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PERFORMANCE_GUIDE
AutoBotSolutions edited this page May 6, 2026
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This comprehensive performance guide covers optimization strategies, monitoring setup, benchmarking procedures, and performance tuning for all 27 integrated systems and 74 API endpoints in the Aurora AI framework.
# Enable advanced monitoring
curl -X GET "http://localhost:8080/api/monitoring/advanced"
# Get performance metrics
curl -X GET "http://localhost:8080/api/monitoring/performance"
# Real-time system metrics
curl -X GET "http://localhost:8080/api/monitoring/metrics"
# System alerts
curl -X GET "http://localhost:8080/api/monitoring/alerts"# Get monitoring analytics
curl -X GET "http://localhost:8080/api/monitoring/analytics"
# Performance prediction
curl -X POST "http://localhost:8080/api/monitoring/predict" \
-H "Content-Type: application/json" \
-d '{"horizon": "24h", "metrics": ["cpu", "memory", "throughput"]}'
# Performance benchmarking
curl -X POST "http://localhost:8080/api/monitoring/benchmark" \
-H "Content-Type: application/json" \
-d '{"type": "comprehensive", "load": "normal", "duration": 300}'# Run comprehensive performance analysis
curl -X POST "http://localhost:8080/api/optimization/analyze" \
-H "Content-Type: application/json" \
-d '{
"scope": "full_system",
"depth": "comprehensive",
"metrics": ["performance", "resource_usage", "efficiency"]
}'
# Execute optimization plan
curl -X POST "http://localhost:8080/api/optimization/execute" \
-H "Content-Type: application/json" \
-d '{
"plan": "auto",
"level": "conservative",
"components": ["database", "memory", "api"]
}'
# Monitor optimization progress
curl -X GET "http://localhost:8080/api/optimization/monitor"# Check resource status
curl -X GET "http://localhost:8080/api/resources/status"
# Optimize resource allocation
curl -X POST "http://localhost:8080/api/resources/optimize" \
-H "Content-Type: application/json" \
-d '{
"scope": "full_system",
"strategy": "balanced",
"target_efficiency": 0.90
}'
# Allocate resources for specific tasks
curl -X POST "http://localhost:8080/api/resources/allocate" \
-H "Content-Type: application/json" \
-d '{
"type": "application",
"application": "Aurora AI Framework",
"resources": {"cpu": "4 cores", "memory": "8GB"},
"priority": "high"
}'# config/database_optimization.yaml
database:
pooling:
enabled: true
min_connections: 5
max_connections: 20
connection_timeout: 30
idle_timeout: 300
query_optimization:
enabled: true
query_cache: true
index_optimization: true
analyze_queries: true
performance:
batch_size: 1000
prefetch_rows: 100
fetch_size: 1000# Database query optimization
class DatabaseOptimizer:
def __init__(self, db_connection):
self.db = db_connection
def optimize_queries(self):
"""Optimize database queries"""
# Enable query cache
self.db.execute("SET query_cache_type = ON")
self.db.execute("SET query_cache_size = 268435456") # 256MB
# Optimize for reads
self.db.execute("SET innodb_read_io_threads = 8")
self.db.execute("SET innodb_write_io_threads = 4")
# Buffer pool optimization
self.db.execute("SET innodb_buffer_pool_size = 1073741824") # 1GB
# Log optimization
self.db.execute("SET innodb_log_file_size = 268435456") # 256MB
self.db.execute("SET innodb_log_buffer_size = 16777216") # 16MB
def analyze_slow_queries(self):
"""Analyze slow queries"""
slow_queries = self.db.execute("""
SELECT query_time, lock_time, rows_sent, rows_examined, sql_text
FROM mysql.slow_log
WHERE query_time > 1.0
ORDER BY query_time DESC
LIMIT 10
""")
return slow_queries.fetchall()# API performance optimization
class APIOptimizer:
def __init__(self, app):
self.app = app
def add_caching_headers(self, response):
"""Add caching headers to responses"""
response.headers['Cache-Control'] = 'public, max-age=300'
response.headers['ETag'] = self.generate_etag(response.data)
return response
def implement_compression(self):
"""Implement response compression"""
from flask_compress import Compress
Compress(self.app)
def add_rate_limiting(self):
"""Add rate limiting"""
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
limiter = Limiter(
app=self.app,
key_func=get_remote_address,
default_limits=["1000 per hour"]
)
return limiter# Async processing for performance
import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
class AsyncProcessor:
def __init__(self, max_workers=4):
self.executor = ThreadPoolExecutor(max_workers=max_workers)
async def process_batch_requests(self, requests_batch):
"""Process batch requests asynchronously"""
tasks = []
for request in requests_batch:
task = asyncio.create_task(
self.process_single_request(request)
)
tasks.append(task)
results = await asyncio.gather(*tasks)
return results
async def process_single_request(self, request):
"""Process single request asynchronously"""
loop = asyncio.get_event_loop()
return await loop.run_in_executor(
self.executor,
self.sync_process_request,
request
)
def sync_process_request(self, request):
"""Synchronous request processing"""
# Actual processing logic here
return {"status": "processed", "data": request}# Memory optimization strategies
class MemoryOptimizer:
def __init__(self):
self.memory_pool = {}
self.cache_size_limit = 1024 * 1024 * 1024 # 1GB
def optimize_memory_usage(self):
"""Optimize memory usage"""
import gc
import psutil
# Force garbage collection
gc.collect()
# Get memory usage
process = psutil.Process()
memory_info = process.memory_info()
# Optimize if memory usage is high
if memory_info.rss > self.cache_size_limit:
self.clear_cache()
gc.collect()
return memory_info
def implement_object_pooling(self):
"""Implement object pooling for frequently used objects"""
class ObjectPool:
def __init__(self, create_func, max_size=100):
self.create_func = create_func
self.max_size = max_size
self.pool = []
def get(self):
if self.pool:
return self.pool.pop()
return self.create_func()
def release(self, obj):
if len(self.pool) < self.max_size:
self.pool.append(obj)
return ObjectPool# Advanced caching implementation
import redis
import pickle
from functools import wraps
class CacheManager:
def __init__(self, redis_host='localhost', redis_port=6379):
self.redis_client = redis.Redis(host=redis_host, port=redis_port)
self.default_ttl = 3600
def cache_result(self, key_prefix, ttl=None):
"""Decorator for caching function results"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
# Generate cache key
cache_key = f"{key_prefix}:{hash(str(args) + str(kwargs))}"
# Try to get from cache
cached_result = self.redis_client.get(cache_key)
if cached_result:
return pickle.loads(cached_result)
# Execute function and cache result
result = func(*args, **kwargs)
self.redis_client.setex(
cache_key,
ttl or self.default_ttl,
pickle.dumps(result)
)
return result
return wrapper
return decorator
def invalidate_cache(self, pattern):
"""Invalidate cache keys matching pattern"""
keys = self.redis_client.keys(pattern)
if keys:
self.redis_client.delete(*keys)# Run hyperparameter optimization
curl -X POST "http://localhost:8080/api/training/hyperopt" \
-H "Content-Type: application/json" \
-d '{
"algorithm": "RandomForest",
"optimization_method": "bayesian",
"max_iterations": 100,
"cv_folds": 5,
"scoring": "accuracy"
}'# Create optimized ensemble
curl -X POST "http://localhost:8080/api/training/ensemble" \
-H "Content-Type: application/json" \
-d '{
"method": "voting",
"models": ["MDL-001", "MDL-002", "MDL-003"],
"weights": [0.4, 0.3, 0.3],
"optimization": true
}'# Performance benchmarking framework
import time
import statistics
from concurrent.futures import ThreadPoolExecutor
class PerformanceBenchmark:
def __init__(self, base_url="http://localhost:8080"):
self.base_url = base_url
self.results = {}
def benchmark_endpoint(self, endpoint, method="GET", data=None,
concurrent_users=10, requests_per_user=100):
"""Benchmark API endpoint"""
def make_request():
start_time = time.time()
try:
if method == "GET":
response = requests.get(f"{self.base_url}{endpoint}", timeout=10)
else:
response = requests.post(
f"{self.base_url}{endpoint}",
json=data,
timeout=10
)
end_time = time.time()
return {
'status_code': response.status_code,
'response_time': end_time - start_time,
'success': response.status_code == 200
}
except Exception as e:
end_time = time.time()
return {
'status_code': 0,
'response_time': end_time - start_time,
'success': False,
'error': str(e)
}
# Run concurrent requests
with ThreadPoolExecutor(max_workers=concurrent_users) as executor:
futures = []
for _ in range(concurrent_users * requests_per_user):
future = executor.submit(make_request)
futures.append(future)
results = [future.result() for future in futures]
# Calculate statistics
response_times = [r['response_time'] for r in results if r['success']]
success_rate = sum(1 for r in results if r['success']) / len(results)
benchmark_results = {
'endpoint': endpoint,
'method': method,
'concurrent_users': concurrent_users,
'total_requests': len(results),
'success_rate': success_rate,
'avg_response_time': statistics.mean(response_times) if response_times else 0,
'min_response_time': min(response_times) if response_times else 0,
'max_response_time': max(response_times) if response_times else 0,
'median_response_time': statistics.median(response_times) if response_times else 0,
'p95_response_time': statistics.quantiles(response_times, n=20)[18] if len(response_times) > 20 else 0,
'p99_response_time': statistics.quantiles(response_times, n=100)[98] if len(response_times) > 100 else 0
}
return benchmark_results
def benchmark_all_endpoints(self):
"""Benchmark all critical endpoints"""
endpoints = [
('/api/status', 'GET'),
('/api/training/status', 'GET'),
('/api/security/status', 'GET'),
('/api/monitoring/advanced', 'GET'),
('/api/data/inventory', 'GET'),
('/api/models/repository', 'GET'),
('/api/inference/status', 'GET'),
('/api/resources/status', 'GET')
]
results = {}
for endpoint, method in endpoints:
print(f"Benchmarking {endpoint}...")
results[endpoint] = self.benchmark_endpoint(endpoint, method)
return results# Load testing framework
class LoadTester:
def __init__(self, base_url="http://localhost:8080"):
self.base_url = base_url
def ramp_up_load_test(self, endpoint, max_users=100, ramp_up_time=60,
test_duration=300):
"""Ramp-up load test"""
import threading
import time
results = []
start_time = time.time()
def user_simulation(user_id):
# Calculate when this user should start
user_start_time = start_time + (user_id * ramp_up_time / max_users)
# Wait for start time
time.sleep(max(0, user_start_time - time.time()))
# Run requests for test duration
while time.time() - start_time < test_duration:
request_start = time.time()
try:
response = requests.get(f"{self.base_url}{endpoint}", timeout=5)
request_time = time.time() - request_start
results.append({
'user_id': user_id,
'timestamp': time.time(),
'response_time': request_time,
'status_code': response.status_code
})
except Exception as e:
results.append({
'user_id': user_id,
'timestamp': time.time(),
'response_time': time.time() - request_start,
'status_code': 0,
'error': str(e)
})
time.sleep(1) # 1 request per second per user
# Start user threads
threads = []
for user_id in range(max_users):
thread = threading.Thread(target=user_simulation, args=(user_id,))
threads.append(thread)
thread.start()
# Wait for all threads to complete
for thread in threads:
thread.join()
return results# Performance monitoring dashboard
class PerformanceDashboard:
def __init__(self, aurora_api_url):
self.api_url = aurora_api_url
def get_system_metrics(self):
"""Get real-time system metrics"""
response = requests.get(f"{self.api_url}/api/monitoring/metrics")
return response.json()
def get_performance_trends(self, hours=24):
"""Get performance trends"""
response = requests.get(f"{self.api_url}/api/monitoring/analytics")
return response.json()
def generate_performance_report(self):
"""Generate comprehensive performance report"""
metrics = self.get_system_metrics()
trends = self.get_performance_trends()
report = {
'timestamp': time.time(),
'current_metrics': metrics,
'performance_trends': trends,
'recommendations': self.generate_recommendations(metrics, trends)
}
return report
def generate_recommendations(self, metrics, trends):
"""Generate performance recommendations"""
recommendations = []
# CPU recommendations
cpu_usage = metrics.get('system_metrics', {}).get('cpu_usage', 0)
if cpu_usage > 80:
recommendations.append("High CPU usage detected - consider scaling or optimization")
# Memory recommendations
memory_usage = metrics.get('system_metrics', {}).get('memory_usage', 0)
if memory_usage > 85:
recommendations.append("High memory usage - consider memory optimization")
# Response time recommendations
response_time = metrics.get('system_metrics', {}).get('avg_response_time', 0)
if response_time > 1000: # 1 second
recommendations.append("High response time - consider API optimization")
return recommendations# config/alerts.yaml
alerts:
enabled: true
thresholds:
cpu_usage:
warning: 70
critical: 85
memory_usage:
warning: 75
critical: 90
disk_usage:
warning: 80
critical: 95
response_time:
warning: 2000 # 2 seconds
critical: 5000 # 5 seconds
error_rate:
warning: 0.05 # 5%
critical: 0.10 # 10%
channels:
email:
enabled: true
recipients:
- "admin@company.com"
- "devops@company.com"
slack:
enabled: true
webhook_url: "${SLACK_WEBHOOK_URL}"
channel: "#aurora-alerts"
webhook:
enabled: true
url: "${ALERT_WEBHOOK_URL}"
cooldown:
warning: 300 # 5 minutes
critical: 60 # 1 minute- Monitor system resource utilization
- Check API response times
- Review error rates
- Analyze database query performance
- Verify cache hit rates
- Run performance benchmarks
- Analyze performance trends
- Optimize slow queries
- Review and update caching strategies
- Check for memory leaks
- Comprehensive performance analysis
- Capacity planning review
- Architecture optimization assessment
- Performance budget review
- Update performance targets
- Response Time: Average API response time
- Throughput: Requests per second
- Error Rate: Percentage of failed requests
- Resource Utilization: CPU, memory, disk usage
- Availability: System uptime percentage
- Cache Hit Rate: Percentage of cache hits
- Database Performance: Query execution time
- Model Training Time: Time to train models
# config/performance_targets.yaml
targets:
response_time:
p50: 100ms
p95: 500ms
p99: 1000ms
throughput:
api_requests_per_second: 1000
model_predictions_per_second: 500
error_rate:
warning: 0.01 # 1%
critical: 0.05 # 5%
resource_utilization:
cpu_usage: 70%
memory_usage: 80%
disk_usage: 85%
availability:
target: 99.9%
monthly_downtime: 43.2 minutesAurora AI Performance Guide
Comprehensive Optimization • Monitoring • Benchmarking • Performance Tuning