Python multiprocessing queue for efficient data management

Within the Python universe, the Python Multiprocessing Module a powerful solution for processing large volumes of data and running multiple processes simultaneously. 

This technology has proven to be a crucial tool for developers and companies looking for ways to optimize computing power and handle demanding tasks. 

The focus on parallel processing and the use of multiprocessing queues have optimized the efficiency of applications, especially in areas where Fast and comprehensive data analysis are required. 

This article takes a detailed look at the basics, functionality and practical application of Python multiprocessing queues and shows how this technology can help solve complex data processing tasks.

python multiprocessing queue basics

Basics of the Python Multiprocessing Queue

Multiprocessing in Python enables the simultaneous execution of several processes, allowing you to increase computing power and realize parallel problem solutions. The module offers functions for process creation, data communication and synchronization for efficient and secure multiprocess applications.

Difference between multithreading and multiprocessing

Multithreading refers to the execution of multiple threads within a single process. Threads share the same address space and resources, which can lead to them accessing shared data and memory. 

However, they also share potential problems such as data inconsistencies or resource conflicts that can arise due to parallel access to shared resources. This can lead to difficulties with synchronization and the creation of race conditions.

Multiprocessing on the other hand, involves the execution of multiple processes, each of which has its own address space and resources. Processes are independent of each other, which means that they do not use shared memory areas and have less potential for conflict. Communication between processes requires explicit mechanisms such as pipes, queues or shared memory, which leads to secure data transfer and fewer synchronization problems.

The multiprocessing module in Python

The multiprocessing module in Python offers an easy way to implement multiprocessing. 

The most important components include:

  • Process: This element enables the creation of processes in Python. It is used to execute a function or method in its own process.
  • Cue: The queue serves as a communication channel between the processes. It allows data to be exchanged securely between different processes by providing a buffer to which processes can write and from which they can read.
  • Pool: The pool enables the creation of a group of processes that can be used together to execute tasks in parallel. This simplifies the management of multiple processes.

The Multiprocessing module provides an abstracted interface for creating and managing processes in Python. It allows developers to take advantage of multiprocessing to improve performance and solve problems more efficiently in certain scenarios.


An example of the use of the multiprocessing queue in Python could be a scenario in which several processes access a shared queue at the same time in order to exchange data and process tasks in parallel.

Suppose you have an application that is to process data from a list. Several processes are to access different elements of this list simultaneously in order to perform certain calculations. 

This is what it looks like in Python:

# Creating a queue for inter-process communication
result_queue = Queue()
# Splitting the data into two parts
chunk1 = data[:len(data)//2]
chunk2 = data[len(data)//2:]
# Creating two processes to work on the data concurrently
process1 = Process(target=worker_function, args=(chunk1, result_queue))
process2 = Process(target=worker_function, args=(chunk2, result_queue))
# Starting the processes
# Waiting for the processes to finish
# Retrieving data from the queue
result1 = result_queue.get()
result2 = result_queue.get()
# Merging the results
final_result = result1 + result2

In this example, the data is split into two parts and two processes each process a part of the data. The results are returned via a shared queue and then combined to produce the final result. The queue allows the processes to securely access the results and combine them to produce the final output.

Addition: Explanation join()

"join()" is a method in Python that is applied to processes or threads to block the main program until the process or thread to which "join()" is applied has ended. 

When "join()" is called on a process, the main program waits until this process has finished before continuing execution. This method is particularly useful to ensure that all started processes or threads are completed before the main program ends.

Areas of application for Python multiprocessing queues

The Python Multiprocessing Queue is used in various areas:

Data processing and analysis

When processing large volumes of data, processes can work in parallel and exchange results securely via the queue to speed up analysis or processing tasks.


A data processing pipeline in which various processes filter, analyze and aggregate data. The queue enables the exchange of partial results for the final analysis.

Simulations and calculations

In simulation-based environments, the queue enables communication between processes that execute different parts of a simulation, thereby reducing the overall computing time.


A physical simulation in which processes calculate various aspects such as temperature, pressure and flow velocity in parallel and combine their results via the queue to create the overall scenario.

Task distribution and parallelization

For complex tasks that can be broken down into independent subtasks, the queue enables the coordinated execution of several processes in order to complete the overall task more quickly.


A distributed task such as the calculation of pi using the Monte Carlo method. Several processes generate random numbers independently of each other and exchange their results via the queue in order to achieve an approximate determination of Pi.

Network applications

In network applications, processes can exchange data via the queue to process requests or respond to incoming messages, resulting in more efficient processing.


A server that has to respond to several requests at the same time. Various processes receive requests and process them in parallel before sending the responses back via the queue.

Machine learning and deep learning

At Machine learning applications models can be trained in different processes or data can be processed in parallel, reducing training times.


At the Training of neural networks several processes can process different parts of the data set and update their weightings. The queue enables the exchange of gradients for joint model training.

The multiprocessing queue is versatile and is well suited to scenarios in which tasks have to be executed in parallel and secure communication between the processes is required.

python multiprocessing queue benefits

Advantages of using Python multiprocessing queues

Parallelism and increased performanceEfficient resource utilization through simultaneous execution of multiple processes on multi-core processors.
Accelerated execution of computationally intensive tasks thanks to improved overall computing power.
Secure data transmissionStructured and secure exchange of data between processes to avoid conflicts.
Prevention of data inconsistencies with simultaneous access to shared resources.
Abstraction of complexitySimplified implementation of multi-process applications by abstracting complex communication mechanisms.
Reduction of concerns regarding synchronization problems between processes.
ScalabilityFlexible adaptation to larger data sets or increased requirements without fundamental architectural changes.
Ability to adapt performance to different workloads and resource availability.
Modularity and flexibilityModular structuring of multi-process applications for independent development and scaling of parts.
Increased flexibility and expandability for various components of the application.

Application and implementation in the company

Below you will find instructions on how to use the Python Multiprocessing Queue and its advantages in your company:

Step 1: Identify potential parallelization opportunities

Think about which tasks in your company could be carried out in parallel to improve performance. Divide these into independent subtasks that can be handled by different processes.


An e-commerce company divides the processing of orders into different subtasks such as inventory checking and shipping information.

Step 2: Develop a clear communication structure

Define which data or results need to be exchanged between the processes. Plan communication via the multiprocessing queues to ensure secure and reliable data exchange.


For the parallel processing of images, use a queue to exchange processed images between the processes.

Step 3: Implementation of processes and queues

Use the multiprocessing module to create and execute processes. Create queue objects for communication between the processes. Ensure that the queues for data exchange are set up correctly.


Creating processes for the simultaneous analysis of customer data and setting up queues for the secure exchange of results between the processes.

Step 4: Starting and synchronizing the processes

Start the created processes and use join() to synchronize their execution. Wait for the processes to finish their tasks before you continue processing the results.


Start processes for the simultaneous calculation of financial reports and use join() to wait for their completion.

Step 5: Error handling and optimization

Implement error handling mechanisms to ensure your application is robust against unexpected events. Monitor resource utilization and performance to identify and resolve bottlenecks.


Implementation of mechanisms for monitoring resource utilization and error handling during parallel processing of database queries.

Step 6: Testing and scaling

Carry out tests to ensure that the multiprocessing queues work as expected. If necessary, adapt the application to accommodate larger data sets or increased requirements.


Testing the use of multiprocessing queues to analyze sales data. 

Step 7: Integration into existing processes

Integrate the implemented multiprocessing queues into your company's existing workflows to benefit from improved performance and scalability.


Integration of multiprocessing queues into a company's order process to reduce processing time and improve customer satisfaction.

Implementing Python multiprocessing queues gives your organization the ability to manage tasks more efficiently and improve application performance. Be sure to take full advantage of parallel processing and continuously monitor and adapt the application.

python multiprocessing queue use cases

Use Cases 

Below you will find some use cases that show how the Python Multiprocessing Queue can support you in your day-to-day business.

Risk analysis and portfolio management


In the financial sector, extensive historical data must be analyzed in order to assess risks and manage portfolios. Analyzing large volumes of data requires efficient processing.


Python multiprocessing queues offer the possibility to run different analyses in parallel processes, which increases the speed of risk assessment and portfolio management.


An investment company uses multiprocessing queues to calculate different risk models simultaneously. This enables them to make faster decisions about the composition and diversification of portfolios.

Insurance claims and actuarial analyses


Insurance companies have to process a large number of insurance claims efficiently in order to assess losses and settle claims.


The use of multiprocessing queues makes it possible to shorten the processing time of insurance claims by processing several claims at the same time.


An insurance company uses queues to process claims in parallel. This enables claims to be assessed more quickly and speeds up payment to policyholders.

Trading algorithms and data processing


In high-frequency trading, the rapid processing of market analyses and the swift execution of trading orders are of crucial importance.


By using multiprocessing queues, you can run different market analyses and trading strategies in parallel in order to react quickly to market movements.


A financial company uses queues to analyze different trading algorithms simultaneously. This leads to a faster response to market changes and improves the execution of trading orders, especially in volatile market phases.

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How to use the Python multiprocessing queue using the example of parallel document processing 

Konfuzio is a Intelligent document processing platformbased on AI-supported OCR and supports companies in transforming unstructured data into valuable insights. Python. is part of Konfuzio's technology stack. 

The platform automates document processing, analysis and extraction and also integrates the use of Python multiprocessing queues to process large amounts of data in parallel. 

Konfuzio uses the possibilities of Python in the processing and organization of documents by means of parallel processes to increase the efficiency and speed of data processing.

This is also the basis for the following application example, which guides you through the use of multiprocessing queues in Python for parallel document processing.

Example: Parallel document processing

Suppose you have a set of documents that need to be analyzed or processed. We'll show you how to use multiprocessing queues in Python to accomplish this task:

Step 1: Importing the required modules

from multiprocessing import Process, Queue

from multiprocessing import Process, Queue

Step 2: Defining the function for document processing

def process_image(image, queue):
    # Perform image processing here
    processed_image = image.rotate(90)

Step 3: Preparing the data and creating the queue

if name == "main":
# Assume 'images' contains a list of image objects
images = [...] # Insert your image list here
# Create the queue for results
result_queue = Queue()

Step 4: Splitting the data for the processes

# Split the data for processes
chunks = [images[i:i + 3] for i in range(0, len(images), 3)]

Step 5: Creating and starting the processes

# Create and start processes for image processing
processes = []
for chunk in chunks:
    p = Process(target=process_image, args=(chunk, result_queue))

Step 6: Completing the processes and calling up the results

# Wait for processes to complete
for p in processes:
# Retrieve processed images from the queue
processed_images = []
while not result_queue.empty():
# Further processing or storing the results
# ...
# Example: Print the number of processed images
print(f "Number of processed images: {len(processed_images)}")

This example shows you how to use Python multiprocessing queues to process several documents in parallel. 

This technique can be applied to a wide variety of tasks to improve processing speed and efficiency.

Conclusion on Python Multiprocessing Queues - Efficient data management in practice

Python multiprocessing queues have established themselves as a valuable tool in data processing. 

They enable the simultaneous processing of large amounts of data and optimize computing power through the use of parallel processes. 

This has increased the efficiency of analyses and forecasts, particularly in sectors such as finance and insurance. 

The flexibility and scalability of this technology open up a wide range of possible applications and help to speed up complex calculations. 

Do you have questions about implementing Python multiprocessing queues or how Konfuzio can help you? Contact us now and get support from our experts to discuss your specific requirements and realize the full potential of this powerful technology.

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