python之如何在 Flask 中并行化任务

我正在向我的 Flask 服务器发送一个 XHR 请求，以便在网络上执行多个 ping

资源

def get(self, site_id): 
    … 
    for printer in printers: 
        hostname = printer['hostname'] 
        response[site_id][hostname] = network_utils.ping(hostname) 
 
    return response 

平

在 shell.execute 下面我使用 subprocess.check_output运行 native ping:

def ping(hostname): 
    command = ['ping', '-c', '1', '-W', '1', '-q', hostname] 
 
    response = shell.execute(command) 
    return output_parser.ping(response['results']) 

输出

{ 
    "test-site": { 
        "avg": 0.093, "max": 0.093, "mdev": 0.0, "min": 0.093, 
        "1.1.1.1": { "avg": null, "max": null, "mdev": null, "min": null}, 
        "1.2.3.4": { "avg": null, "max": null, "mdev": null, "min": null}, 
        "127.0.0.1": { "avg": 0.061, "max": 0.061, "mdev": 0.0, "min": 0.061} 
    } 
} 

问题

ping 是按顺序运行的，这使得请求非常慢(几十秒，我怎样才能加快速度？

请您参考如下方法:

听起来最好的选择是线程，因为您的问题是I/O 限制。我正在使用 Semaphore限制为 5 个线程。

我将响应字典发送到 ping 字典是线程安全的，但你应该阅读 this如果您考虑更复杂的事情。

def get(self, site_id): 
    … 
    semaphore = threading.Semaphore(5) 
    threads = [] 
 
    for printer in printers: 
        hostname = printer['hostname'] 
        threads.append(threading.Thread(target=network_utils.ping, 
                          args=(semaphore, response, site_id, hostname))) 
 
    # Start and wait to all threads to finish 
    map(lambda t: t.start(), threads) 
    map(lambda t: t.join(), threads) 
 
    return response 
 
def ping(semaphore, response, site_id, hostname): 
    semaphore.acquire() 
 
    command = ['ping', '-c', '1', '-W', '1', '-q', hostname] 
    response = shell.execute(command) 
    ping_data = output_parser.ping(response['results']) 
 
    response[site_id][hostname] = ping_data 
 
    semaphore.release()