When building applications that interact with web services, APIs, or need to fetch data from the internet, choosing the right HTTP client is crucial. The HTTP client you select can significantly impact your application's performance, maintainability, and development experience. A well-chosen client can make your code more elegant and efficient, while a poor choice might lead to unnecessary complexity, performance bottlenecks, or maintenance headaches down the line.

In the Python ecosystem, we're fortunate to have several excellent HTTP client libraries available, each with its own strengths and trade-offs. Whether you're building a simple script to fetch data from an API, developing a complex web scraper, or creating a high-performance microservice, understanding the characteristics and capabilities of different HTTP clients will help you make an informed decision that aligns with your project's requirements and constraints.

What is a Python HTTP Client?

A Python HTTP client is a tool used to send HTTP requests, such as GET or POST, to web servers and retrieve information, including raw HTML content from web pages. Since raw HTML can be unstructured and difficult to understand, it is often combined with parsing libraries.

Key considerations include your available resources (e.g., a single machine versus a distributed setup) and your priorities, such as simplicity versus raw performance. For example, a web application that occasionally interacts with a microservice API will have vastly different requirements from a script designed for continuous data scraping.

Additionally, it’s essential to evaluate the long-term viability of the library, ensuring it is well-maintained and likely to remain active in the foreseeable future. By aligning your needs with these factors, you can make a more informed decision.

The HTTP Client Dilemma

With so many Python HTTP clients available, it can be overwhelming to decide which one to use. While some libraries prioritize ease of use, others focus on performance, async support, or low-level control. Understanding the strengths and trade-offs of each option can help you make the right choice for your project.

1. Requests: A widely-used HTTP client in Python favored by both seasoned developers and beginners for its simplicity and efficiency. It is designed to minimize boilerplate code, making HTTP requests straightforward and intuitive.

   It’s built on Python’s urllib3 library allowing you to make requests within a session.

   For example, instead of manually constructing a URL:

   url = "https://petstore.swagger.io/v2/pet/findByStatus?status=available&limit=10"
   response = requests.get(url)

   You can pass query parameters as a dictionary using params, and requests will handle the encoding for you:

   import requests
   
   url = "https://petstore.swagger.io/v2/pet/findByStatus"
   params = {"status": "available", "limit": 10}
   
   response = requests.get(url, params=params)
   
   print(response.url)  # Output: https://petstore.swagger.io/v2/pet/findByStatus?status=available&limit=10
   print(response.json())

   If the application you are working with provides an API, Requests simplifies the process of connecting to it, enabling easy access to specific data. One of its standout features is the built-in JSON decoder, which allows you to seamlessly retrieve and decode JSON data with minimal code, streamlining interactions with APIs.

   Here’s a simple example to illustrate how to use Requests for a GET request:

   import requests
   
   # Simple GET request to fetch a list of available pets
   response = requests.get("https://petstore.swagger.io/v2/pet/findByStatus", params={"status": "available"})
   
   # Check if the request was successful
   if response.status_code == 200:
       # Handling JSON responses
       data = response.json()
       print(data)  # Print the list of available pets
   else:
       print(f"Error: {response.status_code}, {response.text}")

   Pros:

   • Incredibly intuitive API
   • Widespread community support
   • Handles most common use cases effortlessly

   Cons:

   • Synchronous by default
   • No built-in async support

2. HTTPX: A newcomer in the world of Python HTTP clients, designed to meet the needs of modern applications. It stands out by providing both synchronous and asynchronous capabilities in a single, cohesive package, making it a versatile choice for developers.

   Here’s a simple example to illustrate how to use HTTPX for a GET request (sync & async):

   import httpx
   
   # Async request
   async with httpx.AsyncClient() as client:
       response = await client.get('https://petstore.swagger.io/v2/pet/findByStatus?status=available')
   
   # Sync request
   with httpx.Client() as client:
       response = client.get('https://petstore.swagger.io/v2/pet/findByStatus?status=available')

   Pros:

   • Supports both sync and async programming
   • Compatible with Requests API
   • Advanced features like HTTP/2 support

   Cons:

   • Slightly more complex
   • Slightly slower than pure sync libraries

3. Urllib3: The "Bare Metal Option", Urllib3 is ideal for developers who require more control and prefer working closer to the networking layer. It provides a low-level interface for handling HTTP requests, offering greater flexibility and customization for advanced use cases.

   import urllib3
   from urllib3.util.retry import Retry
   from urllib3.exceptions import MaxRetryError
   
   # Create a PoolManager (connection pooling)
   http = urllib3.PoolManager()
   
   # Define custom retry strategy
   retry_strategy = Retry(
       total=5,  # Maximum retry attempts
       backoff_factor=1,  # Exponential backoff (1s, 2s, 4s, etc.)
       status_forcelist=[500, 502, 503, 504],  # Retry on these HTTP statuses
       raise_on_status=True
   )
   
   # Create an HTTP request with custom retry settings
   try:
       response = http.request(
           "GET",
           "https://petstore.swagger.io/v2/pet/findByStatus?status=available",
           retries=retry_strategy,
           timeout=urllib3.Timeout(connect=2.0, read=5.0)  # Fine-grained timeout control
       )
       print(response.data.decode("utf-8"))  # Print response body
   except MaxRetryError:
       print("Request failed after multiple retries")

   Pros:

   • Part of Python's standard library
   • Lowest-level control
   • Connection pooling out of the box

   Cons:

   • Less user-friendly
   • More verbose
   • Requires more manual handling

Choosing Your Champion: A Decision Matrix

When to Use Requests

• Simple, straightforward API calls: Ideal for making basic API requests with minimal setup.
• Synchronous programming: Best suited for synchronous tasks where concurrency is not a priority.
• Maximum readability: Requests' clean and intuitive API promotes ease of understanding and readability.
• Small to medium projects: Well-suited for projects where simplicity and speed of development are key priorities.

When to Use HTTPX

• Projects requiring async capabilities: HTTPX offers asynchronous support out-of-the-box, making it perfect for handling multiple concurrent requests.
• Need for HTTP/2 support: HTTPX includes built-in support for HTTP/2, allowing for optimized communication with modern web services.
• Modern Python applications: Designed for projects leveraging async features and contemporary Python practices.
• Micro-services and concurrent programming: Ideal for distributed systems and micro-services where multiple API requests or services need to be handled concurrently.

When to Use Urllib3

• Low-level network programming: Provides granular control over network connections, making it suitable for advanced network operations.
• Custom networking requirements: Perfect for developers who need to implement custom connection handling or protocol features.
• Performance-critical applications: Offers more direct access to the networking layer, allowing for fine-tuned performance optimizations.
• When you need maximum control: Urllib3 allows full control over HTTP behavior, from connection pooling to retries, giving developers the flexibility to build specialized solutions.

Best Practices Across All Libraries

Regardless of which library you use there are some practices which you should always do when making HTTP requests. These include setting timeouts, handling exceptions, and respecting rate limits.

1. Always use Timeouts

# Prevent hanging requests
requests.get('https://petstore.swagger.io/v2/pet/findByStatus?status=available', timeout=5)

2. Handle Exceptions

try:
    response = requests.get('https://petstore.swagger.io/v2/pet/findByStatus?status=available')
    response.raise_for_status()
except requests.RequestException as e:
    # Log and handle network errors
    print(f"Request failed: {e}")

3. Respect Rate Limits

• Implement Exponential Backoff: Exponential backoff is a strategy where the time between retries increases exponentially with each failed attempt. This helps reduce the load on the server and avoid hitting rate limits, while giving the server time to recover.

import time
import requests
from requests.exceptions import RequestException

def fetch_with_backoff(url, retries=5, backoff_factor=1.0):
    attempt = 0
    while attempt < retries:
        try:
            response = requests.get(url)
            # If the request was successful (status code 200), return the response
            response.raise_for_status()
            return response
        except RequestException as e:
            attempt += 1
            wait_time = backoff_factor * (2 ** (attempt - 1))  # Exponential backoff
            print(f"Request failed: {e}. Retrying in {wait_time} seconds...")
            time.sleep(wait_time)  # Wait before retrying
    print("Max retries reached. Request failed.")
    return None

# Example usage
url = "https://petstore.swagger.io/v2/pet/findByStatus?status=available"
response = fetch_with_backoff(url)

if response:
    data = response.json()
    print(data)
else:
    print("Failed to fetch data.")

Performance Considerations

There are also performance considerations. Some libraries perform faster than others with Urllib3 being the fastest on average and HTTPX being the slowest. All perform well but this is a consideration that may be important in high performance applications.

• Requests: ~100-200 requests/second
• HTTPX: ~80-150 requests/second (async can be much higher)
• Urllib3: ~150-250 requests/second

No One Size Fits All

There is no one-size-fits-all “best” HTTP client. The right choice depends on various factors such as:

• Project requirements: Different projects have different needs, so choose a client that aligns with your goals.
• Async needs: If your project requires asynchronous requests then go with HTTPX.
• Performance constraints: Consider the performance demands of your application, particularly for high-traffic or performance-critical systems.
• Team familiarity: Opt for a client that your team is comfortable with to ensure efficient development and troubleshooting.

Ultimately, the best HTTP client is the one that helps you write clean, readable, and maintainable code while effectively addressing your project’s needs. And if you're using a popular service then ask them to create a Python SDK with Liblab.