In this tutorial, we’ll explore Python’s multiprocessing module that allows us to run multiple processes simultaneously for better performance. We’ll use the Queue class from the concurrent.futures module to distribute workload evenly in chunks between our worker processes. This can be especially useful when dealing with computationally intensive tasks or large datasets.
First, let’s understand how multiprocessing works and why it’s better than using threads for certain use cases. Unlike threads that share a common memory region, each process has its own memory space, which makes them more efficient in terms of resource utilization. However, sharing data between processes can be expensive due to the overhead associated with serializing and deserializing data over interprocess communication channels.
To simplify our implementation, we’ll use a mixin class that adds extra functionality without being consumed by itself. This way, we can reuse code across unrelated classes that implement similar functionality. In this case, we’ll create a ChunkMixin class to encapsulate the formula for generating letter combinations with a given length and range of indices.
Here’s an example implementation:
# Import necessary libraries
import multiprocessing as mp
from concurrent.futures import Queue
import time
from typing import List, Tuple
# Create a ChunkMixin class to encapsulate the formula for generating letter combinations with a given length and range of indices
class ChunkMixin(object):
# Initialize the class with the length of the letter combinations
def __init__(self, length: int) -> None:
self._length = length
# Set the letters to be used for combinations
self._letters = 'abcdefghijklmnopqrstuvwxyz'
# Function to get the range of indices for a given chunk size
def get_indices(self, chunk_size: int) -> List[Tuple]:
indices = []
start = 0
# Loop through the chunk size to get the start and end indices for each chunk
for i in range(chunk_size):
# Calculate the end index by adding the start index and the length of the letter combinations
end = min((start + self._length 1), len(self))
# Append the start and end indices to the list
indices.append((start, end))
# Update the start index for the next chunk
start += self._length
return indices
# Create a Worker class that inherits from mp.Process and ChunkMixin
class Worker(mp.Process, ChunkMixin):
# Initialize the class with a queue and set the chunk size and length of letter combinations
def __init__(self, queue: Queue) -> None:
super().__init__()
self.__queue = queue
self.__chunk_size = 1024
self._length = 6
self._letters = 'abcdefghijklmnopqrstuvwxyz'
# Function to run the process
def run(self) -> None:
# Loop continuously
while True:
# Get the indices and chunk from the queue
indices, chunk = self.queue.get()
# Loop through the indices and perform a computationally intensive task using the given range of indices
for start, end in indices:
# Do some computationally intensive task here using the given range of indices
time.sleep(0.5)
# Mark the task as done in the queue
self.__queue.task_done()
In this example, we’ve created a Worker class that extends both multiprocessing.Process and our ChunkMixin class to encapsulate the formula for generating letter combinations with a given length and range of indices. The run method is where the actual work happens. We receive two arguments from the Queue object: `indices` (a list of tuples representing ranges) and `chunk`, which contains some data that needs to be processed within those ranges.
We iterate over each tuple in `indices` and perform a computationally intensive task using the given range of indices. In this example, we’re sleeping for half a second just to simulate some work being done. Once all tasks have been completed, we call the Queue object’s `task_done()` method to indicate that our current chunk has finished processing.
To use this class in your code, you can create multiple instances of it and start them as processes using multiprocessing.start(). You can then feed data into their respective queues by calling the Queue object’s `put()` method:
# Import the necessary modules
import multiprocessing as mp
# Define a class for our worker process
class Worker(mp.Process):
# Initialize the class with a queue parameter
def __init__(self, queue):
# Call the parent class's __init__ method
super().__init__()
# Set the queue attribute
self.queue = queue
# Define the run method to be executed when the process starts
def run(self):
# Use a while loop to continuously process data from the queue
while True:
# Use the queue's get() method to retrieve data from the queue
data = self.queue.get()
# Check if the data is the 'STOP' signal
if data == 'STOP':
# If it is, break out of the loop and end the process
break
# Otherwise, process the data
else:
# Get the indices and data from the data tuple
indices, data = data
# Process the data using the indices
# (Note: this is just a placeholder, actual processing code would go here)
print(f"Processing data with indices {indices} and data {data}")
# Use the queue's task_done() method to indicate that the current chunk has finished processing
self.queue.task_done()
# Check if the current script is being run directly
if __name__ == '__main__':
# Create a queue with a maximum size of 10
queue = mp.Queue(maxsize=10)
# Create multiple worker processes and start them
for i in range(4):
# Pass in the queue as a parameter to each worker process
Worker(queue).start()
# Feed data into the queues
for chunk_num in range(5):
# Use the ChunkMixin class to get indices for the current chunk
indices = ChunkMixin(6).get_indices(1024)
# Put the indices and some data into the queue as a tuple
queue.put((indices, 'some data'))
# Wait for all tasks to finish processing
queue.join()
In this example, we’re creating four worker processes and starting them using multiprocessing.start(). We then feed five chunks of work into their respective queues by calling the Queue object’s `put()` method with a tuple containing both indices and some data to be processed within those ranges. Once all tasks have been completed, we call the Queue object’s `join()` method to wait for all tasks to finish processing before exiting our program.
This approach can significantly improve performance when dealing with computationally intensive tasks or large datasets because each worker process has its own memory space and doesn’t share data with other processes, which reduces overhead associated with serializing and deserializing data over interprocess communication channels.