[FIXED] How can I speed up parsing with aiohttp workers queue?

Issue

I need to collect 256 urls from the main page, 653 more of them, 11000 more of these 653. I collected 653, guy helped me here.Help me add an async to the worker_iso(q) function, which collects 11000 links from 653, the code is slow, I'm suffering, I will be grateful for any help.Sorry if this is a simple kind of question but I have little experience with asyncio so if anyone could help that would be massively appreciated. UPDATE: I receive this exception with @Andrej Kesely code Thanks

import asyncio
import aiohttp
from bs4 import BeautifulSoup

out = []
iso_standart = []


async def get_soup(session, url):
    async with session.get(url=url) as resp:
        return BeautifulSoup(await resp.text(), "lxml")


async def worker(session, q):
    while True:
        url, link_name, title = await q.get()
        soup = await get_soup(session, url)

        links = soup.select('[data-title="Subcommittee"] a')
        if links:
            for a in links:
                out.append("https://www.iso.org" + a["href"])
        else:
            out.append(url)

        q.task_done()


async def worker_iso(q):
    for urls in out:
        while True:
            response = await q.get(urls)
            soup = BeautifulSoup(await response.text(), "lxml")
            for i in soup.find_all('tr', {'ng-show': 'pChecked || pChecked == null'}):
                a1 = i.find('a').attrs['href']
                print(a1)
                iso_standarts = f'https://www.iso.org{a1}'
                iso_standart.append(iso_standarts)

            q.task_done()


async def main():
    url = "https://www.iso.org/standards-catalogue/browse-by-tc.html"

    async with aiohttp.ClientSession() as session:
        soup = await get_soup(session, url)

        titles = soup.select('td[data-title="Title"]')
        links = soup.select('td[data-title="Committee"] a')

        committees = []
        for a, t in zip(links, titles):
            committees.append(
                [
                    "https://www.iso.org" + a["href"],
                    a.get_text(strip=True),
                    t.get_text(strip=True),
                ]
            )

        queue = asyncio.Queue(maxsize=16)

        tasks = []

        # create 16 workers that will process data in parallel
        for i in range(16):
            task = asyncio.create_task(worker(session, queue))
            tasks.append(task)

        # put some data to worker queue
        for c in tqdm.tqdm(committees):
            await queue.put(c)

        # wait for all data to be processed
        await queue.join()

        # cancel all worker tasks
        for task in tasks:
            task.cancel()

        # Wait until all worker tasks are cancelled.
        for i in range(16):
            task_iso = asyncio.create_task(worker_iso(queue))
            tasks.append(task_iso)
        await asyncio.gather(*tasks, return_exceptions=True)
        print(len(out))


if __name__ == "__main__":
    asyncio.run(main())

Solution

This script will get next ~20k links to iso_standards list (in about a minute):

import tqdm
import asyncio
import aiohttp
from bs4 import BeautifulSoup

out = []
iso_standards = []


async def get_soup(session, url):
    async with session.get(url=url) as resp:
        return BeautifulSoup(await resp.text(), "lxml")


async def worker(session, q):
    while True:
        url, link_name, title = await q.get()
        soup = await get_soup(session, url)

        links = soup.select('[data-title="Subcommittee"] a')
        if links:
            for a in links:
                out.append("https://www.iso.org" + a["href"])
        else:
            out.append(url)

        q.task_done()


async def worker_iso(session, q):
    while True:
        url = await q.get()
        soup = await get_soup(session, url)

        for i in soup.find_all(
            "tr", {"ng-show": "pChecked || pChecked == null"}
        ):
            a1 = i.find("a").attrs["href"]
            iso_standards.append(f"https://www.iso.org{a1}")

        q.task_done()


async def main():
    url = "https://www.iso.org/standards-catalogue/browse-by-tc.html"

    async with aiohttp.ClientSession() as session:
        soup = await get_soup(session, url)

        titles = soup.select('td[data-title="Title"]')
        links = soup.select('td[data-title="Committee"] a')

        committees = []
        for a, t in zip(links, titles):
            committees.append(
                [
                    "https://www.iso.org" + a["href"],
                    a.get_text(strip=True),
                    t.get_text(strip=True),
                ]
            )

        queue = asyncio.Queue(maxsize=16)

        # Phase 1 - Get 653 links:

        tasks = []

        # create 16 workers that will process data in parallel
        for i in range(16):
            task = asyncio.create_task(worker(session, queue))
            tasks.append(task)

        # put some data to worker queue
        for c in tqdm.tqdm(committees):
            await queue.put(c)

        # wait for all data to be processed
        await queue.join()

        # cancel all worker tasks
        for task in tasks:
            task.cancel()

        # Wait until all worker tasks are cancelled.
        await asyncio.gather(*tasks, return_exceptions=True)

        # Phase 2 - Get next 18096 links:

        tasks = []

        # create 16 workers that will process data in parallel
        for i in range(16):
            task = asyncio.create_task(worker_iso(session, queue))
            tasks.append(task)

        # put some data to worker queue
        for c in tqdm.tqdm(out):
            await queue.put(c)

        # wait for all data to be processed
        await queue.join()

        # cancel all worker tasks
        for task in tasks:
            task.cancel()

        # Wait until all worker tasks are cancelled.
        await asyncio.gather(*tasks, return_exceptions=True)

        print(len(iso_standards))


if __name__ == "__main__":
    asyncio.run(main())

Prints:

100%|██████████████████████████████████████████████████████████████████| 256/256 [00:18<00:00, 13.99it/s]
100%|██████████████████████████████████████████████████████████████████| 653/653 [00:42<00:00, 15.47it/s]
21138

Answered By - Andrej Kesely