# Monitoring & Observability Implementation Plan ## 🎯 **Objective** Implement comprehensive monitoring and observability to ensure system reliability, performance, and maintainability. ## 🔴 **Critical Priority - 4 Week Implementation** --- ## 📋 **Phase 1: Metrics Collection (Week 1-2)** ### **1.1 Prometheus Metrics Setup** ```python # File: apps/coordinator-api/src/app/monitoring/metrics.py from prometheus_client import Counter, Histogram, Gauge, Info from prometheus_client.fastapi import metrics from fastapi import FastAPI import time from functools import wraps class ApplicationMetrics: def __init__(self): # Request metrics self.request_count = Counter( 'http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status_code'] ) self.request_duration = Histogram( 'http_request_duration_seconds', 'HTTP request duration in seconds', ['method', 'endpoint'] ) # Business metrics self.active_users = Gauge( 'active_users_total', 'Number of active users' ) self.ai_operations = Counter( 'ai_operations_total', 'Total AI operations performed', ['operation_type', 'status'] ) self.blockchain_transactions = Counter( 'blockchain_transactions_total', 'Total blockchain transactions', ['transaction_type', 'status'] ) # System metrics self.database_connections = Gauge( 'database_connections_active', 'Active database connections' ) self.cache_hit_ratio = Gauge( 'cache_hit_ratio', 'Cache hit ratio' ) def track_request(self, func): """Decorator to track request metrics""" @wraps(func) async def wrapper(*args, **kwargs): start_time = time.time() method = kwargs.get('method', 'unknown') endpoint = kwargs.get('endpoint', 'unknown') try: result = await func(*args, **kwargs) status_code = getattr(result, 'status_code', 200) self.request_count.labels(method=method, endpoint=endpoint, status_code=status_code).inc() return result except Exception as e: self.request_count.labels(method=method, endpoint=endpoint, status_code=500).inc() raise finally: duration = time.time() - start_time self.request_duration.labels(method=method, endpoint=endpoint).observe(duration) return wrapper # Initialize metrics metrics_collector = ApplicationMetrics() # FastAPI integration app = FastAPI() # Add default FastAPI metrics metrics(app) # Custom metrics endpoint @app.get("/metrics") async def custom_metrics(): from prometheus_client import generate_latest return Response(generate_latest(), media_type="text/plain") ``` ### **1.2 Business Metrics Collection** ```python # File: apps/coordinator-api/src/app/monitoring/business_metrics.py from sqlalchemy import func from sqlmodel import Session from datetime import datetime, timedelta from typing import Dict, Any class BusinessMetricsCollector: def __init__(self, db_session: Session): self.db = db_session def get_user_metrics(self) -> Dict[str, Any]: """Collect user-related business metrics""" now = datetime.utcnow() day_ago = now - timedelta(days=1) week_ago = now - timedelta(weeks=1) # Daily active users daily_active = self.db.query(func.count(func.distinct(User.id)))\ .filter(User.last_login >= day_ago).scalar() # Weekly active users weekly_active = self.db.query(func.count(func.distinct(User.id)))\ .filter(User.last_login >= week_ago).scalar() # Total users total_users = self.db.query(func.count(User.id)).scalar() # New users today new_users_today = self.db.query(func.count(User.id))\ .filter(User.created_at >= day_ago).scalar() return { 'daily_active_users': daily_active, 'weekly_active_users': weekly_active, 'total_users': total_users, 'new_users_today': new_users_today } def get_ai_operation_metrics(self) -> Dict[str, Any]: """Collect AI operation metrics""" now = datetime.utcnow() day_ago = now - timedelta(days=1) # Daily AI operations daily_operations = self.db.query(AIOperation)\ .filter(AIOperation.created_at >= day_ago).all() # Operations by type operations_by_type = {} for op in daily_operations: op_type = op.operation_type if op_type not in operations_by_type: operations_by_type[op_type] = {'total': 0, 'success': 0, 'failed': 0} operations_by_type[op_type]['total'] += 1 if op.status == 'success': operations_by_type[op_type]['success'] += 1 else: operations_by_type[op_type]['failed'] += 1 # Average processing time avg_processing_time = self.db.query(func.avg(AIOperation.processing_time))\ .filter(AIOperation.created_at >= day_ago).scalar() or 0 return { 'daily_operations': len(daily_operations), 'operations_by_type': operations_by_type, 'avg_processing_time': float(avg_processing_time) } def get_blockchain_metrics(self) -> Dict[str, Any]: """Collect blockchain-related metrics""" now = datetime.utcnow() day_ago = now - timedelta(days=1) # Daily transactions daily_transactions = self.db.query(BlockchainTransaction)\ .filter(BlockchainTransaction.created_at >= day_ago).all() # Transactions by type transactions_by_type = {} for tx in daily_transactions: tx_type = tx.transaction_type if tx_type not in transactions_by_type: transactions_by_type[tx_type] = 0 transactions_by_type[tx_type] += 1 # Average confirmation time avg_confirmation_time = self.db.query(func.avg(BlockchainTransaction.confirmation_time))\ .filter(BlockchainTransaction.created_at >= day_ago).scalar() or 0 # Failed transactions failed_transactions = self.db.query(func.count(BlockchainTransaction.id))\ .filter(BlockchainTransaction.created_at >= day_ago)\ .filter(BlockchainTransaction.status == 'failed').scalar() return { 'daily_transactions': len(daily_transactions), 'transactions_by_type': transactions_by_type, 'avg_confirmation_time': float(avg_confirmation_time), 'failed_transactions': failed_transactions } # Metrics collection endpoint @app.get("/metrics/business") async def business_metrics(): collector = BusinessMetricsCollector(get_db_session()) metrics = { 'timestamp': datetime.utcnow().isoformat(), 'users': collector.get_user_metrics(), 'ai_operations': collector.get_ai_operation_metrics(), 'blockchain': collector.get_blockchain_metrics() } return metrics ``` ### **1.3 Custom Application Metrics** ```python # File: apps/coordinator-api/src/app/monitoring/custom_metrics.py from prometheus_client import Counter, Histogram, Gauge from contextlib import asynccontextmanager class CustomMetrics: def __init__(self): # AI service metrics self.ai_model_inference_time = Histogram( 'ai_model_inference_duration_seconds', 'Time spent on AI model inference', ['model_name', 'model_type'] ) self.ai_model_requests = Counter( 'ai_model_requests_total', 'Total AI model requests', ['model_name', 'model_type', 'status'] ) # Blockchain metrics self.block_sync_time = Histogram( 'block_sync_duration_seconds', 'Time to sync blockchain blocks' ) self.transaction_queue_size = Gauge( 'transaction_queue_size', 'Number of transactions in queue' ) # Database metrics self.query_execution_time = Histogram( 'database_query_duration_seconds', 'Database query execution time', ['query_type', 'table'] ) self.cache_operations = Counter( 'cache_operations_total', 'Total cache operations', ['operation', 'result'] ) @asynccontextmanager async def time_ai_inference(self, model_name: str, model_type: str): """Context manager for timing AI inference""" start_time = time.time() try: yield self.ai_model_requests.labels( model_name=model_name, model_type=model_type, status='success' ).inc() except Exception: self.ai_model_requests.labels( model_name=model_name, model_type=model_type, status='error' ).inc() raise finally: duration = time.time() - start_time self.ai_model_inference_time.labels( model_name=model_name, model_type=model_type ).observe(duration) @asynccontextmanager async def time_database_query(self, query_type: str, table: str): """Context manager for timing database queries""" start_time = time.time() try: yield finally: duration = time.time() - start_time self.query_execution_time.labels( query_type=query_type, table=table ).observe(duration) # Usage in services custom_metrics = CustomMetrics() class AIService: async def process_request(self, request: dict): model_name = request.get('model', 'default') model_type = request.get('type', 'text') async with custom_metrics.time_ai_inference(model_name, model_type): # AI processing logic result = await self.ai_model.process(request) return result ``` --- ## 📋 **Phase 2: Logging & Alerting (Week 2-3)** ### **2.1 Structured Logging Setup** ```python # File: apps/coordinator-api/src/app/logging/structured_logging.py import structlog import logging from pythonjsonlogger import jsonlogger from typing import Dict, Any import uuid from fastapi import Request # Configure structured logging def configure_logging(): # Configure structlog structlog.configure( processors=[ structlog.stdlib.filter_by_level, structlog.stdlib.add_logger_name, structlog.stdlib.add_log_level, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.TimeStamper(fmt="iso"), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, structlog.processors.UnicodeDecoder(), structlog.processors.JSONRenderer() ], context_class=dict, logger_factory=structlog.stdlib.LoggerFactory(), wrapper_class=structlog.stdlib.BoundLogger, cache_logger_on_first_use=True, ) # Configure standard logging json_formatter = jsonlogger.JsonFormatter( '%(asctime)s %(name)s %(levelname)s %(message)s' ) handler = logging.StreamHandler() handler.setFormatter(json_formatter) logger = logging.getLogger() logger.setLevel(logging.INFO) logger.addHandler(handler) # Request correlation middleware class CorrelationIDMiddleware: def __init__(self, app): self.app = app async def __call__(self, scope, receive, send): if scope["type"] == "http": # Generate or extract correlation ID correlation_id = scope.get("headers", {}).get(b"x-correlation-id") if correlation_id: correlation_id = correlation_id.decode() else: correlation_id = str(uuid.uuid4()) # Add to request state scope["state"] = scope.get("state", {}) scope["state"]["correlation_id"] = correlation_id # Add correlation ID to response headers async def send_wrapper(message): if message["type"] == "http.response.start": headers = list(message.get("headers", [])) headers.append((b"x-correlation-id", correlation_id.encode())) message["headers"] = headers await send(message) await self.app(scope, receive, send_wrapper) else: await self.app(scope, receive, send) # Logging context manager class LoggingContext: def __init__(self, logger, **kwargs): self.logger = logger self.context = kwargs def __enter__(self): return self.logger.bind(**self.context) def __exit__(self, exc_type, exc_val, exc_tb): if exc_type: self.logger.error("Exception occurred", exc_info=(exc_type, exc_val, exc_tb)) # Usage in services logger = structlog.get_logger() class AIService: async def process_request(self, request_id: str, user_id: str, request: dict): with LoggingContext(logger, request_id=request_id, user_id=user_id, service="ai_service"): logger.info("Processing AI request", request_type=request.get('type')) try: result = await self.ai_model.process(request) logger.info("AI request processed successfully", model=request.get('model'), processing_time=result.get('duration')) return result except Exception as e: logger.error("AI request failed", error=str(e), error_type=type(e).__name__) raise ``` ### **2.2 Alert Management System** ```python # File: apps/coordinator-api/src/app/monitoring/alerts.py from enum import Enum from typing import Dict, List, Optional from datetime import datetime, timedelta import asyncio import aiohttp from dataclasses import dataclass class AlertSeverity(str, Enum): LOW = "low" MEDIUM = "medium" HIGH = "high" CRITICAL = "critical" class AlertStatus(str, Enum): FIRING = "firing" RESOLVED = "resolved" SILENCED = "silenced" @dataclass class Alert: name: str severity: AlertSeverity status: AlertStatus message: str labels: Dict[str, str] annotations: Dict[str, str] starts_at: datetime ends_at: Optional[datetime] = None fingerprint: str = "" class AlertManager: def __init__(self): self.alerts: Dict[str, Alert] = {} self.notification_channels = [] self.alert_rules = [] def add_notification_channel(self, channel): """Add notification channel (Slack, email, PagerDuty, etc.)""" self.notification_channels.append(channel) def add_alert_rule(self, rule): """Add alert rule""" self.alert_rules.append(rule) async def check_alert_rules(self): """Check all alert rules and create alerts if needed""" for rule in self.alert_rules: try: should_fire = await rule.evaluate() alert_key = rule.get_alert_key() if should_fire and alert_key not in self.alerts: # Create new alert alert = Alert( name=rule.name, severity=rule.severity, status=AlertStatus.FIRING, message=rule.message, labels=rule.labels, annotations=rule.annotations, starts_at=datetime.utcnow(), fingerprint=alert_key ) self.alerts[alert_key] = alert await self.send_notifications(alert) elif not should_fire and alert_key in self.alerts: # Resolve alert alert = self.alerts[alert_key] alert.status = AlertStatus.RESOLVED alert.ends_at = datetime.utcnow() await self.send_notifications(alert) del self.alerts[alert_key] except Exception as e: logger.error("Error evaluating alert rule", rule=rule.name, error=str(e)) async def send_notifications(self, alert: Alert): """Send alert to all notification channels""" for channel in self.notification_channels: try: await channel.send_notification(alert) except Exception as e: logger.error("Error sending notification", channel=channel.__class__.__name__, error=str(e)) # Alert rule examples class HighErrorRateRule: def __init__(self): self.name = "HighErrorRate" self.severity = AlertSeverity.HIGH self.message = "Error rate is above 5%" self.labels = {"service": "coordinator-api", "type": "error_rate"} self.annotations = {"description": "Error rate has exceeded 5% threshold"} async def evaluate(self) -> bool: # Get error rate from metrics error_rate = await self.get_error_rate() return error_rate > 0.05 # 5% async def get_error_rate(self) -> float: # Query Prometheus for error rate # Implementation depends on your metrics setup return 0.0 # Placeholder def get_alert_key(self) -> str: return f"{self.name}:{self.labels['service']}" # Notification channels class SlackNotificationChannel: def __init__(self, webhook_url: str): self.webhook_url = webhook_url async def send_notification(self, alert: Alert): payload = { "text": f"🚨 {alert.severity.upper()} Alert: {alert.name}", "attachments": [{ "color": self.get_color(alert.severity), "fields": [ {"title": "Message", "value": alert.message, "short": False}, {"title": "Severity", "value": alert.severity.value, "short": True}, {"title": "Status", "value": alert.status.value, "short": True}, {"title": "Started", "value": alert.starts_at.isoformat(), "short": True} ] }] } async with aiohttp.ClientSession() as session: async with session.post(self.webhook_url, json=payload) as response: if response.status != 200: raise Exception(f"Failed to send Slack notification: {response.status}") def get_color(self, severity: AlertSeverity) -> str: colors = { AlertSeverity.LOW: "good", AlertSeverity.MEDIUM: "warning", AlertSeverity.HIGH: "danger", AlertSeverity.CRITICAL: "danger" } return colors.get(severity, "good") # Initialize alert manager alert_manager = AlertManager() # Add notification channels # alert_manager.add_notification_channel(SlackNotificationChannel(slack_webhook_url)) # Add alert rules # alert_manager.add_alert_rule(HighErrorRateRule()) ``` --- ## 📋 **Phase 3: Health Checks & SLA (Week 3-4)** ### **3.1 Comprehensive Health Checks** ```python # File: apps/coordinator-api/src/app/health/health_checks.py from fastapi import APIRouter, HTTPException from typing import Dict, Any, List from datetime import datetime from enum import Enum import asyncio from sqlalchemy import text class HealthStatus(str, Enum): HEALTHY = "healthy" DEGRADED = "degraded" UNHEALTHY = "unhealthy" class HealthCheck: def __init__(self, name: str, check_function, timeout: float = 5.0): self.name = name self.check_function = check_function self.timeout = timeout async def run(self) -> Dict[str, Any]: start_time = datetime.utcnow() try: result = await asyncio.wait_for(self.check_function(), timeout=self.timeout) duration = (datetime.utcnow() - start_time).total_seconds() return { "name": self.name, "status": HealthStatus.HEALTHY, "message": "OK", "duration": duration, "timestamp": start_time.isoformat(), "details": result } except asyncio.TimeoutError: duration = (datetime.utcnow() - start_time).total_seconds() return { "name": self.name, "status": HealthStatus.UNHEALTHY, "message": "Timeout", "duration": duration, "timestamp": start_time.isoformat() } except Exception as e: duration = (datetime.utcnow() - start_time).total_seconds() return { "name": self.name, "status": HealthStatus.UNHEALTHY, "message": str(e), "duration": duration, "timestamp": start_time.isoformat() } class HealthChecker: def __init__(self): self.checks: List[HealthCheck] = [] def add_check(self, check: HealthCheck): self.checks.append(check) async def run_all_checks(self) -> Dict[str, Any]: results = await asyncio.gather(*[check.run() for check in self.checks]) overall_status = HealthStatus.HEALTHY failed_checks = [] for result in results: if result["status"] == HealthStatus.UNHEALTHY: overall_status = HealthStatus.UNHEALTHY failed_checks.append(result["name"]) elif result["status"] == HealthStatus.DEGRADED and overall_status == HealthStatus.HEALTHY: overall_status = HealthStatus.DEGRADED return { "status": overall_status, "timestamp": datetime.utcnow().isoformat(), "checks": results, "failed_checks": failed_checks, "total_checks": len(self.checks), "passed_checks": len(self.checks) - len(failed_checks) } # Health check implementations async def database_health_check(): """Check database connectivity""" async with get_db_session() as session: result = await session.execute(text("SELECT 1")) return {"database": "connected", "query_result": result.scalar()} async def redis_health_check(): """Check Redis connectivity""" redis_client = get_redis_client() await redis_client.ping() return {"redis": "connected"} async def external_api_health_check(): """Check external API connectivity""" async with aiohttp.ClientSession() as session: async with session.get("https://api.openai.com/v1/models", timeout=5) as response: if response.status == 200: return {"openai_api": "connected", "status_code": response.status} else: raise Exception(f"API returned status {response.status}") async def ai_service_health_check(): """Check AI service health""" # Test AI model availability model = get_ai_model() test_result = await model.test_inference("test input") return {"ai_service": "healthy", "model_response_time": test_result.get("duration")} async def blockchain_health_check(): """Check blockchain connectivity""" blockchain_client = get_blockchain_client() latest_block = blockchain_client.get_latest_block() return { "blockchain": "connected", "latest_block": latest_block.number, "block_time": latest_block.timestamp } # Initialize health checker health_checker = HealthChecker() # Add health checks health_checker.add_check(HealthCheck("database", database_health_check)) health_checker.add_check(HealthCheck("redis", redis_health_check)) health_checker.add_check(HealthCheck("external_api", external_api_health_check)) health_checker.add_check(HealthCheck("ai_service", ai_service_health_check)) health_checker.add_check(HealthCheck("blockchain", blockchain_health_check)) # Health check endpoints health_router = APIRouter(prefix="/health", tags=["health"]) @health_router.get("/") async def health_check(): """Basic health check""" return {"status": "healthy", "timestamp": datetime.utcnow().isoformat()} @health_router.get("/detailed") async def detailed_health_check(): """Detailed health check with all components""" return await health_checker.run_all_checks() @health_router.get("/readiness") async def readiness_check(): """Readiness probe for Kubernetes""" result = await health_checker.run_all_checks() if result["status"] == HealthStatus.HEALTHY: return {"status": "ready"} else: raise HTTPException(status_code=503, detail="Service not ready") @health_router.get("/liveness") async def liveness_check(): """Liveness probe for Kubernetes""" # Simple check if the service is responsive return {"status": "alive", "timestamp": datetime.utcnow().isoformat()} ``` ### **3.2 SLA Monitoring** ```python # File: apps/coordinator-api/src/app/monitoring/sla.py from datetime import datetime, timedelta from typing import Dict, Any, List from dataclasses import dataclass from enum import Enum class SLAStatus(str, Enum): COMPLIANT = "compliant" VIOLATED = "violated" WARNING = "warning" @dataclass class SLAMetric: name: str target: float current: float unit: str status: SLAStatus measurement_period: str class SLAMonitor: def __init__(self): self.metrics: Dict[str, SLAMetric] = {} self.sla_definitions = { "availability": {"target": 99.9, "unit": "%", "period": "30d"}, "response_time": {"target": 200, "unit": "ms", "period": "24h"}, "error_rate": {"target": 1.0, "unit": "%", "period": "24h"}, "throughput": {"target": 1000, "unit": "req/s", "period": "1h"} } async def calculate_availability(self) -> SLAMetric: """Calculate service availability""" # Get uptime data from the last 30 days thirty_days_ago = datetime.utcnow() - timedelta(days=30) # Query metrics for availability total_time = 30 * 24 * 60 * 60 # 30 days in seconds downtime = await self.get_downtime(thirty_days_ago) uptime = total_time - downtime availability = (uptime / total_time) * 100 target = self.sla_definitions["availability"]["target"] status = self.get_sla_status(availability, target) return SLAMetric( name="availability", target=target, current=availability, unit="%", status=status, measurement_period="30d" ) async def calculate_response_time(self) -> SLAMetric: """Calculate average response time""" # Get response time metrics from the last 24 hours twenty_four_hours_ago = datetime.utcnow() - timedelta(hours=24) # Query Prometheus for average response time avg_response_time = await self.get_average_response_time(twenty_four_hours_ago) target = self.sla_definitions["response_time"]["target"] status = self.get_sla_status(avg_response_time, target, reverse=True) return SLAMetric( name="response_time", target=target, current=avg_response_time, unit="ms", status=status, measurement_period="24h" ) async def calculate_error_rate(self) -> SLAMetric: """Calculate error rate""" # Get error metrics from the last 24 hours twenty_four_hours_ago = datetime.utcnow() - timedelta(hours=24) total_requests = await self.get_total_requests(twenty_four_hours_ago) error_requests = await self.get_error_requests(twenty_four_hours_ago) error_rate = (error_requests / total_requests) * 100 if total_requests > 0 else 0 target = self.sla_definitions["error_rate"]["target"] status = self.get_sla_status(error_rate, target, reverse=True) return SLAMetric( name="error_rate", target=target, current=error_rate, unit="%", status=status, measurement_period="24h" ) async def calculate_throughput(self) -> SLAMetric: """Calculate system throughput""" # Get request metrics from the last hour one_hour_ago = datetime.utcnow() - timedelta(hours=1) requests_per_hour = await self.get_total_requests(one_hour_ago) requests_per_second = requests_per_hour / 3600 target = self.sla_definitions["throughput"]["target"] status = self.get_sla_status(requests_per_second, target) return SLAMetric( name="throughput", target=target, current=requests_per_second, unit="req/s", status=status, measurement_period="1h" ) def get_sla_status(self, current: float, target: float, reverse: bool = False) -> SLAStatus: """Determine SLA status based on current and target values""" if reverse: # For metrics where lower is better (response time, error rate) if current <= target: return SLAStatus.COMPLIANT elif current <= target * 1.1: # 10% tolerance return SLAStatus.WARNING else: return SLAStatus.VIOLATED else: # For metrics where higher is better (availability, throughput) if current >= target: return SLAStatus.COMPLIANT elif current >= target * 0.9: # 10% tolerance return SLAStatus.WARNING else: return SLAStatus.VIOLATED async def get_sla_report(self) -> Dict[str, Any]: """Generate comprehensive SLA report""" metrics = await asyncio.gather( self.calculate_availability(), self.calculate_response_time(), self.calculate_error_rate(), self.calculate_throughput() ) # Calculate overall SLA status overall_status = SLAStatus.COMPLIANT for metric in metrics: if metric.status == SLAStatus.VIOLATED: overall_status = SLAStatus.VIOLATED break elif metric.status == SLAStatus.WARNING and overall_status == SLAStatus.COMPLIANT: overall_status = SLAStatus.WARNING return { "overall_status": overall_status, "timestamp": datetime.utcnow().isoformat(), "metrics": {metric.name: metric for metric in metrics}, "sla_definitions": self.sla_definitions } # SLA monitoring endpoints @router.get("/monitoring/sla") async def sla_report(): """Get SLA compliance report""" monitor = SLAMonitor() return await monitor.get_sla_report() @router.get("/monitoring/sla/{metric_name}") async def get_sla_metric(metric_name: str): """Get specific SLA metric""" monitor = SLAMonitor() if metric_name == "availability": return await monitor.calculate_availability() elif metric_name == "response_time": return await monitor.calculate_response_time() elif metric_name == "error_rate": return await monitor.calculate_error_rate() elif metric_name == "throughput": return await monitor.calculate_throughput() else: raise HTTPException(status_code=404, detail=f"Metric {metric_name} not found") ``` --- ## 🎯 **Success Metrics & Testing** ### **Monitoring Testing Checklist** ```bash # 1. Metrics collection testing curl http://localhost:8000/metrics curl http://localhost:8000/metrics/business # 2. Health check testing curl http://localhost:8000/health/ curl http://localhost:8000/health/detailed curl http://localhost:8000/health/readiness curl http://localhost:8000/health/liveness # 3. SLA monitoring testing curl http://localhost:8000/monitoring/sla curl http://localhost:8000/monitoring/sla/availability # 4. Alert system testing # - Trigger alert conditions # - Verify notification delivery # - Test alert resolution ``` ### **Performance Requirements** - Metrics collection overhead < 5% CPU - Health check response < 100ms - SLA calculation < 500ms - Alert delivery < 30 seconds ### **Reliability Requirements** - 99.9% monitoring system availability - Complete audit trail for all alerts - Redundant monitoring infrastructure - Automated failover for monitoring components --- ## 📅 **Implementation Timeline** ### **Week 1** - [ ] Prometheus metrics setup - [ ] Business metrics collection - [ ] Custom application metrics ### **Week 2** - [ ] Structured logging implementation - [ ] Alert management system - [ ] Notification channel setup ### **Week 3** - [ ] Comprehensive health checks - [ ] SLA monitoring implementation - [ ] Dashboard configuration ### **Week 4** - [ ] Testing and validation - [ ] Documentation and deployment - [ ] Performance optimization --- **Last Updated**: March 31, 2026 **Owner**: Infrastructure Team **Review Date**: April 7, 2026