Why is it important to consider time and space complexity?
💡 Model Answer
Time and space complexity give a theoretical measure of how an algorithm scales with input size. Considering them helps predict performance, resource usage, and cost in production. For example, an O(n^2) algorithm may be fine for 100 items but become impractical for millions. Space complexity informs memory requirements; an algorithm that uses O(n) space may exceed available RAM on large datasets. By analyzing complexity early, developers can choose more efficient data structures or algorithms, avoid bottlenecks, and ensure the solution remains scalable as data grows.
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