torequests 5.0.2

Async wrapper for requests / aiohttp, and some python crawler toolkits. Let synchronization code enjoy the performance of asynchronous programming. Read more: https://github.com/ClericPy/torequests.

torequests

Briefly speaking, requests & aiohttp wrapper for asynchronous programming rookie, to shorten the code quantity.

Install:

pip install torequests -U

requirements

python2.7 / python3.6+

| requests			# python
| futures 			# python2
| aiohttp >= 3.0.5 	# python3
| uvloop  			# python3

optional:

| psutil
| pyperclip

Features

Inspired by tomorrow, to make async-coding brief & smooth, compatible for win32 / python 2&3.

• Convert any funtions into async-mode with concurrent.futures.
• Wrap requests lib with concurrent.futures to enjoy the concurrent performance.
• Simplify aiohttp, make it requests-like.
• Add FailureException to check the context of request failure.
• Add frequency control, prevent from anti-spider based on frequency check.
• Add retry for request.
• Alenty of common crawler utils.
• Compatible with uniparser

Getting started

1. Async, threads - make functions asynchronous

from torequests import threads, Async
import time


@threads(5)
def test1(n):
    time.sleep(n)
    return 'test1 ok'


def test2(n):
    time.sleep(n)
    return 'test1 ok'


start = int(time.time())
# here async_test2 is same as test1
async_test2 = Async(test2)
future = test1(1)
# future run in non blocking thread pool
print(future, ', %s s passed' % (int(time.time() - start)))
# call future.x will block main thread and get the future.result()
print(future.x, ', %s s passed' % (int(time.time() - start)))
# output:
# <NewFuture at 0x34b1d30 state=running> , 0 s passed
# test1 ok , 1 s passed

2. tPool - thread pool for async-requests

from torequests.main import tPool
from torequests.logs import print_info
from torequests.utils import ttime

req = tPool()
test_url = 'http://p.3.cn'


def callback(task):
    return (len(task.content),
            print_info(
                ttime(task.task_start_time), '-> ', ttime(task.task_end_time),
                ',', round(task.task_cost_time * 1000), 'ms'))


ss = [req.get(test_url, retry=2, callback=callback) for i in range(3)]
# or [i.x for i in ss]
req.x
# task.cx returns the callback_result
ss = [i.cx for i in ss]
print_info(ss)
# [2020-01-21 16:56:23] temp_code.py(11): 2020-01-21 16:56:23 ->  2020-01-21 16:56:23 , 54 ms
# [2020-01-21 16:56:23] temp_code.py(11): 2020-01-21 16:56:23 ->  2020-01-21 16:56:23 , 55 ms
# [2020-01-21 16:56:23] temp_code.py(11): 2020-01-21 16:56:23 ->  2020-01-21 16:56:23 , 57 ms
# [2020-01-21 16:56:23] temp_code.py(18): [(612, None), (612, None), (612, None)]

2.1 Performance.

[3.7.1 (v3.7.1:260ec2c36a, Oct 20 2018, 14:57:15) [MSC v.1915 64 bit (AMD64)]]: 2000 / 2000, 100.0%, cost 4.2121 seconds, 475.0 qps.
[2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]]: 2000 / 2000, 100%, cost 9.4462 seconds, 212.0 qps.

import timeit
from torequests import tPool
import sys

req = tPool()
start_time = timeit.default_timer()
oks = 0
total = 2000
# concurrent all the tasks
tasks = [req.get('http://127.0.0.1:8080') for num in range(total)]
for task in tasks:
    r = task.x
    if r.text == 'ok':
        oks += 1
end_time = timeit.default_timer()
succ_rate = oks * 100 / total
cost_time = round(end_time - start_time, 4)
version = sys.version
qps = round(total / cost_time, 0)
print(
    '[{version}]: {oks} / {total}, {succ_rate}%, cost {cost_time} seconds, {qps} qps.'
    .format(**vars()))

3. Requests - aiohttp-wrapper

from torequests.dummy import Requests
from torequests.logs import print_info
from torequests.utils import ttime
req = Requests(frequencies={'p.3.cn': (2, 1)})


def callback(task):
    return (len(task.content),
            print_info(
                ttime(task.task_start_time), '->', ttime(task.task_end_time),
                ',', round(task.task_cost_time * 1000), 'ms'))


ss = [
    req.get('http://p.3.cn', retry=1, timeout=5, callback=callback)
    for i in range(4)
]
req.x  # this line can be removed
ss = [i.cx for i in ss]
print_info(ss)
# [2020-01-21 18:15:33] temp_code.py(11): 2020-01-21 18:15:32 -> 2020-01-21 18:15:33 , 1060 ms
# [2020-01-21 18:15:33] temp_code.py(11): 2020-01-21 18:15:32 -> 2020-01-21 18:15:33 , 1061 ms
# [2020-01-21 18:15:34] temp_code.py(11): 2020-01-21 18:15:32 -> 2020-01-21 18:15:34 , 2081 ms
# [2020-01-21 18:15:34] temp_code.py(11): 2020-01-21 18:15:32 -> 2020-01-21 18:15:34 , 2081 ms
# [2020-01-21 18:15:34] temp_code.py(20): [(612, None), (612, None), (612, None), (612, None)]

3.1 Performance.

aiohttp is almostly 3 times faster than requests + ThreadPoolExecutor, even without uvloop on windows10.

3.7.1 (v3.7.1:260ec2c36a, Oct 20 2018, 14:57:15) [MSC v.1915 64 bit (AMD64)]
sync_test: 2000 / 2000, 100.0%, cost 1.2965 seconds, 1543.0 qps.
async_test: 2000 / 2000, 100.0%, cost 1.2834 seconds, 1558.0 qps.

Sync usage has little performance lost.

import asyncio
import sys
import timeit

from torequests.dummy import Requests


def sync_test():
    with Requests() as req:
        start_time = timeit.default_timer()
        oks = 0
        total = 2000
        # concurrent all the tasks
        tasks = [req.get('http://127.0.0.1:8080') for num in range(total)]
        req.x
        for task in tasks:
            if task.text == 'ok':
                oks += 1
        end_time = timeit.default_timer()
        succ_rate = oks * 100 / total
        cost_time = round(end_time - start_time, 4)
        qps = round(total / cost_time, 0)
        print(
            f'sync_test: {oks} / {total}, {succ_rate}%, cost {cost_time} seconds, {qps} qps.'
        )


async def async_test():
    req = Requests()
    async with Requests() as req:
        start_time = timeit.default_timer()
        oks = 0
        total = 2000
        # concurrent all the tasks
        tasks = [req.get('http://127.0.0.1:8080') for num in range(total)]
        for task in tasks:
            r = await task
            if r.text == 'ok':
                oks += 1
        end_time = timeit.default_timer()
        succ_rate = oks * 100 / total
        cost_time = round(end_time - start_time, 4)
        qps = round(total / cost_time, 0)
        print(
            f'async_test: {oks} / {total}, {succ_rate}%, cost {cost_time} seconds, {qps} qps.'
        )


if __name__ == "__main__":
    print(sys.version)
    sync_test()
    loop = asyncio.get_event_loop()
    loop.run_until_complete(async_test())

3.2 using torequests.dummy.Requests in async environment.

import asyncio

import uvicorn
from starlette.applications import Starlette
from starlette.responses import PlainTextResponse
from torequests.dummy import Requests

api = Starlette()
api.req = Requests()


@api.route('/')
async def index(req):
    # await for Response or FailureException
    r = await api.req.get('http://p.3.cn', timeout=(1, 1))
    return PlainTextResponse(r.content)


if __name__ == "__main__":
    uvicorn.run(api)

3.3 using torequests.aiohttp_dummy.Requests for better performance.

Removes the frequency_controller & sync usage (task.x) & compatible args of requests for good performance, but remains retry / callback / referer_info.

# -*- coding: utf-8 -*-

from torequests.aiohttp_dummy import Requests
from asyncio import get_event_loop


async def main():
    url = 'http://httpbin.org/json'
    # simple usage, catch_exception defaults to True
    async with Requests() as req:
        r = await req.get(url, retry=1, encoding='u8', timeout=3)
        print(r.ok)
        print(r.url)
        print(r.content[:5])
        print(r.status_code)
        print(r.text[:5])
        print(r.json()['slideshow']['title'])
        print(r.is_redirect)
        print(r.encoding)
    # or use `req = Requests()` without context
    req = Requests()
    r = await req.get(
        url,
        referer_info=123,
        callback=lambda r: r.referer_info + 1,
    )
    print(r)  # 124


get_event_loop().run_until_complete(main())

4. utils: some useful crawler toolkits

ClipboardWatcher: watch your clipboard changing.
Counts: counter while every time being called.
Null: will return self when be called, and alway be False.
Regex: Regex Mapper for string -> regex -> object.
Saver: simple object persistent toolkit with pickle/json.
Timer: timing tool.
UA: some common User-Agents for crawler.
curlparse: translate curl-string into dict of request.
md5: str(obj) -> md5_string.
print_mem: show the proc-mem-cost with psutil, use this only for lazinesssss.
ptime: %Y-%m-%d %H:%M:%S -> timestamp.
ttime: timestamp -> %Y-%m-%d %H:%M:%S
slice_by_size: slice a sequence into chunks, return as a generation of chunks with size.
slice_into_pieces: slice a sequence into n pieces, return a generation of n pieces.
timeago: show the seconds as human-readable.
unique: unique one sequence.
find_one: use regex like Javascript to find one string with index(like [0], [1]).
...

Benchmarks

Benchmark of concurrent is not very necessary and accurate, just take a look.

Test Server: golang net/http

Source code: go_test_server.go

Client Testing Code

Source code: py_test_client.py

Test Result on Windows (without uvloop)

Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz (12 CPUs), ~2.2GHz; 12 logical CPUs.

Windows-10-10.0.18362-SP0
3.7.1 (v3.7.1:260ec2c36a, Oct 20 2018, 14:57:15) [MSC v.1915 64 bit (AMD64)]
['aiohttp(3.6.2)', 'torequests(4.9.14)', 'requests(2.23.0)', 'httpx(0.12.1)']
================================================================================
test_aiohttp             : 2000 / 2000 = 100.0%, cost 1.289s, 1551 qps, 100% standard.
test_dummy               : 2000 / 2000 = 100.0%, cost 1.397s, 1432 qps,  92% standard.
test_aiohttp_dummy       : 2000 / 2000 = 100.0%, cost 1.341s, 1491 qps,  96% standard.
test_httpx               : 2000 / 2000 = 100.0%, cost 4.065s,  492 qps,  32% standard.
test_tPool               : 2000 / 2000 = 100.0%, cost 5.161s,  388 qps,  25% standard.

Test Result on Linux (with uvloop)

Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz; 1 logical CPU.

Linux-4.15.0-13-generic-x86_64-with-Ubuntu-18.04-bionic
3.7.3 (default, Apr  3 2019, 19:16:38)
[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]
['aiohttp(3.6.2)', 'torequests(4.9.14)', 'requests(2.23.0)', 'httpx(0.12.1)']
================================================================================
test_aiohttp             : 2000 / 2000 = 100.0%, cost 0.652s, 3068 qps, 100% standard.
test_dummy               : 2000 / 2000 = 100.0%, cost 0.802s, 2495 qps,  81% standard.
test_aiohttp_dummy       : 2000 / 2000 = 100.0%, cost 0.726s, 2754 qps,  90% standard.
test_httpx               : 2000 / 2000 = 100.0%, cost 2.349s,  852 qps,  28% standard.
test_tPool               : 2000 / 2000 = 100.0%, cost 2.708s,  739 qps,  24% standard.

Conclusion

1. aiohttp is the fastest, for the cython utils
   1. aiohttp's qps is 2866 on 1 cpu linux with uvloop, near to golang's 3300.
2. torequests.dummy.Requests based on aiohttp.
   1. about 8% performance lost without uvloop.
   2. about 20% performance lost with uvloop.
3. torequests.aiohttp_dummy.Requests based on aiohttp.
   1. less than 4% performance lost without uvloop.
   2. less than 10% performance lost with uvloop.
4. httpx is faster than requests + threading, but not very obviously on linux.

PS

golang - net/http 's performance

`2000 / 2000, 100.00 %, cost 0.26 seconds, 7567.38 qps` on windows (12 cpus)
`2000 / 2000, 100.00 %, cost 0.61 seconds, 3302.48 qps` on linux (1 cpu)
   1. slower than windows, because golang benefit from multiple CPU count
   2. linux 1 cpu, but windows is 12

golang http client testing code:

go_test_client.go

Documentation

Document & Usage

License

MIT license