Overview

Data Structures are the containers we use to store information. If an algorithm is the recipe, the data structure is the cupboard where you keep the ingredients.

Core Idea

The core idea is Trade-offs. Different structures are good for different things. Some are fast at reading, others at writing. Some use less memory, others use more.

Formal Definition

A specialized format for organizing, processing, retrieving and storing data.

Intuition

  • The Bookshelf (Array): Books are in a row. Easy to find the 5th book. Hard to insert a book in the middle (you have to scoot everyone over).
  • The Treasure Hunt (Linked List): Each clue points to the next clue. Easy to add a clue. Hard to find the 5th clue (you have to follow the chain).
  • The Dictionary (Hash Table): You look up a word (Key) and find the definition (Value) instantly.

Examples

  • Stack: Last-In, First-Out (LIFO). Like a stack of plates. You can only take the top one. (Used for “Undo” buttons).
  • Queue: First-In, First-Out (FIFO). Like a line at the grocery store.
  • Tree: Hierarchical data. (File systems, HTML DOM).
  • Graph: Networks. (Social networks, Maps).

Common Misconceptions

  • Misconception: One structure to rule them all.
    • Correction: It always depends on the use case. “Premature optimization is the root of all evil,” but choosing the wrong data structure is the root of all slowness.

Applications

  • Databases: B-Trees are used to index data for fast retrieval.
  • Compilers: Abstract Syntax Trees.

Criticism and Limitations

  • Memory Overhead: Complex structures (like graphs) use more memory to store the connections (pointers).

Further Reading

  • The Algorithm Design Manual by Steven Skiena