Space Complexity measures the amount of memory an algorithm uses relative to the input size. Like time complexity, we use Big O notation.
Common space complexities:
O(1) — Constant: fixed number of variables
O(log n) — Logarithmic: recursive call stack depth
O(n) — Linear: one copy of the input
O(n²) — Quadratic: 2D matrix of input size
Important distinction:
- Auxiliary space: extra memory beyond the input
- Total space: input + auxiliary
Examples:
O(1): in-place sorting (Bubble Sort), variable swaps
O(log n): recursive binary search (call stack)
O(n): Merge Sort (temporary arrays), hash tables
O(n²): DP tables, adjacency matrices
Related algorithms
Frequently asked questions
- What is Space Complexity?
- Common space complexities: O(1) — Constant: fixed number of variables O(log n) — Logarithmic: recursive call stack depth O(n) — Linear: one copy of the input O(n²) — Quadratic: 2D matrix of input size
- What is the complexity of Space Complexity?
- Space Complexity is explained with a step-by-step visualization, including time and space complexity where applicable.
- Who is this Space Complexity visualizer for?
- The Space Complexity visualization targets beginner-level learners in the Concepts category. Useful for students, interview prep, and hands-on review.
- What algorithms are related to Space Complexity?
- In the same category (Concepts) you can explore: Big O Notation, Recursion, Two Pointers. Each has an interactive visualization.