使多個請求從CherryPy的網絡服務

Question

我創造出的地圖存儲數據的CherryPy的網絡服務中獲取數據，從客戶端接收鍵和返回相應的數據：

import sys 
import imp 
import cherrypy 

data_source = get_data() # get data from the database and store it in the map 

class Provider: 
    exposed = True 

    def POST(self, key): 
     global data_source 
     data = data_source[key] # get stored data based on given key 
     return data 

if __name__ == '__main__': 
    cherrypy.tree.mount(Provider(), '/Provider',{'/': 
      {'request.dispatch': cherrypy.dispatch.MethodDispatcher()} 
     }) 
    cherrypy.config.update({'server.socket_host': '0.0.0.0', 
      'server.socket_port': 8080, 
     }) 
    cherrypy.server.max_request_body_size = 1048576000 
    cherrypy.engine.start() 
    cherrypy.engine.block() 

然後，另一臺機器上，我創建了一個腳本來向提供者請求數據。使用腳本，就可以scpecify，我想多少併發請求作出：

import requests 
import time 
from threading import Thread 

def make_request(id, key): 
    start = time.time() 
    r = requests.post("http://provider-host/Provider", {'key':key}) 
    end = time.time() 
    print 'Thread {0} takes {1} seconds to finish with status code {2}'.format(id, end - start, r.status_code) 

def start(num, key): 
    ts = [] 
    for i in range(num): 
     t = Thread(target=make_request, args=(i, key)) 
     ts.append(t) 
    for t in ts: t.start() 
    for t in ts: t.join() 

最後，我做一個測試，要求相同的鍵10次，有2種不同的方法：順序和併發。

序貫方法：

time for i in range(10): start(1, 'big_data_key') 

結果是：

Thread 0 takes 2.51558494568 seconds to finish with status code 200 
Thread 0 takes 2.47761011124 seconds to finish with status code 200 
Thread 0 takes 2.66229009628 seconds to finish with status code 200 
Thread 0 takes 2.47381901741 seconds to finish with status code 200 
Thread 0 takes 2.4907720089 seconds to finish with status code 200 
Thread 0 takes 2.93357181549 seconds to finish with status code 200 
Thread 0 takes 2.47671484947 seconds to finish with status code 200 
Thread 0 takes 2.40888786316 seconds to finish with status code 200 
Thread 0 takes 2.6319899559 seconds to finish with status code 200 
Thread 0 takes 2.77075099945 seconds to finish with status code 200 
CPU times: user 1.79 s, sys: 1.06 s, total: 2.85 s 
Wall time: 25.9 s 

併發方法：

time start('138.251.195.251', 10, 'big_data_key') 

結果是：

Thread 5 takes 15.5736939907 seconds to finish with status code 200 
Thread 1 takes 19.4057281017 seconds to finish with status code 200 
Thread 7 takes 21.4743158817 seconds to finish with status code 200 
Thread 8 takes 22.4408829212 seconds to finish with status code 200 
Thread 0 takes 24.1915988922 seconds to finish with status code 200 
Thread 2 takes 24.3175201416 seconds to finish with status code 200 
Thread 6 takes 24.3368370533 seconds to finish with status code 200 
Thread 4 takes 24.3618791103 seconds to finish with status code 200 
Thread 9 takes 24.3891952038 seconds to finish with status code 200 
Thread 3 takes 24.5536601543 seconds to finish with status code 200 
CPU times: user 2.34 s, sys: 1.67 s, total: 4.01 s 
Wall time: 24.6 s 

很明顯，使用併發方法，完成一個請求所需的時間比順序方法中的要高。

所以，我的問題是：是由兩臺機器之間的帶寬造成的下載時間的差異，還是由其他原因造成的，例如， cherrypy有關？如果是由其他原因引起的，我將不勝感激任何處理它的建議。

Answer 1

那麼很明顯你的瓶頸就是網絡。以10.8MiB/s傳輸220MiB應該至少需要20秒。您的實驗需要約25秒，即8.8MiB/s，即在100MBit/s的最大理論容量中有效〜74Mbit/s。考慮到所有可能的測量誤差，這是一個好結果。

串行和並行情況（〜5％）之間的差異表明多路複用無助於解決，因爲瓶頸是網絡帶寬，而不是單個連接限制。

爲了測量CherryPy的影響，你可以設置一個用本地代碼編寫的網頁服務器，我建議nginx，把文件放在那裏，然後嘗試下載10次。對於平行測試，您可以嘗試Apache ab，如ab -n 10 -c 10 http://provider-host/some-big-file。

兼論CherryPy的幾個注意事項：

• CherryPy的是線程服務器，默認的線程池，server.thread_pool，有職工10人，
• 嘗試激活gzip壓縮，'/' : {'tools.gzip.on': True}，在你的配置，這將顯着提升純文本數據。

你也可以看看this question，關於用CherryPy處理大文件下載。