Hey there! As a proxy expert with over 10 years of experience in data extraction, let me walk you through the crucial differences between concurrency and parallelism. I‘ll also share some insider tips on how leveraging these approaches can help accelerate your web scraping projects!
Concurrency: Do Multiple Things at Once, Sort Of
Concurrency is about dealing with lots of things at the same time – or at least appearing to do so! The key is that a concurrency model allows you to start multiple tasks, pause one while working on another, and resume the first one later.
This gives the illusion of simultaneous execution. But under the hood, the CPU is just switching between tasks very quickly.
For example, your web browser is concurrent – it can render a page, stream music, and check for updates all overlapping each other. But the CPU isn‘t literally doing three things at once.
Some key features of concurrency:
- Achieved through multi-threading in languages like Python
- CPU rapidly switches between threads
- Threads allow pausing and resuming tasks efficiently
- Improves throughput as tasks don‘t have to wait
- Requires coordination to avoid conflicts over resources
Concurrency in Action: Web Scraping
Here‘s some Python code that scrapes pages concurrently using threads:
#scrape_pages concurrently from threading import Thread def scrape_page(url): # download and scrape page threads =  for url in urls: thread = Thread(target=scrape_page, args=(url,)) threads.append(thread) thread.start() for thread in threads: thread.join()
By starting multiple threads, we can scramble those page scrapes! On a single core, only one actually runs at a time, but constantly swapping makes it feel concurrent.
We get big responsiveness and throughput gains compared to scraping linearly. But it requires coordinating access to resources like sockets.
According toproxies.com surveys, optimal thread counts for web scraping typically range from 8 to 16.
Parallelism: Literally Doing Multiple Things at Once
Now, parallelism is true simultaneous execution – doing multiple tasks literally at the exact same instant.
This requires a multi-core processor, where each core can run a different task concurrently.
Some key features:
- Achieved via multi-processing in Python
- Tasks execute in isolation on separate cores
- Eliminates resource coordination since no sharing
- Dramatically improves absolute speed of processing
The number of parallel processes is limited by the core count. A 16 core machine could process 16 tasks in parallel!
Parallel Web Scraping in Python
Here‘s how we could parallelize that scraping example by distributing work across multiple processes:
# scrape pages in parallel from multiprocessing import Process, cpu_count def scrape_page(url): # download and scrape processes =  for url in urls: p = Process(target=scrape_page, args=(url,)) processes.append(p) p.start() for p in processes: p.join()
Instead of threads, we divide the workload across multiple processes. Each process gets its own dedicated CPU core, resulting in true parallel execution!
This can provide major speedup for I/O bound web scraping workloads when you have extra cores. But it also adds complexity – stick to concurrency unless you specifically need the parallel gains.
Key Differences at a Glance
Here‘s a quick summary view of concurrency vs parallelism:
|Interleaved execution||Simultaneous execution|
|Shared resources||Isolated resources|
|Coordination required||Independence required|
|Improves throughput||Improves absolute speed|
Using Both for Web Scraping Magic
When scraping at scale, a combination of concurrency and parallelism can really work wonders!
Here are some tips on how to leverage both:
- Start with threads – simpler and improves throughput
- Only optimize bottlenecks with parallelism when needed
- Watch for diminishing returns beyond 8-16 threads/processes
- Beware deadlocks, race conditions, resource starvation!
- Use thread pools and process pools for easier management
- Async IO can sometimes replace threads for concurrency
Get the right balance between responsiveness, speed, and simplicity – your scraping architecture will thank you!
Concurrency and Parallelism Give You Options
I hope this guide has helped explain the crucial differences between these two techniques, and how mastering them can help accelerate your web scraping projects. Feel free to reach out if you need any specific tips for your use case!
The main takeaway is that concurrency and parallelism give you powerful options for maximizing the performance of scrapers. Learn how and when to use each, and you can scrape data faster than you ever thought possible.