Type systems define the rules that govern how values of different types can be used in a programming language, ranging from dynamic runtime checks to static compile-time guarantees. They encompass static vs dynamic typing, structural vs nominal typing, generics and parametric polymorphism, and advanced features like covariance/contravariance, algebraic data types, and type inference. Understanding type systems helps developers write safer, more maintainable code by catching errors early, enabling powerful abstraction patterns, and choosing the right type system trade-offs for their specific problem domain—whether that's the flexibility of gradual typing, the safety of dependent types, or the performance of compile-time type erasure.
What This Cheat Sheet Covers
This topic spans 13 focused tables and 74 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Static vs Dynamic Typing Fundamentals
| Classification | Example | Description |
|---|---|---|
int x = 42;String s = "hello"; | • Types are checked at compile time before execution • the compiler catches type-related errors early, leading to more reliable code | |
x = 42x = "hello" | • Types are checked at runtime during execution • variables can hold values of different types at different times, offering flexibility but detecting errors later | |
let age: number = 25;def greet(name: str) -> str: | • Explicit type declarations added to code • required in some statically-typed languages, optional in gradually-typed languages like TypeScript and Python (mypy). | |
let x = 42;val y = "text" | • Compiler automatically deduces types from context without explicit annotations • common in ML, Haskell, Scala, Rust, and TypeScript |