Combine Pool.map with shared memory Array in Python multiprocessing

James_Dean_7743 0
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I have a very large (read only) array of data that I want to be processed by multiple processes in parallel.

I like the Pool.map function and would like to use it to calculate functions on that data in parallel.

I saw that one can use the Value or Array class to use shared memory data between processes. But when I try to use this I get a RuntimeError: 'SynchronizedString objects should only be shared between processes through inheritance when using the Pool.map function:

Here is a simplified example of what I am trying to do:

from sys import stdin
from multiprocessing import Pool, Array

def count_it( arr, key ):
  count = 0
  for c in arr:
    if c == key:
      count += 1
  return count

if __name__ == '__main__':
  testData = "abcabcs bsdfsdf gdfg dffdgdfg sdfsdfsd sdfdsfsdf"
  # want to share it using shared memory
  toShare = Array('c', testData)

  # this works
  print count_it( toShare, "a" )

  pool = Pool()

  # RuntimeError here
  print pool.map( count_it, [(toShare,key) for key in ["a", "b", "s", "d"]] )

Can anyone tell me what I am doing wrong here?

So what I would like to do is pass info about a newly created shared memory allocated array to the processes after they have been created in the process pool.

shared-memory pool multiprocessing python
4 Answers

If you're seeing:

RuntimeError: Synchronized objects should only be shared between processes through inheritance

Consider using multiprocessing.Manager as it doesn't have this limitation. The manager works considering it presumably runs in a separate process altogether.

import ctypes
import multiprocessing

# Put this in a method or function, otherwise it will run on import from each module:
manager = multiprocessing.Manager()
counter = manager.Value(ctypes.c_ulonglong, 0)
counter_lock = manager.Lock()  # pylint: disable=no-member

with counter_lock:
    counter.value = count = counter.value + 1
user_8322300
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