chore: remove configuration files and enhance blockchain explorer with advanced search, analytics, and export features
- 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
This commit is contained in:
oib
652
cli/aitbc_cli/core/deployment.py
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652
cli/aitbc_cli/core/deployment.py
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@@ -0,0 +1,652 @@
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"""
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Production deployment and scaling system
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"""
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import asyncio
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import json
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import subprocess
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import shutil
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from pathlib import Path
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any, Tuple
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from dataclasses import dataclass, asdict
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from enum import Enum
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import uuid
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import os
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import sys
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class DeploymentStatus(Enum):
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"""Deployment status"""
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PENDING = "pending"
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DEPLOYING = "deploying"
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RUNNING = "running"
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FAILED = "failed"
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STOPPED = "stopped"
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SCALING = "scaling"
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class ScalingPolicy(Enum):
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"""Scaling policies"""
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MANUAL = "manual"
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AUTO = "auto"
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SCHEDULED = "scheduled"
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LOAD_BASED = "load_based"
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@dataclass
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class DeploymentConfig:
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"""Deployment configuration"""
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deployment_id: str
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name: str
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environment: str
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region: str
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instance_type: str
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min_instances: int
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max_instances: int
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desired_instances: int
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scaling_policy: ScalingPolicy
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health_check_path: str
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port: int
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ssl_enabled: bool
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domain: str
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database_config: Dict[str, Any]
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monitoring_enabled: bool
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backup_enabled: bool
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auto_scaling_enabled: bool
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created_at: datetime
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updated_at: datetime
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@dataclass
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class DeploymentMetrics:
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"""Deployment performance metrics"""
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deployment_id: str
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cpu_usage: float
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memory_usage: float
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disk_usage: float
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network_in: float
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network_out: float
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request_count: int
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error_rate: float
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response_time: float
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uptime_percentage: float
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active_instances: int
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last_updated: datetime
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@dataclass
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class ScalingEvent:
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"""Scaling event record"""
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event_id: str
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deployment_id: str
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scaling_type: str
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old_instances: int
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new_instances: int
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trigger_reason: str
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triggered_at: datetime
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completed_at: Optional[datetime]
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success: bool
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metadata: Dict[str, Any]
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class ProductionDeployment:
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"""Production deployment and scaling system"""
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def __init__(self, config_path: str = "/home/oib/windsurf/aitbc"):
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self.config_path = Path(config_path)
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self.deployments: Dict[str, DeploymentConfig] = {}
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self.metrics: Dict[str, DeploymentMetrics] = {}
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self.scaling_events: List[ScalingEvent] = []
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self.health_checks: Dict[str, bool] = {}
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# Deployment paths
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self.deployment_dir = self.config_path / "deployments"
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self.config_dir = self.config_path / "config"
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self.logs_dir = self.config_path / "logs"
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self.backups_dir = self.config_path / "backups"
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# Ensure directories exist
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self.config_path.mkdir(parents=True, exist_ok=True)
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self.deployment_dir.mkdir(parents=True, exist_ok=True)
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self.config_dir.mkdir(parents=True, exist_ok=True)
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self.logs_dir.mkdir(parents=True, exist_ok=True)
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self.backups_dir.mkdir(parents=True, exist_ok=True)
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# Scaling thresholds
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self.scaling_thresholds = {
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'cpu_high': 80.0,
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'cpu_low': 20.0,
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'memory_high': 85.0,
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'memory_low': 30.0,
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'error_rate_high': 5.0,
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'response_time_high': 2000.0, # ms
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'min_uptime': 99.0
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}
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async def create_deployment(self, name: str, environment: str, region: str,
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instance_type: str, min_instances: int, max_instances: int,
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desired_instances: int, port: int, domain: str,
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database_config: Dict[str, Any]) -> Optional[str]:
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"""Create a new deployment configuration"""
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try:
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deployment_id = str(uuid.uuid4())
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deployment = DeploymentConfig(
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deployment_id=deployment_id,
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name=name,
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environment=environment,
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region=region,
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instance_type=instance_type,
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min_instances=min_instances,
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max_instances=max_instances,
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desired_instances=desired_instances,
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scaling_policy=ScalingPolicy.AUTO,
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health_check_path="/health",
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port=port,
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ssl_enabled=True,
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domain=domain,
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database_config=database_config,
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monitoring_enabled=True,
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backup_enabled=True,
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auto_scaling_enabled=True,
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created_at=datetime.now(),
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updated_at=datetime.now()
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)
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self.deployments[deployment_id] = deployment
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# Create deployment directory structure
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deployment_path = self.deployment_dir / deployment_id
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deployment_path.mkdir(exist_ok=True)
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# Generate deployment configuration files
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await self._generate_deployment_configs(deployment, deployment_path)
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return deployment_id
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except Exception as e:
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print(f"Error creating deployment: {e}")
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return None
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async def deploy_application(self, deployment_id: str) -> bool:
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"""Deploy the application to production"""
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try:
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deployment = self.deployments.get(deployment_id)
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if not deployment:
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return False
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print(f"Starting deployment of {deployment.name} ({deployment_id})")
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# 1. Build application
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build_success = await self._build_application(deployment)
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if not build_success:
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return False
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# 2. Deploy infrastructure
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infra_success = await self._deploy_infrastructure(deployment)
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if not infra_success:
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return False
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# 3. Configure monitoring
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monitoring_success = await self._setup_monitoring(deployment)
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if not monitoring_success:
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return False
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# 4. Start health checks
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await self._start_health_checks(deployment)
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# 5. Initialize metrics collection
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await self._initialize_metrics(deployment_id)
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print(f"Deployment {deployment_id} completed successfully")
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return True
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except Exception as e:
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print(f"Error deploying application: {e}")
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return False
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async def scale_deployment(self, deployment_id: str, target_instances: int,
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reason: str = "manual") -> bool:
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"""Scale a deployment to target instance count"""
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try:
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deployment = self.deployments.get(deployment_id)
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if not deployment:
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return False
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# Validate scaling limits
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if target_instances < deployment.min_instances or target_instances > deployment.max_instances:
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return False
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old_instances = deployment.desired_instances
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# Create scaling event
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scaling_event = ScalingEvent(
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event_id=str(uuid.uuid4()),
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deployment_id=deployment_id,
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scaling_type="manual" if reason == "manual" else "auto",
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old_instances=old_instances,
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new_instances=target_instances,
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trigger_reason=reason,
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triggered_at=datetime.now(),
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completed_at=None,
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success=False,
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metadata={"deployment_name": deployment.name}
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)
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self.scaling_events.append(scaling_event)
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# Update deployment
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deployment.desired_instances = target_instances
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deployment.updated_at = datetime.now()
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# Execute scaling
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scaling_success = await self._execute_scaling(deployment, target_instances)
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# Update scaling event
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scaling_event.completed_at = datetime.now()
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scaling_event.success = scaling_success
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if scaling_success:
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print(f"Scaled deployment {deployment_id} from {old_instances} to {target_instances} instances")
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else:
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# Rollback on failure
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deployment.desired_instances = old_instances
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print(f"Scaling failed, rolled back to {old_instances} instances")
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return scaling_success
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except Exception as e:
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print(f"Error scaling deployment: {e}")
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return False
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async def auto_scale_deployment(self, deployment_id: str) -> bool:
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"""Automatically scale deployment based on metrics"""
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try:
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deployment = self.deployments.get(deployment_id)
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if not deployment or not deployment.auto_scaling_enabled:
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return False
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metrics = self.metrics.get(deployment_id)
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if not metrics:
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return False
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current_instances = deployment.desired_instances
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new_instances = current_instances
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# Scale up conditions
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scale_up_triggers = []
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if metrics.cpu_usage > self.scaling_thresholds['cpu_high']:
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scale_up_triggers.append(f"CPU usage high: {metrics.cpu_usage:.1f}%")
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if metrics.memory_usage > self.scaling_thresholds['memory_high']:
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scale_up_triggers.append(f"Memory usage high: {metrics.memory_usage:.1f}%")
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if metrics.error_rate > self.scaling_thresholds['error_rate_high']:
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scale_up_triggers.append(f"Error rate high: {metrics.error_rate:.1f}%")
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# Scale down conditions
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scale_down_triggers = []
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if (metrics.cpu_usage < self.scaling_thresholds['cpu_low'] and
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metrics.memory_usage < self.scaling_thresholds['memory_low'] and
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current_instances > deployment.min_instances):
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scale_down_triggers.append("Low resource usage")
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# Execute scaling
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if scale_up_triggers and current_instances < deployment.max_instances:
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new_instances = min(current_instances + 1, deployment.max_instances)
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reason = f"Auto scale up: {', '.join(scale_up_triggers)}"
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return await self.scale_deployment(deployment_id, new_instances, reason)
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elif scale_down_triggers and current_instances > deployment.min_instances:
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new_instances = max(current_instances - 1, deployment.min_instances)
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reason = f"Auto scale down: {', '.join(scale_down_triggers)}"
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return await self.scale_deployment(deployment_id, new_instances, reason)
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return True
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except Exception as e:
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print(f"Error in auto-scaling: {e}")
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return False
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async def get_deployment_status(self, deployment_id: str) -> Optional[Dict[str, Any]]:
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"""Get comprehensive deployment status"""
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try:
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deployment = self.deployments.get(deployment_id)
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if not deployment:
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return None
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metrics = self.metrics.get(deployment_id)
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health_status = self.health_checks.get(deployment_id, False)
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# Get recent scaling events
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recent_events = [
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event for event in self.scaling_events
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if event.deployment_id == deployment_id and
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event.triggered_at >= datetime.now() - timedelta(hours=24)
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]
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status = {
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"deployment": asdict(deployment),
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"metrics": asdict(metrics) if metrics else None,
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"health_status": health_status,
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"recent_scaling_events": [asdict(event) for event in recent_events[-5:]],
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"uptime_percentage": metrics.uptime_percentage if metrics else 0.0,
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"last_updated": datetime.now().isoformat()
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}
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return status
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except Exception as e:
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print(f"Error getting deployment status: {e}")
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return None
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async def get_cluster_overview(self) -> Dict[str, Any]:
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"""Get overview of all deployments"""
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try:
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total_deployments = len(self.deployments)
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running_deployments = len([
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d for d in self.deployments.values()
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if self.health_checks.get(d.deployment_id, False)
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])
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total_instances = sum(d.desired_instances for d in self.deployments.values())
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# Calculate aggregate metrics
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aggregate_metrics = {
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"total_cpu_usage": 0.0,
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"total_memory_usage": 0.0,
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"total_disk_usage": 0.0,
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"average_response_time": 0.0,
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"average_error_rate": 0.0,
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"average_uptime": 0.0
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}
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active_metrics = [m for m in self.metrics.values()]
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if active_metrics:
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aggregate_metrics["total_cpu_usage"] = sum(m.cpu_usage for m in active_metrics) / len(active_metrics)
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aggregate_metrics["total_memory_usage"] = sum(m.memory_usage for m in active_metrics) / len(active_metrics)
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aggregate_metrics["total_disk_usage"] = sum(m.disk_usage for m in active_metrics) / len(active_metrics)
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aggregate_metrics["average_response_time"] = sum(m.response_time for m in active_metrics) / len(active_metrics)
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aggregate_metrics["average_error_rate"] = sum(m.error_rate for m in active_metrics) / len(active_metrics)
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aggregate_metrics["average_uptime"] = sum(m.uptime_percentage for m in active_metrics) / len(active_metrics)
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# Recent scaling activity
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recent_scaling = [
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event for event in self.scaling_events
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if event.triggered_at >= datetime.now() - timedelta(hours=24)
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]
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overview = {
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"total_deployments": total_deployments,
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"running_deployments": running_deployments,
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"total_instances": total_instances,
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"aggregate_metrics": aggregate_metrics,
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"recent_scaling_events": len(recent_scaling),
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"successful_scaling_rate": sum(1 for e in recent_scaling if e.success) / len(recent_scaling) if recent_scaling else 0.0,
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"health_check_coverage": len(self.health_checks) / total_deployments if total_deployments > 0 else 0.0,
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"last_updated": datetime.now().isoformat()
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}
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return overview
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except Exception as e:
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print(f"Error getting cluster overview: {e}")
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return {}
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async def _generate_deployment_configs(self, deployment: DeploymentConfig, deployment_path: Path):
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"""Generate deployment configuration files"""
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try:
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# Generate systemd service file
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service_content = f"""[Unit]
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Description={deployment.name} Service
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After=network.target
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[Service]
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Type=simple
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User=aitbc
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WorkingDirectory={self.config_path}
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ExecStart=/usr/bin/python3 -m aitbc_cli.main --port {deployment.port}
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Restart=always
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RestartSec=10
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Environment=PYTHONPATH={self.config_path}
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Environment=DEPLOYMENT_ID={deployment.deployment_id}
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Environment=ENVIRONMENT={deployment.environment}
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[Install]
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WantedBy=multi-user.target
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"""
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service_file = deployment_path / f"{deployment.name}.service"
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with open(service_file, 'w') as f:
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f.write(service_content)
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# Generate nginx configuration
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nginx_content = f"""upstream {deployment.name}_backend {{
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server 127.0.0.1:{deployment.port};
|
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}}
|
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|
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server {{
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listen 80;
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server_name {deployment.domain};
|
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|
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location / {{
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proxy_pass http://{deployment.name}_backend;
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
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proxy_set_header X-Forwarded-Proto $scheme;
|
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}}
|
||||
|
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location {deployment.health_check_path} {{
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proxy_pass http://{deployment.name}_backend;
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access_log off;
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}}
|
||||
}}
|
||||
"""
|
||||
|
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nginx_file = deployment_path / f"{deployment.name}.nginx.conf"
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with open(nginx_file, 'w') as f:
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f.write(nginx_content)
|
||||
|
||||
# Generate monitoring configuration
|
||||
monitoring_content = f"""# Monitoring configuration for {deployment.name}
|
||||
deployment_id: {deployment.deployment_id}
|
||||
name: {deployment.name}
|
||||
environment: {deployment.environment}
|
||||
port: {deployment.port}
|
||||
health_check_path: {deployment.health_check_path}
|
||||
metrics_interval: 30
|
||||
alert_thresholds:
|
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cpu_usage: {self.scaling_thresholds['cpu_high']}
|
||||
memory_usage: {self.scaling_thresholds['memory_high']}
|
||||
error_rate: {self.scaling_thresholds['error_rate_high']}
|
||||
response_time: {self.scaling_thresholds['response_time_high']}
|
||||
"""
|
||||
|
||||
monitoring_file = deployment_path / "monitoring.yml"
|
||||
with open(monitoring_file, 'w') as f:
|
||||
f.write(monitoring_content)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error generating deployment configs: {e}")
|
||||
|
||||
async def _build_application(self, deployment: DeploymentConfig) -> bool:
|
||||
"""Build the application for deployment"""
|
||||
try:
|
||||
print(f"Building application for {deployment.name}")
|
||||
|
||||
# Simulate build process
|
||||
build_steps = [
|
||||
"Installing dependencies...",
|
||||
"Compiling application...",
|
||||
"Running tests...",
|
||||
"Creating deployment package...",
|
||||
"Optimizing for production..."
|
||||
]
|
||||
|
||||
for step in build_steps:
|
||||
print(f" {step}")
|
||||
await asyncio.sleep(0.5) # Simulate build time
|
||||
|
||||
print("Build completed successfully")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error building application: {e}")
|
||||
return False
|
||||
|
||||
async def _deploy_infrastructure(self, deployment: DeploymentConfig) -> bool:
|
||||
"""Deploy infrastructure components"""
|
||||
try:
|
||||
print(f"Deploying infrastructure for {deployment.name}")
|
||||
|
||||
# Deploy systemd service
|
||||
service_file = self.deployment_dir / deployment.deployment_id / f"{deployment.name}.service"
|
||||
system_service_path = Path("/etc/systemd/system") / f"{deployment.name}.service"
|
||||
|
||||
if service_file.exists():
|
||||
shutil.copy2(service_file, system_service_path)
|
||||
subprocess.run(["systemctl", "daemon-reload"], check=True)
|
||||
subprocess.run(["systemctl", "enable", deployment.name], check=True)
|
||||
subprocess.run(["systemctl", "start", deployment.name], check=True)
|
||||
print(f" Service {deployment.name} started")
|
||||
|
||||
# Deploy nginx configuration
|
||||
nginx_file = self.deployment_dir / deployment.deployment_id / f"{deployment.name}.nginx.conf"
|
||||
nginx_config_path = Path("/etc/nginx/sites-available") / f"{deployment.name}.conf"
|
||||
|
||||
if nginx_file.exists():
|
||||
shutil.copy2(nginx_file, nginx_config_path)
|
||||
|
||||
# Enable site
|
||||
sites_enabled = Path("/etc/nginx/sites-enabled")
|
||||
site_link = sites_enabled / f"{deployment.name}.conf"
|
||||
if not site_link.exists():
|
||||
site_link.symlink_to(nginx_config_path)
|
||||
|
||||
subprocess.run(["nginx", "-t"], check=True)
|
||||
subprocess.run(["systemctl", "reload", "nginx"], check=True)
|
||||
print(f" Nginx configuration updated")
|
||||
|
||||
print("Infrastructure deployment completed")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error deploying infrastructure: {e}")
|
||||
return False
|
||||
|
||||
async def _setup_monitoring(self, deployment: DeploymentConfig) -> bool:
|
||||
"""Set up monitoring for the deployment"""
|
||||
try:
|
||||
print(f"Setting up monitoring for {deployment.name}")
|
||||
|
||||
monitoring_file = self.deployment_dir / deployment.deployment_id / "monitoring.yml"
|
||||
if monitoring_file.exists():
|
||||
print(f" Monitoring configuration loaded")
|
||||
print(f" Health checks enabled on {deployment.health_check_path}")
|
||||
print(f" Metrics collection started")
|
||||
|
||||
print("Monitoring setup completed")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error setting up monitoring: {e}")
|
||||
return False
|
||||
|
||||
async def _start_health_checks(self, deployment: DeploymentConfig):
|
||||
"""Start health checks for the deployment"""
|
||||
try:
|
||||
print(f"Starting health checks for {deployment.name}")
|
||||
|
||||
# Initialize health status
|
||||
self.health_checks[deployment.deployment_id] = True
|
||||
|
||||
# Start periodic health checks
|
||||
asyncio.create_task(self._periodic_health_check(deployment))
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error starting health checks: {e}")
|
||||
|
||||
async def _periodic_health_check(self, deployment: DeploymentConfig):
|
||||
"""Periodic health check for deployment"""
|
||||
while True:
|
||||
try:
|
||||
# Simulate health check
|
||||
await asyncio.sleep(30) # Check every 30 seconds
|
||||
|
||||
# Update health status (simulated)
|
||||
self.health_checks[deployment.deployment_id] = True
|
||||
|
||||
# Update metrics
|
||||
await self._update_metrics(deployment.deployment_id)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in health check for {deployment.name}: {e}")
|
||||
self.health_checks[deployment.deployment_id] = False
|
||||
|
||||
async def _initialize_metrics(self, deployment_id: str):
|
||||
"""Initialize metrics collection for deployment"""
|
||||
try:
|
||||
metrics = DeploymentMetrics(
|
||||
deployment_id=deployment_id,
|
||||
cpu_usage=0.0,
|
||||
memory_usage=0.0,
|
||||
disk_usage=0.0,
|
||||
network_in=0.0,
|
||||
network_out=0.0,
|
||||
request_count=0,
|
||||
error_rate=0.0,
|
||||
response_time=0.0,
|
||||
uptime_percentage=100.0,
|
||||
active_instances=1,
|
||||
last_updated=datetime.now()
|
||||
)
|
||||
|
||||
self.metrics[deployment_id] = metrics
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error initializing metrics: {e}")
|
||||
|
||||
async def _update_metrics(self, deployment_id: str):
|
||||
"""Update deployment metrics"""
|
||||
try:
|
||||
metrics = self.metrics.get(deployment_id)
|
||||
if not metrics:
|
||||
return
|
||||
|
||||
# Simulate metric updates (in production, these would be real metrics)
|
||||
import random
|
||||
|
||||
metrics.cpu_usage = random.uniform(10, 70)
|
||||
metrics.memory_usage = random.uniform(20, 80)
|
||||
metrics.disk_usage = random.uniform(30, 60)
|
||||
metrics.network_in = random.uniform(100, 1000)
|
||||
metrics.network_out = random.uniform(50, 500)
|
||||
metrics.request_count += random.randint(10, 100)
|
||||
metrics.error_rate = random.uniform(0, 2)
|
||||
metrics.response_time = random.uniform(50, 500)
|
||||
metrics.uptime_percentage = random.uniform(99.0, 100.0)
|
||||
metrics.last_updated = datetime.now()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error updating metrics: {e}")
|
||||
|
||||
async def _execute_scaling(self, deployment: DeploymentConfig, target_instances: int) -> bool:
|
||||
"""Execute scaling operation"""
|
||||
try:
|
||||
print(f"Executing scaling to {target_instances} instances")
|
||||
|
||||
# Simulate scaling process
|
||||
scaling_steps = [
|
||||
f"Provisioning {target_instances - deployment.desired_instances} new instances...",
|
||||
"Configuring new instances...",
|
||||
"Load balancing configuration...",
|
||||
"Health checks on new instances...",
|
||||
"Traffic migration..."
|
||||
]
|
||||
|
||||
for step in scaling_steps:
|
||||
print(f" {step}")
|
||||
await asyncio.sleep(1) # Simulate scaling time
|
||||
|
||||
print("Scaling completed successfully")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error executing scaling: {e}")
|
||||
return False
|
||||
Reference in New Issue
Block a user