Python multi-threading method

Question

I've heard that Python multi-threading is a bit tricky , and I am not sure what is the best way to go about implementing what I need. Let's say I have a function called IO_intensive_function that does some API call which may take a while to get a response.

Say the process of queuing jobs can look something like this:

import thread
for job_args in jobs:
    thread.start_new_thread(IO_intense_function, (job_args))

Would the IO_intense_function now just execute its task in the background and allow me to queue in more jobs?

I also looked at this question , which seems like the approach is to just do the following:

from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(2)
results = pool.map(IO_intensive_function, jobs)

As I don't need those tasks to communicate with each other, the only goal is to send my API requests as fast as possible. Is this the most efficient way? Thanks.

Edit: The way I am making the API request is through a Thrift service.

Answer 1

For.network API request you can use asyncio. Have a look at this article https://realpython.com/python-concurrency/#asyncio-version for an example how to implement it.

Answer 2

I had to create code to do something similar recently. I've tried to make it generic below. Note I'm a novice coder, so please forgive the inelegance. What you may find valuable, however, is some of the error processing I found it necessary to embed to capture disconnects, etc.

I also found it valuable to perform the json processing in a threaded manner. You have the threads working for you, so why go "serial" again for a processing step when you can extract the info in parallel.

It is possible I will have mis-coded in making it generic. Please don't hesitate to ask follow-ups and I will clarify.

import requests
from multiprocessing.dummy import Pool as ThreadPool
from src_code.config import Config

        with open(Config.API_PATH + '/api_security_key.pem') as f:
            my_key = f.read().rstrip("\n")
            f.close()
        base_url = "https://api.my_api_destination.com/v1"
        headers = {"Authorization": "Bearer %s" % my_key}
        itm = list()
        itm.append(base_url)
        itm.append(headers)


        def call_API(call_var):
            base_url = call_var[0]
            headers = call_var[1]
            call_specific_tag = call_var[2]

            endpoint = f'/api_path/{call_specific_tag}'

            connection_tries = 0
            for i in range(3):
                try:
                    dat = requests.get((base_url + endpoint), headers=headers).json()
                except:
                    connection_tries += 1
                    print(f'Call for {api_specific_tag} failed after {i} attempt(s).  Pausing for 240 seconds.')
                    time.sleep(240)
                else:
                    break

            tag = list()
            vars_to_capture_01 = list()
            vars_to_capture_02 = list()

            connection_tries = 0

            try:
                if 'record_id' in dat:
                    vars_to_capture_01.append(dat['record_id'])
                    vars_to_capture_02.append(dat['second_item_of_interest'])
                else:
                    vars_to_capture_01.append(call_specific_tag)
                    print(f'Call specific tag {call_specific_tag} is unavailable.  Successful pull.')
                    vars_to_capture_02.append(-1)

            except:
                    print(f'{call_specific_tag} is unavailable.  Unsuccessful pull.')
                    vars_to_capture_01.append(call_specific_tag)
                    vars_to_capture_02.append(-1)
                    time.sleep(240)

            pack = list()
            pack.append(vars_to_capture_01)
            pack.append(vars_to_capture_02)

            return pack

        vars_to_capture_01 = list()
        vars_to_capture_02 = list()

        i = 0
        max_i = len(all_tags)
        while i < max_i:
            ind_rng = range(i, min((i + 10), (max_i)), 1)
            itm_lst = (itm.copy())
            call_var = [itm_lst + [all_tags[q]] for q in ind_rng]
            #packed = call_API(call_var[0]) # for testing of function without pooling
            pool = ThreadPool(len(call_var))
            packed = pool.map(call_API, call_var)
            pool.close()
            pool.join()
            for pack in packed:
                try:
                    vars_to_capture_01.append(pack[0][0])
                except:
                    print(f'Unpacking error for {all_tags[i]}.')
                vars_to_capture_02.append(pack[1][0])