Optimizing Python Code for Production

As both a software engineer and ML practitioner, I’ve learned that Python performance optimization is crucial for production systems. Here are key strategies I’ve employed in my projects and some that I’ve gathered online

Memory Management

1. Generator Functions

Instead of loading large datasets into memory:

# Bad
def load_data():
    return [process(line) for line in open('large_file.txt')]

# Good
def load_data():
    for line in open('large_file.txt'):
        yield process(line)

2. Memory Profiling

Using memory_profiler to identify bottlenecks:

@profile
def memory_heavy_function():
    data = []
    for i in range(1000000):
        data.append(i)
    return data

Computational Optimization

1. Vectorization

Replace loops with NumPy operations:

# Slow
result = []
for x, y in zip(arr1, arr2):
    result.append(x * y)

# Fast
result = np.multiply(arr1, arr2)

2. Caching

Using functools.lru_cache for expensive computations:

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_computation(x):
    return sum(i * i for i in range(x))

Parallel Processing

1. Multiprocessing

For CPU-bound tasks:

from multiprocessing import Pool

def process_chunk(data):
    return [complex_computation(x) for x in data]

with Pool() as pool:
    results = pool.map(process_chunk, data_chunks)

2. Asyncio

For I/O-bound operations:

async def fetch_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.json()

async def main():
    tasks = [fetch_data(url) for url in urls]
    results = await asyncio.gather(*tasks)

Production Considerations

  1. Error Handling

    • Proper exception handling
    • Logging and monitoring
    • Graceful degradation

  2. Resource Management

    • Connection pooling
    • Thread pool sizing
    • Memory limits

Remember: profile first, optimize second, and always measure the impact of your optimizations.