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Performance Troubleshooting Guide

This guide helps identify and resolve performance issues in the AI Assistant System.

Overview

Performance issues can manifest as slow response times, high resource usage, or poor throughput. This guide provides systematic approaches to diagnose and fix these problems.

Performance Monitoring

1. Key Metrics

Monitor these critical performance indicators:

from app.core.monitoring import PerformanceMonitor

monitor = PerformanceMonitor()

# Track response times
response_time = monitor.measure_response_time(
    lambda: generate_response(prompt)
)

# Track resource usage
cpu_usage = monitor.get_cpu_usage()
memory_usage = monitor.get_memory_usage()
disk_io = monitor.get_disk_io()
network_io = monitor.get_network_io()

# Track AI-specific metrics
token_usage = monitor.get_token_usage()
model_latency = monitor.get_model_latency()
cache_hit_rate = monitor.get_cache_hit_rate()

2. Performance Dashboard

Create a dashboard for real-time monitoring:

from app.core.monitoring import PerformanceDashboard

dashboard = PerformanceDashboard()

# Add metrics to dashboard
dashboard.add_metric("Response Time", "response_time", "ms")
dashboard.add_metric("CPU Usage", "cpu_usage", "%")
dashboard.add_metric("Memory Usage", "memory_usage", "%")
dashboard.add_metric("Token Throughput", "token_throughput", "tokens/s")

# Set up alerts
dashboard.add_alert("High Response Time", "response_time > 5000")
dashboard.add_alert("High CPU Usage", "cpu_usage > 80")
dashboard.add_alert("Low Cache Hit Rate", "cache_hit_rate < 0.5")

Common Performance Issues

1. Slow Response Times

Symptoms: - Requests taking longer than expected - User complaints about slowness - Timeouts occurring

Diagnosis:

from app.core.profiling import ResponseTimeProfiler

profiler = ResponseTimeProfiler()

@profiler.profile_response_time
async def profile_slow_response(prompt):
    # Profile each step
    with profiler.step("preprocessing"):
        preprocessed = preprocess_prompt(prompt)

    with profiler.step("model_request"):
        response = await model.generate(preprocessed)

    with profiler.step("postprocessing"):
        result = postprocess_response(response)

    return result

# Analyze the profile
profile_data = profiler.get_profile_data()
print(f"Step timings: {profile_data.step_timings}")
print(f"Bottleneck: {profile_data.bottleneck}")

Solutions:

  1. Optimize prompt preprocessing: 

    def optimize_preprocessing(prompt):
    # Cache expensive operations
    if prompt in preprocessing_cache:
        return preprocessing_cache[prompt]

    # Use faster algorithms
    optimized = fast_preprocess(prompt)
    preprocessing_cache[prompt] = optimized
    return optimized

  2. Implement request batching: 

    from app.core.optimization import BatchProcessor

batch_processor = BatchProcessor(batch_size=10)

async def batch_requests(requests):
    return await batch_processor.process_batch(requests)

  3. Use streaming for long responses: 

    async def stream_response(prompt):
    async for chunk in model.generate_stream(prompt):
        yield chunk

2. High Resource Usage

Symptoms: - High CPU or memory consumption - System becoming unresponsive - Resource exhaustion errors

Diagnosis:

import psutil
from app.core.profiling import ResourceProfiler

resource_profiler = ResourceProfiler()

def profile_resource_usage():
    process = psutil.Process()

    # Get current resource usage
    cpu_percent = process.cpu_percent()
    memory_info = process.memory_info()
    memory_percent = process.memory_percent()

    # Profile memory usage
    memory_profiler.profile_memory()

    # Profile CPU usage
    cpu_profiler.profile_cpu()

    return {
        "cpu_percent": cpu_percent,
        "memory_mb": memory_info.rss / 1024 / 1024,
        "memory_percent": memory_percent
    }

Solutions:

  1. Implement memory pooling: 

    from app.core.optimization import MemoryPool

memory_pool = MemoryPool(initial_size=100, max_size=1000)

def get_memory_resource():
    return memory_pool.acquire()

def release_memory_resource(resource):
    memory_pool.release(resource)

  2. Optimize data structures: 

    # Use generators instead of lists
def process_large_dataset(data):
    for item in data_generator(data):  # Generator, not list
        yield process_item(item)

# Use more efficient data structures
from collections import deque
task_queue = deque(maxlen=1000)  # Bounded queue

  3. Implement rate limiting: 

    from app.core.optimization import RateLimiter

rate_limiter = RateLimiter(max_requests=100, time_window=60)

@rate_limiter.limit
async def limited_request(prompt):
    return await model.generate(prompt)

3. Poor Throughput

Symptoms: - Low number of requests per second - Queue buildup - System falling behind

Diagnosis:

from app.core.monitoring import ThroughputMonitor

throughput_monitor = ThroughputMonitor()

async def measure_throughput():
    start_time = time.time()
    request_count = 0

    async for request in get_requests():
        await process_request(request)
        request_count += 1

        if request_count % 100 == 0:
            elapsed = time.time() - start_time
            throughput = request_count / elapsed
            print(f"Current throughput: {throughput:.2f} req/s")

Solutions:

  1. Implement connection pooling: 

    from app.core.optimization import ConnectionPool

connection_pool = ConnectionPool(min_connections=5, max_connections=20)

async def get_connection():
    return await connection_pool.acquire()

  2. Use asynchronous processing: 

    import asyncio

async def process_requests_concurrently(requests):
    semaphore = asyncio.Semaphore(10)  # Limit concurrency

    async def process_with_semaphore(request):
        async with semaphore:
            return await process_request(request)

    tasks = [process_with_semaphore(req) for req in requests]
    return await asyncio.gather(*tasks)

  3. Optimize database queries: 

    from app.core.optimization import QueryOptimizer

query_optimizer = QueryOptimizer()

# Use batch queries
def batch_get_items(ids):
    return query_optimizer.batch_query(
        "SELECT * FROM items WHERE id IN %s",
        (tuple(ids),)
    )

# Add indexes
query_optimizer.add_index("items", "user_id")

AI-Specific Performance Optimization

1. Token Optimization

Reduce token usage for better performance:

from app.core.optimization import TokenOptimizer

token_optimizer = TokenOptimizer()

def optimize_tokens(prompt):
    # Remove redundant content
    optimized = token_optimizer.remove_redundancy(prompt)

    # Use more concise language
    optimized = token_optimizer.make_concise(optimized)

    # Limit context
    optimized = token_optimizer.limit_context(optimized, max_tokens=2000)

    return optimized

2. Model Selection

Choose appropriate models for tasks:

from app.core.optimization import ModelSelector

model_selector = ModelSelector()

def select_model_for_task(task):
    complexity = model_selector.analyze_complexity(task)

    if complexity < 0.3:
        return "gpt-3.5-turbo"  # Fast and cheap
    elif complexity < 0.7:
        return "gpt-4"  # Balanced
    else:
        return "claude-3-opus"  # High quality

3. Caching Strategy

Implement intelligent caching:

from app.core.optimization import SmartCache

smart_cache = SmartCache(
    ttl=3600,  # 1 hour
    similarity_threshold=0.9
)

@smart_cache.cached_response
async def cached_generate(prompt):
    # Check for similar cached responses
    similar = smart_cache.find_similar(prompt)
    if similar and similar.similarity > 0.9:
        return similar.response

    # Generate new response
    response = await model.generate(prompt)
    smart_cache.store(prompt, response)
    return response

Performance Testing

1. Load Testing

Test system performance under load:

from app.core.testing import LoadTester

load_tester = LoadTester()

async def run_load_test():
    # Configure test parameters
    test_config = {
        "concurrent_users": 50,
        "duration": 300,  # 5 minutes
        "ramp_up": 30,    # 30 seconds
        "requests_per_second": 10
    }

    # Run the test
    results = await load_tester.run_test(test_config)

    # Analyze results
    print(f"Average response time: {results.avg_response_time}ms")
    print(f"95th percentile: {results.p95_response_time}ms")
    print(f"Throughput: {results.throughput} req/s")
    print(f"Error rate: {results.error_rate}%")

2. Stress Testing

Find system limits:

from app.core.testing import StressTester

stress_tester = StressTester()

async def run_stress_test():
    # Gradually increase load
    for load in [10, 50, 100, 200, 500]:
        print(f"Testing with {load} concurrent users")

        results = await stress_tester.test_load(
            concurrent_users=load,
            duration=60
        )

        if results.error_rate > 5:  # 5% error threshold
            print(f"System limit reached at {load} users")
            break

Performance Tuning Checklist

1. Application Level

  • [ ] Profile code to identify bottlenecks
  • [ ] Optimize algorithms and data structures
  • [ ] Implement caching where appropriate
  • [ ] Use connection pooling for external resources
  • [ ] Optimize database queries
  • [ ] Implement request batching

2. Infrastructure Level

  • [ ] Right-size compute resources
  • [ ] Use load balancing for high availability
  • [ ] Implement CDN for static content
  • [ ] Optimize network configuration
  • [ ] Use appropriate storage solutions

3. AI Model Level

  • [ ] Select appropriate models for tasks
  • [ ] Optimize prompts for efficiency
  • [ ] Implement token optimization
  • [ ] Use model-specific optimizations
  • [ ] Consider fine-tuning for specific tasks

Monitoring and Alerting

1. Performance Alerts

Set up alerts for performance issues:

from app.core.monitoring import PerformanceAlerts

alerts = PerformanceAlerts()

# Response time alerts
alerts.add_alert(
    name="High Response Time",
    condition="response_time > 5000",
    severity="warning"
)

# Resource usage alerts
alerts.add_alert(
    name="High CPU Usage",
    condition="cpu_usage > 80",
    severity="critical"
)

# Throughput alerts
alerts.add_alert(
    name="Low Throughput",
    condition="throughput < 10",
    severity="warning"
)

2. Performance Reports

Generate regular performance reports:

from app.core.reporting import PerformanceReporter

reporter = PerformanceReporter()

# Daily performance report
daily_report = reporter.generate_daily_report()
reporter.send_report(daily_report, recipients=["team@example.com"])

# Weekly performance summary
weekly_summary = reporter.generate_weekly_summary()
reporter.send_report(weekly_summary, recipients=["manager@example.com"])

Conclusion

Performance optimization is an ongoing process that requires continuous monitoring and improvement. By implementing the strategies outlined in this guide, you can identify and resolve performance issues before they impact your users.

Remember that performance tuning is about finding the right balance between speed, cost, and quality. Regularly review your performance metrics and adjust your optimization strategies based on your specific requirements and constraints.