Python 使用多线程触发动态 url

Question

我是 Python-Threading 的新手，我看过很多帖子，但我真的不明白如何使用它。但是我试图完成我的任务，我想检查我是否以正确的方法完成它。

任务是：读取包含大约 20K 条记录的大型 CSV，从每条记录中获取 ID 并为 CSV 的每条记录触发 HTTP API 调用。

t1 = time.time()
file_data_obj = csv.DictReader(open(file_path, 'rU')) 
threads = []
for record in file_data_obj:
      apiurl = https://www.api-server.com?id=record.get("acc_id", "")
      thread = threading.Thread(target=requests.get, args=(apiurl,))
      thread.start()
      threads.append(thread)

t2 = time.time()

for thread in threads:
    thread.join()

print("Total time required to process a file - {} Secs".format(t2-t1))

有20K条记录，是否会开启20K个线程？或者 OS/Python 会处理吗？如果是，我们可以限制它吗？
如何收集 requests.get 返回的响应？
t2 - t1 真的会给 mw 处理整个文件所需的时间吗？

Answer 1

As there are 20K records, would it start 20K threads? OR OS/Python will handle it? If yes, can we restrict it?

是 - 每次迭代都会启动一个线程。线程的最大数量取决于您的 OS.

How can I grab the response returned by requests.get?

如果您只想使用 threading 模块，则必须使用 Queue。 Threads return None 按照设计，因此您必须在 Thread 和您 main 循环之间实现一条通信线路。

from queue import Queue
from threading import Thread
import time

# A thread that produces data
q = Queue()



def return_get(q, apiurl):
    q.put(requests.get(apiurl)

for record in file_data_obj:
    apiurl = https://www.api-server.com?id=record.get("acc_id", "")
    t = threading.Thread(target=return_get, args=(q, apiurl))
    t.start()
    threads.append(t)

for thread in threads:
    thread.join()

while not q.empty:
    r = q.get()  # Fetches the first item on the queue
    print(r.text)

另一种方法是使用工作池。

from concurrent.futures import ThreadPoolExecutor
from queue import Queue
import urllib.request

threads = []

pool = ThreadPoolExecutor(10)

# Submit work to the pool
for record in file_data_obj:
    apiurl = https://www.api-server.com?id=record.get("acc_id", "")
    t = pool.submit(fetch_url, 'http://www.python.org')
    threads.append(t)

for t in threads:
    print(t.result())

Answer 2

您可以使用 ThreadPoolExecutor

检索单个页面并报告 URL 和内容

def load_url(url, timeout):
    with urllib.request.urlopen(url, timeout=timeout) as conn:
        return conn.read()

创建具有 N 个 worker 的池执行器

with concurrent.futures.ThreadPoolExecutor(max_workers=N_workers) as executor:
    # Start the load operations and mark each future with its URL
    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
        try:
            data = future.result()
        except Exception as exc:
            print('%r generated an exception: %s' % (url, exc))
        else:
            print('%r page is %d bytes' % (url, len(data)))

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