Monitor Performance¶

This guide covers how to monitor and optimize the performance of aider-lint-fixer.

Performance Monitoring¶

Basic Performance Metrics¶

# Run with timing information
python -m aider_lint_fixer --profile --output-stats

# Memory usage monitoring
python -m memory_profiler aider_lint_fixer/main.py

Built-in Monitoring¶

# Enable performance monitoring
export PERFORMANCE_MONITORING=true
export LOG_LEVEL=DEBUG

python -m aider_lint_fixer --monitor-performance

Key Performance Indicators (KPIs)¶

Processing Speed¶

Files processed per minute
Lines of code analyzed per second
Time to first lint result

Resource Usage¶

Memory consumption
CPU utilization
Disk I/O patterns

Quality Metrics¶

Issues detected per file
False positive rate
Fix success rate

Performance Profiling¶

CPU Profiling¶

# Profile CPU usage
python -m cProfile -o profile.stats -m aider_lint_fixer

# Analyze profile data
python -c "
import pstats
p = pstats.Stats('profile.stats')
p.sort_stats('cumulative').print_stats(20)
"

Memory Profiling¶

# Install memory profiler
pip install memory-profiler psutil

# Profile memory usage
python -m memory_profiler aider_lint_fixer/main.py

# Line-by-line memory profiling
@profile
def analyze_file(filepath):
    # Function implementation
    pass

I/O Profiling¶

# Monitor file I/O
python -m aider_lint_fixer --io-profile

# Use iotop for system-wide I/O monitoring
sudo iotop -p $(pgrep -f aider_lint_fixer)

Performance Optimization¶

Configuration Tuning¶

# config/performance.yml
performance:
  max_workers: 4          # Parallel processing
  chunk_size: 100         # Files per batch
  cache_size: 1000        # Result cache size
  timeout: 30             # Operation timeout

optimization:
  enable_caching: true
  use_incremental: true   # Only process changed files
  skip_large_files: true  # Skip files > 1MB

Parallel Processing¶

# Enable parallel processing
python -m aider_lint_fixer --workers 4 --parallel

# Fine-tune worker count
import multiprocessing
optimal_workers = multiprocessing.cpu_count() - 1

Caching Strategies¶

# Enable result caching
export ENABLE_CACHE=true
export CACHE_DIR=~/.cache/aider-lint-fixer

# Clear cache when needed
python -m aider_lint_fixer --clear-cache

Monitoring Tools¶

System Monitoring¶

# htop for real-time monitoring
htop -p $(pgrep -f aider_lint_fixer)

# iostat for I/O statistics
iostat -x 1

# vmstat for memory statistics
vmstat 1

Application Monitoring¶

# Custom performance monitoring
import time
import psutil
import logging

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

    while True:
        cpu_percent = process.cpu_percent()
        memory_mb = process.memory_info().rss / 1024 / 1024

        logging.info(f"CPU: {cpu_percent}%, Memory: {memory_mb}MB")
        time.sleep(60)

Cloud Monitoring¶

# CloudWatch (AWS)
cloudwatch:
  metrics:
    - name: ProcessingTime
      unit: Seconds
    - name: MemoryUsage
      unit: Bytes
    - name: ErrorRate
      unit: Percent

# Application Insights (Azure)
appinsights:
  instrumentation_key: your_key
  track_dependencies: true
  track_requests: true

Performance Benchmarking¶

Baseline Measurements¶

# Create performance baseline
python scripts/benchmark.py --baseline --output baseline.json

# Compare current performance
python scripts/benchmark.py --compare baseline.json

Load Testing¶

# Test with large codebases
python -m aider_lint_fixer --test-performance \
  --large-files 1000 \
  --max-filesize 10MB

# Stress testing
python scripts/stress_test.py --duration 300 --concurrent 10

Regression Testing¶

# Performance regression tests
def test_performance_regression():
    start_time = time.time()

    # Run linting operation
    result = lint_large_codebase()

    duration = time.time() - start_time
    assert duration < PERFORMANCE_THRESHOLD

Performance Alerts¶

Threshold-Based Alerts¶

# alerts.yml
alerts:
  high_memory_usage:
    threshold: 1GB
    action: log_warning

  slow_processing:
    threshold: 5min
    action: send_notification

  high_error_rate:
    threshold: 10%
    action: stop_processing

Automated Monitoring¶

# Performance monitoring script
import psutil
import time
import smtplib

def check_performance():
    process = get_aider_process()

    if process.memory_percent() > 80:
        send_alert("High memory usage detected")

    if get_processing_time() > 300:
        send_alert("Processing taking too long")

Performance Reporting¶

Daily Reports¶

# Generate daily performance report
python scripts/generate_report.py --daily --format html

# Key metrics to include:
# - Average processing time
# - Peak memory usage
# - Files processed
# - Error rates

Performance Dashboard¶

# Simple dashboard with matplotlib
import matplotlib.pyplot as plt
import pandas as pd

def create_performance_dashboard():
    df = load_performance_data()

    fig, axes = plt.subplots(2, 2, figsize=(12, 8))

    # Processing time trend
    axes[0,0].plot(df['timestamp'], df['processing_time'])
    axes[0,0].set_title('Processing Time Trend')

    # Memory usage
    axes[0,1].plot(df['timestamp'], df['memory_usage'])
    axes[0,1].set_title('Memory Usage')

    # Throughput
    axes[1,0].bar(df['hour'], df['files_processed'])
    axes[1,0].set_title('Files Processed per Hour')

    # Error rate
    axes[1,1].plot(df['timestamp'], df['error_rate'])
    axes[1,1].set_title('Error Rate')

    plt.tight_layout()
    plt.savefig('performance_dashboard.png')

Troubleshooting Performance Issues¶

Common Performance Problems¶

High Memory Usage
Reduce batch size
Enable garbage collection
Use streaming for large files
Slow Processing
Increase parallel workers
Enable caching
Skip unnecessary checks
High CPU Usage
Optimize regex patterns
Reduce worker count
Use more efficient algorithms

Diagnostic Commands¶

# Memory leaks
python -m aider_lint_fixer --debug-memory

# CPU bottlenecks
python -m aider_lint_fixer --profile-cpu

# I/O issues
python -m aider_lint_fixer --trace-io

Monitor Performance¶

Performance Monitoring¶

Basic Performance Metrics¶

Built-in Monitoring¶

Key Performance Indicators (KPIs)¶

Processing Speed¶

Resource Usage¶

Quality Metrics¶

Performance Profiling¶

CPU Profiling¶

Memory Profiling¶

I/O Profiling¶

Performance Optimization¶

Configuration Tuning¶

Parallel Processing¶

Caching Strategies¶

Monitoring Tools¶

System Monitoring¶

Application Monitoring¶

Cloud Monitoring¶

Performance Benchmarking¶

Baseline Measurements¶

Load Testing¶

Regression Testing¶

Performance Alerts¶

Threshold-Based Alerts¶

Automated Monitoring¶

Performance Reporting¶

Daily Reports¶

Performance Dashboard¶

Troubleshooting Performance Issues¶

Common Performance Problems¶

Diagnostic Commands¶

Performance Best Practices¶

Development¶

Deployment¶

Maintenance¶

Next Steps¶