Python ThreadPoolExecutor 未正确执行

Question

Im using concurrent.futures library to do a for-loop with multithreading. It needs to do the for loop every time with all 5 parameters. Now i have reached the point that my do_something_parallel -function only prints "test1" and nothing more.

The problem now is that inside the do_something_parallel -function it does not recognize item. Because when i print the error it says AttributeError: <unknown>.Name . Inside the for-loop i also tried to print item.Name and there it works.

from concurrent.futures import ThreadPoolExecutor

do_something_parallel(x, par2, par3, par4, par5):
    print("test1")
    print(str(x.Value))
    print("test2")

main():
    for i in range(0,38):
        with ThreadPoolExecutor(max_workers=4) as executor:
            futures = set()
                for x in range(0,5):
                    print(str(item.Name)
                    f = executor.submit(do_something_parallel, x, par2, par3, par4, par5)
                    futures.add(f)

Answer 1

You have to take ThreadPoolExecutor outside of the iterator, and then the pattern will be like this:

from concurrent import futures
from concurrent.futures import ThreadPoolExecutor

def do_something(*args, **kwargs):
    """ Stub function to use with futures - your processing logic """
    print("Do something in parallel")
    return "result processed"

def main():
   
    # The important part - concurrent futures 
    # - set number of workers as the number of jobs to process
    
    # The number of workers you want to run in parallel
    workers_range = 3

    with ThreadPoolExecutor(len(your_range)) as executor:
        # Use list jobs for concurrent futures
        # Use list scraped_results for results
        jobs = []
        results_done = []
        # Here you identify how many parallel tasks you want
        # and what value you'll send to them
        values = ["value1", "value2", "value3"] # as per workers_range 

        for value in values:
            # Pass some keyword arguments if needed - per job    
            kw = {"some_param": value}

            # Here we iterate 'number of dates' times, could be different
            # We're adding scrape_func, could be different function per call
            jobs.append(executor.submit(do_something, **kw))

        # Once parallell processing is complete, iterate over results
        for job in futures.as_completed(jobs):
            # Read result from future
            result_done = job.result()
            # Append to the list of results
            results_done.append(result_done)

        # Iterate over results scraped and do whatever is needed
        for result in results_done:
            print("Do something with me {}".format(result))

Just follow that pattern to get it working.