# Efficiently Monitor and Log Memory Usage of a Python Application  

This snippet demonstrates how to monitor and log memory usage of a Python application in real-time, helping identify memory leaks or inefficient resource usage.  

```python
import tracemalloc
import logging
from datetime import datetime
import time

def setup_memory_monitor(interval=10, log_file='memory_usage.log'):
    """Monitor memory usage at regular intervals and log results."""
    logging.basicConfig(
        filename=log_file,
        level=logging.INFO,
        format='%(asctime)s - %(message)s'
    )
    tracemalloc.start()

    try:
        while True:
            current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            snapshot = tracemalloc.take_snapshot()
            top_stats = snapshot.statistics('lineno')

            logging.info(f"Memory usage at {current_time}:")
            for stat in top_stats[:5]:  # Log top 5 memory-consuming lines
                logging.info(f"{stat.count} blocks - {stat.size / 1024:.2f} KiB - {stat.traceback}")

            time.sleep(interval)
    except KeyboardInterrupt:
        tracemalloc.stop()
        print("Memory monitoring stopped.")

if __name__ == '__main__':
    print("Starting memory monitor... (Press Ctrl+C to stop)")
    setup_memory_monitor(interval=5)  # Check every 5 seconds
```

## Why This Is Useful  
- **Identifies Memory Leaks:** Helps detect unexpected memory growth in long-running applications.  
- **Optimizes Performance:** Reveals which parts of your code consume the most memory.  
- **Lightweight:** Uses Python's built-in `tracemalloc` module without external dependencies.  

## How to Use  
1. **Run the Script:** Execute the script alongside your main application (or modify it to monitor a specific function).  
2. **Analyze Output:** Check the generated `memory_usage.log` file for detailed memory statistics.  
3. **Adjust Interval:** Change the `interval` parameter (in seconds) to balance granularity and overhead.  

Example log output:  
```
2024-01-15 14:30:00 - Memory usage at 2024-01-15 14:30:00:  
42 blocks - 1280.50 KiB - File "app.py", line 15  
12 blocks - 640.25 KiB - File "utils.py", line 8  
```  

**Note:** For production use, consider integrating this with tools like `logging.handlers.RotatingFileHandler` for log rotation.