8 Advanced Python Context Managers and Decorators for Production Systems

#programming #devto #python #softwareengineering

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Python's context managers and decorators transform how we handle resources and modify behavior. They encapsulate setup and teardown logic while keeping code clean. I've found these patterns invaluable in production systems, especially when dealing with transactions, errors, and resource management. Let me share eight advanced techniques I regularly use.

Stateful context managers maintain internal state across operations. Consider a database transaction system tracking multiple operations before commit. Here's a practical implementation:

class StatefulTransaction:
    def __init__(self):
        self.operations = []
        self.active = False

    def __enter__(self):
        self.active = True
        return self

    def add_operation(self, sql):
        if not self.active:
            raise RuntimeError("Transaction not active")
        self.operations.append(sql)

    def __exit__(self, exc_type, _, __):
        if not exc_type:
            print(f"Committing: {self.operations}")
            # Actual commit logic here
        else:
            print(f"Rolling back: {exc_type.__name__}")
        self.active = False

# Real-world usage
with StatefulTransaction() as tx:
    tx.add_operation("UPDATE accounts SET balance = balance - 100")
    tx.add_operation("UPDATE accounts SET balance = balance + 100")
    # Transaction auto-commits if no exceptions

Retry decorators handle transient failures gracefully. This pattern saved me during API integrations where network glitches were common. Here's an enhanced version with exponential backoff:

import time
import random
from functools import wraps

def retry(max_attempts=4, initial_delay=0.1, backoff=2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            delay = initial_delay
            for attempt in range(1, max_attempts + 1):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts:
                        raise
                    jitter = random.uniform(0.5, 1.5)
                    sleep_time = delay * jitter
                    print(f"Attempt {attempt} failed. Retrying in {sleep_time:.2f}s")
                    time.sleep(sleep_time)
                    delay *= backoff
        return wrapper
    return decorator

@retry(max_attempts=3, initial_delay=0.5)
def call_flaky_service(endpoint):
    # Simulate 30% failure rate
    if random.random() < 0.3:
        raise ConnectionError("Service timeout")
    return "Response data"

Timing context managers help identify bottlenecks. I use this daily when optimizing data pipelines:

from time import perf_counter
from contextlib import contextmanager

@contextmanager
def timer(description):
    start = perf_counter()
    try:
        yield
    finally:
        elapsed = perf_counter() - start
        print(f"{description}: {elapsed:.4f} seconds")

# Practical application
with timer("Matrix multiplication"):
    import numpy as np
    a = np.random.rand(1000, 1000)
    b = np.random.rand(1000, 1000)
    np.dot(a, b)

Combined decorator-context managers simplify resource handling. This factory pattern creates reusable resource managers:

from contextlib import contextmanager

def resource_manager(resource_type):
    @contextmanager
    def manager(*args, **kwargs):
        resource = resource_type(*args, **kwargs)
        try:
            print(f"Initialized {resource}")
            yield resource
        finally:
            resource.cleanup()
            print(f"Released {resource}")
    return manager

@resource_manager
class DatabaseConnection:
    def __init__(self, connection_string):
        self.conn_string = connection_string
        # Actual connection logic

    def cleanup(self):
        # Close connections
        print(f"Closing {self.conn_string}")

# Usage
@DatabaseConnection("postgres://user:pass@localhost")
def run_queries(db):
    print(f"Querying with {db.conn_string}")

Memoization with expiration balances performance and freshness. I implemented this for financial data feeds:

from functools import wraps
import time

def expiring_cache(ttl=60):
    def decorator(func):
        cache = {}
        @wraps(func)
        def wrapper(*args):
            key = args
            if key in cache:
                result, timestamp = cache[key]
                if time.time() - timestamp < ttl:
                    return result
            result = func(*args)
            cache[key] = (result, time.time())
            return result
        return wrapper
    return decorator

@expiring_cache(ttl=10)
def get_live_stock_price(symbol):
    print(f"Fetching live price for {symbol}")
    # Simulate API call
    return f"{symbol}: {100 + time.time() % 10:.2f}"

Temporary environment context managers isolate configuration changes. This prevents "env var pollution" in tests:

import os
from contextlib import contextmanager

@contextmanager
def scoped_env(**overrides):
    original = {}
    for key in overrides:
        original[key] = os.environ.get(key)
        os.environ[key] = str(overrides[key])
    try:
        yield
    finally:
        for key, value in original.items():
            if value is None:
                os.environ.pop(key, None)
            else:
                os.environ[key] = value

# Test scenario
print("Before:", os.getenv("DEBUG"))
with scoped_env(DEBUG="1", MAX_WORKERS="4"):
    print("Inside:", os.getenv("DEBUG"))
print("After:", os.getenv("DEBUG"))

Runtime type enforcement decorators catch errors early. I enforce contracts in data processing pipelines:

def validate_types(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        annotations = func.__annotations__
        for name, value in zip(func.__code__.co_varnames, args):
            if name in annotations and not isinstance(value, annotations[name]):
                raise TypeError(f"{name} requires {annotations[name]}, got {type(value)}")
        return func(*args, **kwargs)
    return wrapper

@validate_types
def transform_data(inputs: list, coefficient: float) -> dict:
    return {k: v * coefficient for k, v in enumerate(inputs)}

# Valid call
transform_data([1,2,3], 1.5)
# Invalid call
transform_data("text", 0.5)  # Raises TypeError

Async context managers manage resources in concurrent code. This pattern is crucial for modern web applications:

import aiohttp
import asyncio
from contextlib import asynccontextmanager

@asynccontextmanager
async def managed_session(timeout=10):
    session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout))
    try:
        yield session
    finally:
        await session.close()

async def fetch_multiple(urls):
    async with managed_session() as session:
        tasks = [session.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [await r.text() for r in responses]

# Practical usage
urls = ["https://httpbin.org/get"] * 3
results = asyncio.run(fetch_multiple(urls))
print(f"Fetched {len(results)} responses")

These patterns demonstrate Python's flexibility in resource management and behavior modification. By encapsulating complex logic, they reduce boilerplate and prevent common errors. In my experience, mastering these techniques leads to more robust and maintainable systems. Each serves distinct purposes but shares a common goal: making complex operations simple and safe.

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