43 KiB
43 KiB
Performance Optimization Engine
Intelligent Performance Analysis and Optimization for Enhanced BMAD System
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.
Performance Optimization Architecture
Comprehensive Performance Framework
performance_optimization_architecture:
analysis_capabilities:
profiling_analysis:
- cpu_profiling: "Profile CPU usage patterns and hotspots"
- memory_profiling: "Analyze memory allocation and usage patterns"
- io_profiling: "Profile I/O operations and bottlenecks"
- network_profiling: "Analyze network usage and latency"
- database_profiling: "Profile database queries and performance"
bottleneck_identification:
- computational_bottlenecks: "Identify CPU-intensive operations"
- memory_bottlenecks: "Find memory allocation and leak issues"
- io_bottlenecks: "Detect I/O performance issues"
- network_bottlenecks: "Identify network latency and throughput issues"
- synchronization_bottlenecks: "Find concurrency and locking issues"
scalability_analysis:
- load_scalability: "Analyze performance under increasing load"
- data_scalability: "Assess performance with growing data volumes"
- user_scalability: "Test performance with more concurrent users"
- resource_scalability: "Evaluate scaling with additional resources"
- architectural_scalability: "Assess architectural scaling limitations"
performance_regression_detection:
- automated_regression_detection: "Detect performance regressions automatically"
- baseline_comparison: "Compare against performance baselines"
- trend_analysis: "Analyze performance trends over time"
- threshold_monitoring: "Monitor performance against defined thresholds"
- early_warning_system: "Provide early warnings for degradation"
optimization_capabilities:
code_optimization:
- algorithm_optimization: "Optimize algorithms for better performance"
- data_structure_optimization: "Choose optimal data structures"
- loop_optimization: "Optimize loop structures and iterations"
- function_optimization: "Optimize function calls and parameters"
- compiler_optimization: "Leverage compiler optimizations"
memory_optimization:
- memory_leak_detection: "Detect and fix memory leaks"
- garbage_collection_optimization: "Optimize garbage collection"
- caching_optimization: "Implement intelligent caching strategies"
- memory_pool_optimization: "Optimize memory allocation patterns"
- object_lifecycle_optimization: "Optimize object creation and destruction"
database_optimization:
- query_optimization: "Optimize database queries for performance"
- index_optimization: "Optimize database indexes"
- schema_optimization: "Optimize database schema design"
- connection_pool_optimization: "Optimize database connection pooling"
- transaction_optimization: "Optimize database transactions"
network_optimization:
- api_optimization: "Optimize API calls and responses"
- data_transfer_optimization: "Optimize data transfer efficiency"
- connection_optimization: "Optimize network connections"
- protocol_optimization: "Choose optimal network protocols"
- cdn_optimization: "Optimize content delivery networks"
system_optimization:
- resource_allocation_optimization: "Optimize system resource allocation"
- process_optimization: "Optimize process scheduling and execution"
- thread_optimization: "Optimize threading and concurrency"
- configuration_optimization: "Optimize system configurations"
- infrastructure_optimization: "Optimize infrastructure deployment"
monitoring_capabilities:
real_time_monitoring:
- performance_metrics_monitoring: "Monitor performance metrics in real-time"
- resource_usage_monitoring: "Monitor resource usage continuously"
- error_rate_monitoring: "Monitor error rates and patterns"
- user_experience_monitoring: "Monitor user experience metrics"
- system_health_monitoring: "Monitor overall system health"
predictive_monitoring:
- performance_forecasting: "Forecast future performance trends"
- capacity_planning: "Predict capacity requirements"
- failure_prediction: "Predict potential performance failures"
- optimization_opportunity_detection: "Predict optimization opportunities"
- scaling_requirement_prediction: "Predict scaling requirements"
alerting_system:
- intelligent_alerting: "Provide intelligent performance alerts"
- threshold_based_alerts: "Alert based on performance thresholds"
- anomaly_detection_alerts: "Alert on performance anomalies"
- predictive_alerts: "Alert on predicted performance issues"
- escalation_workflows: "Automate alert escalation workflows"
Performance Optimization Implementation
import asyncio
import psutil
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple, Callable
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import time
import threading
import multiprocessing
import resource
import gc
import sys
import tracemalloc
import cProfile
import pstats
import io
from contextlib import contextmanager
import statistics
from collections import defaultdict, deque
import json
import pickle
class PerformanceMetricType(Enum):
CPU_USAGE = "cpu_usage"
MEMORY_USAGE = "memory_usage"
IO_OPERATIONS = "io_operations"
NETWORK_LATENCY = "network_latency"
DATABASE_RESPONSE_TIME = "database_response_time"
API_RESPONSE_TIME = "api_response_time"
THROUGHPUT = "throughput"
ERROR_RATE = "error_rate"
class OptimizationType(Enum):
ALGORITHM = "algorithm"
MEMORY = "memory"
DATABASE = "database"
NETWORK = "network"
CACHING = "caching"
CONCURRENCY = "concurrency"
CONFIGURATION = "configuration"
class PerformanceLevel(Enum):
EXCELLENT = "excellent"
GOOD = "good"
FAIR = "fair"
POOR = "poor"
CRITICAL = "critical"
@dataclass
class PerformanceMetric:
"""
Represents a performance metric measurement
"""
metric_type: PerformanceMetricType
value: float
unit: str
timestamp: datetime
context: Dict[str, Any] = field(default_factory=dict)
tags: List[str] = field(default_factory=list)
@dataclass
class PerformanceBottleneck:
"""
Represents an identified performance bottleneck
"""
bottleneck_id: str
type: str
severity: PerformanceLevel
description: str
location: str
impact_assessment: Dict[str, Any]
optimization_suggestions: List[str] = field(default_factory=list)
estimated_improvement: float = 0.0
implementation_complexity: str = "medium"
@dataclass
class OptimizationRecommendation:
"""
Represents a performance optimization recommendation
"""
recommendation_id: str
optimization_type: OptimizationType
title: str
description: str
expected_improvement: Dict[str, float]
implementation_effort: str
risk_level: str
code_changes: List[Dict[str, Any]] = field(default_factory=list)
configuration_changes: List[Dict[str, Any]] = field(default_factory=list)
validation_steps: List[str] = field(default_factory=list)
class PerformanceOptimizationEngine:
"""
Advanced performance optimization engine with intelligent analysis and recommendations
"""
def __init__(self, claude_code_interface, config=None):
self.claude_code = claude_code_interface
self.config = config or {
'profiling_enabled': True,
'real_time_monitoring': True,
'optimization_threshold': 0.2, # 20% improvement threshold
'baseline_window_hours': 24,
'alert_thresholds': {
'cpu_usage': 80.0,
'memory_usage': 85.0,
'response_time': 2.0,
'error_rate': 5.0
},
'auto_optimization_enabled': False
}
# Core performance components
self.profiler = PerformanceProfiler(self.config)
self.bottleneck_detector = BottleneckDetector(self.config)
self.optimizer = PerformanceOptimizer(self.claude_code, self.config)
self.monitor = PerformanceMonitor(self.config)
# Analysis components
self.analyzer = PerformanceAnalyzer(self.config)
self.predictor = PerformancePredictor(self.config)
self.recommender = OptimizationRecommender(self.config)
# Specialized optimizers
self.code_optimizer = CodeOptimizer(self.claude_code, self.config)
self.database_optimizer = DatabaseOptimizer(self.config)
self.memory_optimizer = MemoryOptimizer(self.config)
self.network_optimizer = NetworkOptimizer(self.config)
# State management
self.performance_history = defaultdict(deque)
self.active_optimizations = {}
self.optimization_history = []
# Monitoring state
self.monitoring_active = False
self.alert_handlers = []
self.baseline_metrics = {}
async def perform_comprehensive_performance_analysis(self, target_application, analysis_scope=None):
"""
Perform comprehensive performance analysis of an application
"""
analysis_session = {
'session_id': generate_uuid(),
'start_time': datetime.utcnow(),
'target_application': target_application,
'analysis_scope': analysis_scope or 'full',
'profiling_results': {},
'bottlenecks': [],
'optimization_recommendations': [],
'performance_metrics': {},
'baseline_comparison': {}
}
try:
# Establish performance baseline
baseline_metrics = await self.establish_performance_baseline(target_application)
analysis_session['baseline_metrics'] = baseline_metrics
# Perform profiling analysis
profiling_results = await self.profiler.profile_application(target_application)
analysis_session['profiling_results'] = profiling_results
# Identify bottlenecks
bottlenecks = await self.bottleneck_detector.identify_bottlenecks(
profiling_results,
baseline_metrics
)
analysis_session['bottlenecks'] = bottlenecks
# Analyze performance patterns
performance_analysis = await self.analyzer.analyze_performance_patterns(
profiling_results,
self.performance_history
)
analysis_session['performance_analysis'] = performance_analysis
# Generate optimization recommendations
optimization_recommendations = await self.recommender.generate_recommendations(
bottlenecks,
performance_analysis,
target_application
)
analysis_session['optimization_recommendations'] = optimization_recommendations
# Predict performance trends
performance_predictions = await self.predictor.predict_performance_trends(
self.performance_history,
performance_analysis
)
analysis_session['performance_predictions'] = performance_predictions
# Compare against historical baselines
baseline_comparison = await self.compare_against_baselines(
profiling_results,
self.baseline_metrics
)
analysis_session['baseline_comparison'] = baseline_comparison
except Exception as e:
analysis_session['error'] = str(e)
finally:
analysis_session['end_time'] = datetime.utcnow()
analysis_session['analysis_duration'] = (
analysis_session['end_time'] - analysis_session['start_time']
).total_seconds()
return analysis_session
async def establish_performance_baseline(self, target_application):
"""
Establish performance baseline for the application
"""
baseline_session = {
'baseline_id': generate_uuid(),
'timestamp': datetime.utcnow(),
'application': target_application,
'metrics': {},
'measurement_duration': 300 # 5 minutes
}
# Collect baseline metrics over measurement period
start_time = time.time()
measurement_duration = baseline_session['measurement_duration']
metrics_collector = {
'cpu_usage': [],
'memory_usage': [],
'io_operations': [],
'network_latency': [],
'response_times': []
}
# Collect metrics for the specified duration
while time.time() - start_time < measurement_duration:
# Collect system metrics
cpu_percent = psutil.cpu_percent(interval=1)
memory_info = psutil.virtual_memory()
io_counters = psutil.disk_io_counters()
net_io = psutil.net_io_counters()
metrics_collector['cpu_usage'].append(cpu_percent)
metrics_collector['memory_usage'].append(memory_info.percent)
if io_counters:
metrics_collector['io_operations'].append(
io_counters.read_count + io_counters.write_count
)
# Wait before next measurement
await asyncio.sleep(5)
# Calculate baseline statistics
for metric_name, values in metrics_collector.items():
if values:
baseline_session['metrics'][metric_name] = {
'mean': statistics.mean(values),
'median': statistics.median(values),
'std_dev': statistics.stdev(values) if len(values) > 1 else 0,
'min': min(values),
'max': max(values),
'p95': np.percentile(values, 95),
'p99': np.percentile(values, 99)
}
# Store baseline for future comparisons
self.baseline_metrics[target_application] = baseline_session
return baseline_session
async def apply_optimization_recommendation(self, recommendation: OptimizationRecommendation, target_application):
"""
Apply a performance optimization recommendation
"""
optimization_session = {
'session_id': generate_uuid(),
'recommendation_id': recommendation.recommendation_id,
'start_time': datetime.utcnow(),
'target_application': target_application,
'pre_optimization_metrics': {},
'post_optimization_metrics': {},
'changes_applied': [],
'validation_results': {},
'success': False
}
try:
# Capture pre-optimization performance metrics
pre_metrics = await self.capture_performance_snapshot(target_application)
optimization_session['pre_optimization_metrics'] = pre_metrics
# Apply optimization based on type
if recommendation.optimization_type == OptimizationType.ALGORITHM:
changes = await self.code_optimizer.apply_algorithm_optimization(
recommendation,
target_application
)
elif recommendation.optimization_type == OptimizationType.MEMORY:
changes = await self.memory_optimizer.apply_memory_optimization(
recommendation,
target_application
)
elif recommendation.optimization_type == OptimizationType.DATABASE:
changes = await self.database_optimizer.apply_database_optimization(
recommendation,
target_application
)
elif recommendation.optimization_type == OptimizationType.NETWORK:
changes = await self.network_optimizer.apply_network_optimization(
recommendation,
target_application
)
else:
changes = await self.optimizer.apply_generic_optimization(
recommendation,
target_application
)
optimization_session['changes_applied'] = changes
# Wait for optimization to take effect
await asyncio.sleep(30) # 30 seconds stabilization period
# Capture post-optimization performance metrics
post_metrics = await self.capture_performance_snapshot(target_application)
optimization_session['post_optimization_metrics'] = post_metrics
# Validate optimization effectiveness
validation_results = await self.validate_optimization_effectiveness(
pre_metrics,
post_metrics,
recommendation.expected_improvement
)
optimization_session['validation_results'] = validation_results
optimization_session['success'] = validation_results.get('effective', False)
# Store optimization results
self.optimization_history.append(optimization_session)
except Exception as e:
optimization_session['error'] = str(e)
optimization_session['success'] = False
# Attempt rollback if possible
if 'changes_applied' in optimization_session:
rollback_result = await self.rollback_optimization(
optimization_session['changes_applied']
)
optimization_session['rollback_result'] = rollback_result
finally:
optimization_session['end_time'] = datetime.utcnow()
optimization_session['optimization_duration'] = (
optimization_session['end_time'] - optimization_session['start_time']
).total_seconds()
return optimization_session
async def capture_performance_snapshot(self, target_application):
"""
Capture a comprehensive performance snapshot
"""
snapshot = {
'timestamp': datetime.utcnow(),
'application': target_application,
'system_metrics': {},
'application_metrics': {},
'resource_usage': {}
}
# Capture system metrics
snapshot['system_metrics'] = {
'cpu_usage': psutil.cpu_percent(interval=1),
'memory_usage': psutil.virtual_memory().percent,
'disk_usage': psutil.disk_usage('/').percent,
'load_average': psutil.getloadavg() if hasattr(psutil, 'getloadavg') else [0, 0, 0]
}
# Capture application-specific metrics (would integrate with application monitoring)
snapshot['application_metrics'] = {
'response_time': await self.measure_response_time(target_application),
'throughput': await self.measure_throughput(target_application),
'error_rate': await self.measure_error_rate(target_application),
'concurrent_users': await self.measure_concurrent_users(target_application)
}
# Capture resource usage
process_info = self.get_process_info(target_application)
if process_info:
snapshot['resource_usage'] = {
'cpu_percent': process_info.cpu_percent(),
'memory_info': process_info.memory_info()._asdict(),
'io_counters': process_info.io_counters()._asdict() if process_info.io_counters() else {},
'num_threads': process_info.num_threads(),
'open_files': len(process_info.open_files()) if process_info.open_files() else 0
}
return snapshot
async def measure_response_time(self, target_application):
"""
Measure application response time
"""
# This would integrate with actual application monitoring
# For now, return simulated measurement
import random
return random.uniform(0.1, 2.0) # Simulated response time in seconds
async def measure_throughput(self, target_application):
"""
Measure application throughput
"""
# This would integrate with actual application monitoring
# For now, return simulated measurement
import random
return random.uniform(100, 1000) # Simulated requests per second
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
# 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.
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