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Mastering Python 2E

You're reading from   Mastering Python 2E Write powerful and efficient code using the full range of Python's capabilities

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Product type Paperback
Published in May 2022
Last Updated in May 2022
Publisher Packt
ISBN-13 9781800207721
Length 710 pages
Edition 2nd Edition
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Author (1):
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Rick Hattem Rick Hattem
Author Profile Icon Rick Hattem
Rick Hattem
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Table of Contents (21) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Interactive Python Interpreters 3. Pythonic Syntax and Common Pitfalls 4. Pythonic Design Patterns 5. Functional Programming – Readability Versus Brevity 6. Decorators – Enabling Code Reuse by Decorating 7. Generators and Coroutines – Infinity, One Step at a Time 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. asyncio – Multithreading without Threads 14. Multiprocessing – When a Single CPU Core Is Not Enough 15. Scientific Python and Plotting 16. Artificial Intelligence 17. Extensions in C/C++, System Calls, and C/C++ Libraries 18. Packaging – Creating Your Own Libraries or Applications 19. Other Books You May Enjoy
20. Index

Performance monitoring

So far, we have seen how to measure and improve both CPU and memory performance, but there is one part we have completely skipped over. Performance changes due to external factors such as growing amounts of data are very hard to predict. In real-life applications, bottlenecks aren’t constant. They change all the time and code that was once extremely fast might bog down as soon as more load is applied.

Because of that, I recommend implementing a monitoring solution that tracks the performance of anything and everything over time. The big problem with performance monitoring is that you can’t know what will slow down in the future and what the cause is going to be. I’ve even had websites slow down because of Memcached and Redis calls. These are memory-only caching servers that respond well within a millisecond, which makes slowdowns highly unlikely, until you do over 100 cache calls and the latency toward the cache server increases from...

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