claude-home/development/debugging.md

title	description	type	domain	tags
Python Debugging Reference	Python debugging techniques including pdb/breakpoint usage, logging setup, memory profiling with tracemalloc, cProfile performance profiling, and SQLAlchemy query debugging.	reference	development	python debugging pdb logging profiling sqlalchemy memory

Python Debugging Reference

Common Error Patterns

ImportError / ModuleNotFoundError

# Check Python path
python -c "import sys; print('\n'.join(sys.path))"

# Check installed packages
pip list
pip show <package_name>

# Install missing packages
pip install -r requirements.txt
pip install <package_name>

Virtual Environment Issues

# Check current environment
which python
which pip

# Recreate virtual environment
rm -rf venv/
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate   # Windows
pip install -r requirements.txt

Memory Issues

import tracemalloc
import psutil
import os

# Memory profiling
tracemalloc.start()
# ... your code ...
current, peak = tracemalloc.get_traced_memory()
print(f"Current memory usage: {current / 1024 / 1024:.1f} MB")
print(f"Peak memory usage: {peak / 1024 / 1024:.1f} MB")

# System memory
process = psutil.Process(os.getpid())
print(f"RSS memory: {process.memory_info().rss / 1024 / 1024:.1f} MB")

Debugging Tools

Using pdb debugger

import pdb

def problematic_function():
    x = 10
    pdb.set_trace()  # Execution will pause here
    y = x * 2
    return y

# Or use breakpoint() in Python 3.7+
def modern_debug():
    x = 10
    breakpoint()  # Same as pdb.set_trace()
    return x

Logging setup

import logging

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('debug.log'),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)

def example_function():
    logger.debug("Starting function")
    try:
        result = risky_operation()
        logger.info(f"Operation successful: {result}")
        return result
    except Exception as e:
        logger.error(f"Operation failed: {e}", exc_info=True)
        raise

Performance Debugging

Profile code execution

import cProfile
import pstats

# Profile a function
cProfile.run('your_function()', 'profile_output.prof')

# Analyze results
stats = pstats.Stats('profile_output.prof')
stats.sort_stats('cumulative').print_stats(10)

Line-by-line profiling

# Install line_profiler
pip install line_profiler

# Add @profile decorator to functions
# Run with:
kernprof -l -v your_script.py

Check for memory leaks

import gc
import objgraph

# Show most common types
objgraph.show_most_common_types()

# Track object growth
objgraph.show_growth()

Database Debugging

SQLAlchemy query debugging

import logging

# Enable SQL query logging
logging.basicConfig()
logging.getLogger('sqlalchemy.engine').setLevel(logging.INFO)
logging.getLogger('sqlalchemy.dialects').setLevel(logging.DEBUG)
logging.getLogger('sqlalchemy.pool').setLevel(logging.DEBUG)

Connection issues

# Test database connection
try:
    engine.connect()
    print("Database connection successful")
except Exception as e:
    print(f"Database connection failed: {e}")