Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • DSA
  • Interview Problems on Graph
  • Practice Graph
  • MCQs on Graph
  • Graph Tutorial
  • Graph Representation
  • Graph Properties
  • Types of Graphs
  • Graph Applications
  • BFS on Graph
  • DFS on Graph
  • Graph VS Tree
  • Transpose Graph
  • Dijkstra's Algorithm
  • Minimum Spanning Tree
  • Prim’s Algorithm
  • Topological Sorting
  • Floyd Warshall Algorithm
  • Strongly Connected Components
  • Advantages & Disadvantages
Open In App
Next Article:
C Program for Depth First Search or DFS for a Graph
Next article icon

Depth First Search or DFS for a Graph

Last Updated : 29 Mar, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report
Try it on GfG Practice
redirect icon

In Depth First Search (or DFS) for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through that adjacent vertex. This is similar to a tree, where we first completely traverse the left subtree and then move to the right subtree. The key difference is that, unlike trees, graphs may contain cycles (a node may be visited more than once). To avoid processing a node multiple times, we use a boolean visited array.

Example:

Note : There can be multiple DFS traversals of a graph according to the order in which we pick adjacent vertices. Here we pick vertices as per the insertion order.

Input: adj = [[1, 2], [0, 2], [0, 1, 3, 4], [2], [2]]

Input_undirected_Graph

Output: [0 1 2 3 4]
Explanation: The source vertex s is 0. We visit it first, then we visit an adjacent.
Start at 0: Mark as visited. Output: 0
Move to 1: Mark as visited. Output: 1
Move to 2: Mark as visited. Output: 2
Move to 3: Mark as visited. Output: 3 (backtrack to 2)
Move to 4: Mark as visited. Output: 4 (backtrack to 2, then backtrack to 1, then to 0)

Not that there can be more than one DFS Traversals of a Graph. For example, after 1, we may pick adjacent 2 instead of 0 and get a different DFS. Here we pick in the insertion order.

Input: [[2,3,1], [0], [0,4], [0], [2]]

Input_undirected_Graph2

Output: [0 2 4 3 1]
Explanation: DFS Steps:

Start at 0: Mark as visited. Output: 0
Move to 2: Mark as visited. Output: 2
Move to 4: Mark as visited. Output: 4 (backtrack to 2, then backtrack to 0)
Move to 3: Mark as visited. Output: 3 (backtrack to 0)
Move to 1: Mark as visited. Output: 1 (backtrack to 0)

Table of Content

  • DFS from a Given Source of Undirected Graph:
  • DFS for Complete Traversal of Disconnected Undirected Graph

DFS from a Given Source of Undirected Graph:

The algorithm starts from a given source and explores all reachable vertices from the given source. It is similar to Preorder Tree Traversal where we visit the root, then recur for its children. In a graph, there might be loops. So we use an extra visited array to make sure that we do not process a vertex again.

Let us understand the working of Depth First Search with the help of the following Illustration: for the source as 0.


C++
#include <bits/stdc++.h> using namespace std;  // Recursive function for DFS traversal void dfsRec(vector<vector<int>> &adj, vector<bool> &visited, int s, vector<int> &res) {      visited[s] = true;      res.push_back(s);      // Recursively visit all adjacent vertices     // that are not visited yet     for (int i : adj[s])         if (visited[i] == false)             dfsRec(adj, visited, i, res); }  // Main DFS function that initializes the visited array // and call DFSRec vector<int> DFS(vector<vector<int>> &adj) {     vector<bool> visited(adj.size(), false);     vector<int> res;     dfsRec(adj, visited, 0, res);     return res; }  // To add an edge in an undirected graph void addEdge(vector<vector<int>> &adj, int s, int t) {     adj[s].push_back(t);     adj[t].push_back(s); }  int main() {     int V = 5;     vector<vector<int>> adj(V);      // Add edges     vector<vector<int>> edges = {{1, 2}, {1, 0}, {2, 0}, {2, 3}, {2, 4}};     for (auto &e : edges)         addEdge(adj, e[0], e[1]);      // Starting vertex for DFS     vector<int> res = DFS(adj); // Perform DFS starting from the source verte 0;      for (int i = 0; i < V; i++)         cout << res[i] << " "; } 
Java
import java.util.*;  public class DFSGraph {     // Recursive function for DFS traversal     private static void     dfsRec(ArrayList<ArrayList<Integer> > adj,            boolean[] visited, int s, ArrayList<Integer> res)     {         visited[s] = true;         res.add(s);          // Recursively visit all adjacent vertices that are         // not visited yet         for (int i : adj.get(s)) {             if (!visited[i]) {                 dfsRec(adj, visited, i, res);             }         }     }      // Main DFS function that initializes the visited array     // and calls dfsRec     public static ArrayList<Integer>     DFS(ArrayList<ArrayList<Integer> > adj)     {         boolean[] visited = new boolean[adj.size()];         ArrayList<Integer> res = new ArrayList<>();         dfsRec(adj, visited, 0, res);         return res;     }      // To add an edge in an undirected graph     public static void     addEdge(ArrayList<ArrayList<Integer> > adj, int s,             int t)     {         adj.get(s).add(t);         adj.get(t).add(s);     }      public static void main(String[] args)     {         int V = 5;         ArrayList<ArrayList<Integer> > adj             = new ArrayList<>();          // Initialize adjacency list         for (int i = 0; i < V; i++) {             adj.add(new ArrayList<>());         }          // Add edges         int[][] edges= { { 1, 2 },{ 1, 0 },{ 2, 0 },{ 2, 3 },{ 2, 4 } };          for (int[] e : edges)         {             addEdge(adj, e[0], e[1]);         }          // Perform DFS starting from vertex 0         ArrayList<Integer> res = DFS(adj);          for (int i = 0; i < res.size(); i++) {             System.out.print(res.get(i) + " ");         }     } } 
Python
def dfsRec(adj, visited, s, res):     visited[s] = True     res.append(s)      # Recursively visit all adjacent vertices that are not visited yet     for i in range(len(adj)):         if adj[s][i] == 1 and not visited[i]:             dfsRec(adj, visited, i, res)   def DFS(adj):     visited = [False] * len(adj)     res = []     dfsRec(adj, visited, 0, res)  # Start DFS from vertex 0     return res   def add_edge(adj, s, t):     adj[s][t] = 1     adj[t][s] = 1  # Since it's an undirected graph   # Driver code V = 5 adj = [[0] * V for _ in range(V)]  # Adjacency matrix  # Define the edges of the graph edges = [(1, 2), (1, 0), (2, 0), (2, 3), (2, 4)]  # Populate the adjacency matrix with edges for s, t in edges:     add_edge(adj, s, t)  res = DFS(adj)  # Perform DFS print(" ".join(map(str, res))) 
C#
using System; using System.Collections.Generic;  class DFSGraph {     // Recursive function for DFS traversal     static void DfsRec(List<int>[] adj, bool[] visited,                        int s, List<int> res)     {         visited[s] = true;         res.Add(s);          // Recursively visit all adjacent vertices that are         // not visited yet         foreach(int i in adj[s])         {             if (!visited[i]) {                 DfsRec(adj, visited, i, res);             }         }     }      // Main DFS function that initializes the visited array     // and calls DfsRec     static List<int> DFS(List<int>[] adj)     {         bool[] visited = new bool[adj.Length];         List<int> res = new List<int>();         DfsRec(adj, visited, 0, res);         return res;     }      // To add an edge in an undirected graph     static void AddEdge(List<int>[] adj, int s, int t)     {         adj[s].Add(t);         adj[t].Add(s);     }      static void Main()     {         int V = 5;         List<int>[] adj = new List<int>[ V ];          // Initialize adjacency list         for (int i = 0; i < V; i++) {             adj[i] = new List<int>();         }          // Add edges         int[, ] edges = {             { 1, 2 }, { 1, 0 }, { 2, 0 }, { 2, 3 }, { 2, 4 }         };          for (int i = 0; i < edges.GetLength(0); i++) {             AddEdge(adj, edges[i, 0], edges[i, 1]);         }          // Perform DFS starting from vertex 0         List<int> res = DFS(adj);          foreach(int i in res) { Console.Write(i + " "); }     } } 
JavaScript
function dfsRec(adj, visited, s, res) {     visited[s] = true;     res.push(s);      // Recursively visit all adjacent vertices that are not     // visited yet     for (let i = 0; i < adj.length; i++) {         if (adj[s][i] === 1 && !visited[i]) {             dfsRec(adj, visited, i, res);         }     } }  function DFS(adj) {     let visited = new Array(adj.length).fill(false);     let res = [];     dfsRec(adj, visited, 0, res); // Start DFS from vertex 0     return res; }  function addEdge(adj, s, t) {     adj[s][t] = 1;     adj[t][s] = 1; // Since it's an undirected graph }  // Driver code let V = 5; let adj = Array.from(     {length : V},     () => new Array(V).fill(0)); // Adjacency matrix  // Define the edges of the graph let edges =     [ [ 1, 2 ], [ 1, 0 ], [ 2, 0 ], [ 2, 3 ], [ 2, 4 ] ];  // Populate the adjacency matrix with edges edges.forEach(([ s, t ]) => addEdge(adj, s, t));  let res = DFS(adj); // Perform DFS console.log(res.join(" ")); 

Output
0 1 2 3 4 

Time complexity: O(V + E), where V is the number of vertices and E is the number of edges in the graph.
Auxiliary Space: O(V + E), since an extra visited array of size V is required, And stack size for recursive calls to dfsRec function.

Please refer Complexity Analysis of Depth First Search for details.

DFS for Complete Traversal of Disconnected Undirected Graph

The above implementation takes a source as an input and prints only those vertices that are reachable from the source and would not print all vertices in case of disconnected graph. Let us now talk about the algorithm that prints all vertices without any source and the graph maybe disconnected.

The idea is simple, instead of calling DFS for a single vertex, we call the above implemented DFS for all all non-visited vertices one by one.

C++
#include <bits/stdc++.h> using namespace std;  void addEdge(vector<vector<int>> &adj, int s, int t) {     adj[s].push_back(t);     adj[t].push_back(s); }  // Recursive function for DFS traversal void dfsRec(vector<vector<int>> &adj, vector<bool> &visited, int s, vector<int> &res) {     // Mark the current vertex as visited     visited[s] = true;      res.push_back(s);      // Recursively visit all adjacent vertices that are not visited yet     for (int i : adj[s])         if (visited[i] == false)             dfsRec(adj, visited, i, res); }  // Main DFS function to perform DFS for the entire graph vector<int> DFS(vector<vector<int>> &adj) {     vector<bool> visited(adj.size(), false);     vector<int> res;     // Loop through all vertices to handle disconnected graph     for (int i = 0; i < adj.size(); i++)     {         if (visited[i] == false)         {             // If vertex i has not been visited,             // perform DFS from it             dfsRec(adj, visited, i, res);         }     }      return res; }  int main() {     int V = 6;     // Create an adjacency list for the graph     vector<vector<int>> adj(V);      // Define the edges of the graph     vector<vector<int>> edges = {{1, 2}, {2, 0}, {0, 3}, {4, 5}};      // Populate the adjacency list with edges     for (auto &e : edges)         addEdge(adj, e[0], e[1]);     vector<int> res = DFS(adj);      for (auto it : res)         cout << it << " ";     return 0; } 
Java
import java.util.*;  public class GfG {     // Function to add an edge to the adjacency list     public static void     addEdge(ArrayList<ArrayList<Integer> > adj, int s,             int t)     {         adj.get(s).add(t);         adj.get(t).add(s);     }      // Recursive function for DFS traversal     private static void     dfsRec(ArrayList<ArrayList<Integer> > adj,            boolean[] visited, int s, ArrayList<Integer> res)     {         visited[s] = true;         res.add(s);          // Recursively visit all adjacent vertices that are         // not visited yet         for (int i : adj.get(s)) {             if (!visited[i]) {                 dfsRec(adj, visited, i, res);             }         }     }      // Main DFS function to perform DFS for the entire graph     public static ArrayList<Integer>     DFS(ArrayList<ArrayList<Integer> > adj)     {         boolean[] visited = new boolean[adj.size()];         ArrayList<Integer> res = new ArrayList<>();          // Loop through all vertices to handle disconnected         // graphs         for (int i = 0; i < adj.size(); i++) {             if (!visited[i]) {                 dfsRec(adj, visited, i, res);             }         }          return res;     }      public static void main(String[] args)     {         int V = 6;         // Create an adjacency list for the graph         ArrayList<ArrayList<Integer> > adj             = new ArrayList<>();          // Initialize adjacency list         for (int i = 0; i < V; i++) {             adj.add(new ArrayList<>());         }          // Define the edges of the graph         int[][] edges             = { { 1, 2 }, { 2, 0 }, { 0, 3 }, { 4, 5 } };          // Populate the adjacency list with edges         for (int[] e : edges) {             addEdge(adj, e[0], e[1]);         }          // Perform DFS         ArrayList<Integer> res = DFS(adj);          // Print the DFS traversal result         for (int num : res) {             System.out.print(num + " ");         }     } } 
Python
# Create an adjacency list for the graph from collections import defaultdict   def add_edge(adj, s, t):     adj[s].append(t)     adj[t].append(s)  # Recursive function for DFS traversal   def dfs_rec(adj, visited, s, res):     # Mark the current vertex as visited     visited[s] = True     res.append(s)      # Recursively visit all adjacent vertices that are not visited yet     for i in adj[s]:         if not visited[i]:             dfs_rec(adj, visited, i, res)  # Main DFS function to perform DFS for the entire graph   def dfs(adj):     visited = [False] * len(adj)     res = []     # Loop through all vertices to handle disconnected graph     for i in range(len(adj)):         if not visited[i]:             # If vertex i has not been visited,             # perform DFS from it             dfs_rec(adj, visited, i, res)     return res   V = 6 # Create an adjacency list for the graph adj = defaultdict(list)  # Define the edges of the graph edges = [[1, 2], [2, 0], [0, 3], [4, 5]]  # Populate the adjacency list with edges for e in edges:     add_edge(adj, e[0], e[1]) res = dfs(adj)  print(' '.join(map(str, res))) 
C#
using System; using System.Collections.Generic;  class GfG {     // Function to add an edge to the adjacency list     static void AddEdge(List<int>[] adj, int s, int t)     {         adj[s].Add(t);         adj[t].Add(s);     }      // Recursive function for DFS traversal     static void DfsRec(List<int>[] adj, bool[] visited,                        int s, List<int> res)     {         visited[s] = true;         res.Add(s);          // Recursively visit all adjacent vertices that are         // not visited yet         foreach(int i in adj[s])         {             if (!visited[i]) {                 DfsRec(adj, visited, i, res);             }         }     }      // Main DFS function to perform DFS for the entire graph     static List<int> DFS(List<int>[] adj)     {         bool[] visited = new bool[adj.Length];         List<int> res = new List<int>();          // Loop through all vertices to handle disconnected         // graphs         for (int i = 0; i < adj.Length; i++) {             if (!visited[i]) {                 DfsRec(adj, visited, i, res);             }         }          return res;     }      static void Main()     {         int V = 6;         // Create an adjacency list for the graph         List<int>[] adj = new List<int>[ V ];          // Initialize adjacency list         for (int i = 0; i < V; i++) {             adj[i] = new List<int>();         }          // Define the edges of the graph         int[, ] edges             = { { 1, 2 }, { 2, 0 }, { 0, 3 }, { 4, 5 } };          // Populate the adjacency list with edges         for (int i = 0; i < edges.GetLength(0); i++) {             AddEdge(adj, edges[i, 0], edges[i, 1]);         }          // Perform DFS         List<int> res = DFS(adj);          // Print the DFS traversal result         foreach(int num in res)         {             Console.Write(num + " ");         }     } } 
JavaScript
function addEdge(adj, s, t) {     adj[s].push(t);     adj[t].push(s); }  // Recursive function for DFS traversal function dfsRec(adj, visited, s, res) {     visited[s] = true;     res.push(s);      // Recursively visit all adjacent vertices that are not visited yet     for (let i of adj[s]) {         if (!visited[i]) {             dfsRec(adj, visited, i, res);         }     } }  // Main DFS function to perform DFS for the entire graph function DFS(adj) {     let visited = new Array(adj.length).fill(false);     let res = [];      // Loop through all vertices to handle disconnected graphs     for (let i = 0; i < adj.length; i++) {         if (!visited[i]) {             dfsRec(adj, visited, i, res);         }     }      return res; }  // Main Execution let V = 6; // Create an adjacency list for the graph let adj = Array.from({ length: V }, () => []);  let edges = [[1, 2], [2, 0], [0, 3], [4, 5]];  // Populate the adjacency list with edges for (let e of edges) {     addEdge(adj, e[0], e[1]); }  // Perform DFS let res = DFS(adj);  // Print the DFS traversal result console.log(res.join(" ")); 

Output
0 2 1 3 4 5 

Time complexity: O(V + E). Note that the time complexity is same here because we visit every vertex at most once and every edge is traversed at most once (in directed) and twice in undirected.
Auxiliary Space: O(V + E), since an extra visited array of size V is required, And stack size for recursive calls to dfsRec function.

Related Articles:

  • Depth First Search or DFS on Directed Graph
  • Breadth First Search or BFS for a Graph

Next Article
C Program for Depth First Search or DFS for a Graph

K

kartik
Improve
Article Tags :
  • Graph
  • Algorithms
  • DSA
  • DFS
  • graph-basics
Practice Tags :
  • Algorithms
  • DFS
  • Graph

Similar Reads

    Depth First Search or DFS for a Graph
    In Depth First Search (or DFS) for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through that adjacent vertex. This is similar to a tree, where we first completely traverse the left subtree and
    13 min read

    DFS in different language

    C Program for Depth First Search or DFS for a Graph
    Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. The only catch here is, that, unlike trees, graphs may contain cycles (a node may be visited twice). To avoid processing a node more than once, use a boolean visited array. A graph can have more than one DFS tr
    4 min read
    Depth First Search or DFS for a Graph - Python
    Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. The only catch here is, that, unlike trees, graphs may contain cycles (a node may be visited twice). To avoid processing a node more than once, use a Boolean visited array. A graph can have more than one DFS tr
    4 min read
    Java Program for Depth First Search or DFS for a Graph
    Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. Prerequisite: Graph knowledge is important to understand the concept of DFS. What is DFS?DFS or Depth First Traversal is the traversing algorithm. DFS can be used to approach the elements of a Graph. To avoid p
    3 min read
    Iterative Depth First Traversal of Graph
    Given a directed Graph, the task is to perform Depth First Search of the given graph.Note: Start DFS from node 0, and traverse the nodes in the same order as adjacency list.Note : There can be multiple DFS traversals of a graph according to the order in which we pick adjacent vertices. Here we pick
    10 min read
    Applications, Advantages and Disadvantages of Depth First Search (DFS)
    Depth First Search is a widely used algorithm for traversing a graph. Here we have discussed some applications, advantages, and disadvantages of the algorithm. Applications of Depth First Search:1. Detecting cycle in a graph: A graph has a cycle if and only if we see a back edge during DFS. So we ca
    4 min read
    Difference between BFS and DFS
    Breadth-First Search (BFS) and Depth-First Search (DFS) are two fundamental algorithms used for traversing or searching graphs and trees. This article covers the basic difference between Breadth-First Search and Depth-First Search.Difference between BFS and DFSParametersBFSDFSStands forBFS stands fo
    2 min read
    Depth First Search or DFS for disconnected Graph
    Given a Disconnected Graph, the task is to implement DFS or Depth First Search Algorithm for this Disconnected Graph. Example: Input: Disconnected Graph Output: 0 1 2 3 Algorithm for DFS on Disconnected Graph:In the post for Depth First Search for Graph, only the vertices reachable from a given sour
    7 min read
    Printing pre and post visited times in DFS of a graph
    Depth First Search (DFS) marks all the vertices of a graph as visited. So for making DFS useful, some additional information can also be stored. For instance, the order in which the vertices are visited while running DFS. Pre-visit and Post-visit numbers are the extra information that can be stored
    8 min read
    Tree, Back, Edge and Cross Edges in DFS of Graph
    Given a directed graph, the task is to identify tree, forward, back and cross edges present in the graph.Note: There can be multiple answers.Example:Input: GraphOutput:Tree Edges: 1->2, 2->4, 4->6, 1->3, 3->5, 5->7, 5->8 Forward Edges: 1->8 Back Edges: 6->2 Cross Edges: 5-
    9 min read
    Transitive Closure of a Graph using DFS
    Given a directed graph, find out if a vertex v is reachable from another vertex u for all vertex pairs (u, v) in the given graph. Here reachable means that there is a path from vertex u to v. The reach-ability matrix is called transitive closure of a graph. For example, consider below graph: GraphTr
    8 min read

    Variations of DFS implementations

    Implementation of DFS using adjacency matrix
    Depth First Search (DFS) has been discussed in this article which uses adjacency list for the graph representation. In this article, adjacency matrix will be used to represent the graph.Adjacency matrix representation: In adjacency matrix representation of a graph, the matrix mat[][] of size n*n (wh
    8 min read
    Graph implementation using STL for competitive programming | Set 1 (DFS of Unweighted and Undirected)
    We have introduced Graph basics in Graph and its representations. In this post, a different STL-based representation is used that can be helpful to quickly implement graphs using vectors. The implementation is for the adjacency list representation of the graph. Following is an example undirected and
    7 min read
    Graph implementation using STL for competitive programming | Set 2 (Weighted graph)
    In Set 1, unweighted graph is discussed. In this post, weighted graph representation using STL is discussed. The implementation is for adjacency list representation of weighted graph. Undirected Weighted Graph We use two STL containers to represent graph: vector : A sequence container. Here we use i
    7 min read
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences