This is useful for situations where you have a large dataset that needs to be accessed simultaneously by different parts of your program.
Now, let me introduce you to Python’s Shared Memory Manager (SMM). It’s not as exciting as it sounds, but trust me, it’s worth knowing about! The SMM is part of the mmap module in Python 3.4 and later versions. This means that if you’re using an older version of Python or a different language altogether, you might need to look into other options for shared memory management.
But let’s assume you’re working with Python 3.5 (or higher) and want to use the SMM to share data between processes. Here are some tips:
1. First, create your dataset in a separate file using any text editor or programming language of your choice. For example, if you have a CSV file called “data.csv” with columns for “name”, “age”, and “gender”, it might look something like this:
# This script is used to create a dataset in a separate file using a text editor or programming language of your choice.
# The dataset will be used to share data between processes.
# First, import the necessary libraries for creating and manipulating datasets.
import pandas as pd # Importing the pandas library for data analysis and manipulation.
# Next, create a new dataframe using the pandas library and assign it to a variable called "dataset".
dataset = pd.DataFrame(columns=['name', 'age', 'gender']) # The columns parameter specifies the column names for the dataframe.
# Now, add data to the dataframe by creating a list of dictionaries, with each dictionary representing a row of data.
data = [{'name': 'Alice', 'age': 25, 'gender': 'F'},
{'name': 'Bob', 'age': 30, 'gender': 'M'},
{'name': 'Charlie', 'age': 45, 'gender': 'M'}]
# Use the pandas library to append the data to the dataframe.
dataset = dataset.append(data, ignore_index=True) # The ignore_index parameter ensures that the index of the dataframe is automatically generated.
# Finally, save the dataframe as a CSV file using the to_csv() function.
dataset.to_csv('data.csv', index=False) # The index parameter is set to False to avoid saving the index column in the CSV file.
# The resulting CSV file will have the following format:
# name, age, gender
# Alice, 25, F
# Bob, 30, M
# Charlie, 45, M
# ...
2. Next, open the file using Python’s mmap module and create a shared memory object:
# Import the necessary modules
import mmap # Importing the mmap module for creating a shared memory object
from os import path # Importing the path module from the os library for file path operations
# Define the file name, mode, access permissions, and size of the shared memory object
filename = "data.csv" # Assigning the file name to a variable
mode = "r+" # Assigning the mode to "r+" for reading and writing
access = 0o644 # Assigning the access permissions to 0o644, readable by everyone and writable only by owner
size = int(mmap.popen("wc -c < {}".format(filename), shell=True).stdout.readline().strip()) # Using the popen function from the mmap module to get the size of the file in bytes and converting it to an integer
# Open the file and create the shared memory object
with open(filename, "r") as f: # Opening the file in read mode and assigning it to a variable "f"
data = mmap.mmap(f.fileno(), size) # Using the mmap function to create a shared memory object using the file descriptor and size obtained earlier
In this example, we’re using the `wc -c < {}` command to get the number of bytes in our CSV file (which is stored in a variable called `size`) and then creating a shared memory object with that size. We also set the mode to "r+" for reading/writing access and the permissions to 0o644, which means that everyone can read it but only the owner can write to it. 3. Now you're ready to share your data between processes! Here's an example of how to do this using Python's multiprocessing module:
# Import necessary libraries
import mmap # for creating shared memory objects
from os import path, stat # for file operations
from time import sleep # for time delay
from random import randint # for generating random numbers
from multiprocessing import Process, Queue # for creating multiple processes and queues for inter-process communication
# Set variables for file name, mode, access permissions, and size of shared memory object
filename = "data.csv"
mode = "r+" # for reading/writing
access = 0o644 # readable by everyone, writable only by owner
size = int(mmap.popen("wc -c < {}".format(filename), shell=True).stdout.readline().strip()) # get the size of the file in bytes
# Define a function for the reader process
def reader():
with open(filename, "r") as f: # open the file in read mode
data = mmap.mmap(f.fileno(), size) # create a shared memory object for the file
while True:
# read a random line from the shared memory object and print it to console
offset = randint(0, size - 16384) # generate a random offset within the size of the shared memory object
chunk_size = min(stat(filename).st_size - offset, 16384) # calculate the chunk size to read, ensuring it doesn't exceed the file size
data.seek(offset) # move the cursor to the random offset
line = data.read(chunk_size).decode("utf-8") # read the chunk of data and decode it from bytes to string
print(line[:-1]) # remove the newline character at the end of the line and print it to console
# Define a function for the writer process
def writer():
with open(filename, "r+") as f: # open the file in read/write mode
data = mmap.mmap(f.fileno(), size) # create a shared memory object for the file
while True:
# read a random line from the shared memory object and modify it in place
offset = randint(0, size - 16384) # generate a random offset within the size of the shared memory object
chunk_size = min(stat(filename).st_size - offset, 16384) # calculate the chunk size to read, ensuring it doesn't exceed the file size
data.seek(offset) # move the cursor to the random offset
line = data.read(chunk_size).decode("utf-8") # read the chunk of data and decode it from bytes to string
new_line = "{} {}".format(line[:randint(0, len(line))], line[len(line)-randint(1, 5):]) # modify the line randomly by adding a random string at the beginning and end
data.seek(offset) # move the cursor back to the random offset
data.write(new_line.encode("utf-8")) # encode the modified line back to bytes and write it to the shared memory object
# Main function
if __name__ == "__main__":
reader_queue = Queue() # create a queue for inter-process communication for the reader process
writer_queue = Queue() # create a queue for inter-process communication for the writer process
for i in range(2): # create two processes, one for reading and one for writing
if i == 0:
p = Process(target=reader) # assign the reader function to the process
else:
p = Process(target=writer) # assign the writer function to the process
p.daemon = True # set the process as a daemon so it doesn't block program termination
p.start() # start the process
while True:
line_to_print = reader_queue.get() # get the line to print from the reader process
print("Reading: {}".format(line_to_print)) # print the line to console
writer_queue.put(randint(0, len(line_to_print))) # send the index of a random character to be modified by the writer process
for i in range(2):
p.join() # wait for both processes to finish before terminating program
In this example, we’re using Python’s multiprocessing module to create two processes: one for reading and one for writing. The reader process reads a random line from the shared memory object and prints it to console, while the writer process modifies a random line in place (by randomly selecting a character within that line).
To make this work with our SMM setup, we’re using Python’s Queue module to send messages between processes. The main program creates two queues: one for sending lines to be printed by the reader process and another for sending random indices of characters to modify in the writer process.
And that’s it! With these tips, you should now have a better understanding of how Python’s Shared Memory Manager works and how to use it effectively.