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DSA Course: Interview Patterns and Problem Solving
Module 1: Arrays
Best Time to Buy and Sell Stock: Greedy Pattern
Maximum Subarray: Kadane Pattern
Move Zeroes: Two pointers Pattern
Contains Duplicate: Set Pattern
Valid Anagram: Frequency map Pattern
Longest Substring Without Repeating Characters: Sliding window Pattern
Valid Palindrome: Two pointers Pattern
Longest Palindromic Substring: Expand around center Pattern
Group Anagrams: Hash key Pattern
Binary Search: Classic search Pattern
Search Insert Position: Lower bound Pattern
First Bad Version: Predicate search Pattern
Search in Rotated Sorted Array: Rotated search Pattern
Find Minimum in Rotated Sorted Array: Rotated minimum Pattern
Valid Parentheses: Stack matching Pattern
Min Stack: Auxiliary stack Pattern
Daily Temperatures: Monotonic stack Pattern
Next Greater Element I: Monotonic stack Pattern
Evaluate Reverse Polish Notation: Stack evaluation Pattern
Reverse Linked List: Pointer reversal Pattern
Merge Two Sorted Lists: Dummy node Pattern
Linked List Cycle: Fast and slow pointers Pattern
Middle of the Linked List: Fast and slow pointers Pattern
Remove Nth Node From End: Two pointers Pattern
Binary Tree Traversals: DFS recursion Pattern
Maximum Depth of Binary Tree: Height recursion Pattern
Binary Tree Level Order Traversal: BFS queue Pattern
Validate Binary Search Tree: Range bounds Pattern
Lowest Common Ancestor: Recursive split Pattern
Connected Components: Adjacency DFS Pattern
Number of Islands: Grid DFS Pattern
Flood Fill: Boundary DFS Pattern
Clone Graph: Hash Map DFS Pattern
Course Schedule: Topological Sort Pattern
Union Find Components: Disjoint Set Pattern
Shortest Path in Unweighted Graph: BFS Distance Pattern
Climbing Stairs: Fibonacci DP Pattern
House Robber: Pick or Skip DP Pattern
Coin Change: Minimum Coins DP Pattern
Longest Increasing Subsequence: Binary Search DP Pattern
Longest Common Subsequence: 2D DP Pattern
0/1 Knapsack: Capacity DP Pattern
Longest Consecutive Sequence: Hash Set Pattern
Subarray Sum Equals K: Prefix Sum Hashmap Pattern
First Unique Character: Frequency Map Pattern
Find Duplicates: Frequency Map Pattern
Ransom Note: Character Availability Pattern
Sort Colors: Dutch National Flag Pattern
Next Permutation: Pivot and Suffix Reversal Pattern
Merge Intervals: Sort and Sweep Pattern
Find First and Last Position: Boundary Binary Search Pattern
Search a 2D Matrix: Flattened Binary Search Pattern
Subsets: Pick or Skip Recursion Pattern
Generate Parentheses: Valid State Backtracking Pattern
Combination Sum: Reuse Choice Backtracking Pattern
N-Queens: Constraint Backtracking Pattern
Word Search: Grid Backtracking Pattern
Kth Largest Element: Size-K Min-Heap Pattern
Top K Frequent Elements: Frequency Heap Pattern
Merge K Sorted Lists: Min-Heap Multiway Merge Pattern
Median Finder: Two Heaps Pattern
Task Scheduler: Greedy Max-Heap Pattern
Jump Game: Farthest Reach Greedy Pattern
Gas Station: Greedy Reset Pattern
Non-overlapping Intervals: Earliest End Greedy Pattern
Minimum Arrows to Burst Balloons: Interval End Greedy Pattern
Partition Labels: Last Occurrence Greedy Pattern
Single Number: XOR Cancellation Pattern
Power of Two: n and n-1 Pattern
Number of 1 Bits: Brian Kernighan Pattern
Single Number III: Rightmost Set Bit Pattern
XOR From 1 to N: Modulo Cycle Pattern
Prime Check: Square Root Trial Division Pattern
Sieve of Eratosthenes: Prime Marking Pattern
GCD: Euclidean Remainder Pattern
Binary Exponentiation: Fast Power Pattern
Modular Inverse: Extended Euclid Pattern
Implement Trie: Prefix Tree Pattern
Longest Common Prefix: Single Branch Trie Pattern
LRU Cache: Hash Map Plus Recency List Pattern
Segment Tree: Range Sum Query Pattern
Fenwick Tree: Binary Indexed Prefix Sum Pattern
CONTENTS

Contains Duplicate: Set Pattern

Use a set to detect whether any value has appeared before.

DSA Course: Interview Patterns and Problem Solving
Module 1: Arrays
dsa
data structures and algorithms
+4
May 28, 2026
30
A

Learning Outcome

After this lesson, you should be able to identify duplicate detection problems and solve them with a set in one pass.

Problem Statement

Given an integer array nums, return true if any value appears at least twice. Return false if all values are distinct.

InputOutputWhy
[1, 2, 3, 1]trueThe value 1 appears twice.
[1, 2, 3, 4]falseEvery value appears once.

Brute Force Approach

Compare every element with every later element. If any pair has equal values, return true.

This is simple but slow because it performs repeated comparisons, leading to O(n^2) time.

Optimized Approach

Use a set to store values already seen. During the scan, if the current value is already in the set, a duplicate exists.

This turns repeated pair checks into average O(1) membership checks.

Exact Pseudocode

seen = empty set
for value in nums:
  if value exists in seen:
    return true
  add value to seen
return false

Reference Code

class Solution:
    def containsDuplicate(self, nums):
        seen = set()

        for value in nums:
            if value in seen:
                return True
            seen.add(value)

        return False

Sample Dry Run

valueseen before checkAction
1{}Add 1
2{1}Add 2
3{1, 2}Add 3
1{1, 2, 3}1 already exists, return true

Complexity

MeasureValueReason
TimeO(n)Each value is checked once.
SpaceO(n)The set may store all values if there are no duplicates.

Edge Cases

  • Empty or single-element array should return false if allowed.
  • Duplicate appears immediately, such as [1, 1].
  • Large negative and positive values.

Interview Checklist

  • Use a set when only existence matters.
  • Return as soon as a duplicate is found.
  • Do not sort if the prompt asks for linear time.

FAQs

Can sorting solve this?

Yes, sort and check neighbors, but that is O(n log n). A set gives average O(n).

Why is space O(n)?

If all values are unique, every value is stored in the set.

What is the core pattern?

Seen-set duplicate detection.

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Contains Duplicate - Set Pattern Practice Quiz
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Lesson 4 of 4 in Module 1: Arrays
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Valid Anagram: Frequency map Pattern
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