When we start learning to code, we often get excited about solving problems that work. But as we move forward, writing code that just works isn’t enough, we also need to think about how well it works. That’s where time and space complexity come in.
What is Time Complexity?
Time complexity measures how the running time of your program grows as the size of the input increases.
- For example, if you loop through an array of
n elements once, the time complexity is O(n).
- If you have a nested loop, it could be O(n²).
Instead of counting actual seconds, we use this notation (Big O) to understand how fast or slow an algorithm scales.
What is Space Complexity?
Space complexity refers to the amount of memory your algorithm uses as the input grows. This includes memory for:
- Variables
- Data structures (arrays, hash maps, etc.)
- Function call stacks
For example, if you just use a few variables, it’s O(1) (constant space). But if you store all input data again in another structure, it may be O(n).
Why Does It Matter?
- Efficiency in real-world systems: Google search, YouTube recommendations, or payment apps handle billions of operations. Optimized code saves huge amounts of time and resources.
- Interviews: Almost every coding interview involves analyzing and improving complexity.
- Scalability: Writing code without considering complexity might work for small inputs, but fail miserably at scale.
A Simple Example
Let’s say you want to find if a number exists in a list:
- Linear Search: Check one by one → O(n) time.
- Binary Search: Requires sorted list, but finds the number in O(log n) time.
Both give correct answers, but one is clearly more efficient.
Wrapping Up
Understanding space and time complexity helps you write smarter code, not just working code. Think of it as learning the grammar of efficient programming, you can form sentences without it, but true fluency comes when you master it.
I’ll be sharing a full DSA learning series on our dev group, walking step by step through concepts and problems. You can also explore my growing list of DSA articles here.