3df0a9ed62
- Remove dev/scripts/check-file-organization.sh (obsolete organization checker) - Remove dev/scripts/community_onboarding.py (unused 559-line automation script) - Update gpu_miner_host.py log path from /home/oib/windsurf/aitbc to /opt/aitbc - Add service status and standardization badges to README.md
719 lines
27 KiB
Python
719 lines
27 KiB
Python
"""
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AITBC Production Monitoring and Analytics
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This module provides comprehensive monitoring and analytics capabilities
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for the AITBC production environment, including metrics collection,
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alerting, and dashboard generation.
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"""
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import asyncio
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import json
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import logging
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import time
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass, asdict
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from pathlib import Path
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import subprocess
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import psutil
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import aiohttp
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import statistics
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@dataclass
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class SystemMetrics:
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"""System performance metrics."""
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timestamp: float
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cpu_percent: float
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memory_percent: float
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disk_usage: float
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network_io: Dict[str, int]
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process_count: int
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load_average: List[float]
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@dataclass
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class ApplicationMetrics:
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"""Application performance metrics."""
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timestamp: float
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active_users: int
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api_requests: int
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response_time_avg: float
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response_time_p95: float
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error_rate: float
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throughput: float
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cache_hit_rate: float
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@dataclass
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class BlockchainMetrics:
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"""Blockchain network metrics."""
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timestamp: float
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block_height: int
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gas_price: float
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transaction_count: int
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network_hashrate: float
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peer_count: int
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sync_status: str
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@dataclass
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class SecurityMetrics:
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"""Security monitoring metrics."""
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timestamp: float
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failed_logins: int
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suspicious_ips: int
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security_events: int
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vulnerability_scans: int
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blocked_requests: int
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audit_log_entries: int
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class ProductionMonitor:
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"""Production monitoring system."""
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def __init__(self, config_path: str = "config/monitoring_config.json"):
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self.config = self._load_config(config_path)
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self.logger = self._setup_logging()
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self.metrics_history = {
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"system": [],
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"application": [],
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"blockchain": [],
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"security": []
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}
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self.alerts = []
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self.dashboards = {}
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def _load_config(self, config_path: str) -> Dict:
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"""Load monitoring configuration."""
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default_config = {
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"collection_interval": 60, # seconds
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"retention_days": 30,
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"alert_thresholds": {
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"cpu_percent": 80,
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"memory_percent": 85,
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"disk_usage": 90,
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"error_rate": 5.0,
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"response_time_p95": 2000, # ms
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"failed_logins": 10,
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"security_events": 5
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},
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"endpoints": {
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"health": "https://api.aitbc.dev/health",
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"metrics": "https://api.aitbc.dev/metrics",
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"blockchain": "https://api.aitbc.dev/blockchain/stats",
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"security": "https://api.aitbc.dev/security/stats"
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},
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"notifications": {
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"slack_webhook": os.getenv("SLACK_WEBHOOK_URL"),
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"email_smtp": os.getenv("SMTP_SERVER"),
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"pagerduty_key": os.getenv("PAGERDUTY_KEY")
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}
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}
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config_file = Path(config_path)
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if config_file.exists():
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with open(config_file, 'r') as f:
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user_config = json.load(f)
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default_config.update(user_config)
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return default_config
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def _setup_logging(self) -> logging.Logger:
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"""Setup logging for monitoring system."""
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logger = logging.getLogger("production_monitor")
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logger.setLevel(logging.INFO)
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if not logger.handlers:
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handler = logging.StreamHandler()
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formatter = logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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return logger
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async def collect_system_metrics(self) -> SystemMetrics:
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"""Collect system performance metrics."""
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try:
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# CPU metrics
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cpu_percent = psutil.cpu_percent(interval=1)
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load_avg = list(psutil.getloadavg())
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# Memory metrics
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memory = psutil.virtual_memory()
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memory_percent = memory.percent
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# Disk metrics
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disk = psutil.disk_usage('/')
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disk_usage = (disk.used / disk.total) * 100
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# Network metrics
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network = psutil.net_io_counters()
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network_io = {
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"bytes_sent": network.bytes_sent,
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"bytes_recv": network.bytes_recv,
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"packets_sent": network.packets_sent,
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"packets_recv": network.packets_recv
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}
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# Process metrics
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process_count = len(psutil.pids())
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return SystemMetrics(
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timestamp=time.time(),
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cpu_percent=cpu_percent,
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memory_percent=memory_percent,
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disk_usage=disk_usage,
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network_io=network_io,
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process_count=process_count,
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load_average=load_avg
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)
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except Exception as e:
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self.logger.error(f"Error collecting system metrics: {e}")
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return None
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async def collect_application_metrics(self) -> ApplicationMetrics:
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"""Collect application performance metrics."""
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try:
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async with aiohttp.ClientSession() as session:
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# Get metrics from application
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async with session.get(self.config["endpoints"]["metrics"]) as response:
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if response.status == 200:
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data = await response.json()
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return ApplicationMetrics(
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timestamp=time.time(),
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active_users=data.get("active_users", 0),
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api_requests=data.get("api_requests", 0),
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response_time_avg=data.get("response_time_avg", 0),
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response_time_p95=data.get("response_time_p95", 0),
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error_rate=data.get("error_rate", 0),
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throughput=data.get("throughput", 0),
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cache_hit_rate=data.get("cache_hit_rate", 0)
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)
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# Fallback metrics if API is unavailable
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return ApplicationMetrics(
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timestamp=time.time(),
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active_users=0,
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api_requests=0,
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response_time_avg=0,
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response_time_p95=0,
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error_rate=0,
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throughput=0,
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cache_hit_rate=0
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)
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except Exception as e:
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self.logger.error(f"Error collecting application metrics: {e}")
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return None
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async def collect_blockchain_metrics(self) -> BlockchainMetrics:
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"""Collect blockchain network metrics."""
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(self.config["endpoints"]["blockchain"]) as response:
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if response.status == 200:
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data = await response.json()
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return BlockchainMetrics(
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timestamp=time.time(),
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block_height=data.get("block_height", 0),
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gas_price=data.get("gas_price", 0),
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transaction_count=data.get("transaction_count", 0),
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network_hashrate=data.get("network_hashrate", 0),
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peer_count=data.get("peer_count", 0),
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sync_status=data.get("sync_status", "unknown")
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)
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return BlockchainMetrics(
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timestamp=time.time(),
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block_height=0,
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gas_price=0,
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transaction_count=0,
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network_hashrate=0,
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peer_count=0,
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sync_status="unknown"
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)
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except Exception as e:
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self.logger.error(f"Error collecting blockchain metrics: {e}")
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return None
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async def collect_security_metrics(self) -> SecurityMetrics:
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"""Collect security monitoring metrics."""
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(self.config["endpoints"]["security"]) as response:
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if response.status == 200:
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data = await response.json()
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return SecurityMetrics(
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timestamp=time.time(),
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failed_logins=data.get("failed_logins", 0),
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suspicious_ips=data.get("suspicious_ips", 0),
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security_events=data.get("security_events", 0),
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vulnerability_scans=data.get("vulnerability_scans", 0),
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blocked_requests=data.get("blocked_requests", 0),
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audit_log_entries=data.get("audit_log_entries", 0)
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)
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return SecurityMetrics(
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timestamp=time.time(),
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failed_logins=0,
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suspicious_ips=0,
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security_events=0,
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vulnerability_scans=0,
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blocked_requests=0,
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audit_log_entries=0
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)
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except Exception as e:
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self.logger.error(f"Error collecting security metrics: {e}")
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return None
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async def collect_all_metrics(self) -> Dict[str, Any]:
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"""Collect all metrics."""
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tasks = [
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self.collect_system_metrics(),
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self.collect_application_metrics(),
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self.collect_blockchain_metrics(),
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self.collect_security_metrics()
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]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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return {
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"system": results[0] if not isinstance(results[0], Exception) else None,
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"application": results[1] if not isinstance(results[1], Exception) else None,
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"blockchain": results[2] if not isinstance(results[2], Exception) else None,
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"security": results[3] if not isinstance(results[3], Exception) else None
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}
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async def check_alerts(self, metrics: Dict[str, Any]) -> List[Dict]:
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"""Check metrics against alert thresholds."""
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alerts = []
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thresholds = self.config["alert_thresholds"]
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# System alerts
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if metrics["system"]:
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sys_metrics = metrics["system"]
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if sys_metrics.cpu_percent > thresholds["cpu_percent"]:
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alerts.append({
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"type": "system",
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"metric": "cpu_percent",
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"value": sys_metrics.cpu_percent,
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"threshold": thresholds["cpu_percent"],
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"severity": "warning" if sys_metrics.cpu_percent < 90 else "critical",
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"message": f"High CPU usage: {sys_metrics.cpu_percent:.1f}%"
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})
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if sys_metrics.memory_percent > thresholds["memory_percent"]:
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alerts.append({
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"type": "system",
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"metric": "memory_percent",
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"value": sys_metrics.memory_percent,
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"threshold": thresholds["memory_percent"],
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"severity": "warning" if sys_metrics.memory_percent < 95 else "critical",
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"message": f"High memory usage: {sys_metrics.memory_percent:.1f}%"
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})
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if sys_metrics.disk_usage > thresholds["disk_usage"]:
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alerts.append({
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"type": "system",
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"metric": "disk_usage",
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"value": sys_metrics.disk_usage,
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"threshold": thresholds["disk_usage"],
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"severity": "critical",
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"message": f"High disk usage: {sys_metrics.disk_usage:.1f}%"
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})
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# Application alerts
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if metrics["application"]:
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app_metrics = metrics["application"]
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if app_metrics.error_rate > thresholds["error_rate"]:
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alerts.append({
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"type": "application",
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"metric": "error_rate",
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"value": app_metrics.error_rate,
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"threshold": thresholds["error_rate"],
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"severity": "warning" if app_metrics.error_rate < 10 else "critical",
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"message": f"High error rate: {app_metrics.error_rate:.1f}%"
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})
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if app_metrics.response_time_p95 > thresholds["response_time_p95"]:
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alerts.append({
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"type": "application",
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"metric": "response_time_p95",
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"value": app_metrics.response_time_p95,
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"threshold": thresholds["response_time_p95"],
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"severity": "warning",
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"message": f"High response time: {app_metrics.response_time_p95:.0f}ms"
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})
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# Security alerts
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if metrics["security"]:
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sec_metrics = metrics["security"]
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if sec_metrics.failed_logins > thresholds["failed_logins"]:
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alerts.append({
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"type": "security",
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"metric": "failed_logins",
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"value": sec_metrics.failed_logins,
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"threshold": thresholds["failed_logins"],
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"severity": "warning",
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"message": f"High failed login count: {sec_metrics.failed_logins}"
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})
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if sec_metrics.security_events > thresholds["security_events"]:
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alerts.append({
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"type": "security",
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"metric": "security_events",
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"value": sec_metrics.security_events,
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"threshold": thresholds["security_events"],
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"severity": "critical",
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"message": f"High security events: {sec_metrics.security_events}"
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})
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return alerts
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async def send_alert(self, alert: Dict) -> bool:
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"""Send alert notification."""
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try:
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# Log alert
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self.logger.warning(f"ALERT: {alert['message']}")
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# Send to Slack
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if self.config["notifications"]["slack_webhook"]:
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await self._send_slack_alert(alert)
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# Send to PagerDuty for critical alerts
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if alert["severity"] == "critical" and self.config["notifications"]["pagerduty_key"]:
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await self._send_pagerduty_alert(alert)
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# Store alert
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alert["timestamp"] = time.time()
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self.alerts.append(alert)
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return True
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except Exception as e:
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self.logger.error(f"Error sending alert: {e}")
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return False
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async def _send_slack_alert(self, alert: Dict) -> bool:
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"""Send alert to Slack."""
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try:
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webhook_url = self.config["notifications"]["slack_webhook"]
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color = {
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"warning": "warning",
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"critical": "danger",
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"info": "good"
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}.get(alert["severity"], "warning")
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payload = {
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"text": f"AITBC Alert: {alert['message']}",
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"attachments": [{
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"color": color,
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"fields": [
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{"title": "Type", "value": alert["type"], "short": True},
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{"title": "Metric", "value": alert["metric"], "short": True},
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{"title": "Value", "value": str(alert["value"]), "short": True},
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{"title": "Threshold", "value": str(alert["threshold"]), "short": True},
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{"title": "Severity", "value": alert["severity"], "short": True}
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],
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"timestamp": int(time.time())
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}]
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}
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|
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async with aiohttp.ClientSession() as session:
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async with session.post(webhook_url, json=payload) as response:
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return response.status == 200
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|
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except Exception as e:
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self.logger.error(f"Error sending Slack alert: {e}")
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return False
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|
|
|
async def _send_pagerduty_alert(self, alert: Dict) -> bool:
|
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"""Send alert to PagerDuty."""
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try:
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api_key = self.config["notifications"]["pagerduty_key"]
|
|
|
|
payload = {
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"routing_key": api_key,
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"event_action": "trigger",
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"payload": {
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"summary": f"AITBC Alert: {alert['message']}",
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"source": "aitbc-monitor",
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"severity": alert["severity"],
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"timestamp": datetime.now().isoformat(),
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"custom_details": alert
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}
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}
|
|
|
|
async with aiohttp.ClientSession() as session:
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|
async with session.post(
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"https://events.pagerduty.com/v2/enqueue",
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json=payload
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) as response:
|
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return response.status == 202
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error sending PagerDuty alert: {e}")
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return False
|
|
|
|
async def generate_dashboard(self) -> Dict:
|
|
"""Generate monitoring dashboard data."""
|
|
try:
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|
# Get recent metrics (last hour)
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|
cutoff_time = time.time() - 3600
|
|
|
|
recent_metrics = {
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"system": [m for m in self.metrics_history["system"] if m.timestamp > cutoff_time],
|
|
"application": [m for m in self.metrics_history["application"] if m.timestamp > cutoff_time],
|
|
"blockchain": [m for m in self.metrics_history["blockchain"] if m.timestamp > cutoff_time],
|
|
"security": [m for m in self.metrics_history["security"] if m.timestamp > cutoff_time]
|
|
}
|
|
|
|
dashboard = {
|
|
"timestamp": time.time(),
|
|
"status": "healthy",
|
|
"alerts": self.alerts[-10:], # Last 10 alerts
|
|
"metrics": {
|
|
"current": await self.collect_all_metrics(),
|
|
"trends": self._calculate_trends(recent_metrics),
|
|
"summaries": self._calculate_summaries(recent_metrics)
|
|
}
|
|
}
|
|
|
|
# Determine overall status
|
|
critical_alerts = [a for a in self.alerts if a.get("severity") == "critical"]
|
|
if critical_alerts:
|
|
dashboard["status"] = "critical"
|
|
elif self.alerts:
|
|
dashboard["status"] = "warning"
|
|
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|
return dashboard
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error generating dashboard: {e}")
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|
return {"status": "error", "error": str(e)}
|
|
|
|
def _calculate_trends(self, recent_metrics: Dict) -> Dict:
|
|
"""Calculate metric trends."""
|
|
trends = {}
|
|
|
|
for metric_type, metrics in recent_metrics.items():
|
|
if not metrics:
|
|
continue
|
|
|
|
# Calculate trend for each numeric field
|
|
if metric_type == "system" and metrics:
|
|
trends["system"] = {
|
|
"cpu_trend": self._calculate_trend([m.cpu_percent for m in metrics]),
|
|
"memory_trend": self._calculate_trend([m.memory_percent for m in metrics]),
|
|
"disk_trend": self._calculate_trend([m.disk_usage for m in metrics])
|
|
}
|
|
|
|
elif metric_type == "application" and metrics:
|
|
trends["application"] = {
|
|
"response_time_trend": self._calculate_trend([m.response_time_avg for m in metrics]),
|
|
"error_rate_trend": self._calculate_trend([m.error_rate for m in metrics]),
|
|
"throughput_trend": self._calculate_trend([m.throughput for m in metrics])
|
|
}
|
|
|
|
return trends
|
|
|
|
def _calculate_trend(self, values: List[float]) -> str:
|
|
"""Calculate trend direction."""
|
|
if len(values) < 2:
|
|
return "stable"
|
|
|
|
# Simple linear regression to determine trend
|
|
n = len(values)
|
|
x = list(range(n))
|
|
|
|
x_mean = sum(x) / n
|
|
y_mean = sum(values) / n
|
|
|
|
numerator = sum((x[i] - x_mean) * (values[i] - y_mean) for i in range(n))
|
|
denominator = sum((x[i] - x_mean) ** 2 for i in range(n))
|
|
|
|
if denominator == 0:
|
|
return "stable"
|
|
|
|
slope = numerator / denominator
|
|
|
|
if slope > 0.1:
|
|
return "increasing"
|
|
elif slope < -0.1:
|
|
return "decreasing"
|
|
else:
|
|
return "stable"
|
|
|
|
def _calculate_summaries(self, recent_metrics: Dict) -> Dict:
|
|
"""Calculate metric summaries."""
|
|
summaries = {}
|
|
|
|
for metric_type, metrics in recent_metrics.items():
|
|
if not metrics:
|
|
continue
|
|
|
|
if metric_type == "system" and metrics:
|
|
summaries["system"] = {
|
|
"avg_cpu": statistics.mean([m.cpu_percent for m in metrics]),
|
|
"max_cpu": max([m.cpu_percent for m in metrics]),
|
|
"avg_memory": statistics.mean([m.memory_percent for m in metrics]),
|
|
"max_memory": max([m.memory_percent for m in metrics]),
|
|
"avg_disk": statistics.mean([m.disk_usage for m in metrics])
|
|
}
|
|
|
|
elif metric_type == "application" and metrics:
|
|
summaries["application"] = {
|
|
"avg_response_time": statistics.mean([m.response_time_avg for m in metrics]),
|
|
"max_response_time": max([m.response_time_p95 for m in metrics]),
|
|
"avg_error_rate": statistics.mean([m.error_rate for m in metrics]),
|
|
"total_requests": sum([m.api_requests for m in metrics]),
|
|
"avg_throughput": statistics.mean([m.throughput for m in metrics])
|
|
}
|
|
|
|
return summaries
|
|
|
|
async def store_metrics(self, metrics: Dict) -> None:
|
|
"""Store metrics in history."""
|
|
try:
|
|
timestamp = time.time()
|
|
|
|
# Add to history
|
|
if metrics["system"]:
|
|
self.metrics_history["system"].append(metrics["system"])
|
|
if metrics["application"]:
|
|
self.metrics_history["application"].append(metrics["application"])
|
|
if metrics["blockchain"]:
|
|
self.metrics_history["blockchain"].append(metrics["blockchain"])
|
|
if metrics["security"]:
|
|
self.metrics_history["security"].append(metrics["security"])
|
|
|
|
# Cleanup old metrics
|
|
cutoff_time = timestamp - (self.config["retention_days"] * 24 * 3600)
|
|
|
|
for metric_type in self.metrics_history:
|
|
self.metrics_history[metric_type] = [
|
|
m for m in self.metrics_history[metric_type]
|
|
if m.timestamp > cutoff_time
|
|
]
|
|
|
|
# Save to file
|
|
await self._save_metrics_to_file()
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error storing metrics: {e}")
|
|
|
|
async def _save_metrics_to_file(self) -> None:
|
|
"""Save metrics to file."""
|
|
try:
|
|
metrics_file = Path("data/metrics_history.json")
|
|
metrics_file.parent.mkdir(exist_ok=True)
|
|
|
|
# Convert dataclasses to dicts for JSON serialization
|
|
serializable_history = {}
|
|
for metric_type, metrics in self.metrics_history.items():
|
|
serializable_history[metric_type] = [
|
|
asdict(m) if hasattr(m, '__dict__') else m
|
|
for m in metrics
|
|
]
|
|
|
|
with open(metrics_file, 'w') as f:
|
|
json.dump(serializable_history, f, indent=2)
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error saving metrics to file: {e}")
|
|
|
|
async def run_monitoring_cycle(self) -> None:
|
|
"""Run a complete monitoring cycle."""
|
|
try:
|
|
# Collect metrics
|
|
metrics = await self.collect_all_metrics()
|
|
|
|
# Store metrics
|
|
await self.store_metrics(metrics)
|
|
|
|
# Check alerts
|
|
alerts = await self.check_alerts(metrics)
|
|
|
|
# Send alerts
|
|
for alert in alerts:
|
|
await self.send_alert(alert)
|
|
|
|
# Generate dashboard
|
|
dashboard = await self.generate_dashboard()
|
|
|
|
# Log summary
|
|
self.logger.info(f"Monitoring cycle completed. Status: {dashboard['status']}")
|
|
if alerts:
|
|
self.logger.warning(f"Generated {len(alerts)} alerts")
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error in monitoring cycle: {e}")
|
|
|
|
async def start_monitoring(self) -> None:
|
|
"""Start continuous monitoring."""
|
|
self.logger.info("Starting production monitoring")
|
|
|
|
while True:
|
|
try:
|
|
await self.run_monitoring_cycle()
|
|
await asyncio.sleep(self.config["collection_interval"])
|
|
|
|
except KeyboardInterrupt:
|
|
self.logger.info("Monitoring stopped by user")
|
|
break
|
|
except Exception as e:
|
|
self.logger.error(f"Error in monitoring loop: {e}")
|
|
await asyncio.sleep(60) # Wait before retrying
|
|
|
|
|
|
# CLI interface
|
|
async def main():
|
|
"""Main CLI interface."""
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser(description="AITBC Production Monitoring")
|
|
parser.add_argument("--start", action="store_true", help="Start monitoring")
|
|
parser.add_argument("--collect", action="store_true", help="Collect metrics once")
|
|
parser.add_argument("--dashboard", action="store_true", help="Generate dashboard")
|
|
parser.add_argument("--alerts", action="store_true", help="Check alerts")
|
|
|
|
args = parser.parse_args()
|
|
|
|
monitor = ProductionMonitor()
|
|
|
|
if args.start:
|
|
await monitor.start_monitoring()
|
|
|
|
elif args.collect:
|
|
metrics = await monitor.collect_all_metrics()
|
|
print(json.dumps(metrics, indent=2, default=str))
|
|
|
|
elif args.dashboard:
|
|
dashboard = await monitor.generate_dashboard()
|
|
print(json.dumps(dashboard, indent=2, default=str))
|
|
|
|
elif args.alerts:
|
|
metrics = await monitor.collect_all_metrics()
|
|
alerts = await monitor.check_alerts(metrics)
|
|
print(json.dumps(alerts, indent=2, default=str))
|
|
|
|
else:
|
|
print("Use --help to see available options")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|