What is Parallel Execution?

Man, I feel your pain. Viscerally.

Seven years ago, I practically melted a battered Lenovo ThinkPad trying to pull off this exact same stunt with a wildly chaotic real estate scraper. Whenever you finally snap and ask, what is parallel execution?, the internet reliably throws a wall of dense, suffocating computer science jargon directly at your face. It sucks.

Let's fix this mess right now.

To put it bluntly, what is parallel execution? It essentially means forcing your machine to juggle multiple active tasks at the exact same physical moment, rather than making those tasks stand in a soul-crushing, single-file line. But here is the massive trap you fell into—and trust me, almost every self-taught programmer steps on this exact landmine.

The Two Flavors of Concurrency

You used multiprocessing (specifically, the ProcessPoolExecutor). That approach literally clones your entire Python brain into multiple independent, isolated workers.

It demands immense CPU power.

Since your specific scraping script is waiting around for slow, distant external web servers to respond (this is known as being Network I/O bound), throwing raw CPU muscle at the bottleneck is like buying a massive industrial dump truck just to deliver a single paper envelope. Your processor maxes out trying to manage all those heavy clones. Your fan screams. Your laptop completely locks up.

If you keep digging into the core question of what is parallel execution?, you will inevitably stumble across multithreading. For scraping, multithreading is your golden ticket.

Threads share the exact same memory space. When Thread A asks a website for data and sits there twiddling its thumbs waiting for the server, Thread B instantly steps up and fires off a completely different network request. Your processor barely breaks a sweat, because it is merely coordinating network traffic rather than doing heavy mathematical lifting.

Why Your CSV Became a Jumbled Nightmare

You mentioned your final data was totally corrupted. That happens because multiple rogue workers tried to write text to the exact same file at the exact same millisecond.

Absolute chaos.

To stop this dead in its tracks, you absolutely cannot let your threads touch the same shared variables blindly. How do we bypass this data collision?

• The Easy Hack: Just write individual, temporary JSON files per URL scraped to a local folder, then mash them all together into one CSV at the very end. (That trick alone saved my sanity back in 2018).
• The Pro Move: Use a thread-safe Queue. Let your threads scrape the raw HTML, but force them to dump that data into a synchronized waiting line. Then, have one single, dedicated worker pull from that line and safely write to your CSV.

Here is the corrected mental map for your architecture going forward:

So, to finally answer your burning question: What is parallel execution in your specific context? It is utilizing a pool of lightweight threads to overlap your agonizing waiting times.

Drop the process pool today. Switch your script over to a `ThreadPoolExecutor` with maybe 10 or 15 workers max. You will undoubtedly slice that painful 14-hour waiting period down to maybe forty-five minutes. Keep your scope tight, don't overcomplicate the memory sharing, and your script will run flawlessly.

Let me know if that clears up the confusion!

The previous reply entirely nailed the basic multithreading concept, but I'm going to throw a giant, disruptive wrench into this discussion.

Stop using threads.

Seriously. While a thread pool absolutely patches up that frozen laptop situation initially, it secretly conceals a terrifying memory trap the very second you attempt scaling past a handful of concurrent workers.

Three years ago, I stubbornly tried scraping 400,000 heavily delayed e-commerce product pages using a monstrous 500-thread pool. The operating system basically panicked. Constant context-switching between that many physical threads ate up so much RAM my remote server collapsed like a wet paper napkin. Whenever a frustrated beginner asks me what is parallel execution? regarding massive web scraping today, I point them away from threading entirely.

The Asynchronous Cheat Code

If you want the absolute, definitive answer to what is parallel execution? for agonizing network bottlenecks, you must abandon traditional executors and embrace Asynchronous I/O (specifically Python's asyncio paired with aiohttp).

Async operates as a totally different beast. Instead of spawning dozens of independent threads that blindly fight for system resources, async relies on one single, blindingly fast event loop.

When your script fires a request to a slow server, it doesn't just sit blockaded. It instantly fires off 999 other requests. The absolute microsecond any random server finally replies, the loop seamlessly catches the inbound data. No messy threads. No corrupted CSV data collisions. Just pure, unadulterated speed.

Your Immediate Action Plan

Here is how you drastically redefine what is parallel execution? in your exact codebase without completely losing your sanity:

• Drop standard libraries: Swap out your basic requests module for aiohttp. Standard requests strictly block execution—which totally ruins the magic.
• Batch your URLs: Don't feed all 50,000 URLs simultaneously. Chunk them into manageable blocks of 500 using asyncio.gather.
• Install a speed limit: (This is a literal lifesaver). Always add an asyncio.Semaphore(100) to throttle active connections. Async is so violently fast it essentially mimics a targeted DDoS attack, which will automatically get your IP banned by Cloudflare in three seconds flat.

Threading is exactly like hiring ten average workers to stand around waiting. Async is basically hiring one hyper-active superhuman who literally never stops moving. Try rewriting just a tiny ten-URL test script with asyncio—you'll be blown away by the speed increase.