Make Concurrent Requests in Python: An Expert‘s Guide

Building high-performance web scrapers involves downloading data from websites programmatically and at scale. A key technique is to make concurrent requests to fetch pages in parallel rather than sequentially. This allows you to maximize throughput and evade bottlenecks.

In this comprehensive guide, you‘ll learn different methods for enabling concurrency in Python scrapers using threads, processes, asyncio, queues, and more.

Why Concurrent Scraping Matters

Before diving into code, let‘s highlight why it‘s critical to make concurrent requests when web scraping:

1. Faster data extraction

By fetching multiple pages simultaneously, scrapers can achieve orders of magnitude better throughput than sequential requests:

2. Overcome throttling/blocking

Websites commonly limit how often you can request pages to combat scraping. Concurrency lets you stay under these thresholds.

3. Simpler asynchronous code

Concurrency frameworks handle the intricate details of threading, synchronization, and non-blocking I/O behind the scenes.

Now let‘s explore various ways to unlock these benefits in a Python context…

Threading in Python

The simplest form of Python concurrency is threads – lightweight subprocesses that share memory with the main application. The built-in threading module makes spun up threads straightforward:

from threading import Thread

def print_nums(nums):
  for n in nums:
    print(n)

nums = [1, 2, 3, 4]
t = Thread(target=print_nums, args=(nums,))
t.start() // Launch thread!

We instantiate a Thread object, passing in our target function and arguments. Calling .start() dispatches execution to the new thread.

To adapt this for web scraping:

def scrape(url):
  data = download(url) 
  return data

threads = []
urls = [url1, url2, ...] 

for url in urls:
  thread = Thread(target=scrape, args=(url))
  thread.start()
  threads.append(thread)

for thread in threads:
  thread.join() // Wait for completion

Here we spin up a thread per URL to fetch, eventually joining to collect results.

Pros

• Simple API
• Shared memory makes passing data easy

Cons

• Static number of threads
• State/race conditions
• GIL limits parallelism

multiprocessing

To sidestep Python‘s Global Interpreter Lock for true parallel execution, we can utilize multiprocessing instead. This fires up actual OS-level processes for each task:

import multiprocessing

def worker(num):
   print(‘Worker:‘, num)

if __name__ == ‘__main__‘:
   jobs = []
   for i in range(5):
      p = multiprocessing.Process(target=worker, args=(i,))
      jobs.append(p)
      p.start()

   for proc in jobs:
      proc.join() 

This dynamically spawns five processes that run concurrently.

To make this work for web scraping:

import multiprocessing
import requests

def scrape(url):
  return requests.get(url)

pool = multiprocessing.Pool(processes=20)

urls = [url1, url2...]
results = pool.map(scrape, urls)
pool.close()

We set up a process pool, use pool.map() to dispatch scrape jobs, then collect output. Much simpler than manually managing processes!

Pros:

• Avoids GIL for better parallelism
• Process isolation improves stability

Cons:

• Higher memory overhead
• IPC overhead

AsyncIO in Python

AsyncIO provides infrastructure for single-threaded concurrency using cooperative multitasking and an event loop:

import asyncio

async def scrape(url):
   data = await fetch(url)
   return data

async def main():
   urls = [url1, url2...] 
   tasks = []

   for url in urls:
      task = asyncio.create_task(scrape(url))
      tasks.append(task)

   await asyncio.gather(*tasks)

asyncio.run(main())

Async methods await promises instead of blocking. The event loop interleaving allows concurrency despite using a single thread.

Pros

• Efficient for I/O-bound work
• Simple to use with await
• Fast switching between tasks

Cons

• Added complexity of asynchronous code
• Not useful for CPU-heavy work

How to Choose?

With multiple options available, here is guidance on which concurrency approach to use for scrapers:

Goal	Recommended Model
Simplest code	threading
Avoid GIL restraints	multiprocessing
Asynchronous workflows	AsyncIO
Cross-platform (incl. Windows)	threading

AsyncIO is best for I/O-bound tasks like network requests, while multiprocessing suits CPU-intensive work. Threading offers the easiest API despite GIL drawbacks.

Now let‘s explore some more advanced patterns…

Coordinating Scraper Concurrency

When scaling up an army of scraper threads/processes, coordination becomes vital to prevent unnecessary execution and wasted resources. Helpful primitives include:

Queues

Use a queue as a buffer for URLs to be scraped:

from queue import Queue

q = Queue()

for url in initial_list:
   q.put(url)

def scraper():
   while True:
      url = q.get()
      scrape_page(url)
      q.task_done() 

Queues give you fine-grained control over scrapers pulling jobs.

Semaphores

To limit active threads, use a Semaphore guarded resource:

from threading import Semaphore

max_threads = 30
sem = Semaphore(max_threads)

def scrape(url):
   with sem:
      # scraping logic
      ...

This restricts the number of concurrent operations.

Pools

For fixed worker counts, utilize a pool:

from multiprocessing import Pool 

pool = Pool(20)

def scraper(url):
   ...

pool.map(scraper, urls)

Pools abstract the complexity of scaling to a set degree of parallelism.

Integrating Proxies

When scraping aggressively, you‘ll likely need proxies to mask scrapers and avoid IP blocks. Here‘s one method:

import proxies

def scrape(url):
   proxy = next(proxies) // Get proxy

   with requests.Session() as session:
      session.proxies = {"http": proxy, "https": proxy}
      return session.get(url)  

By rotating different proxies across threads, you can scrape under the radar at scale.

Key Takeaways

Making concurrent requests allows you to maximize the throughput and performance of Python scrapers. As highlighted, there are several solid options:

• Threading – Simplest API using shared memory
• Multiprocessing – Avoid GIL by using separate processes
• AsyncIO – Asynchronous event loop for I/O concurrency
• Queues – Coordinate distribution of scraping work
• Semaphores – Limit number of threads accessing resources
• Proxies – Rotate IPs randomly to avoid blocks

With these tools and patterns, you can extract data from websites at blazing speeds! Let me know if you have any other questions.