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Mastering Concurrency in Python

You're reading from   Mastering Concurrency in Python Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343052
Length 446 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (22) Chapters Close

Preface 1. Advanced Introduction to Concurrent and Parallel Programming FREE CHAPTER 2. Amdahl's Law 3. Working with Threads in Python 4. Using the with Statement in Threads 5. Concurrent Web Requests 6. Working with Processes in Python 7. Reduction Operators in Processes 8. Concurrent Image Processing 9. Introduction to Asynchronous Programming 10. Implementing Asynchronous Programming in Python 11. Building Communication Channels with asyncio 12. Deadlocks 13. Starvation 14. Race Conditions 15. The Global Interpreter Lock 16. Designing Lock-Based and Mutex-Free Concurrent Data Structures 17. Memory Models and Operations on Atomic Types 18. Building a Server from Scratch 19. Testing, Debugging, and Scheduling Concurrent Applications 20. Assessments 21. Other Books You May Enjoy

Summary

In this chapter, we studied the theoretical differences between lock-based and mutex-free data structures: a lock-based data structure uses a locking mechanism to protect the integrity of its data, while a mutex-free one does not. We analyzed the problem of race conditions that can occur in poorly-designed data structures, and looked at how to address it in both situations.

In our example of the concurrent lock-based counter data structure, we considered the design of approximate counters, as well as the improved scalability that the design can offer. In our analysis of the concurrent network data structure, we studied the RCU technique, which isolates reading instructions from updating instructions, with the goal of maintaining the integrity of the concurrent data structure.

In the next chapter, we will look at another set of advanced concepts in Python concurrent programming...

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