🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 122 (from laksa051)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa122.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://proxiesapi.com/articles/combining-asyncio-and-multiprocessing-in-python\')), getAhrefsUnparsedNoserviceFromURL(\'https://proxiesapi.com/articles/combining-asyncio-and-multiprocessing-in-python\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/proxiesapi.com\/articles\/combining-asyncio-and-multiprocessing-in-python","crawl_time":1775423763,"first_indexed_time":1710671997,"http_code":200,"src_unparsed":"com,proxiesapi!\/articles\/combining-asyncio-and-multiprocessing-in-python s443","src_root_hash":"8457903495155892322","history_drop_reason":null,"meta_title":"Combining AsyncIO and Multiprocessing in Python | ProxiesAPI","meta_descriptions":["Python's asyncio library and multiprocessing module can be combined for improved resource utilization and cleaner code. Data passing between the two requires caution."],"attrs_boilerpipe_text":"P\nython's asyncio library provides infrastructure for writing asynchronous code using the async\/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined?\nThe short answer is\nyes, with some care around passing data between async code and multiprocessing\n.\nWhy Combine AsyncIO and Multiprocessing?\nThere are a few potential benefits to using AsyncIO and multiprocessing together in Python:\nImproved resource utilization\n- AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage.\nSimplified asynchronous code\n- AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this.\nAvoid callback hell\n- Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides\nasync\n\/\nawait\nto mitigate this.\nPassing Data Between Processes\nThe main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary.\nThe safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example:\nimport asyncio\nfrom multiprocessing import Queue\n\nqueue = Queue() \n\nasync def async_worker(queue):\n  data = await get_data()  \n  queue.put(data)\n\ndef mp_worker(queue):\n  data = queue.get()\n  process(data)\nSo in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds.","attrs_markdown":"Scrape Like a Pro! [Get Your Free API Key](https:\/\/proxiesapi.com\/articles\/assets\/r.php?pid=816)\n\n[![](https:\/\/proxiesapi.com\/assets\/img\/ProxiesApi_logo_nud.png)](https:\/\/proxiesapi.com\/)\n\n[Documentation](https:\/\/proxiesapi.com\/documentation.php) [Pricing](https:\/\/proxiesapi.com\/pricing.php) [Blog](https:\/\/proxiesapi.com\/articles\/) [Login](https:\/\/app.proxiesapi.com\/login\/index.php) [Try Proxies API for Free](https:\/\/proxiesapi.com\/articles\/assets\/r.php?pid=816)\n\n# Combining AsyncIO and Multiprocessing in Python\n[Mohan Ganesan](https:\/\/www.linkedin.com\/in\/mohanganesanproxiesapi)\n\nMar 17, 2024 · 2 min read\n\nPython's asyncio library provides infrastructure for writing asynchronous code using the async\/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined?\n\nThe short answer is **yes, with some care around passing data between async code and multiprocessing**.\n\n## Why Combine AsyncIO and Multiprocessing?\nThere are a few potential benefits to using AsyncIO and multiprocessing together in Python:\n\n**Improved resource utilization** - AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage.\n\n**Simplified asynchronous code** - AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this.\n\n**Avoid callback hell** - Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides\nasync\n\/\nawait\nto mitigate this.\n## Passing Data Between Processes\nThe main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary.\n\nThe safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example:\n\nCopy\n```\nimport asyncio\nfrom multiprocessing import Queue\n\nqueue = Queue() \n\nasync def async_worker(queue):\n  data = await get_data()  \n  queue.put(data)\n\ndef mp_worker(queue):\n  data = queue.get()\n  process(data)\n```\n\nSo in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds.\n\n### Browse by tags:\n[Python](https:\/\/proxiesapi.com\/articles\/tag-Python)[performance](https:\/\/proxiesapi.com\/articles\/tag-performance)[asyncio](https:\/\/proxiesapi.com\/articles\/tag-asyncio)[asynchronous code](https:\/\/proxiesapi.com\/articles\/tag-asynchronous+code)[multiprocessing](https:\/\/proxiesapi.com\/articles\/tag-multiprocessing)[resource utilization](https:\/\/proxiesapi.com\/articles\/tag-resource+utilization)[data passing](https:\/\/proxiesapi.com\/articles\/tag-data+passing)\n\n### Browse by language:\n[C\\#](https:\/\/proxiesapi.com\/articles\/csharp) [PHP](https:\/\/proxiesapi.com\/articles\/php) [Python](https:\/\/proxiesapi.com\/articles\/python) [JavaScript](https:\/\/proxiesapi.com\/articles\/javascript) [Rust](https:\/\/proxiesapi.com\/articles\/rust) [Ruby](https:\/\/proxiesapi.com\/articles\/ruby) [Go](https:\/\/proxiesapi.com\/articles\/go) [C++](https:\/\/proxiesapi.com\/articles\/cplusplus) [Objective-C](https:\/\/proxiesapi.com\/articles\/objectivec) [Scala](https:\/\/proxiesapi.com\/articles\/scala) [Elixir](https:\/\/proxiesapi.com\/articles\/elixir) [Kotlin](https:\/\/proxiesapi.com\/articles\/kotlin) [Perl](https:\/\/proxiesapi.com\/articles\/perl) [R](https:\/\/proxiesapi.com\/articles\/r) [Java](https:\/\/proxiesapi.com\/articles\/java)\n\n## The easiest way to do Web Scraping\nGet HTML from any page with a simple API call. We handle proxy rotation, browser identities, automatic retries, CAPTCHAs, JavaScript rendering, etc automatically for you\n\n[Try ProxiesAPI for free](https:\/\/proxiesapi.com\/articles\/assets\/r.php?pid=816)\n\ncurl \"http:\/\/api.proxiesapi.com\/?key=API\\_KEY\\&url=https:\/\/example.com\"\n\n\\<!doctype html\\>  \n\\<html\\>  \n \\<head\\>  \n \\<title\\>Example Domain\\<\/title\\>  \n \\<meta charset=\"utf-8\" \/\\>  \n \\<meta http-equiv=\"Content-type\" content=\"text\/html; charset=utf-8\" \/\\>  \n \\<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" \/\\>  \n ...\n\nTired of getting blocked while scraping the web?   \n  \n Get access to 1,000 free API credits, no credit card required\\!\n\n[Try for free](https:\/\/proxiesapi.com\/articles\/assets\/r.php?pid=816)\n\nX\n\n## Don't leave just yet\\!\nEnter your email below to claim your free API key:","attrs_readable_markdown":"Python's asyncio library provides infrastructure for writing asynchronous code using the async\/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined?\n\nThe short answer is **yes, with some care around passing data between async code and multiprocessing**.\n\n## Why Combine AsyncIO and Multiprocessing?\nThere are a few potential benefits to using AsyncIO and multiprocessing together in Python:\n\n**Improved resource utilization** - AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage.\n\n**Simplified asynchronous code** - AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this.\n\n**Avoid callback hell** - Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides\nasync\n\/\nawait\nto mitigate this.\n## Passing Data Between Processes\nThe main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary.\n\nThe safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example:\n```\nimport asyncio\nfrom multiprocessing import Queue\n\nqueue = Queue() \n\nasync def async_worker(queue):\n  data = await get_data()  \n  queue.put(data)\n\ndef mp_worker(queue):\n  data = queue.get()\n  process(data)\n```\nSo in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds.","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

6 days ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.2 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://proxiesapi.com/articles/combining-asyncio-and-multiprocessing-in-python
Last Crawled	2026-04-05 21:16:03 (6 days ago)
First Indexed	2024-03-17 10:39:57 (2 years ago)
HTTP Status Code	200
Meta Title	Combining AsyncIO and Multiprocessing in Python \| ProxiesAPI
Meta Description	Python's asyncio library and multiprocessing module can be combined for improved resource utilization and cleaner code. Data passing between the two requires caution.
Meta Canonical	null
Boilerpipe Text	P ython's asyncio library provides infrastructure for writing asynchronous code using the async/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined? The short answer is yes, with some care around passing data between async code and multiprocessing . Why Combine AsyncIO and Multiprocessing? There are a few potential benefits to using AsyncIO and multiprocessing together in Python: Improved resource utilization - AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage. Simplified asynchronous code - AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this. Avoid callback hell - Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides async / await to mitigate this. Passing Data Between Processes The main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary. The safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example: import asyncio from multiprocessing import Queue queue = Queue() async def async_worker(queue): data = await get_data() queue.put(data) def mp_worker(queue): data = queue.get() process(data) So in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds.
Markdown	Scrape Like a Pro! [Get Your Free API Key](https://proxiesapi.com/articles/assets/r.php?pid=816) [![](https://proxiesapi.com/assets/img/ProxiesApi_logo_nud.png)](https://proxiesapi.com/) [Documentation](https://proxiesapi.com/documentation.php) [Pricing](https://proxiesapi.com/pricing.php) [Blog](https://proxiesapi.com/articles/) [Login](https://app.proxiesapi.com/login/index.php) [Try Proxies API for Free](https://proxiesapi.com/articles/assets/r.php?pid=816) # Combining AsyncIO and Multiprocessing in Python [Mohan Ganesan](https://www.linkedin.com/in/mohanganesanproxiesapi) Mar 17, 2024 · 2 min read Python's asyncio library provides infrastructure for writing asynchronous code using the async/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined? The short answer is yes, with some care around passing data between async code and multiprocessing. ## Why Combine AsyncIO and Multiprocessing? There are a few potential benefits to using AsyncIO and multiprocessing together in Python: Improved resource utilization - AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage. Simplified asynchronous code - AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this. Avoid callback hell - Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides async / await to mitigate this. ## Passing Data Between Processes The main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary. The safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example: Copy ``` import asyncio from multiprocessing import Queue queue = Queue() async def async_worker(queue): data = await get_data() queue.put(data) def mp_worker(queue): data = queue.get() process(data) ``` So in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds. ### Browse by tags: [Python](https://proxiesapi.com/articles/tag-Python)[performance](https://proxiesapi.com/articles/tag-performance)[asyncio](https://proxiesapi.com/articles/tag-asyncio)[asynchronous code](https://proxiesapi.com/articles/tag-asynchronous+code)[multiprocessing](https://proxiesapi.com/articles/tag-multiprocessing)[resource utilization](https://proxiesapi.com/articles/tag-resource+utilization)[data passing](https://proxiesapi.com/articles/tag-data+passing) ### Browse by language: [C\#](https://proxiesapi.com/articles/csharp) [PHP](https://proxiesapi.com/articles/php) [Python](https://proxiesapi.com/articles/python) [JavaScript](https://proxiesapi.com/articles/javascript) [Rust](https://proxiesapi.com/articles/rust) [Ruby](https://proxiesapi.com/articles/ruby) [Go](https://proxiesapi.com/articles/go) [C++](https://proxiesapi.com/articles/cplusplus) [Objective-C](https://proxiesapi.com/articles/objectivec) [Scala](https://proxiesapi.com/articles/scala) [Elixir](https://proxiesapi.com/articles/elixir) [Kotlin](https://proxiesapi.com/articles/kotlin) [Perl](https://proxiesapi.com/articles/perl) [R](https://proxiesapi.com/articles/r) [Java](https://proxiesapi.com/articles/java) ## The easiest way to do Web Scraping Get HTML from any page with a simple API call. We handle proxy rotation, browser identities, automatic retries, CAPTCHAs, JavaScript rendering, etc automatically for you [Try ProxiesAPI for free](https://proxiesapi.com/articles/assets/r.php?pid=816) curl "http://api.proxiesapi.com/?key=API\_KEY\&url=https://example.com" \<!doctype html\> \<html\> \<head\> \<title\>Example Domain\</title\> \<meta charset="utf-8" /\> \<meta http-equiv="Content-type" content="text/html; charset=utf-8" /\> \<meta name="viewport" content="width=device-width, initial-scale=1" /\> ... Tired of getting blocked while scraping the web? Get access to 1,000 free API credits, no credit card required\! [Try for free](https://proxiesapi.com/articles/assets/r.php?pid=816) X ## Don't leave just yet\! Enter your email below to claim your free API key:
Readable Markdown	Python's asyncio library provides infrastructure for writing asynchronous code using the async/await syntax. Meanwhile, the multiprocessing module allows spawning processes to leverage multiple CPUs for parallel execution. Can these tools be combined? The short answer is yes, with some care around passing data between async code and multiprocessing. ## Why Combine AsyncIO and Multiprocessing? There are a few potential benefits to using AsyncIO and multiprocessing together in Python: Improved resource utilization - AsyncIO allows non-blocking IO in a single thread, freeing up resources while IO is in progress. Multiprocessing fully utilizes multiple CPUs for CPU-bound parallel work. Using both can maximize resource usage. Simplified asynchronous code - AsyncIO provides a nice high-level interface for asynchronous logic in Python. But it runs in a single thread, so CPU-bound work will block the event loop. Offloading CPU work to other processes prevents this. Avoid callback hell - Multiprocessing avoids complications with threading and the GIL. But async code with lots of callbacks can get complex. AsyncIO provides async / await to mitigate this. ## Passing Data Between Processes The main catch with mixing AsyncIO and multiprocessing is that concurrent data structures like Python queues are not compatible across the boundary. The safest approach is to use multiprocessing queues, pipes or shared memory to pass data between the async event loop and processes. For example: ``` import asyncio from multiprocessing import Queue queue = Queue() async def async_worker(queue): data = await get_data() queue.put(data) def mp_worker(queue): data = queue.get() process(data) ``` So in summary - AsyncIO and multiprocessing absolutely can combine forces in Python for improved performance, resource utilization and cleaner code. Just be careful in how data flows between the two worlds.
Shard	122 (laksa)
Root Hash	8457903495155892322
Unparsed URL	com,proxiesapi!/articles/combining-asyncio-and-multiprocessing-in-python s443