Are you just starting out with programming, preparing for technical interviews or beginning to code in 2026? Then you’ve probably run into time complexity and space complexity. They may sound intimidating, but in fact they are easy to grasp.
In this article, I will try to explain time complexity and space complexity using analogies and examples.
What Is Time Complexity?
Time complexity tells you how the runtime of your algorithm depends on the input size.
More precisely: Time complexity gives you the opportunity to understand how long it will take to execute your algorithm depending on how big the input is.
It cannot be measured in seconds (since it depends on your computer). But it can give you an estimate of how your algorithm will perform with 1 million records for example, and whether it will stay efficient or become too slow.
An analogy from real life:
Imagine you are looking for your friend’s contact in a phone book.
The inefficient approach: You will need to check each page individually. In case you have a 1000-page phone book, you may need to look at all 1000 pages in the worst case.
The efficient approach: You will open the phone book right in the middle, decide what half to choose and continue like that until you find the contact.
Using time complexity, you will be able to analyze both of these approaches knowing that your phone book is becoming bigger and bigger.
What Is Space Complexity?
Space complexity tells you the amount of RAM required by your algorithm as the input increases.
More precisely: What additional space will my algorithm require except for the input itself?
An analogy from real life:
While arranging your closet, you will need plenty of space to store your things outside your closet or you will not need any at all.
The former approach would be inefficient while the latter would be more optimal.
Big O Notation – Language of Complexity
We usually employ Big O Notation to describe time and space complexity.
| Big O | Name | How it Grows | Real Example |
| O(1) | Constant Time | Same speed regardless of size | Checking if a number is even |
| O(log n) | Logarithmic | Very slow growth | Binary Search |
| O(n) | Linear Time | Grows proportionally | Looping through an array once |
| O(n log n) | Linearithmic | Efficient for sorting | Merge Sort, Quick Sort |
| O(n²) | Quadratic | Gets slow quickly | Bubble Sort, nested loops |
| O(2ⁿ) | Exponential | Extremely slow | Recursive solutions without memoization |
def find_max(arr):
max_num = arr[0]
for num in arr: # This loop runs n times
if num > max_num:
max_num = num
return max_num
- Time Complexity: O(n) — You have to look at every element once.
- Space Complexity: O(1) — Only using a few extra variables.
Example 2: Checking for Duplicates (Naive Way)
def has_duplicates(arr):
for i in range(len(arr)):
for j in range(i+1, len(arr)): # Nested loops
if arr[i] == arr[j]:
return True
return False
Time Complexity: O(n²) – very ineffective algorithm for large arrays
Algorithm using HashSet: O(n)
Examples of Space Complexities
Task: reverse array
Ineffective algorithm: use additional array – O(n) space complexity
Effective algorithm: swap elements from both ends – O(1) space complexity
Importance of Time & Space Complexities in 2026
Technical interviews: The majority of coding questions in technical interviews in FAANG companies is related to time & space complexities.
Implementation: Ineffective algorithm will cost you millions of dollars to implement in cloud services such as AWS, Azure, Google Cloud.
Scalability: Algorithm which works for 100 users doesn’t work for 100k users because of its inefficiency.
Cost optimization: Effective algorithm helps to save millions of dollars on servers in cloud services.
How to Estimate Time & Space Complexities of Algorithms?
Identify the basic operation
Count the number of operations depending on n
Remove lower order terms and constants
Consider memory consumption by variables, arrays, recursion stack, etc.
Advice to Estimate Time & Space Complexities
One loop: O(n) complexity
Two nested loops: O(n²)
Divide the task in half: O(log n)
Recursion without memoization: exponential complexity in the worst case
Mistakes Students Make When Estimating Complexities
Ignoring the worst-case scenario
Forgetting about memory consumption by recursion stack
Believing that it works well on their laptop
Optimizing the code after being asked about complexities by the interviewer
Advice for Beginners
Start solving simple tasks on LeetCode or HackerRank and calculating time & space complexities of different solutions.
Learn algorithms: binary search, two pointers, sliding window, DFS, BFS, basic dynamic programming.
Learn how to estimate complexities – the most important skill to pass technical interviews.
Code in Python (very simple syntax), but know what is going on in other languages.
Importance of Time & Space Complexities
Netflix recommends movies to millions of people thanks to effective algorithms.
Google processes billions of requests per day thanks to effective data structures and algorithms.
Your banking application processes transactions effectively thanks to effective time complexity.
Conclusion
Not anymore. Extremely useful way to write effective code. Learn them and become an awesome programmer, pass technical interviews and build scalable applications.
Start with basics. Every time you solve a coding question, answer:
What is time complexity?
How to improve it?
What is space complexity?
How to improve it?
Learning these concepts will be one of the quickest ways to become a professional software engineer in 2026.
Good programmers do not only make code work. They make it work effectively. Calculate complexity now and watch your skills grow quickly!
Practice, practice and learn more.
FAQ
What is time complexity in simple terms?
It’s a method to measure algorithm efficiency. Allows to estimate how fast the algorithm runs depending on input size.
What is space complexity?
It’s a method to estimate memory usage of an algorithm while working with increasing input sizes.
What is Big O notation?
Big O notation estimates upper bound of the algorithm growth rate. The lower order is the better (O(1) is the best, O(n²) is very inefficient).
Why should beginners learn time & space complexities?
It will allow them to write effective code, pass technical interviews and build scalable applications.
What is the difference between best case, average case and worst case complexity?
Worst case is the most important one since it shows how the algorithm behaves in the most difficult conditions.
Is O(n log n) effective?
Yes, quite effective and used in effective sorting algorithms like Merge Sort.
How can I improve time complexity?
Use effective data structures (hash table for lookup), sort the data, use two pointers, sliding window, etc.
Does programming language affect time complexity?
Affects constant factor, but does not affect Big O notation. Algorithm is more important than language.
Is space complexity as important as time complexity?
Time complexity is more important, but in limited environments (mobile applications, embedded systems) space is important too.
