Downloading a large file in parts using multiple parallel threads


I have a use case, where a large remote file needs to be downloaded in parts, by using multiple threads.
Each thread must run simultaneously (in parallel), grabbing a specific part of the file.
The expectation is to combine the parts into a single (original) file, once all parts were successfully downloaded.

Perhaps using the requests library could do the job, but then I am not sure how I would multithread this into a solution that combines the chunks together.

url = ''
headers = {"Range": "bytes=0-1000000"}  # first megabyte
r = get(url, headers=headers)

I was also thinking of using curl where Python would orchestrate the downloads, but I am not sure that’s the correct way to go. It just seems to be too complex and swaying away from the vanilla Python solution. Something like this:

curl --range 200000000-399999999 -o file.iso.part2

Can someone explain how you’d go about something like this? Or post a code example of something that works in Python 3? I usually find the Python-related answers quite easily, but the solution to this problem seems to be eluding me.

Asked By: jjj



You could use grequests to download in parallel.

import grequests

URL = ''
CHUNK_SIZE = 104857600  # 100 MB

_start, _stop = 0, 0
for x in range(4):  # file size is > 300MB, so we download in 4 parts. 
    _start = _stop
    _stop = 104857600 * (x + 1)
    HEADERS.append({"Range": "bytes=%s-%s" % (_start, _stop)})

rs = (grequests.get(URL, headers=h) for h in HEADERS)
downloads =

with open('/tmp/debian-10.1.0-amd64-netinst.iso', 'ab') as f:
    for download in downloads:

PS: I did not check if the Ranges are correctly determinded and if the downloaded md5sum matches! This should just show in general how it could work.

Answered By: Maurice Meyer

Here is a version using Python 3 with Asyncio, it’s just an example, it can be improved, but you should be able to get everything you need.

  • get_size: Send an HEAD request to get the size of the file
  • download_range: Download a single chunk
  • download: Download all the chunks and merge them
import asyncio
import concurrent.futures
import functools
import requests
import os

URL = ''
OUTPUT = 'video.mp4'

async def get_size(url):
    response = requests.head(url)
    size = int(response.headers['Content-Length'])
    return size

def download_range(url, start, end, output):
    headers = {'Range': f'bytes={start}-{end}'}
    response = requests.get(url, headers=headers)

    with open(output, 'wb') as f:
        for part in response.iter_content(1024):

async def download(run, loop, url, output, chunk_size=1000000):
    file_size = await get_size(url)
    chunks = range(0, file_size, chunk_size)

    tasks = [
            start + chunk_size - 1,
        for i, start in enumerate(chunks)

    await asyncio.wait(tasks)

    with open(output, 'wb') as o:
        for i in range(len(chunks)):
            chunk_path = f'{output}.part{i}'

            with open(chunk_path, 'rb') as s:


if __name__ == '__main__':
    executor = concurrent.futures.ThreadPoolExecutor(max_workers=3)
    loop = asyncio.new_event_loop()
    run = functools.partial(loop.run_in_executor, executor)


            download(run, loop, URL, OUTPUT)
Answered By: bug

The best way i found is to use a module called pySmartDL.

step 1: pip install pySmartDL

step 2: for downloading the file you could use

from pySmartDL import SmartDL
obj = SmartDL(url, destination)

Note: This gives you a download meter by default.

In case you need to hook the download progress to a gui you could use

obj = SmartDL(url, dest,progress_bar=False)
while not obj.isFinished():
    download_precentage = round(obj.get_progress()*100,2)

if you want to use more threads you can use

obj = SmartDL(url, destination,threads=7) #by default thread = 5

you can find many more features from the project page



Project page:

Bugs and Issues:

Answered By: Jishnu

You can also you use ThreadPoolExecutor (or ProcessPoolExecutor) from concurrent.futures instead of using asyncio. The following shows how to modify bug’s answer by using ThreadPoolExecutor:

Bonus: The following snippet also uses tqdm to show a progress bar of the download. If you don’t want to use tqdm then just comment out the block below with tqdm(total=file_size . . .. More information on tqdm is here which can be installed with pip install tqdm. Btw, tqdm can also be used with asyncio.

import requests
import concurrent.futures
from concurrent.futures import as_completed
from tqdm import tqdm
import os

def download_part(url_and_headers_and_partfile):
    url, headers, partfile = url_and_headers_and_partfile
    response = requests.get(url, headers=headers)
    # setting same as below in the main block, but not necessary:
    chunk_size = 1024*1024 

    # Need size to make tqdm work.
    with open(partfile, 'wb') as f:
        for chunk in response.iter_content(chunk_size):
            if chunk:
    return size

def make_headers(start, chunk_size):
    end = start + chunk_size - 1
    return {'Range': f'bytes={start}-{end}'}

url = ''
file_name = 'video.mp4'
response = requests.get(url, stream=True)
file_size = int(response.headers.get('content-length', 0))
chunk_size = 1024*1024

chunks = range(0, file_size, chunk_size)
my_iter = [[url, make_headers(chunk, chunk_size), f'{file_name}.part{i}'] for i, chunk in enumerate(chunks)] 

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    jobs = [executor.submit(download_part, i) for i in my_iter]

    with tqdm(total=file_size, unit='iB', unit_scale=True, unit_divisor=chunk_size, leave=True, colour='cyan') as bar:
        for job in as_completed(jobs):
            size = job.result()

with open(file_name, 'wb') as outfile:
    for i in range(len(chunks)):
        chunk_path = f'{file_name}.part{i}'
        with open(chunk_path, 'rb') as s:
Answered By: Inspired_Blue