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When Speed Matters: Big O Basics

Learn to recognize when algorithm efficiency matters and when simple is better

Part of Search & Sort Algorithms

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Big O: The Language of Scaling

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Big O: The Language of Scaling

Big O describes how time grows as data grows:

  • O(1): Constant - same time regardless of size
  • O(log N): Logarithmic - doubles data, adds one step
  • O(N): Linear - doubles data, doubles time
  • O(N log N): Linearithmic - efficient sorts
  • O(N²): Quadratic - doubles data, 4× time

The notation ignores constants. It's about growth patterns, not exact times.

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Quiz: 3 Questions

Test your understanding with this quiz.

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You have two algorithms: Algorithm A takes 100 operations for 10 items (O(N²)), Algorithm B takes 50 operations for 10 items (O(N log N)). Which performs better for 1,000 items?

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Recognizing Scalability Problems

Complete this exercise and get AI-powered feedback.

Recognizing Scalability Problems

Big O thinking helps you predict what breaks at scale before building.

Think about: Apps, websites, processes, or systems you use. Which would struggle with 10x or 100x more data?