Binary TreesBinary Trees36
  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Inorder Traversal of a Binary Tree using Recursion
  4. 4Inorder Traversal of a Binary Tree using Iteration
  5. 5Postorder Traversal of a Binary Tree Using Recursion
  6. 6Postorder Traversal of a Binary Tree using Iteration
  7. 7Level Order Traversal of a Binary Tree using Recursion
  8. 8Level Order Traversal of a Binary Tree using Iteration
  9. 9Reverse Level Order Traversal of a Binary Tree using Iteration
  10. 10Reverse Level Order Traversal of a Binary Tree using Recursion
  11. 11Find Height of a Binary Tree
  12. 12Find Diameter of a Binary Tree
  13. 13Find Mirror of a Binary Tree
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree
GraphsGraphs46
  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

Find Longest Consecutive Sequence in an Array Optimal HashSet Solution

Problem Statement

Given an unsorted array of integers, your task is to find the length of the longest sequence of consecutive numbers that appear in the array.

  • A consecutive sequence is a group of numbers where each number is exactly 1 more than the previous number.
  • The numbers in the array can be in any order and may contain duplicates.
  • You need to return the length of the longest such sequence.

If the array is empty, return 0.

Examples

Input Array Longest Consecutive Sequence Length Description
[100, 4, 200, 1, 3, 2] [1, 2, 3, 4] 4 Longest consecutive sequence starts at 1 and ends at 4
[0, 3, 7, 2, 5, 8, 4, 6, 0, 1] [0, 1, 2, 3, 4, 5, 6, 7, 8] 9 All elements from 0 to 8 form the longest consecutive sequence
[10, 30, 20] [10] 1 No two numbers are consecutive, so each number alone is a sequence
[1, 9, 3, 10, 2, 20] [1, 2, 3] 3 Consecutive sequence from 1 to 3
[5, 5, 5, 5] [5] 1 All elements are duplicates, so only one unique number exists
[] [] 0 Empty array, no sequence exists

Visualization Player

Solution

The goal is to find the longest chain of consecutive numbers that appear in the array, even though the array itself might be completely unsorted.

A consecutive sequence means a series of numbers like [3, 4, 5, 6] — where each number is 1 more than the previous one. We are not concerned with the order of elements in the original array, just whether the numbers exist to form a sequence.

Understanding the Problem Through Cases

Case 1: Normal case with random numbers
In an array like [100, 4, 200, 1, 3, 2], even though the numbers are jumbled, there's a consecutive sequence [1, 2, 3, 4]. We return 4, the length of that sequence.

Case 2: All numbers already in order
If the array is like [1, 2, 3, 4, 5], then the whole array forms one big sequence. We return the array’s length.

Case 3: Duplicates in the array
For arrays like [5, 5, 5], duplicates don’t help us. We only care about unique numbers. So, we ignore repeats and count the length of the sequence among distinct values.

Case 4: Disconnected numbers
If the numbers have big gaps between them like [10, 30, 50], then each number is its own sequence. The answer will be 1.

Case 5: Empty array
If no numbers are given, then there’s no sequence. So the answer is simply 0.

How We Efficiently Solve It

We put all numbers into a HashSet to remove duplicates and allow fast lookups. Then for each number, we check whether it could be the start of a new sequence (i.e., num - 1 is not in the set). If it is a valid start, we keep checking num + 1, num + 2, ... to count how long the sequence continues.

This way, we avoid re-checking numbers that are part of earlier sequences and ensure we process each number only once. The result is a very fast and scalable solution that works even for large arrays.

⏱️ Time Complexity

Since we use a set and visit each number once, the time complexity is O(n), where n is the number of elements in the array.

Algorithm Steps

  1. Given an unsorted array arr of integers.
  2. Insert all elements into a HashSet for constant-time lookups.
  3. Initialize max_length = 0.
  4. For each element num in arr:
  5. → Check if num - 1 is not in the set (meaning num is the start of a new sequence).
  6. → If so, initialize current_length = 1 and incrementally check for num + 1, num + 2, ... while they exist in the set, incrementing current_length.
  7. → Update max_length with the maximum of max_length and current_length.
  8. Return max_length.

Code

Python
JavaScript
Java
C++
C
def longest_consecutive_sequence(arr):
    num_set = set(arr)
    max_length = 0
    
    for num in arr:
        if num - 1 not in num_set:
            current = num
            length = 1
            while current + 1 in num_set:
                current += 1
                length += 1
            max_length = max(max_length, length)

    return max_length

# Sample Input
arr = [100, 4, 200, 1, 3, 2]
print("Longest Consecutive Sequence Length:", longest_consecutive_sequence(arr))

Time Complexity

CaseTime ComplexityExplanation
Best CaseO(n)In the best case, each element is checked only once, and very few sequences are formed. The HashSet operations are constant time on average.
Average CaseO(n)Each number is visited once when building the set and at most once again when scanning for sequence starts, resulting in linear time.
Worst CaseO(n)Even in the worst case, each element is processed at most twice—once during insertion and once during sequence scanning. So time remains linear.

Space Complexity

O(n)

Explanation: A HashSet is used to store all unique elements of the array for quick lookups, which requires linear space in terms of the input size.