I expect to have maybe something like 100k URLs from different domains. I wrote this code which has a list of URLs in all_urls
and forms N
threads to run in one batch. Currently I'm using threading
module to make these requests in parallel.
import requests
import os
import threading
import time
all_urls = [] # a list of URLs to request, can have up to 100k
global success, fail
success = 0
fail = 0
def func(url_to_request):
global success, fail
try:
r = requests.get(url_to_request, timeout=5)
c = r.content
success = success +1
except:
fail = fail +1
return
batch_count = 1
N = 200 # number of threads
all_threads_urls = []
time_start = time.time()
for item in all_urls:
all_threads_urls.append(item)
if all_urls.index(item) == len(all_urls)-1 or len(all_threads_urls) == N:
# call it
all_threads = []
for link in all_threads_urls:
current_thread = threading.Thread(target=func, args=(link,))
all_threads.append(current_thread)
current_thread.start()
for thr in all_threads:
thr.join()
all_threads_urls = [] # for the next batch
time_end = time.time()
print "Request number", all_urls.index(item)+1, "Good:", success, "Bad:", fail, "Duration:", round(time_end - time_start,2 ), "seconds."
time_start = time_end
Results for this are a bit weird, it seems that the script starts very fast but then slows down a lot (see image). Printed durations are for each batch.
Can someone explain what is the bottleneck here? Is there maybe a better module for this or there is no way around this?