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Data Structures & Algorithms (DSA) Guide for Google Tech interviews

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Google is known for its rigorous and highly competitive technical interviews. These interviews are designed to assess a candidate’s problem-solving abilities, technical knowledge, and cultural fit with the company. Preparing for technical interviews at top companies like Google requires a solid understanding of Data Structures and Algorithms (DSA). These topics form the foundation of any programming interview and are crucial for solving complex problems efficiently. This article aims to provide a comprehensive guide for mastering DSA to excel in Google tech interviews.


Before diving into complex algorithms, it is crucial to have a strong grasp of the basic data structures such as arrays, linked lists, stacks, queues, and trees. Additionally, understanding concepts like recursion, sorting, searching, and hashing is essential. Reviewing introductory books or online courses can help build a strong foundation.

a) Data Structures:

Data structures are a way to organize and store data efficiently. They provide a way to access and manipulate data. Here are some fundamental data structures:

  • Arrays: A collection of elements, each identified by an index or a key.
  • Linked Lists: A data structure where each element points to the next one, forming a chain.
  • Stacks: A linear data structure that follows the Last-In-First-Out (LIFO) principle.
  • Queues: A linear data structure that follows the First-In-First-Out (FIFO) principle.
  • Trees: A hierarchical data structure with a root element and child elements.
  • Graphs: A collection of nodes connected by edges.

b) Algorithms:

Algorithms are step-by-step procedures or instructions for solving a specific problem or performing a task. They define the logic for solving problems using data structures. Here are some key aspects of algorithms:

  • Efficiency: Algorithms aim to solve problems efficiently, considering factors like time complexity (how long it takes to run) and space complexity (how much memory it uses).
  • Correctness: Algorithms must produce the correct output for all valid inputs.
  • Scalability: Algorithms should work well with both small and large datasets.
  • Optimization: Some algorithms can be optimized to perform better or use fewer resources.

c) Basic Operations:

Understanding common operations on data structures is crucial:

  • Insertion: Adding an element to a data structure.
  • Deletion: Removing an element from a data structure.
  • Search: Finding a specific element in a data structure.
  • Traversal: Visiting each element in a data structure.

d) Time and Space Complexity:

Analysing the efficiency of algorithms is a fundamental skill. Time complexity describes how the running time of an algorithm increases with the size of the input, typically expressed using Big O notation (e.g., O(n), O(log n)). Space complexity describes how memory usage increases with input size.

e) Sorting and Searching:

Sorting and searching algorithms are essential:

  • Sorting Algorithms: Techniques for arranging elements in a specific order, such as quicksort, merge sort and bubble sort.
  • Searching Algorithms: Methods for finding an element in a data structure, like binary search and linear search.

f) Recursion:

Recursion is a technique where a function calls itself to solve a problem. It’s often used in algorithms dealing with tree-like structures.

g) Dynamic Programming:

Dynamic programming is a technique used to solve complex problems by breaking them into smaller subproblems and solving each subproblem only once, storing the results in a table to avoid redundant computations.

h) Greedy Algorithms:

Greedy algorithms make the locally optimal choice at each step with the hope of finding a globally optimal solution. They are often used for optimization problems.

i) Graph Algorithms:

Graph algorithms deal with problems involving networks and relationships between data points. Examples include breadth-first search (BFS) and depth-first search (DFS).

j) Hashing:

Hashing is a technique used to map data to a fixed-size array, allowing for efficient data retrieval.


Google interviews often include questions based on popular data structures, such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Gain a deep understanding of their properties, implementation, and typical use cases. Be fluent in manipulating and traversing these data structures efficiently.

a) Arrays:

Arrays are collections of elements stored at contiguous memory locations. To master arrays:

  • Understand indexing and accessing elements.
  • Learn common array operations like insertion, deletion, and searching.
  • Practice solving problems involving arrays to improve your problem-solving skills.

b) Linked Lists:

Linked lists consist of nodes where each node points to the next one. To master linked lists:

  • Learn about singly linked lists, doubly linked lists, and circular linked lists.
  • Understand insertion, deletion, and traversal operations.
  • Practice reversing a linked list and solving problems related to linked lists.

c) Stacks and Queues:

Stacks follow the Last-In-First-Out (LIFO) principle, while queues follow the First-In-First-Out (FIFO) principle. To master stacks and queues:

  • Understand basic operations like push and pop for stacks and enqueue and dequeue for queues.
  • Learn about the use cases of stacks and queues.
  • Practice implementing these data structures and solving problems like evaluating expressions with stacks or implementing a queue using stacks.

d) Trees:

Trees are hierarchical data structures with nodes connected by edges. To master trees:

  • Learn about binary trees, binary search trees (BSTs), and balanced trees (e.g., AVL or Red-Black trees).
  • Understand tree traversal techniques like inorder, preorder, and postorder.
  • Practice tree-related algorithms such as finding the lowest common ancestor, tree serialization, and constructing a balanced BST.

e) Graphs:

Graphs represent relationships between data points. To master graphs:

  • Learn about different types of graphs (e.g., directed and undirected) and common representations (e.g., adjacency matrix and adjacency list).
  • Understand graph traversal algorithms like depth-first search (DFS) and breadth-first search (BFS).
  • Practice solving problems involving graphs, such as finding the shortest path or detecting cycles.

f) Hash Tables:

Hash tables provide efficient data retrieval using key-value pairs. To master hash tables:

  • Understand the concept of hashing and collision resolution techniques.
  • Learn how to implement a basic hash table and perform operations like insertion, deletion, and retrieval.
  • Practice solving problems involving hash tables, such as implementing a cache or solving problems with unique element occurrences.

g) Heaps:

Heaps are specialized trees used for priority queue operations. To master heaps:

  • Learn about min-heaps and max-heaps.
  • Understand basic operations like insertion and extraction of minimum or maximum elements.
  • Practice solving problems involving heaps, like finding the kth smallest or largest element.

h) Arrays vs. Linked Lists vs. Others:

Master the ability to choose the appropriate data structure for a given problem by understanding their strengths and weaknesses.

i) Algorithms and Time Complexity:

Understand how to analyze the time and space complexity of operations on these data structures.

j) Real-World Applications:

Explore real-world applications of these data structures in software development and problem-solving.

To master common data structures, continuous practice, and problem-solving are essential. Online coding platforms like LeetCode, HackerRank, and Codeforces provide a wealth of problems to hone your skills. Additionally, consider taking online courses or reading textbooks on data structures and algorithms to deepen your understanding and gain practical experience.


Google interviewers often assess candidates’ problem-solving skills by evaluating their knowledge of algorithmic techniques. Focus on mastering techniques like dynamic programming, greedy algorithms, divide and conquer, backtracking, and more. Practice implementing these techniques to solve a wide variety of problems.

Here’s a roadmap to help you become proficient in algorithmic techniques:

a) Dynamic Programming:

  • Learn dynamic programming techniques for solving problems by breaking them into smaller subproblems.
  • Practice solving dynamic programming problems, which often involve optimization tasks.

b) Greedy Algorithms:

  • Study greedy algorithms that make locally optimal choices at each step, aiming for a globally optimal solution.
  • Practice solving problems where greedy algorithms are applicable.

c) Divide and Conquer:

  • Understand the divide and conquer strategy, which involves breaking a problem into smaller subproblems, solving them, and combining the results.
  • Practice solving problems using divide and conquer, such as merge sort and the fast exponentiation algorithm.

d) Solve Algorithmic Problems:

To succeed in Google tech interviews, practice solving algorithmic problems regularly. Leverage coding platforms like LeetCode, HackerRank, or CodeSignal, which offer a vast collection of interview problems and provide a platform for testing solutions. These platforms often categorize problems based on data structures and algorithms, allowing you to focus on specific areas for improvement.

e) Graph Algorithms:

  • Learn graph algorithms like breadth-first search (BFS) and depth-first search (DFS) for traversing graphs.
  • Study graph algorithms for solving problems like finding the shortest path (Dijkstra’s, Bellman-Ford) and detecting cycles.

f) Backtracking:

  • Explore backtracking algorithms for solving problems that require trying out different possibilities and undoing choices if they don’t lead to a solution.
  • Practice solving problems involving permutations, combinations, and Sudoku using backtracking.
  • Efficient coding is a critical aspect of Google tech interviews. Understand how to analyze the time and space complexity of an algorithm. Recognize the best data structure and algorithm to use in different problem scenarios based on their time and space requirements. Optimizing your code becomes essential when solving problems with large input sizes.

g) Competitive Programming:

  • Consider participating in competitive programming competitions on platforms like Codeforces and TopCoder.
  • Competitive programming sharpens your problem-solving skills and exposes you to a wide range of algorithmic challenges.


Simulate real interview scenarios by participating in mock interviews. Use platforms like Pramp, and, or hire a professional mentor to conduct structured interviews and provide feedback. Mock interviews are invaluable for boosting your confidence, improving problem-solving techniques, and refining your communication skills.


To succeed in Google tech interviews, practice solving algorithmic problems regularly. Leverage coding platforms like LeetCode, HackerRank, or CodeSignal, which offer a vast collection of interview problems and provide a platform for testing solutions. These platforms often categorize problems based on data structures and algorithms, allowing you to focus on specific areas for improvement.


Google, like many other top-tech companies, has its unique set of interview patterns. Review common Google interview questions and study the topics that frequently come up. This helps you familiarize yourself with the types of problems you might encounter and the level of complexity expected. Additionally, prepare for system design interviews, as these are often an integral part of the Google interview process.


Join online communities and discussion forums focused on technical interviews. Engaging with others who are also preparing for Google interviews allows you to learn from their experiences and gain valuable insights. Collaborate on solving problems together, discuss approaches, and share coding resources. This collaborative environment can enhance your problem-solving skills and knowledge.

To excel in Google tech interviews, a solid understanding of Data Structures and Algorithms is crucial. By focusing on mastering basic concepts, practicing problem-solving techniques, and analyzing time and space complexities, you can enhance your chances of success. Continuous practice, solving real-world problems, and seeking guidance from peers and mentors will ultimately lead you toward achieving your goals.

Last Updated : 05 Dec, 2023
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