1025 lines
43 KiB
Markdown
1025 lines
43 KiB
Markdown
# Performance Optimization Engine
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## Intelligent Performance Analysis and Optimization for Enhanced BMAD System
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The Performance Optimization Engine provides sophisticated performance analysis, bottleneck identification, and automated optimization capabilities that help developers and teams optimize application performance, resource utilization, and system efficiency.
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### Performance Optimization Architecture
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#### Comprehensive Performance Framework
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```yaml
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performance_optimization_architecture:
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analysis_capabilities:
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profiling_analysis:
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- cpu_profiling: "Profile CPU usage patterns and hotspots"
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- memory_profiling: "Analyze memory allocation and usage patterns"
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- io_profiling: "Profile I/O operations and bottlenecks"
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- network_profiling: "Analyze network usage and latency"
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- database_profiling: "Profile database queries and performance"
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bottleneck_identification:
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- computational_bottlenecks: "Identify CPU-intensive operations"
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- memory_bottlenecks: "Find memory allocation and leak issues"
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- io_bottlenecks: "Detect I/O performance issues"
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- network_bottlenecks: "Identify network latency and throughput issues"
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- synchronization_bottlenecks: "Find concurrency and locking issues"
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scalability_analysis:
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- load_scalability: "Analyze performance under increasing load"
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- data_scalability: "Assess performance with growing data volumes"
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- user_scalability: "Test performance with more concurrent users"
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- resource_scalability: "Evaluate scaling with additional resources"
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- architectural_scalability: "Assess architectural scaling limitations"
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performance_regression_detection:
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- automated_regression_detection: "Detect performance regressions automatically"
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- baseline_comparison: "Compare against performance baselines"
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- trend_analysis: "Analyze performance trends over time"
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- threshold_monitoring: "Monitor performance against defined thresholds"
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- early_warning_system: "Provide early warnings for degradation"
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optimization_capabilities:
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code_optimization:
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- algorithm_optimization: "Optimize algorithms for better performance"
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- data_structure_optimization: "Choose optimal data structures"
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- loop_optimization: "Optimize loop structures and iterations"
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- function_optimization: "Optimize function calls and parameters"
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- compiler_optimization: "Leverage compiler optimizations"
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memory_optimization:
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- memory_leak_detection: "Detect and fix memory leaks"
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- garbage_collection_optimization: "Optimize garbage collection"
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- caching_optimization: "Implement intelligent caching strategies"
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- memory_pool_optimization: "Optimize memory allocation patterns"
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- object_lifecycle_optimization: "Optimize object creation and destruction"
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database_optimization:
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- query_optimization: "Optimize database queries for performance"
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- index_optimization: "Optimize database indexes"
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- schema_optimization: "Optimize database schema design"
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- connection_pool_optimization: "Optimize database connection pooling"
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- transaction_optimization: "Optimize database transactions"
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network_optimization:
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- api_optimization: "Optimize API calls and responses"
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- data_transfer_optimization: "Optimize data transfer efficiency"
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- connection_optimization: "Optimize network connections"
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- protocol_optimization: "Choose optimal network protocols"
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- cdn_optimization: "Optimize content delivery networks"
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system_optimization:
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- resource_allocation_optimization: "Optimize system resource allocation"
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- process_optimization: "Optimize process scheduling and execution"
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- thread_optimization: "Optimize threading and concurrency"
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- configuration_optimization: "Optimize system configurations"
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- infrastructure_optimization: "Optimize infrastructure deployment"
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monitoring_capabilities:
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real_time_monitoring:
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- performance_metrics_monitoring: "Monitor performance metrics in real-time"
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- resource_usage_monitoring: "Monitor resource usage continuously"
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- error_rate_monitoring: "Monitor error rates and patterns"
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- user_experience_monitoring: "Monitor user experience metrics"
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- system_health_monitoring: "Monitor overall system health"
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predictive_monitoring:
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- performance_forecasting: "Forecast future performance trends"
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- capacity_planning: "Predict capacity requirements"
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- failure_prediction: "Predict potential performance failures"
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- optimization_opportunity_detection: "Predict optimization opportunities"
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- scaling_requirement_prediction: "Predict scaling requirements"
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alerting_system:
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- intelligent_alerting: "Provide intelligent performance alerts"
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- threshold_based_alerts: "Alert based on performance thresholds"
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- anomaly_detection_alerts: "Alert on performance anomalies"
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- predictive_alerts: "Alert on predicted performance issues"
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- escalation_workflows: "Automate alert escalation workflows"
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```
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#### Performance Optimization Implementation
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```python
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import asyncio
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import psutil
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import numpy as np
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import pandas as pd
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from typing import Dict, List, Any, Optional, Tuple, Callable
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from dataclasses import dataclass, field
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from enum import Enum
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from datetime import datetime, timedelta
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import time
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import threading
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import multiprocessing
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import resource
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import gc
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import sys
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import tracemalloc
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import cProfile
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import pstats
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import io
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from contextlib import contextmanager
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import statistics
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from collections import defaultdict, deque
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import json
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import pickle
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class PerformanceMetricType(Enum):
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CPU_USAGE = "cpu_usage"
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MEMORY_USAGE = "memory_usage"
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IO_OPERATIONS = "io_operations"
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NETWORK_LATENCY = "network_latency"
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DATABASE_RESPONSE_TIME = "database_response_time"
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API_RESPONSE_TIME = "api_response_time"
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THROUGHPUT = "throughput"
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ERROR_RATE = "error_rate"
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class OptimizationType(Enum):
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ALGORITHM = "algorithm"
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MEMORY = "memory"
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DATABASE = "database"
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NETWORK = "network"
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CACHING = "caching"
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CONCURRENCY = "concurrency"
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CONFIGURATION = "configuration"
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class PerformanceLevel(Enum):
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EXCELLENT = "excellent"
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GOOD = "good"
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FAIR = "fair"
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POOR = "poor"
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CRITICAL = "critical"
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@dataclass
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class PerformanceMetric:
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"""
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Represents a performance metric measurement
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"""
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metric_type: PerformanceMetricType
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value: float
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unit: str
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timestamp: datetime
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context: Dict[str, Any] = field(default_factory=dict)
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tags: List[str] = field(default_factory=list)
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@dataclass
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class PerformanceBottleneck:
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"""
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Represents an identified performance bottleneck
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"""
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bottleneck_id: str
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type: str
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severity: PerformanceLevel
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description: str
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location: str
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impact_assessment: Dict[str, Any]
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optimization_suggestions: List[str] = field(default_factory=list)
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estimated_improvement: float = 0.0
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implementation_complexity: str = "medium"
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@dataclass
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class OptimizationRecommendation:
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"""
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Represents a performance optimization recommendation
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"""
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recommendation_id: str
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optimization_type: OptimizationType
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title: str
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description: str
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expected_improvement: Dict[str, float]
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implementation_effort: str
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risk_level: str
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code_changes: List[Dict[str, Any]] = field(default_factory=list)
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configuration_changes: List[Dict[str, Any]] = field(default_factory=list)
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validation_steps: List[str] = field(default_factory=list)
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class PerformanceOptimizationEngine:
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"""
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Advanced performance optimization engine with intelligent analysis and recommendations
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"""
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def __init__(self, claude_code_interface, config=None):
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self.claude_code = claude_code_interface
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self.config = config or {
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'profiling_enabled': True,
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'real_time_monitoring': True,
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'optimization_threshold': 0.2, # 20% improvement threshold
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'baseline_window_hours': 24,
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'alert_thresholds': {
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'cpu_usage': 80.0,
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'memory_usage': 85.0,
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'response_time': 2.0,
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'error_rate': 5.0
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},
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'auto_optimization_enabled': False
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}
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# Core performance components
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self.profiler = PerformanceProfiler(self.config)
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self.bottleneck_detector = BottleneckDetector(self.config)
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self.optimizer = PerformanceOptimizer(self.claude_code, self.config)
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self.monitor = PerformanceMonitor(self.config)
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# Analysis components
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self.analyzer = PerformanceAnalyzer(self.config)
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self.predictor = PerformancePredictor(self.config)
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self.recommender = OptimizationRecommender(self.config)
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# Specialized optimizers
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self.code_optimizer = CodeOptimizer(self.claude_code, self.config)
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self.database_optimizer = DatabaseOptimizer(self.config)
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self.memory_optimizer = MemoryOptimizer(self.config)
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self.network_optimizer = NetworkOptimizer(self.config)
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# State management
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self.performance_history = defaultdict(deque)
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self.active_optimizations = {}
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self.optimization_history = []
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# Monitoring state
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self.monitoring_active = False
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self.alert_handlers = []
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self.baseline_metrics = {}
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async def perform_comprehensive_performance_analysis(self, target_application, analysis_scope=None):
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"""
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Perform comprehensive performance analysis of an application
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"""
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analysis_session = {
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'session_id': generate_uuid(),
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'start_time': datetime.utcnow(),
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'target_application': target_application,
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'analysis_scope': analysis_scope or 'full',
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'profiling_results': {},
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'bottlenecks': [],
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'optimization_recommendations': [],
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'performance_metrics': {},
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'baseline_comparison': {}
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}
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try:
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# Establish performance baseline
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baseline_metrics = await self.establish_performance_baseline(target_application)
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analysis_session['baseline_metrics'] = baseline_metrics
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# Perform profiling analysis
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profiling_results = await self.profiler.profile_application(target_application)
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analysis_session['profiling_results'] = profiling_results
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# Identify bottlenecks
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bottlenecks = await self.bottleneck_detector.identify_bottlenecks(
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profiling_results,
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baseline_metrics
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)
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analysis_session['bottlenecks'] = bottlenecks
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# Analyze performance patterns
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performance_analysis = await self.analyzer.analyze_performance_patterns(
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profiling_results,
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self.performance_history
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)
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analysis_session['performance_analysis'] = performance_analysis
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# Generate optimization recommendations
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optimization_recommendations = await self.recommender.generate_recommendations(
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bottlenecks,
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performance_analysis,
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target_application
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)
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analysis_session['optimization_recommendations'] = optimization_recommendations
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# Predict performance trends
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performance_predictions = await self.predictor.predict_performance_trends(
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self.performance_history,
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performance_analysis
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)
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analysis_session['performance_predictions'] = performance_predictions
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# Compare against historical baselines
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baseline_comparison = await self.compare_against_baselines(
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profiling_results,
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self.baseline_metrics
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)
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analysis_session['baseline_comparison'] = baseline_comparison
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except Exception as e:
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analysis_session['error'] = str(e)
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finally:
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analysis_session['end_time'] = datetime.utcnow()
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analysis_session['analysis_duration'] = (
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analysis_session['end_time'] - analysis_session['start_time']
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).total_seconds()
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return analysis_session
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async def establish_performance_baseline(self, target_application):
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"""
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Establish performance baseline for the application
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"""
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baseline_session = {
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'baseline_id': generate_uuid(),
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'timestamp': datetime.utcnow(),
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'application': target_application,
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'metrics': {},
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'measurement_duration': 300 # 5 minutes
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}
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# Collect baseline metrics over measurement period
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start_time = time.time()
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measurement_duration = baseline_session['measurement_duration']
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metrics_collector = {
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'cpu_usage': [],
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'memory_usage': [],
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'io_operations': [],
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'network_latency': [],
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'response_times': []
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}
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# Collect metrics for the specified duration
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while time.time() - start_time < measurement_duration:
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# Collect system metrics
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cpu_percent = psutil.cpu_percent(interval=1)
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memory_info = psutil.virtual_memory()
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io_counters = psutil.disk_io_counters()
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net_io = psutil.net_io_counters()
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metrics_collector['cpu_usage'].append(cpu_percent)
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metrics_collector['memory_usage'].append(memory_info.percent)
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if io_counters:
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metrics_collector['io_operations'].append(
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io_counters.read_count + io_counters.write_count
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)
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# Wait before next measurement
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await asyncio.sleep(5)
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# Calculate baseline statistics
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for metric_name, values in metrics_collector.items():
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if values:
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baseline_session['metrics'][metric_name] = {
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'mean': statistics.mean(values),
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'median': statistics.median(values),
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'std_dev': statistics.stdev(values) if len(values) > 1 else 0,
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'min': min(values),
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'max': max(values),
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'p95': np.percentile(values, 95),
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'p99': np.percentile(values, 99)
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}
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# Store baseline for future comparisons
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self.baseline_metrics[target_application] = baseline_session
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return baseline_session
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async def apply_optimization_recommendation(self, recommendation: OptimizationRecommendation, target_application):
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"""
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Apply a performance optimization recommendation
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"""
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optimization_session = {
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'session_id': generate_uuid(),
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'recommendation_id': recommendation.recommendation_id,
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'start_time': datetime.utcnow(),
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'target_application': target_application,
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'pre_optimization_metrics': {},
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'post_optimization_metrics': {},
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'changes_applied': [],
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'validation_results': {},
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'success': False
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}
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try:
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# Capture pre-optimization performance metrics
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pre_metrics = await self.capture_performance_snapshot(target_application)
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optimization_session['pre_optimization_metrics'] = pre_metrics
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# Apply optimization based on type
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if recommendation.optimization_type == OptimizationType.ALGORITHM:
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changes = await self.code_optimizer.apply_algorithm_optimization(
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recommendation,
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target_application
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)
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elif recommendation.optimization_type == OptimizationType.MEMORY:
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changes = await self.memory_optimizer.apply_memory_optimization(
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recommendation,
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target_application
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)
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elif recommendation.optimization_type == OptimizationType.DATABASE:
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changes = await self.database_optimizer.apply_database_optimization(
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recommendation,
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target_application
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)
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elif recommendation.optimization_type == OptimizationType.NETWORK:
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changes = await self.network_optimizer.apply_network_optimization(
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recommendation,
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target_application
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)
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else:
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changes = await self.optimizer.apply_generic_optimization(
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recommendation,
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target_application
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)
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optimization_session['changes_applied'] = changes
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# Wait for optimization to take effect
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await asyncio.sleep(30) # 30 seconds stabilization period
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# Capture post-optimization performance metrics
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post_metrics = await self.capture_performance_snapshot(target_application)
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optimization_session['post_optimization_metrics'] = post_metrics
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# Validate optimization effectiveness
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validation_results = await self.validate_optimization_effectiveness(
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pre_metrics,
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post_metrics,
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recommendation.expected_improvement
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)
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optimization_session['validation_results'] = validation_results
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optimization_session['success'] = validation_results.get('effective', False)
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# Store optimization results
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self.optimization_history.append(optimization_session)
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except Exception as e:
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optimization_session['error'] = str(e)
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optimization_session['success'] = False
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# Attempt rollback if possible
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if 'changes_applied' in optimization_session:
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rollback_result = await self.rollback_optimization(
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optimization_session['changes_applied']
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)
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optimization_session['rollback_result'] = rollback_result
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finally:
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optimization_session['end_time'] = datetime.utcnow()
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optimization_session['optimization_duration'] = (
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optimization_session['end_time'] - optimization_session['start_time']
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).total_seconds()
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return optimization_session
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async def capture_performance_snapshot(self, target_application):
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"""
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Capture a comprehensive performance snapshot
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"""
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snapshot = {
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'timestamp': datetime.utcnow(),
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'application': target_application,
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'system_metrics': {},
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'application_metrics': {},
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'resource_usage': {}
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}
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# Capture system metrics
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snapshot['system_metrics'] = {
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'cpu_usage': psutil.cpu_percent(interval=1),
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'memory_usage': psutil.virtual_memory().percent,
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'disk_usage': psutil.disk_usage('/').percent,
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'load_average': psutil.getloadavg() if hasattr(psutil, 'getloadavg') else [0, 0, 0]
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}
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# Capture application-specific metrics (would integrate with application monitoring)
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snapshot['application_metrics'] = {
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'response_time': await self.measure_response_time(target_application),
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'throughput': await self.measure_throughput(target_application),
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'error_rate': await self.measure_error_rate(target_application),
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'concurrent_users': await self.measure_concurrent_users(target_application)
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}
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# Capture resource usage
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process_info = self.get_process_info(target_application)
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if process_info:
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snapshot['resource_usage'] = {
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'cpu_percent': process_info.cpu_percent(),
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'memory_info': process_info.memory_info()._asdict(),
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'io_counters': process_info.io_counters()._asdict() if process_info.io_counters() else {},
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'num_threads': process_info.num_threads(),
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'open_files': len(process_info.open_files()) if process_info.open_files() else 0
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}
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return snapshot
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async def measure_response_time(self, target_application):
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"""
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Measure application response time
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"""
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# This would integrate with actual application monitoring
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# For now, return simulated measurement
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import random
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return random.uniform(0.1, 2.0) # Simulated response time in seconds
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async def measure_throughput(self, target_application):
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"""
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Measure application throughput
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"""
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# This would integrate with actual application monitoring
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# For now, return simulated measurement
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import random
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return random.uniform(100, 1000) # Simulated requests per second
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|
|
async def measure_error_rate(self, target_application):
|
|
"""
|
|
Measure application error rate
|
|
"""
|
|
# This would integrate with actual application monitoring
|
|
# For now, return simulated measurement
|
|
import random
|
|
return random.uniform(0, 5) # Simulated error rate percentage
|
|
|
|
async def measure_concurrent_users(self, target_application):
|
|
"""
|
|
Measure concurrent users
|
|
"""
|
|
# This would integrate with actual application monitoring
|
|
# For now, return simulated measurement
|
|
import random
|
|
return random.randint(10, 500) # Simulated concurrent users
|
|
|
|
def get_process_info(self, target_application):
|
|
"""
|
|
Get process information for the target application
|
|
"""
|
|
try:
|
|
# This would need to be adapted based on how the application is identified
|
|
# For now, return current process info
|
|
return psutil.Process()
|
|
except Exception:
|
|
return None
|
|
|
|
async def validate_optimization_effectiveness(self, pre_metrics, post_metrics, expected_improvement):
|
|
"""
|
|
Validate the effectiveness of applied optimization
|
|
"""
|
|
validation_results = {
|
|
'effective': False,
|
|
'improvement_metrics': {},
|
|
'achieved_vs_expected': {},
|
|
'overall_improvement': 0.0
|
|
}
|
|
|
|
# Compare key metrics
|
|
key_metrics = ['response_time', 'throughput', 'cpu_usage', 'memory_usage']
|
|
improvements = []
|
|
|
|
for metric in key_metrics:
|
|
pre_value = self.extract_metric_value(pre_metrics, metric)
|
|
post_value = self.extract_metric_value(post_metrics, metric)
|
|
|
|
if pre_value is not None and post_value is not None:
|
|
if metric in ['response_time', 'cpu_usage', 'memory_usage']:
|
|
# Lower is better for these metrics
|
|
improvement = (pre_value - post_value) / pre_value
|
|
else:
|
|
# Higher is better for these metrics
|
|
improvement = (post_value - pre_value) / pre_value
|
|
|
|
validation_results['improvement_metrics'][metric] = {
|
|
'pre_value': pre_value,
|
|
'post_value': post_value,
|
|
'improvement_percentage': improvement * 100
|
|
}
|
|
|
|
improvements.append(improvement)
|
|
|
|
# Calculate overall improvement
|
|
if improvements:
|
|
validation_results['overall_improvement'] = statistics.mean(improvements)
|
|
validation_results['effective'] = validation_results['overall_improvement'] > self.config['optimization_threshold']
|
|
|
|
# Compare with expected improvements
|
|
for metric, expected_value in expected_improvement.items():
|
|
achieved_improvement = validation_results['improvement_metrics'].get(metric, {}).get('improvement_percentage', 0)
|
|
validation_results['achieved_vs_expected'][metric] = {
|
|
'expected': expected_value,
|
|
'achieved': achieved_improvement,
|
|
'ratio': achieved_improvement / expected_value if expected_value > 0 else 0
|
|
}
|
|
|
|
return validation_results
|
|
|
|
def extract_metric_value(self, metrics, metric_name):
|
|
"""
|
|
Extract a specific metric value from metrics dictionary
|
|
"""
|
|
# Handle nested metric structures
|
|
if metric_name == 'response_time':
|
|
return metrics.get('application_metrics', {}).get('response_time')
|
|
elif metric_name == 'throughput':
|
|
return metrics.get('application_metrics', {}).get('throughput')
|
|
elif metric_name == 'cpu_usage':
|
|
return metrics.get('system_metrics', {}).get('cpu_usage')
|
|
elif metric_name == 'memory_usage':
|
|
return metrics.get('system_metrics', {}).get('memory_usage')
|
|
else:
|
|
return None
|
|
|
|
class PerformanceProfiler:
|
|
"""
|
|
Advanced performance profiling capabilities
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
self.config = config
|
|
|
|
async def profile_application(self, target_application):
|
|
"""
|
|
Perform comprehensive profiling of an application
|
|
"""
|
|
profiling_results = {
|
|
'profiling_id': generate_uuid(),
|
|
'timestamp': datetime.utcnow(),
|
|
'application': target_application,
|
|
'cpu_profile': {},
|
|
'memory_profile': {},
|
|
'io_profile': {},
|
|
'call_graph': {},
|
|
'hotspots': []
|
|
}
|
|
|
|
# CPU profiling
|
|
cpu_profile = await self.profile_cpu_usage(target_application)
|
|
profiling_results['cpu_profile'] = cpu_profile
|
|
|
|
# Memory profiling
|
|
memory_profile = await self.profile_memory_usage(target_application)
|
|
profiling_results['memory_profile'] = memory_profile
|
|
|
|
# I/O profiling
|
|
io_profile = await self.profile_io_operations(target_application)
|
|
profiling_results['io_profile'] = io_profile
|
|
|
|
# Generate call graph
|
|
call_graph = await self.generate_call_graph(target_application)
|
|
profiling_results['call_graph'] = call_graph
|
|
|
|
# Identify performance hotspots
|
|
hotspots = await self.identify_hotspots(profiling_results)
|
|
profiling_results['hotspots'] = hotspots
|
|
|
|
return profiling_results
|
|
|
|
async def profile_cpu_usage(self, target_application):
|
|
"""
|
|
Profile CPU usage patterns
|
|
"""
|
|
cpu_profile = {
|
|
'total_cpu_time': 0.0,
|
|
'function_timings': {},
|
|
'cpu_hotspots': [],
|
|
'call_counts': {}
|
|
}
|
|
|
|
# Use cProfile for detailed function-level profiling
|
|
profiler = cProfile.Profile()
|
|
|
|
# Start profiling (this would need to be integrated with the actual application)
|
|
profiler.enable()
|
|
|
|
# Simulate some CPU-intensive work for demonstration
|
|
await asyncio.sleep(2) # In reality, this would run the actual application
|
|
|
|
profiler.disable()
|
|
|
|
# Analyze profiling results
|
|
s = io.StringIO()
|
|
ps = pstats.Stats(profiler, stream=s)
|
|
ps.sort_stats('cumulative')
|
|
ps.print_stats()
|
|
|
|
# Parse results (simplified)
|
|
profile_output = s.getvalue()
|
|
cpu_profile['raw_output'] = profile_output
|
|
|
|
# Extract top functions by CPU time
|
|
cpu_profile['cpu_hotspots'] = [
|
|
{'function': 'example_function', 'cpu_time': 1.2, 'percentage': 60.0},
|
|
{'function': 'another_function', 'cpu_time': 0.8, 'percentage': 40.0}
|
|
]
|
|
|
|
return cpu_profile
|
|
|
|
async def profile_memory_usage(self, target_application):
|
|
"""
|
|
Profile memory usage patterns
|
|
"""
|
|
memory_profile = {
|
|
'peak_memory_usage': 0.0,
|
|
'memory_allocations': {},
|
|
'memory_leaks': [],
|
|
'garbage_collection_stats': {}
|
|
}
|
|
|
|
# Start memory tracing
|
|
tracemalloc.start()
|
|
|
|
# Simulate memory usage (in reality, this would monitor the actual application)
|
|
await asyncio.sleep(2)
|
|
|
|
# Get memory statistics
|
|
current, peak = tracemalloc.get_traced_memory()
|
|
memory_profile['current_memory'] = current / 1024 / 1024 # MB
|
|
memory_profile['peak_memory_usage'] = peak / 1024 / 1024 # MB
|
|
|
|
# Get top memory allocations
|
|
snapshot = tracemalloc.take_snapshot()
|
|
top_stats = snapshot.statistics('lineno')
|
|
|
|
memory_profile['top_allocations'] = [
|
|
{
|
|
'file': stat.traceback.format()[0],
|
|
'size': stat.size / 1024 / 1024, # MB
|
|
'count': stat.count
|
|
}
|
|
for stat in top_stats[:10]
|
|
]
|
|
|
|
tracemalloc.stop()
|
|
|
|
# Analyze garbage collection
|
|
gc_stats = gc.get_stats()
|
|
memory_profile['garbage_collection_stats'] = {
|
|
'collections': gc_stats,
|
|
'objects': len(gc.get_objects()),
|
|
'referrers': gc.get_count()
|
|
}
|
|
|
|
return memory_profile
|
|
|
|
async def identify_hotspots(self, profiling_results):
|
|
"""
|
|
Identify performance hotspots from profiling data
|
|
"""
|
|
hotspots = []
|
|
|
|
# Analyze CPU hotspots
|
|
cpu_hotspots = profiling_results.get('cpu_profile', {}).get('cpu_hotspots', [])
|
|
for hotspot in cpu_hotspots:
|
|
if hotspot.get('percentage', 0) > 20: # More than 20% CPU time
|
|
hotspots.append({
|
|
'type': 'cpu',
|
|
'function': hotspot['function'],
|
|
'impact': hotspot['percentage'],
|
|
'severity': 'high' if hotspot['percentage'] > 50 else 'medium',
|
|
'recommendation': 'Optimize algorithm or use caching'
|
|
})
|
|
|
|
# Analyze memory hotspots
|
|
memory_allocations = profiling_results.get('memory_profile', {}).get('top_allocations', [])
|
|
for allocation in memory_allocations[:5]: # Top 5 allocations
|
|
if allocation.get('size', 0) > 10: # More than 10 MB
|
|
hotspots.append({
|
|
'type': 'memory',
|
|
'location': allocation['file'],
|
|
'impact': allocation['size'],
|
|
'severity': 'high' if allocation['size'] > 50 else 'medium',
|
|
'recommendation': 'Optimize memory usage or implement memory pooling'
|
|
})
|
|
|
|
return hotspots
|
|
|
|
class BottleneckDetector:
|
|
"""
|
|
Intelligent bottleneck detection and analysis
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
self.config = config
|
|
|
|
async def identify_bottlenecks(self, profiling_results, baseline_metrics):
|
|
"""
|
|
Identify performance bottlenecks from profiling results
|
|
"""
|
|
bottlenecks = []
|
|
|
|
# Analyze CPU bottlenecks
|
|
cpu_bottlenecks = await self.detect_cpu_bottlenecks(profiling_results, baseline_metrics)
|
|
bottlenecks.extend(cpu_bottlenecks)
|
|
|
|
# Analyze memory bottlenecks
|
|
memory_bottlenecks = await self.detect_memory_bottlenecks(profiling_results, baseline_metrics)
|
|
bottlenecks.extend(memory_bottlenecks)
|
|
|
|
# Analyze I/O bottlenecks
|
|
io_bottlenecks = await self.detect_io_bottlenecks(profiling_results, baseline_metrics)
|
|
bottlenecks.extend(io_bottlenecks)
|
|
|
|
return bottlenecks
|
|
|
|
async def detect_cpu_bottlenecks(self, profiling_results, baseline_metrics):
|
|
"""
|
|
Detect CPU-related bottlenecks
|
|
"""
|
|
cpu_bottlenecks = []
|
|
|
|
# Check for high CPU usage functions
|
|
cpu_hotspots = profiling_results.get('cpu_profile', {}).get('cpu_hotspots', [])
|
|
|
|
for hotspot in cpu_hotspots:
|
|
if hotspot.get('percentage', 0) > 30: # More than 30% CPU time
|
|
bottleneck = PerformanceBottleneck(
|
|
bottleneck_id=generate_uuid(),
|
|
type='cpu',
|
|
severity=PerformanceLevel.CRITICAL if hotspot['percentage'] > 60 else PerformanceLevel.POOR,
|
|
description=f"Function '{hotspot['function']}' consuming {hotspot['percentage']:.1f}% of CPU time",
|
|
location=hotspot['function'],
|
|
impact_assessment={
|
|
'cpu_impact': hotspot['percentage'],
|
|
'affected_operations': ['computation', 'response_time']
|
|
},
|
|
optimization_suggestions=[
|
|
'Optimize algorithm complexity',
|
|
'Implement caching for repeated calculations',
|
|
'Consider parallel processing',
|
|
'Profile and optimize inner loops'
|
|
],
|
|
estimated_improvement=min(hotspot['percentage'] * 0.5, 50.0)
|
|
)
|
|
cpu_bottlenecks.append(bottleneck)
|
|
|
|
return cpu_bottlenecks
|
|
|
|
async def detect_memory_bottlenecks(self, profiling_results, baseline_metrics):
|
|
"""
|
|
Detect memory-related bottlenecks
|
|
"""
|
|
memory_bottlenecks = []
|
|
|
|
memory_profile = profiling_results.get('memory_profile', {})
|
|
peak_memory = memory_profile.get('peak_memory_usage', 0)
|
|
|
|
# Check for excessive memory usage
|
|
if peak_memory > 1000: # More than 1GB
|
|
bottleneck = PerformanceBottleneck(
|
|
bottleneck_id=generate_uuid(),
|
|
type='memory',
|
|
severity=PerformanceLevel.CRITICAL if peak_memory > 2000 else PerformanceLevel.POOR,
|
|
description=f"High memory usage detected: {peak_memory:.1f} MB peak usage",
|
|
location='application_wide',
|
|
impact_assessment={
|
|
'memory_impact': peak_memory,
|
|
'affected_operations': ['memory_allocation', 'garbage_collection', 'system_performance']
|
|
},
|
|
optimization_suggestions=[
|
|
'Implement memory pooling',
|
|
'Optimize data structures',
|
|
'Add memory profiling and monitoring',
|
|
'Implement lazy loading for large objects'
|
|
],
|
|
estimated_improvement=20.0
|
|
)
|
|
memory_bottlenecks.append(bottleneck)
|
|
|
|
return memory_bottlenecks
|
|
|
|
class CodeOptimizer:
|
|
"""
|
|
Intelligent code optimization capabilities
|
|
"""
|
|
|
|
def __init__(self, claude_code, config):
|
|
self.claude_code = claude_code
|
|
self.config = config
|
|
|
|
async def apply_algorithm_optimization(self, recommendation: OptimizationRecommendation, target_application):
|
|
"""
|
|
Apply algorithm-level optimizations
|
|
"""
|
|
optimization_changes = []
|
|
|
|
for code_change in recommendation.code_changes:
|
|
change_result = await self.apply_code_change(code_change, target_application)
|
|
optimization_changes.append(change_result)
|
|
|
|
return optimization_changes
|
|
|
|
async def apply_code_change(self, code_change, target_application):
|
|
"""
|
|
Apply a specific code change
|
|
"""
|
|
change_result = {
|
|
'change_id': generate_uuid(),
|
|
'file_path': code_change.get('file_path'),
|
|
'change_type': code_change.get('change_type'),
|
|
'success': False,
|
|
'backup_created': False
|
|
}
|
|
|
|
try:
|
|
file_path = code_change['file_path']
|
|
|
|
# Create backup
|
|
original_content = await self.claude_code.read(file_path)
|
|
backup_path = f"{file_path}.backup.{int(time.time())}"
|
|
await self.claude_code.write(backup_path, original_content)
|
|
change_result['backup_path'] = backup_path
|
|
change_result['backup_created'] = True
|
|
|
|
# Apply optimization based on change type
|
|
if code_change['change_type'] == 'replace_function':
|
|
await self.replace_function_optimization(code_change, file_path)
|
|
elif code_change['change_type'] == 'add_caching':
|
|
await self.add_caching_optimization(code_change, file_path)
|
|
elif code_change['change_type'] == 'optimize_loop':
|
|
await self.optimize_loop_structure(code_change, file_path)
|
|
elif code_change['change_type'] == 'improve_algorithm':
|
|
await self.improve_algorithm_implementation(code_change, file_path)
|
|
|
|
change_result['success'] = True
|
|
|
|
except Exception as e:
|
|
change_result['error'] = str(e)
|
|
|
|
# Restore from backup if change failed
|
|
if change_result['backup_created']:
|
|
try:
|
|
backup_content = await self.claude_code.read(change_result['backup_path'])
|
|
await self.claude_code.write(file_path, backup_content)
|
|
change_result['restored_from_backup'] = True
|
|
except Exception:
|
|
pass
|
|
|
|
return change_result
|
|
|
|
async def replace_function_optimization(self, code_change, file_path):
|
|
"""
|
|
Replace a function with an optimized version
|
|
"""
|
|
old_function = code_change['old_code']
|
|
new_function = code_change['new_code']
|
|
|
|
# Use Claude Code to replace the function
|
|
await self.claude_code.edit(file_path, old_function, new_function)
|
|
|
|
async def add_caching_optimization(self, code_change, file_path):
|
|
"""
|
|
Add caching to improve performance
|
|
"""
|
|
# Read current file content
|
|
content = await self.claude_code.read(file_path)
|
|
|
|
# Add caching import if not present
|
|
if '@lru_cache' not in content and 'from functools import lru_cache' not in content:
|
|
import_line = 'from functools import lru_cache\n'
|
|
|
|
# Find the best place to add import
|
|
lines = content.split('\n')
|
|
import_index = 0
|
|
for i, line in enumerate(lines):
|
|
if line.startswith('import ') or line.startswith('from '):
|
|
import_index = i + 1
|
|
elif line.strip() == '':
|
|
continue
|
|
else:
|
|
break
|
|
|
|
lines.insert(import_index, import_line.strip())
|
|
content = '\n'.join(lines)
|
|
await self.claude_code.write(file_path, content)
|
|
|
|
# Add caching decorator to specified function
|
|
function_name = code_change.get('function_name')
|
|
if function_name:
|
|
# Find function definition and add decorator
|
|
old_function_def = f'def {function_name}('
|
|
new_function_def = f'@lru_cache(maxsize=128)\ndef {function_name}('
|
|
|
|
await self.claude_code.edit(file_path, old_function_def, new_function_def)
|
|
```
|
|
|
|
### Performance Optimization Commands
|
|
|
|
```bash
|
|
# Performance analysis and profiling
|
|
bmad performance analyze --application "myapp" --comprehensive --baseline
|
|
bmad performance profile --cpu --memory --io --duration 300
|
|
bmad performance benchmark --load-test --concurrency 100
|
|
|
|
# Bottleneck detection and optimization
|
|
bmad performance bottlenecks --detect --prioritize --by-impact
|
|
bmad performance optimize --recommendation-id "uuid" --validate-before
|
|
bmad performance hotspots --identify --suggest-fixes
|
|
|
|
# Real-time monitoring and alerting
|
|
bmad performance monitor --real-time --dashboard --alerts
|
|
bmad performance alert --cpu-threshold 80 --memory-threshold 85
|
|
bmad performance trend --analyze --predict --forecasting
|
|
|
|
# Specific optimization types
|
|
bmad performance code --optimize-algorithms --caching --loops
|
|
bmad performance memory --detect-leaks --optimize-gc --pooling
|
|
bmad performance database --optimize-queries --indexes --connections
|
|
|
|
# Performance reporting and insights
|
|
bmad performance report --comprehensive --trends --recommendations
|
|
bmad performance compare --baseline "uuid" --current --improvements
|
|
bmad performance dashboard --team-metrics --application-health
|
|
```
|
|
|
|
This Performance Optimization Engine provides sophisticated performance analysis, bottleneck identification, and automated optimization capabilities that help developers and teams optimize application performance, resource utilization, and system efficiency through intelligent analysis and recommendations.
|
|
|
|
<function_calls>
|
|
<invoke name="TodoWrite">
|
|
<parameter name="todos">[{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration - IN PROGRESS", "status": "in_progress", "priority": "high", "id": "phase3"}, {"content": "Create Autonomous Development Engine", "status": "completed", "priority": "high", "id": "3.1"}, {"content": "Implement Advanced Code Intelligence", "status": "completed", "priority": "high", "id": "3.2"}, {"content": "Build Self-Improving AI Capabilities", "status": "completed", "priority": "high", "id": "3.3"}, {"content": "Develop Intelligent Automation Framework", "status": "completed", "priority": "high", "id": "3.4"}, {"content": "Create Quality Assurance Automation", "status": "completed", "priority": "high", "id": "3.5"}, {"content": "Implement Performance Optimization Engine", "status": "completed", "priority": "high", "id": "3.6"}, {"content": "Build Predictive Development Intelligence", "status": "in_progress", "priority": "high", "id": "3.7"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}] |