Last Updated on November 19, 2022

Process-safe is the concept of thread safety applied to concurrency with processes.

In this tutorial, you will discover process safety in Python.

Let’s get started.

What is Thread-Safe

Thread-safe refers to program code that can be executed free of concurrency errors by multiple threads concurrently.

Primarily, it refers to the fact that the code is free of race conditions.

A race condition is a bug in concurrency programming.

It is a failure case where the behavior of the program is dependent upon the order of execution by two or more threads. This means the behavior of the program will not be predictable, possibly changing each time it is run.

There are many types of race conditions, although a common type of race condition is when two or more threads attempt to change the same data variable.

You can see examples of race conditions in threads and how to fix them in these tutorials:

• Race Condition With a Shared Variable in Python
• How to Fix a Race Condition With Timing in Python

Next, let’s take a look at process-safe.

What is Process-Safe

Process-safe refers to program code that can be executed free of concurrency errors by multiple processes concurrently.

It is the concept of “thread-safe” applied to processes, where processes are the unit of concurrency instead of threads.

Thread safety is a major concern of concurrent programming using threads. This is because threads have shared memory within the process, meaning that concurrent access of the same data or variables can lead to race conditions.

Processes do not have direct shared memory and therefore are not subject to the same concerns of race conditions.

Nevertheless, processes do simulate shared memory using socket connections and files and may need to protect simulated shared program state or data from race conditions due to timing and concurrent modification.

You can learn more about race conditions between processes in the tutorial:

• Multiprocessing Race Conditions in Python

Happily, some tools used for inter-process communication provide some process safety, such as queues.

Let’s take a closer look at how we can ensure process safety in our multiprocessing programs.

How to Ensure Process-Safety

Many of the tools provided for process-based concurrency in Python are described as being explicitly process-safe, and may also be thread-safe.

This includes queues in the multiprocessing.Queue class.

For example:

Queues are thread and process safe.

— multiprocessing — Process-based parallelism

This means that processes may get() and put() items from and to the queue concurrently without fear of a race condition.

You can learn more about to how to use queues with multiple processes in the tutorial:

• Multiprocessing Queue in Python

It also applies to shared ctypes such as the multiprocessing.Value and multiprocessing.Array classes.

For example:

These shared objects will be process and thread-safe.

— multiprocessing — Process-based parallelism

This means that multiple processes may access and change the values of shared ctypes without fear of race conditions.

Interestingly, shared ctypes can also be configured to be process-unsafe.

If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

— multiprocessing — Process-based parallelism

You can learn more about shared ctypes in the tutorial:

• Multiprocessing Shared ctypes in Python

Process safety is provided under the covers by synchronization primitives.

This includes objects such as mutex locks via the multiprocessing.Lock class.

You can learn more about mutex locks for processes in the tutorial:

• Multiprocessing Lock in Python

Synchronization primitives are used less in process-based concurrency given that processes do not have direct shared memory and instead simulated shared memory using files and sockets.

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program.

— multiprocessing — Process-based parallelism

Nevertheless, it is still possible for processes to suffer race conditions if simulated shared data and program state is not treated as critical sections and protected appropriately.

Further Reading

This section provides additional resources that you may find helpful.

Python Multiprocessing Books

• Python Multiprocessing Jump-Start, Jason Brownlee (my book!)
• Multiprocessing API Interview Questions
• Multiprocessing API Cheat Sheet

I would also recommend specific chapters in the books:

• Effective Python, Brett Slatkin, 2019.
  • See: Chapter 7: Concurrency and Parallelism
• High Performance Python, Ian Ozsvald and Micha Gorelick, 2020.
  • See: Chapter 9: The multiprocessing Module
• Python in a Nutshell, Alex Martelli, et al., 2017.
  • See: Chapter: 14: Threads and Processes

Guides

• Python Multiprocessing: The Complete Guide
• Python Multiprocessing Pool: The Complete Guide
• Python ProcessPoolExecutor: The Complete Guide

APIs

• multiprocessing — Process-based parallelism
• PEP 371 - Addition of the multiprocessing package

References

• Thread (computing), Wikipedia.
• Process (computing), Wikipedia.

Takeaways

You now know about process safety in Python.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

Photo by Ivan Bandura on Unsplash