globals() does not work with concurrent.futures module?

Question

This is my code without concurrent.futures:

import finviz

def finvizxl(ticker):
        data = finviz.get_stock(ticker)
        globals()[ticker] = data

finvizxl('AAPL')

print(AAPL)

print(AAPL) returns stock information on AAPL using the finviz module and the finviz.get_stock method. The globals() function converted my 'AAPL' string to a variable to store data

This is my code with concurrent.futures:

import finviz
import concurrent.futures

def finvizxl(ticker):
    data = finviz.get_stock(ticker)
    globals()[ticker] = data
    
if __name__ == '__main__':
    with concurrent.futures.ProcessPoolExecutor() as executor:
        x = ['AAPL', 'TSLA', 'GME']
        executor.map(finvizxl, x)

print(AAPL)

My Goal is to run my function finvizxl() concurrently and store data in the converted strings 'AAPL', 'TSLA', 'GME' respectively, that way when i type print(aapl), the stock information for the respective ticker will be returned. However, i am getting this error:

NameError: name 'AAPL' is not defined

When i put print(AAPL) in the function instead,

def finvizxl(ticker):
    data = finviz.get_stock(ticker)
    locals()[ticker] = list(data.values())
    print(AAPL)
    
if __name__ == '__main__':
    with concurrent.futures.ProcessPoolExecutor() as executor:
        x = ['AAPL', 'TSLA', 'GME']
        executor.map(finvizxl, x)

The program runs but nothing is returned.

However, when i do this:

def finvizxl(ticker):
    data = finviz.get_stock(ticker)
    locals()[ticker] = list(data.values())
    print(locals()[ticker])
    
if __name__ == '__main__':
    with concurrent.futures.ProcessPoolExecutor() as executor:
        x = ['AAPL', 'TSLA', 'GME']
        executor.map(finvizxl, x)

All 3 of my tickers ['AAPL', 'TSLA', 'GME'] have their information returned in the terminal.

I am probably missing something basic as i just started learning python. Thanks for your help.

Answer 1

Not if you're using the process pool, no. Each pool item has its own process, with a completely separate Python interpreter and a completely separate memory space. concurrent tries to shuffle over the things you need, but there is no live connection.

If you need to ship results back, you might consider using a multiprocessing.Queue. That knows how to marshal things across the process boundary.