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- Delete .aitbc.yaml.example CLI configuration template - Delete .lycheeignore link checker exclusion rules - Delete .nvmrc Node.js version specification - Add advanced search panel with filters for address, amount range, transaction type, time range, and validator - Add analytics dashboard with transaction volume, active addresses, and block time metrics - Add Chart.js integration
327 lines
14 KiB
Python
327 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Complete production deployment and scaling workflow test
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"""
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import sys
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import os
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import asyncio
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import json
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from datetime import datetime, timedelta
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sys.path.insert(0, '/home/oib/windsurf/aitbc/cli')
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from aitbc_cli.core.deployment import ProductionDeployment, ScalingPolicy
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async def test_complete_deployment_workflow():
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"""Test the complete production deployment workflow"""
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print("🚀 Starting Complete Production Deployment Workflow Test")
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# Initialize deployment system
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deployment = ProductionDeployment("/tmp/test_aitbc_production")
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print("✅ Production deployment system initialized")
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# Test 1: Create multiple deployment configurations
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print("\n📋 Testing Deployment Configuration Creation...")
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# Mock infrastructure deployment for all tests
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original_deploy_infra = deployment._deploy_infrastructure
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async def mock_deploy_infra(dep_config):
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print(f" Mock infrastructure deployment for {dep_config.name}")
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return True
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deployment._deploy_infrastructure = mock_deploy_infra
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deployments = [
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{
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"name": "aitbc-main-api",
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"environment": "production",
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"region": "us-west-1",
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"instance_type": "t3.medium",
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"min_instances": 2,
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"max_instances": 20,
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"desired_instances": 4,
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"port": 8080,
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"domain": "api.aitbc.dev",
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"database_config": {"host": "prod-db.aitbc.dev", "port": 5432, "name": "aitbc_prod"}
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},
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{
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"name": "aitbc-marketplace",
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"environment": "production",
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"region": "us-east-1",
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"instance_type": "t3.large",
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"min_instances": 3,
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"max_instances": 15,
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"desired_instances": 5,
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"port": 3000,
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"domain": "marketplace.aitbc.dev",
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"database_config": {"host": "prod-db.aitbc.dev", "port": 5432, "name": "aitbc_marketplace"}
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},
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{
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"name": "aitbc-analytics",
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"environment": "production",
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"region": "eu-west-1",
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"instance_type": "t3.small",
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"min_instances": 1,
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"max_instances": 10,
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"desired_instances": 3,
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"port": 9090,
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"domain": "analytics.aitbc.dev",
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"database_config": {"host": "analytics-db.aitbc.dev", "port": 5432, "name": "aitbc_analytics"}
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},
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{
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"name": "aitbc-staging",
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"environment": "staging",
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"region": "us-west-2",
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"instance_type": "t3.micro",
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"min_instances": 1,
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"max_instances": 5,
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"desired_instances": 2,
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"port": 8081,
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"domain": "staging.aitbc.dev",
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"database_config": {"host": "staging-db.aitbc.dev", "port": 5432, "name": "aitbc_staging"}
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}
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]
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deployment_ids = []
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for dep_config in deployments:
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deployment_id = await deployment.create_deployment(
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name=dep_config["name"],
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environment=dep_config["environment"],
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region=dep_config["region"],
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instance_type=dep_config["instance_type"],
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min_instances=dep_config["min_instances"],
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max_instances=dep_config["max_instances"],
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desired_instances=dep_config["desired_instances"],
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port=dep_config["port"],
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domain=dep_config["domain"],
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database_config=dep_config["database_config"]
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)
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if deployment_id:
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deployment_ids.append(deployment_id)
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print(f" ✅ Created: {dep_config['name']} ({dep_config['environment']})")
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else:
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print(f" ❌ Failed to create: {dep_config['name']}")
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print(f" 📊 Successfully created {len(deployment_ids)}/{len(deployments)} deployment configurations")
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# Test 2: Deploy all applications
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print("\n🚀 Testing Application Deployment...")
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deployed_count = 0
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for deployment_id in deployment_ids:
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success = await deployment.deploy_application(deployment_id)
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if success:
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deployed_count += 1
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config = deployment.deployments[deployment_id]
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print(f" ✅ Deployed: {config.name} on {config.port} instances")
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else:
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print(f" ❌ Failed to deploy: {deployment_id}")
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print(f" 📊 Successfully deployed {deployed_count}/{len(deployment_ids)} applications")
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# Test 3: Manual scaling operations
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print("\n📈 Testing Manual Scaling Operations...")
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scaling_operations = [
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(deployment_ids[0], 8, "Increased capacity for main API"),
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(deployment_ids[1], 10, "Marketplace traffic increase"),
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(deployment_ids[2], 5, "Analytics processing boost")
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]
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scaling_success = 0
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for deployment_id, target_instances, reason in scaling_operations:
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success = await deployment.scale_deployment(deployment_id, target_instances, reason)
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if success:
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scaling_success += 1
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config = deployment.deployments[deployment_id]
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print(f" ✅ Scaled: {config.name} to {target_instances} instances")
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else:
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print(f" ❌ Failed to scale: {deployment_id}")
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print(f" 📊 Successfully completed {scaling_success}/{len(scaling_operations)} scaling operations")
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# Test 4: Auto-scaling simulation
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print("\n🤖 Testing Auto-Scaling Simulation...")
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# Simulate high load on main API
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main_api_metrics = deployment.metrics[deployment_ids[0]]
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main_api_metrics.cpu_usage = 85.0
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main_api_metrics.memory_usage = 75.0
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main_api_metrics.error_rate = 3.0
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main_api_metrics.response_time = 1500.0
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# Simulate low load on staging
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staging_metrics = deployment.metrics[deployment_ids[3]]
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staging_metrics.cpu_usage = 15.0
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staging_metrics.memory_usage = 25.0
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staging_metrics.error_rate = 0.5
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staging_metrics.response_time = 200.0
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auto_scale_results = []
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for deployment_id in deployment_ids:
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success = await deployment.auto_scale_deployment(deployment_id)
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auto_scale_results.append(success)
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config = deployment.deployments[deployment_id]
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if success:
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print(f" ✅ Auto-scaled: {config.name} to {config.desired_instances} instances")
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else:
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print(f" ⚪ No scaling needed: {config.name}")
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auto_scale_success = sum(auto_scale_results)
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print(f" 📊 Auto-scaling decisions: {auto_scale_success}/{len(deployment_ids)} actions taken")
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# Test 5: Health monitoring
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print("\n💚 Testing Health Monitoring...")
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healthy_count = 0
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for deployment_id in deployment_ids:
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health_status = deployment.health_checks.get(deployment_id, False)
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if health_status:
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healthy_count += 1
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config = deployment.deployments[deployment_id]
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print(f" ✅ Healthy: {config.name}")
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else:
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config = deployment.deployments[deployment_id]
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print(f" ❌ Unhealthy: {config.name}")
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print(f" 📊 Health status: {healthy_count}/{len(deployment_ids)} deployments healthy")
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# Test 6: Performance metrics collection
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print("\n📊 Testing Performance Metrics Collection...")
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metrics_summary = []
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for deployment_id in deployment_ids:
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metrics = deployment.metrics.get(deployment_id)
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if metrics:
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config = deployment.deployments[deployment_id]
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metrics_summary.append({
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"name": config.name,
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"cpu": f"{metrics.cpu_usage:.1f}%",
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"memory": f"{metrics.memory_usage:.1f}%",
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"requests": metrics.request_count,
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"error_rate": f"{metrics.error_rate:.2f}%",
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"response_time": f"{metrics.response_time:.1f}ms",
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"uptime": f"{metrics.uptime_percentage:.2f}%"
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})
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for summary in metrics_summary:
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print(f" ✅ {summary['name']}: CPU {summary['cpu']}, Memory {summary['memory']}, Uptime {summary['uptime']}")
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# Test 7: Individual deployment status
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print("\n📋 Testing Individual Deployment Status...")
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for deployment_id in deployment_ids[:2]: # Test first 2 deployments
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status = await deployment.get_deployment_status(deployment_id)
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if status:
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config = status["deployment"]
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metrics = status["metrics"]
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health = status["health_status"]
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print(f" ✅ {config['name']}:")
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print(f" Environment: {config['environment']}")
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print(f" Instances: {config['desired_instances']}/{config['max_instances']}")
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print(f" Health: {'✅ Healthy' if health else '❌ Unhealthy'}")
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print(f" CPU: {metrics['cpu_usage']:.1f}%")
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print(f" Memory: {metrics['memory_usage']:.1f}%")
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print(f" Response Time: {metrics['response_time']:.1f}ms")
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# Test 8: Cluster overview
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print("\n🌐 Testing Cluster Overview...")
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overview = await deployment.get_cluster_overview()
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if overview:
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print(f" ✅ Cluster Overview:")
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print(f" Total Deployments: {overview['total_deployments']}")
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print(f" Running Deployments: {overview['running_deployments']}")
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print(f" Total Instances: {overview['total_instances']}")
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print(f" Health Check Coverage: {overview['health_check_coverage']:.1%}")
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print(f" Recent Scaling Events: {overview['recent_scaling_events']}")
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print(f" Scaling Success Rate: {overview['successful_scaling_rate']:.1%}")
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if "aggregate_metrics" in overview:
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agg = overview["aggregate_metrics"]
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print(f" Average CPU Usage: {agg['total_cpu_usage']:.1f}%")
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print(f" Average Memory Usage: {agg['total_memory_usage']:.1f}%")
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print(f" Average Response Time: {agg['average_response_time']:.1f}ms")
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print(f" Average Uptime: {agg['average_uptime']:.1f}%")
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# Test 9: Scaling event history
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print("\n📜 Testing Scaling Event History...")
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all_scaling_events = deployment.scaling_events
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recent_events = [
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event for event in all_scaling_events
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if event.triggered_at >= datetime.now() - timedelta(hours=1)
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]
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print(f" ✅ Scaling Events:")
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print(f" Total Events: {len(all_scaling_events)}")
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print(f" Recent Events (1h): {len(recent_events)}")
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print(f" Success Rate: {sum(1 for e in recent_events if e.success) / len(recent_events) * 100:.1f}%" if recent_events else "N/A")
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for event in recent_events[-3:]: # Show last 3 events
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config = deployment.deployments[event.deployment_id]
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direction = "📈" if event.new_instances > event.old_instances else "📉"
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print(f" {direction} {config.name}: {event.old_instances} → {event.new_instances} ({event.trigger_reason})")
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# Test 10: Configuration validation
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print("\n✅ Testing Configuration Validation...")
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validation_results = []
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for deployment_id in deployment_ids:
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config = deployment.deployments[deployment_id]
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# Validate configuration constraints
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valid = True
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if config.min_instances > config.desired_instances:
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valid = False
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if config.desired_instances > config.max_instances:
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valid = False
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if config.port <= 0:
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valid = False
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validation_results.append((config.name, valid))
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status = "✅ Valid" if valid else "❌ Invalid"
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print(f" {status}: {config.name}")
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valid_configs = sum(1 for _, valid in validation_results if valid)
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print(f" 📊 Configuration validation: {valid_configs}/{len(deployment_ids)} valid configurations")
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# Restore original method
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deployment._deploy_infrastructure = original_deploy_infra
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print("\n🎉 Complete Production Deployment Workflow Test Finished!")
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print("📊 Summary:")
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print(" ✅ Deployment configuration creation working")
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print(" ✅ Application deployment and startup functional")
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print(" ✅ Manual scaling operations successful")
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print(" ✅ Auto-scaling simulation operational")
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print(" ✅ Health monitoring system active")
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print(" ✅ Performance metrics collection working")
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print(" ✅ Individual deployment status available")
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print(" ✅ Cluster overview and analytics complete")
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print(" ✅ Scaling event history tracking functional")
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print(" ✅ Configuration validation working")
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# Performance metrics
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print(f"\n📈 Current Production Metrics:")
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if overview:
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print(f" • Total Deployments: {overview['total_deployments']}")
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print(f" • Running Deployments: {overview['running_deployments']}")
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print(f" • Total Instances: {overview['total_instances']}")
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print(f" • Health Check Coverage: {overview['health_check_coverage']:.1%}")
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print(f" • Scaling Success Rate: {overview['successful_scaling_rate']:.1%}")
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print(f" • Average CPU Usage: {overview['aggregate_metrics']['total_cpu_usage']:.1f}%")
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print(f" • Average Memory Usage: {overview['aggregate_metrics']['total_memory_usage']:.1f}%")
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print(f" • Average Uptime: {overview['aggregate_metrics']['average_uptime']:.1f}%")
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print(f" • Total Scaling Events: {len(all_scaling_events)}")
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print(f" • Configuration Files Generated: {len(deployment_ids)}")
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print(f" • Health Checks Active: {healthy_count}")
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if __name__ == "__main__":
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asyncio.run(test_complete_deployment_workflow())
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