如何在Python中将API响应时间减少89.30％

如果您要构建快速且可扩展的应用程序，则API响应时间是要寻找的重要因素。

在处理客户的项目时，我需要完成一项任务，需要为该项目集成第三方API。这意味着客户的项目API响应时间现在也将取决于此第三方API。

如果我们只需要对API进行单个查询并返回该查询的结果，那么一切都会很好，当我们需要进行多个查询并为我们进行的每个查询返回结果时，情况会变得越来越糟。

为了演示如何有效地发出多个请求，我们将使用此简单的Rhyming word API。

例如，假设我们要为[“ hello”，“ mellow”，“ cat”，“ rat”，“ dog”，“ frog”，“ mouse”，“ sparrow”，“男人”，“妇女”]可能会想到实现这样的代码。

import requests, time

words = ["hello", "mellow", "cat", "rat", "dog", "frog", "mouse", "sparrow", "man", "women"]

def make_req_syncronously(words_arr):
    final_res = []
    for word in words_arr:
        url = f"https://api.datamuse.com/words?rel_rhy={word}&max=100"
        response = requests.get(url)
        json_response = response.json()
        for item in json_response:
            rhyming_word = item.get("word", "")
            final_res.append({
     "word": word, "rhyming_word": rhyming_word})
    return final_res

without_async_start_time = time.time()
response = make_req_syncronously(words)
time_without_async = time.time() - without_async_start_time

print("total time for with synchronous execution >> ", time_without_async, " seconds")

您会看到这种方法大约需要9.479秒的时间来搜索“单词”列表中存在的每个单词的押韵单词。您能想象您打开一个网站并花10秒钟来加载吗？只是因为有人在后端写了这个可怕的代码。

为了更好地理解这一点，请查看此图，了解我们如何以这种同步方式发出请求。

如您所见，我们向（“ hello”）单词发出第一个请求，然后等待它返回结果，然后将其附加到我们要返回的列表中，列表中的每个单词都相同。这是实现它们的极其耗时的方法。

那么，我们该如何改善呢？

好吧，这就是异步/等待为我们提供帮助的地方。让我们了解如何使用以下Python代码同时发出多个请求。

import asyncio
import aiohttp  # external library
import time

words = ["hello", "mellow", "cat", "rat", "dog", "frog", "mouse", "sparrow", "man", "women"]

async def main():
    headers = {
     'content-type': 'application/json'}
    async with aiohttp.ClientSession(headers=headers) as session:
        tasks = []  # for storing all the tasks we will create in the next step
        for word in words:
            task = asyncio.ensure_future(get_rhyming_words(session, word))  # means get this process started and move on
            tasks.append(task)

        # .gather() will collect the result from every single task from tasks list
        # here we use await to wait till all the requests have been satisfied
        all_results = await asyncio.gather(*tasks)
        combined_list = merge_lists(all_results)
        return combined_list


async def get_rhyming_words(session, word):
    url = f"https://api.datamuse.com/words?rel_rhy={word}&max=1000"
    async with session.get(url) as response:
        result_data = await response.json()
        return result_data


async_func_start_time = time.time()
response2 = asyncio.get_event_loop().run_until_complete(main(words))
time_with_async = time.time() - async_func_start_time

print("\nTotal time with async/await execution >> ", time_with_async, " seconds")

print("\nTotal time with async/await execution >> ", time_with_async, " seconds")

在这里，我们为每个请求创建一个Task，在Python中也称为Couritine。在此实现中，我们开始一个任务，但不等待它完成就可以继续前进并开始一个新任务。

最后，在完成所有任务的创建之后，我们等待直到获得每个任务的结果，然后我们简单地使用merge_lists函数合并任务的所有结果，并返回包含所有结果的组合列表。

在单词列表中为所有单词搜索有韵词的时间大约为1秒，这比我们以前的方法要好得多。

请求与时间的关系图现在看起来像

正如您在图表中看到的那样，当我们发起最终请求时，我们将已经完成一些先前的请求。

这是供您比较这两种实现的Python代码。

_import_ requests, time

words = ["hello", "mellow", "cat", "rat", "dog", "frog", "mouse", "sparrow", "man", "women"]

_def_ make_req_syncronously(words_arr):
    final_res = []
    _for_ word _in_ words_arr:
        url = f"https://api.datamuse.com/words?rel_rhy={word}&max=100"
        response = requests.get(url)
        json_response = response.json()
        _for_ item _in_ json_response:
            rhyming_word = item.get("word", "")
            final_res.append({
     "word": word, "rhyming_word": rhyming_word})
    _return_ final_res

without_async_start_time = time.time()
response = make_req_syncronously(words)
time_without_async = time.time() - without_async_start_time
_#  
print_ ("total time for with synchronous execution >> ", time_without_async, " seconds")

_import_ asyncio
_import_ aiohttp _# external library_


_def_ merge_lists(results_from_fc):
    _"""  
 Function for merging multiple lists  
 """_ combined_list = []
    _for_ li _in_ results_from_fc:
        combined_list.extend(li)

    _return_ combined_list


_async def_ main():
    headers = {
     'content-type': 'application/json'}
    _async with_ aiohttp.ClientSession(headers=headers) _as_ session:
        tasks = [] _# for storing all the tasks we will create in the next step  
 for_ word _in_ words:
            task = asyncio.ensure_future(get_rhyming_words(session, word)) _# means get this process started and move on_  
tasks.append(task)

        _# .gather() will collect the result from every single task from tasks list  
 # here we use await to wait till all the requests have been satisfied_ all_results = _await_ asyncio.gather(*tasks)
        combined_list = merge_lists(all_results)
        _return_ combined_list


_async def_ get_rhyming_words(session, word):
    url = f"https://api.datamuse.com/words?rel_rhy={word}&max=1000"
    _async with_ session.get(url) _as_ response:
        result_data = _await_ response.json()
        _return_ result_data


async_func_start_time = time.time()
response2 = asyncio.get_event_loop().run_until_complete(main())
time_with_async = time.time() - async_func_start_time

_print_("\nTotal time with async/await execution >> ", time_with_async, " seconds")

total_improvement = (time_without_async - time_with_async) / time_without_async * 100
_print_(f"\n{'*' * 100}\n{' ' * 32}Improved by {total_improvement} %\n{'*' * 100}")

感谢您抽出宝贵的时间阅读我的博客。