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About the Book
Mastering Performant Code in Python is a hands-on blueprint for seasoned Python developers who want to go beyond theory and actually build the data-structure and optimisation skills the job market rewards. If you can read a Big-O graph, write a class, and run a unit test, this book picks up from there and takes you all the way to production-ready, profiled, and benchmarked code.
- Why this book?
- Implementation-first: every concept is introduced by writing it, testing it, timing it. You don’t just read about AVL trees or Bloom filters—you ship them, with type hints and 100 % test coverage .
- Performance obsession: each chapter ends with side-by-side speed and memory tables so you can see exactly when a hand-rolled structure outpaces a Python built-in .
- Real-world focus: text-editor buffers, in-memory DBs and caching layers show up as worked examples, proving the techniques survive outside the REPL .
- What you’ll master
- CPython internals—how lists resize, how dict hashing really works, and the memory layout that makes some operations O(1)O(1) and others O(n)O(n) .
- Fifteen+ data structures built from scratch, from dynamic arrays through balanced trees to probabilistic filters, each wrapped in modern Python idioms (dataclasses, context managers, mypy-friendly types) .
- A profiler’s toolbox:
timeit,cProfile,tracemalloc, plus statistical benchmarking harnesses you can drop into any codebase . - Production optimisation moves—
__slots__, object pools, Cython fall-backs, and a full deployment pipeline that bakes in performance tests and CI/CD hooks . - How you’ll learn
- A repeatable seven-step chapter pattern (Motivation → Theory → Implementation → Tests → Benchmarks → Applications → Exercises) keeps the pace brisk yet structured .
- Over fifty graded exercises—many open-ended—push you to tweak growth factors, hunt memory leaks, and make thread-safe variants until the knowledge sticks .
- Zero external dependencies: the entire journey runs on the standard library so you spend time learning fundamentals, not wrangling installs .
By the final page you’ll have a personal toolbox of battle-tested data structures, the instinct to profile before you guess, and the confidence that comes from watching your code outrun the stock implementations. If your next milestone is a system that has to stay fast at scale—or an interview where “implement an LRU cache” is just the warm-up—Mastering Performant Code in Python will get you there.
About the Author
Jayasimha Raghavan is a seasoned software engineer and performance optimization specialist with over 25 years of experience building scalable, high-performance systems at Fortune 500 companies. Currently serving as Founding Engineer and Agentic AI Lead at Unskript Inc, he has architected AI systems that reduce incident response times by 65% through autonomous workflows.
Jayasimha holds two granted patents in Voice over IP technology and advanced certifications from Stanford University in Cybersecurity and Natural Language Processing. His expertise spans from low-level algorithmic optimization to high-level system architecture, with particular focus on Python performance engineering and distributed systems processing billions of events daily.
"Mastering Performant Code" represents his commitment to sharing 25+ years of hard-won insights about writing efficient, production-grade software that scales.