Performance monitoring and optimization for MCP are crucial for ensuring efficient system operation. Here are detailed monitoring strategies and optimization methods:
Performance Monitoring Architecture
MCP performance monitoring should consider following aspects:
- Metrics Collection: Collect key system operation metrics
- Performance Analysis: Analyze performance bottlenecks and optimization opportunities
- Real-time Monitoring: Monitor system status and performance in real-time
- Alerting Mechanism: Timely detect and notify performance issues
- Optimization Strategy: Optimize performance based on monitoring data
1. Metrics Collection System
from dataclasses import dataclass from typing import Dict, List, Optional from datetime import datetime import time @dataclass class Metric: """Performance metric""" name: str value: float timestamp: datetime tags: Dict[str, str] = None def __post_init__(self): if self.tags is None: self.tags = {} class MetricsCollector: """Metrics collector""" def __init__(self): self.metrics: List[Metric] = [] self.counters: Dict[str, int] = {} self.gauges: Dict[str, float] = {} self.histograms: Dict[str, List[float]] = {} def increment_counter(self, name: str, value: int = 1, tags: Dict[str, str] = None): """Increment counter""" key = self._make_key(name, tags) self.counters[key] = self.counters.get(key, 0) + value # Record metric self._record_metric(name, self.counters[key], tags) def set_gauge(self, name: str, value: float, tags: Dict[str, str] = None): """Set gauge value""" key = self._make_key(name, tags) self.gauges[key] = value # Record metric self._record_metric(name, value, tags) def record_histogram(self, name: str, value: float, tags: Dict[str, str] = None): """Record histogram value""" key = self._make_key(name, tags) if key not in self.histograms: self.histograms[key] = [] self.histograms[key].append(value) # Limit history size if len(self.histograms[key]) > 1000: self.histograms[key] = self.histograms[key][-1000:] # Record metric self._record_metric(name, value, tags) def _record_metric(self, name: str, value: float, tags: Dict[str, str] = None): """Record metric""" metric = Metric( name=name, value=value, timestamp=datetime.now(), tags=tags or {} ) self.metrics.append(metric) # Limit metrics history size if len(self.metrics) > 10000: self.metrics = self.metrics[-10000:] def _make_key(self, name: str, tags: Dict[str, str] = None) -> str: """Generate metric key""" if not tags: return name tag_str = ",".join([f"{k}={v}" for k, v in sorted(tags.items())]) return f"{name}{{{tag_str}}}" def get_metrics( self, name: str = None, since: datetime = None ) -> List[Metric]: """Get metrics""" metrics = self.metrics if name: metrics = [m for m in metrics if m.name == name] if since: metrics = [m for m in metrics if m.timestamp >= since] return metrics def get_histogram_stats( self, name: str, tags: Dict[str, str] = None ) -> Optional[Dict]: """Get histogram statistics""" key = self._make_key(name, tags) if key not in self.histograms: return None values = self.histograms[key] if not values: return None sorted_values = sorted(values) return { "count": len(values), "sum": sum(values), "avg": sum(values) / len(values), "min": min(values), "max": max(values), "p50": self._percentile(sorted_values, 50), "p90": self._percentile(sorted_values, 90), "p95": self._percentile(sorted_values, 95), "p99": self._percentile(sorted_values, 99), } def _percentile(self, sorted_values: List[float], percentile: int) -> float: """Calculate percentile""" if not sorted_values: return 0.0 index = int(len(sorted_values) * percentile / 100) return sorted_values[min(index, len(sorted_values) - 1)]
2. Performance Profiler
from functools import wraps from typing import Callable, Optional import time class PerformanceProfiler: """Performance profiler""" def __init__(self, metrics_collector: MetricsCollector): self.metrics_collector = metrics_collector def profile_function( self, metric_name: str, tags: Dict[str, str] = None ): """Function performance profiling decorator""" def decorator(func: Callable): @wraps(func) async def async_wrapper(*args, **kwargs): start_time = time.time() try: result = await func(*args, **kwargs) # Record execution time execution_time = time.time() - start_time self.metrics_collector.record_histogram( f"{metric_name}_duration", execution_time, tags ) # Record success counter self.metrics_collector.increment_counter( f"{metric_name}_success", tags=tags ) return result except Exception as e: # Record failure counter self.metrics_collector.increment_counter( f"{metric_name}_error", tags=tags ) raise e @wraps(func) def sync_wrapper(*args, **kwargs): start_time = time.time() try: result = func(*args, **kwargs) # Record execution time execution_time = time.time() - start_time self.metrics_collector.record_histogram( f"{metric_name}_duration", execution_time, tags ) # Record success counter self.metrics_collector.increment_counter( f"{metric_name}_success", tags=tags ) return result except Exception as e: # Record failure counter self.metrics_collector.increment_counter( f"{metric_name}_error", tags=tags ) raise e # Return appropriate wrapper based on function type if asyncio.iscoroutinefunction(func): return async_wrapper else: return sync_wrapper return decorator def profile_context( self, metric_name: str, tags: Dict[str, str] = None ): """Context manager performance profiling""" class ProfileContext: def __init__(self, profiler, name, tags): self.profiler = profiler self.name = name self.tags = tags self.start_time = None def __enter__(self): self.start_time = time.time() return self def __exit__(self, exc_type, exc_val, exc_tb): execution_time = time.time() - self.start_time self.profiler.metrics_collector.record_histogram( f"{self.name}_duration", execution_time, self.tags ) if exc_type is None: self.profiler.metrics_collector.increment_counter( f"{self.name}_success", tags=self.tags ) else: self.profiler.metrics_collector.increment_counter( f"{self.name}_error", tags=self.tags ) return False return ProfileContext(self, metric_name, tags)
3. Real-time Monitoring System
import asyncio from typing import Dict, List, Callable, Optional class RealTimeMonitor: """Real-time monitor""" def __init__( self, metrics_collector: MetricsCollector, check_interval: int = 5 ): self.metrics_collector = metrics_collector self.check_interval = check_interval self.alerts: List[Dict] = [] self.alert_rules: List[Dict] = [] self.subscribers: List[Callable] = [] self.running = False def add_alert_rule( self, name: str, metric_name: str, condition: str, threshold: float, severity: str = "warning" ): """Add alert rule""" self.alert_rules.append({ "name": name, "metric_name": metric_name, "condition": condition, "threshold": threshold, "severity": severity }) def subscribe(self, callback: Callable): """Subscribe to alerts""" self.subscribers.append(callback) def unsubscribe(self, callback: Callable): """Unsubscribe from alerts""" if callback in self.subscribers: self.subscribers.remove(callback) async def start(self): """Start monitoring""" self.running = True while self.running: await self.check_alerts() await asyncio.sleep(self.check_interval) async def stop(self): """Stop monitoring""" self.running = False async def check_alerts(self): """Check alerts""" for rule in self.alert_rules: try: alert = await self._evaluate_rule(rule) if alert: self.alerts.append(alert) await self._notify_subscribers(alert) except Exception as e: print(f"Failed to check alert rule: {rule['name']}, error: {e}") async def _evaluate_rule(self, rule: Dict) -> Optional[Dict]: """Evaluate alert rule""" metric_name = rule["metric_name"] condition = rule["condition"] threshold = rule["threshold"] # Get recent metrics recent_metrics = self.metrics_collector.get_metrics( metric_name, since=datetime.now() - timedelta(minutes=1) ) if not recent_metrics: return None # Calculate aggregate value values = [m.value for m in recent_metrics] avg_value = sum(values) / len(values) # Check condition triggered = False if condition == "greater_than": triggered = avg_value > threshold elif condition == "less_than": triggered = avg_value < threshold elif condition == "equals": triggered = avg_value == threshold elif condition == "not_equals": triggered = avg_value != threshold if triggered: return { "rule_name": rule["name"], "metric_name": metric_name, "current_value": avg_value, "threshold": threshold, "condition": condition, "severity": rule["severity"], "timestamp": datetime.now() } return None async def _notify_subscribers(self, alert: Dict): """Notify subscribers""" for callback in self.subscribers: try: await callback(alert) except Exception as e: print(f"Failed to notify subscriber: {e}") def get_recent_alerts( self, since: datetime = None, severity: str = None ) -> List[Dict]: """Get recent alerts""" alerts = self.alerts if since: alerts = [a for a in alerts if a["timestamp"] >= since] if severity: alerts = [a for a in alerts if a["severity"] == severity] return alerts
4. Performance Optimization Strategy
from typing import Dict, List, Optional import asyncio from functools import lru_cache class PerformanceOptimizer: """Performance optimizer""" def __init__(self, metrics_collector: MetricsCollector): self.metrics_collector = metrics_collector self.optimization_strategies = {} def register_strategy( self, name: str, strategy: Callable, priority: int = 0 ): """Register optimization strategy""" self.optimization_strategies[name] = { "strategy": strategy, "priority": priority } async def optimize(self, context: Dict) -> Dict: """Execute optimization""" results = {} # Sort strategies by priority sorted_strategies = sorted( self.optimization_strategies.items(), key=lambda x: x[1]["priority"], reverse=True ) for name, strategy_info in sorted_strategies: try: result = await strategy_info["strategy"](context) results[name] = result except Exception as e: results[name] = {"error": str(e)} return results # Cache optimization strategy class CacheOptimizer: """Cache optimizer""" def __init__(self, max_size: int = 1000, ttl: int = 300): self.max_size = max_size self.ttl = ttl self.cache: Dict[str, tuple] = {} def get(self, key: str) -> Optional[any]: """Get cached value""" if key not in self.cache: return None value, timestamp = self.cache[key] # Check if expired if time.time() - timestamp > self.ttl: del self.cache[key] return None return value def set(self, key: str, value: any): """Set cached value""" # Check cache size if len(self.cache) >= self.max_size: self._evict() self.cache[key] = (value, time.time()) def _evict(self): """Evict cache entry""" # Simple implementation: random eviction if self.cache: key = next(iter(self.cache)) del self.cache[key] # Connection pool optimization class ConnectionPoolOptimizer: """Connection pool optimizer""" def __init__(self, min_size: int = 5, max_size: int = 20): self.min_size = min_size self.max_size = max_size self.pool = asyncio.Queue() self.created = 0 self.lock = asyncio.Lock() async def acquire(self) -> any: """Acquire connection""" try: # Try to get connection from pool connection = await asyncio.wait_for( self.pool.get(), timeout=1.0 ) return connection except asyncio.TimeoutError: # No available connection in pool, create new one async with self.lock: if self.created < self.max_size: connection = await self._create_connection() self.created += 1 return connection # Wait for other connections to be released return await self.pool.get() async def release(self, connection: any): """Release connection""" await self.pool.put(connection) async def _create_connection(self) -> any: """Create new connection""" # Implement specific connection creation logic pass # Batch processing optimization class BatchProcessor: """Batch processor""" def __init__(self, batch_size: int = 100, timeout: float = 1.0): self.batch_size = batch_size self.timeout = timeout self.buffer: List = [] self.lock = asyncio.Lock() async def add(self, item: any): """Add item to batch""" async with self.lock: self.buffer.append(item) if len(self.buffer) >= self.batch_size: await self._flush() async def _flush(self): """Flush buffer""" if not self.buffer: return batch = self.buffer.copy() self.buffer.clear() # Process batch await self._process_batch(batch) async def _process_batch(self, batch: List): """Process batch""" # Implement specific batch processing logic pass async def start_periodic_flush(self): """Start periodic flush""" while True: await asyncio.sleep(self.timeout) async with self.lock: await self._flush()
5. Performance Report Generator
from typing import Dict, List from datetime import datetime, timedelta class PerformanceReportGenerator: """Performance report generator""" def __init__(self, metrics_collector: MetricsCollector): self.metrics_collector = metrics_collector def generate_report( self, start_time: datetime, end_time: datetime ) -> Dict: """Generate performance report""" report = { "period": { "start": start_time, "end": end_time }, "summary": {}, "metrics": {}, "alerts": [], "recommendations": [] } # Generate summary report["summary"] = self._generate_summary(start_time, end_time) # Generate metrics details report["metrics"] = self._generate_metrics_detail( start_time, end_time ) # Generate optimization recommendations report["recommendations"] = self._generate_recommendations( report["metrics"] ) return report def _generate_summary( self, start_time: datetime, end_time: datetime ) -> Dict: """Generate summary""" summary = { "total_requests": 0, "successful_requests": 0, "failed_requests": 0, "average_response_time": 0.0, "p95_response_time": 0.0, "p99_response_time": 0.0 } # Get request metrics success_metrics = self.metrics_collector.get_metrics( "request_success", since=start_time ) error_metrics = self.metrics_collector.get_metrics( "request_error", since=start_time ) duration_metrics = self.metrics_collector.get_metrics( "request_duration", since=start_time ) summary["successful_requests"] = len(success_metrics) summary["failed_requests"] = len(error_metrics) summary["total_requests"] = ( summary["successful_requests"] + summary["failed_requests"] ) if duration_metrics: durations = [m.value for m in duration_metrics] summary["average_response_time"] = sum(durations) / len(durations) summary["p95_response_time"] = self._percentile(durations, 95) summary["p99_response_time"] = self._percentile(durations, 99) return summary def _generate_metrics_detail( self, start_time: datetime, end_time: datetime ) -> Dict: """Generate metrics details""" metrics_detail = {} # Get all metric names metric_names = set(m.name for m in self.metrics_collector.metrics) for metric_name in metric_names: metrics = self.metrics_collector.get_metrics( metric_name, since=start_time ) if not metrics: continue values = [m.value for m in metrics] metrics_detail[metric_name] = { "count": len(values), "sum": sum(values), "avg": sum(values) / len(values), "min": min(values), "max": max(values), "p50": self._percentile(values, 50), "p90": self._percentile(values, 90), "p95": self._percentile(values, 95), "p99": self._percentile(values, 99), } return metrics_detail def _generate_recommendations( self, metrics_detail: Dict ) -> List[str]: """Generate optimization recommendations""" recommendations = [] # Check response time if "request_duration" in metrics_detail: avg_response_time = metrics_detail["request_duration"]["avg"] p95_response_time = metrics_detail["request_duration"]["p95"] if avg_response_time > 1.0: recommendations.append( "Average response time exceeds 1 second, recommend optimizing slow queries or adding cache" ) if p95_response_time > 5.0: recommendations.append( "P95 response time exceeds 5 seconds, recommend checking performance bottlenecks" ) # Check error rate if "request_error" in metrics_detail: error_count = metrics_detail["request_error"]["count"] if error_count > 100: recommendations.append( "High error count, recommend checking error logs and fixing issues" ) return recommendations def _percentile(self, sorted_values: List[float], percentile: int) -> float: """Calculate percentile""" if not sorted_values: return 0.0 sorted_values = sorted(sorted_values) index = int(len(sorted_values) * percentile / 100) return sorted_values[min(index, len(sorted_values) - 1)]
Best Practices:
- Comprehensive Monitoring: Monitor all key metrics, including requests, response time, error rate, etc.
- Real-time Alerting: Set reasonable alert thresholds to detect performance issues in time
- Performance Analysis: Regularly analyze performance data to identify optimization opportunities
- Cache Optimization: Use cache appropriately to reduce duplicate calculations and queries
- Connection Pool: Use connection pool to optimize database and network connections
- Batch Processing: Use batch processing to improve throughput
Through comprehensive performance monitoring and optimization, you can ensure efficient and stable operation of MCP system.