers_data = [ { "name": "Production-Web-01", "ip": "192.168.1.10", "type": "Ubuntu 22.04", "description": "Haupt-Webserver für Production", "cpu_type": "Intel Core i7-12700K", "cpu_cores": 12, "gpu_type": None, "ram_size": 34359738368, # 32 GB in Bytes "disk_space": "512 GB NVMe SSD", "is_online": True }, { "name": "Dev-Server", "ip": "192.168.1.20", "type": "Debian 12", "description": "Entwicklungs- und Testserver", "cpu_type": "AMD Ryzen 9 5900X", "cpu_cores": 12, "gpu_type": None, "ram_size": 68719476736, # 64 GB in Bytes "disk_space": "1 TB NVMe SSD", "is_online": True }, { "name": "GPU-Server-ML", "ip": "192.168.1.30", "type": "Ubuntu 22.04 LTS", "description": "Machine Learning Training Server", "cpu_type": "AMD Ryzen Threadripper 3970X", "cpu_cores": 32, "gpu_type": "NVIDIA RTX 4090", "ram_size": 137438953472, # 128 GB in Bytes "disk_space": "2 TB NVMe SSD", "is_online": True }, { "name": "Backup-Server", "ip": "192.168.1.40", "type": "Ubuntu 20.04", "description": "Backup und Storage Server", "cpu_type": "Intel Xeon E5-2680 v4", "cpu_cores": 14, "gpu_type": None, "ram_size": 17179869184, # 16 GB in Bytes "disk_space": "10 TB HDD RAID5", "is_online": False }, { "name": "Docker-Host-01", "ip": "192.168.1.50", "type": "Ubuntu 22.04", "description": "Docker Container Host", "cpu_type": "Intel Xeon Gold 6248R", "cpu_cores": 24, "gpu_type": None, "ram_size": 68719476736, # 64 GB in Bytes "disk_space": "2 TB NVMe SSD", "is_online": True } ] server_ids = [] for server in servers_data: last_seen = datetime.utcnow() - timedelta(minutes=random.randint(0, 30)) if server["is_online"] else datetime.utcnow() - timedelta(hours=random.randint(2, 48)) cursor.execute(""" INSERT INTO Servers ( Name, IPAddress, Type, Description, CpuType, CpuCores, GpuType, RamSize, DiskSpace, CPU_Load_Warning, CPU_Load_Critical, CPU_Temp_Warning, CPU_Temp_Critical, RAM_Load_Warning, RAM_Load_Critical, GPU_Load_Warning, GPU_Load_Critical, GPU_Temp_Warning, GPU_Temp_Critical, Disk_Usage_Warning, Disk_Usage_Critical, DISK_Temp_Warning, DISK_Temp_Critical, CreatedAt, IsOnline, LastSeen, IsVerified ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( server["name"], server["ip"], server["type"], server["description"], server["cpu_type"], server["cpu_cores"], server["gpu_type"], server["ram_size"], server["disk_space"], 75.0, 90.0, # CPU Load 80.0, 90.0, # CPU Temp 85.0, 95.0, # RAM Load 75.0, 90.0, # GPU Load 70.0, 80.0, # GPU Temp 75.0, 90.0, # Disk Usage 34.0, 36.0, # Disk Temp datetime.utcnow() - timedelta(days=random.randint(30, 365)), server["is_online"], last_seen, True )) server_ids.append(cursor.lastrowid) print(f" ✓ Server '{server['name']}' erstellt (ID: {cursor.lastrowid})") conn.commit() # ============================================================================ # 3. METRICS - Server-Metriken erstellen (letzte 48 Stunden) # ============================================================================ print("\n📈 Erstelle Server-Metriken (letzte 48 Stunden)...") metrics_count = 0 for server_id in server_ids: # Finde den Server cursor.execute("SELECT IsOnline FROM Servers WHERE Id = ?", (server_id,)) is_online = cursor.fetchone()[0] if not is_online: continue # Keine Metriken für offline Server # Erstelle Metriken für die letzten 48 Stunden (alle 5 Minuten) start_time = datetime.utcnow() - timedelta(hours=48) current_time = start_time # Basis-Werte für realistische Schwankungen base_cpu = random.uniform(20, 40) base_ram = random.uniform(40, 60) base_gpu = random.uniform(10, 30) if server_id == server_ids[2] else 0 # Nur GPU-Server while current_time <= datetime.utcnow(): # Realistische Schwankungen cpu_load = max(0, min(100, base_cpu + random.gauss(0, 15))) cpu_temp = 30 + (cpu_load * 0.5) + random.gauss(0, 3) ram_load = max(0, min(100, base_ram + random.gauss(0, 10))) gpu_load = max(0, min(100, base_gpu + random.gauss(0, 20))) if base_gpu > 0 else 0 gpu_temp = 25 + (gpu_load * 0.6) + random.gauss(0, 3) if gpu_load > 0 else 0 gpu_vram_usage = gpu_load * 0.8 if gpu_load > 0 else 0 disk_usage = random.uniform(40, 75) disk_temp = random.uniform(28, 35) net_in = random.uniform(1000000, 10000000) # 1-10 Mbps in Bits net_out = random.uniform(500000, 5000000) # 0.5-5 Mbps in Bits cursor.execute(""" INSERT INTO Metrics ( ServerId, Timestamp, CPU_Load, CPU_Temp, GPU_Load, GPU_Temp, GPU_Vram_Size, GPU_Vram_Usage, RAM_Size, RAM_Load, DISK_Size, DISK_Usage, DISK_Temp, NET_In, NET_Out ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( server_id, current_time, cpu_load, cpu_temp, gpu_load, gpu_temp, 24.0, gpu_vram_usage, # 24 GB VRAM 64.0, ram_load, 512.0, disk_usage, disk_temp, net_in, net_out )) metrics_count += 1 current_time += timedelta(minutes=5) print(f" ✓ Server {server_id}: {metrics_count} Metriken erstellt") conn.commit() print(f"✅ Insgesamt {metrics_count} Metriken erstellt") # ============================================================================ # 4. IMAGES - Docker Images # ============================================================================ print("\n🐳 Erstelle Docker Images...") images_data = [ ("nginx", "latest"), ("nginx", "alpine"), ("postgres", "15"), ("postgres", "14-alpine"), ("redis", "7-alpine"), ("node", "18-alpine"), ("python", "3.11-slim"), ("mysql", "8.0"), ("traefik", "v2.10"), ("portainer", "latest") ] image_ids = {} for name, tag in images_data: cursor.execute(""" INSERT INTO Images (Name, Tag) VALUES (?, ?) """, (name, tag)) image_ids[f"{name}:{tag}"] = cursor.lastrowid print(f" ✓ Image '{name}:{tag}' erstellt") conn.commit() # ============================================================================ # 5. CONTAINERS - Docker Container # ============================================================================ print("\n📦 Erstelle Docker Container...") # Nur für Server, die online sind online_server_ids = [sid for sid in server_ids[:2]] # Erste 2 Server haben Container containers_data = [ # Production-Web-01 ("nginx-web", "abc123def456", "nginx:latest", online_server_ids[0], True), ("postgres-db", "def456ghi789", "postgres:15", online_server_ids[0], True), ("redis-cache", "ghi789jkl012", "redis:7-alpine", online_server_ids[0], True), ("traefik-proxy", "jkl012mno345", "traefik:v2.10", online_server_ids[0], True), # Dev-Server ("dev-nginx", "mno345pqr678", "nginx:alpine", online_server_ids[1], True), ("dev-postgres", "pqr678stu901", "postgres:14-alpine", online_server_ids[1], False), ("dev-redis", "stu901vwx234", "redis:7-alpine", online_server_ids[1], True), ("test-app", "vwx234yz567", "node:18-alpine", online_server_ids[1], True), ("portainer", "yz567abc890", "portainer:latest", online_server_ids[1], True), ] container_ids = [] for name, container_id, image, server_id, is_running in containers_data: cursor.execute(""" INSERT INTO Containers (Name, ContainerId, Image, ServerId, IsRunning) VALUES (?, ?, ?, ?, ?) """, (name, container_id, image, server_id, is_running)) container_ids.append(cursor.lastrowid) status = "🟢 Running" if is_running else "🔴 Stopped" print(f" ✓ Container '{name}' erstellt - {status}") conn.commit() # ============================================================================ # 6. CONTAINER METRICS - Container-Metriken (letzte 24 Stunden) # ============================================================================ print("\n📊 Erstelle Container-Metriken (letzte 24 Stunden)...") container_metrics_count = 0 for container_id in container_ids: # Prüfe ob Container läuft cursor.execute("SELECT IsRunning FROM Containers WHERE Id = ?", (container_id,)) is_running = cursor.fetchone()[0] if not is_running: continue # Keine Metriken für gestoppte Container # Erstelle Metriken für die letzten 24 Stunden (alle 5 Minuten) start_time = datetime.utcnow() - timedelta(hours=24) current_time = start_time # Basis-Werte für Container (meist niedriger als Host) base_cpu = random.uniform(5, 15) base_ram = random.uniform(10, 30) while current_time <= datetime.utcnow(): cpu_load = max(0, min(100, base_cpu + random.gauss(0, 8))) cpu_temp = 30 + (cpu_load * 0.5) + random.gauss(0, 2) ram_load = max(0, min(100, base_ram + random.gauss(0, 5))) ram_size = random.uniform(0.5, 4.0) # Container nutzen weniger RAM cursor.execute(""" INSERT INTO ContainerMetrics ( ContainerId, Timestamp, CPU_Load, CPU_Temp, RAM_Size, RAM_Load ) VALUES (?, ?, ?, ?, ?, ?) """, ( container_id, current_time, cpu_load, cpu_temp, ram_size, ram_load )) container_metrics_count += 1 current_time += timedelta(minutes=5) conn.commit() print(f"✅ Insgesamt {container_metrics_count} Container-Metriken erstellt") # ============================================================================ # 7. LOG EVENTS - Log-Einträge erstellen # ============================================================================ print("\n📝 Erstelle Log Events...") log_messages = [ ("Info", "Server erfolgreich gestartet", None, None), ("Info", "Backup abgeschlossen", server_ids[3], None), ("Warning", "CPU-Auslastung über 80%", server_ids[0], None), ("Info", "Container gestartet", server_ids[0], container_ids[0]), ("Error", "Datenbank-Verbindung fehlgeschlagen", server_ids[1], container_ids[5]), ("Warning", "Speicherplatz unter 25%", server_ids[1], None), ("Info", "Update installiert", server_ids[2], None), ("Info", "Container neu gestartet", server_ids[0], container_ids[2]), ("Warning", "GPU-Temperatur über 75°C", server_ids[2], None), ("Info", "Netzwerk-Check erfolgreich", server_ids[0], None), ] for level, message, server_id, container_id in log_messages: timestamp = datetime.utcnow() - timedelta(hours=random.randint(0, 48)) cursor.execute(""" INSERT INTO LogEvents (Timestamp, Message, Level, ServerId, ContainerId) VALUES (?, ?, ?, ?, ?) """, (timestamp, message, level, server_id, container_id)) print(f" ✓ Log: [{level}] {message}") conn.commit() # ============================================================================ # 8. TAGS - Tags für Server/Container # ============================================================================ print("\n🏷️ Erstelle Tags...") tags_data = ["production", "development", "backup", "docker", "monitoring", "critical"] for tag_name in tags_data: cursor.execute(""" INSERT INTO Tags (Name) VALUES (?) """, (tag_name,)) print(f" ✓ Tag '{tag_name}' erstellt") conn.commit() # ============================================================================ # Abschluss # ============================================================================ print("\n" + "="*60) print("✅ Testdaten erfolgreich erstellt!") print("="*60) # Statistiken ausgeben cursor.execute("SELECT COUNT(*) FROM Servers") server_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM Containers") container_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM Metrics") metrics_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM ContainerMetrics") container_metrics_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM LogEvents") log_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM Images") image_count = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM Tags") tag_count = cursor.fetchone()[0] print(f""" 📊 STATISTIK: 🖥️ Server: {server_count} 📦 Container: {container_count} 📈 Server-Metriken: {metrics_count} 📊 Container-Metriken: {container_metrics_count} 📝 Log Events: {log_count} 🐳 Images: {image_count} 🏷️ Tags: {tag_count} 💡 HINWEIS: - User-Tabelle wurde nicht verändert - Metriken wurden für die letzten 48 Stunden generiert - Server 'Backup-Server' ist offline (für Tests) - Container 'dev-postgres' ist gestoppt (für Tests) - Die Datenbank befindet sich unter: {DB_PATH} """) # Verbindung schließen conn.close() print("🔒 Datenbankverbindung geschlossen\n")