5 ms·
Show HN: Dead easy Python for executing many HTTP requests asynchronously
- joshlk 6y agoNeeded when executing 1 million API requests for example. Use the full force of your machine by utilising async IO via the excellent library Trio. It has been tested in the wild with more than 10 million requests. Automatically handles errors and executes retries. Also provides helper functions for executing embarrassingly parallel async coroutines.
- glic3rinu 6y agowhen you say parallel you mean the work is distributed across multiple cores? e.g. multiple python worker processes. Or is all work running single threaded in a single process (concurrent IO, but not true parallelism)?
- joshlk 6y agoAll running in a single thread - but the work is executed asynchronously. This is preferred for I/O heavy throughput such as many HTTP requests.
- nurettin 6y agoWhy not distribute the concurrent work over multiple processes to get the most out of multiple cores? More event loops, better performance, and maybe more text preprocessing.
- joshlk 6y agoThat would be a good idea, but it's non-trivial in Python as multi-processing async support is patchy. If you have to choose multi-processing vs async for HTTP requests, from experience, async is much faster (as your not CPU bound but IO bound) and easier to use.
- nurettin 6y agoI do both when I'm scaling to millions of requests per day. I also do some cpu bound preprocessing after the io bound async functions end.
- genidoi 6y agoLook into Go, specifically the colly pkg. Learnt Go just so I could use this library after wrestling with highly parralelized Python web scraping. The 1k/requests/sec/core figure is true and frankly deadly, and Go borrows a lot of useful stuff from Python like array slicing. It was pretty remarkable to go from 32 cores being maxed for 100 concurrent workers to... A 10-15% usage spike on a 4 core XPS :) https://github.com/gocolly/colly https://github.com/gocolly/colly
- deleted 6y ago[deleted]
- throwaway189262 6y agoReally it's better to use a fast language like Go or Java for huge loads like this. Our typical CRUD Spring backends can handle ~15,000 requests a second with no optimization. I appreciate all the effort that's gone into making Python faster but I think it's still the wrong tool for the job at this point if heavy load is involved
- rovr138 6y agoIf the rest of their stack is in python, not having to manage another language means that everyone else can switch to this and quickly keep going. There is a space for go and other languages, but also for libraries like these.
- hansvm 6y agoNice job! Is vanilla Python networking is still dog slow, on the order of 1k requests per second per core on mid-range consumer hardware no matter how you wire it all together?
- roywiggins 6y agoOh, handy. This would have solved the exact problem I had last month (which I solved with just-good-enough fiddling with async python, which I'm not comfortable in yet)
- eatonphil 6y agoI'm curious why one would suggest using this over the built-in ThreadPoolExecutor class? Here's a complete, similar example from the Python docs: https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example https://docs.python.org/3/library/concurrent.futures.html#th.... Granted that example is more lines of code than the README for this library. But the core of it is fairly straightforward: rsps = [] with concurrent.futures.ThreadPoolExecutor(max_workers=N) as executor: futures = [executor.submit(requests.get, url) for url in urls] for future in concurrent.futures.as_completed(futures): rsps.append(future.result)
- offtop5 6y agoGreat point , adding more libraries should always be a last resort. When using lambdas adding Python libraries are an absolute pain in the but, I'll use this example in the future
- joshlk 6y agoThis wont work for a large number of requests as the throughput plateaus quit rapidly with a large number of threads due to the GIL and blocking I/O. This is the main reason async was introduced into Python. Also `many-requests` has further logic to deal with retries and bad responses.
- eatonphil 6y agoYeah, I guess swapping out requests for an async version of requests would be an improvement on my example. But I think requests already handles retries on its own.
- joshlk 6y agoIts not simple to just "swap out" the requests library with an async one. This is why I wrote this package :) It deals with a lot of the headaches one would run into by trying just that.
- devxpy 6y ago
- texuf 6y agoI've been out of python for a while, why is the package name and the module name different? `install many-requests` vs `import many_requests`
- stevesimmons 6y agomany-requests is the name the package was registered with on pypi.org. So you install with: pip install many-requests. That installation creates a folder named many_requests inside the site-packages directory on your PYTHON_PATH. It uses underscore rather than hyphen because modules/packages must be legal Python names. A hyphen would be parsed as subtraction.
- comfydragon 6y ago> It uses underscore rather than hyphen because modules/packages must be legal Python names. A hyphen would be parsed as subtraction. And inversely, PyPI normalizes the package name (the name you give to `pip install`) to use hyphens instead of underscores. https://www.python.org/dev/peps/pep-0503/#normalized-names https://www.python.org/dev/peps/pep-0503/#normalized-names
- stevesimmons 6y agoI have a somewhat related question: What is the current best practice way to parallelize reading many parquet files on S3 using pyarrow? Deep down, it's just lots of HTTP requests.