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Big O notation: Python to Rust

本课程是 Rust for Data Engineering 专项课程的一部分

位教师：Noah Gift

包含在中

了解更多

5个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

6 小时完成

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

6 小时完成

灵活的计划

自行安排学习进度

要了解的详细信息

可分享的证书

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本课程是 Rust for Data Engineering 专项课程专项课程的一部分

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该课程共有5个模块

Big O notation: Python to Rust is a hands-on algorithmic complexity course for engineers transitioning from Python to Rust who want to reason rigorously about how their code scales. You will learn Big O, Big Theta, and Big Omega notation; analyze the time and space complexity of common operations on Python and Rust data structures (list/Vec, dict/HashMap, set/HashSet, tuple, slice, BTreeMap); and compare measured performance in both languages on identical workloads. The course covers amortized analysis, recursion and master theorem, worst case versus expected case, the cost of allocation and borrowing, and how Rust's ownership model affects real-world constants even when asymptotic complexity is identical. You will profile Python code with cProfile and timeit, profile Rust code with criterion and perf, and translate Python algorithms (search, sort, hashing, graph traversal, dynamic programming) into idiomatic Rust while keeping or improving complexity guarantees. By the end of the course, you will be able to predict performance, choose appropriate data structures, justify rewrites from Python to Rust on quantitative grounds, and communicate trade-offs to a team. Part of the Rust for Data Engineering specialization.

单元详情

Set the foundation: define what "complexity" actually means as a claim, and meet the three modes of proof — analytical, empirical, and structural — you'll use to defend complexity claims throughout the course. Learn to recognize falsifiable vs. unfalsifiable performance claims and build the worked-example habit you'll need in later modules.

涵盖的内容

3个视频7篇阅读材料

3个视频总计4分钟

1.1.1 What Complexity Means And Three Modes1分钟
1.2.1 Reading A Complexity Claim1分钟
1.3.1 Depyler Transpiler Shortcut1分钟

7篇阅读材料总计70分钟

About This Course10分钟
Key Terms: What "Complexity" Means + Three Modes of Proof10分钟
Reflection: What "Complexity" Means + Three Modes of Proof10分钟
Key Terms: Reading a Complexity Claim — Falsifiability10分钟
Reflection: Reading a Complexity Claim — Falsifiability10分钟
Key Terms: Depyler — the Transpiler Shortcut10分钟
Reflection: Depyler — the Transpiler Shortcut10分钟

Empirical proof in practice: measure runtime with reproducible benchmarks. Three head-to-head Python→Rust translations — list comprehension to iterator, dict lookup to HashMap, and sorted() to sort_unstable — let you read benchmark output, control for noise, and decide when measured speedups are real and when they're artifacts.

涵盖的内容

3个视频6篇阅读材料

3个视频总计4分钟

2.1.1 List Comp To Iterator1分钟
2.2.1 Dict Lookup To Hashmap1分钟
2.3.1 Sorted To Sort Unstable1分钟

6篇阅读材料总计60分钟

Key Terms: List Comprehension → Iterator10分钟
Reflection: List Comprehension → Iterator10分钟
Key Terms: x in dict → HashMap Lookup10分钟
Reflection: x in dict → HashMap Lookup10分钟
Key Terms: sorted() → sort_unstable10分钟
Reflection: sorted() → sort_unstable10分钟

Structural proof in practice: use the type system to make incorrect programs impossible to compile. Translating Optional[T] to Option<T>, try/except to Result<T,E>, and ad-hoc state machines to Rust enums turns runtime errors into compile-time errors — a structural guarantee no benchmark can refute.

涵盖的内容

3个视频6篇阅读材料

3个视频总计3分钟

3.1.1 Optional To Option1分钟
3.2.1 Try Except To Result1分钟
3.3.1 Mutable Default To Ownership1分钟

6篇阅读材料总计60分钟

Key Terms: Optional[T] → Option<T>10分钟
Reflection: Optional[T] → Option<T>10分钟
Key Terms: try/except → Result<T, E>10分钟
Reflection: try/except → Result<T, E>10分钟
Key Terms: Mutable Default Arg → Ownership10分钟
Reflection: Mutable Default Arg → Ownership10分钟

Translation with runtime consequences: each translation in this module replaces a Python construct (generators, subprocess calls, parallel loops) with a Rust equivalent that carries different runtime guarantees — memory profile, error surface, parallelism model — not just "the same thing, but faster."

涵盖的内容

3个视频6篇阅读材料

3个视频总计3分钟

4.1.1 Generator To Iterator1分钟
4.2.1 Subprocess To Command1分钟
4.3.1 Threading To Rayon1分钟

6篇阅读材料总计60分钟

Key Terms: Generator → Iterator10分钟
Reflection: Generator → Iterator10分钟
Key Terms: Subprocess → Command10分钟
Reflection: Subprocess → Command10分钟
Key Terms: Threading → Rayon10分钟
Reflection: Threading → Rayon10分钟

Capstone: bring all three modes of proof together on a real translation. Two case studies — a three-mode playbook end to end, and a deliberate "when NOT to translate" example — train your judgment about when a Python→Rust port pays off and when it would just add cost without measurable benefit.