Searching in Data Structure: Different Search Algorithms and Their Applications

Did you know? In 1977, two mathematicians, frustrated by the slowness of existing text search methods, created the Boyer-Moore algorithm over lunch, forever changing the speed of text searching!

Searching in data structures is the process of finding specific data within a collection. It’s essential for organizing and retrieving information quickly. However, with so many search algorithms available, choosing the right one can be tricky. 

In this article, you’ll explore different search algorithms, how they work, and when to use them.

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What is Searching in Data Structure?

Searching in data structure is the process of identifying the location or verifying the presence of a specific element within a dataset.

In simpler terms, searching helps you find what you’re looking for in a collection of data—like finding a contact in your phonebook, retrieving a customer record from a database, or locating a file on your computer. It ensures fast and efficient information retrieval, which is vital for applications like search engines, online shopping, and streaming platforms, where quick access to information enhances user experience.

By making data retrieval fast and efficient, searching plays a key role in applications like search engines, online shopping platforms, and even video streaming services.

Also Read: Understanding Types of Data: Why is Data Important, its 4 Types, Job Prospects, and More

What are the Types of Searching Algorithms in Data Structures? 

Searching algorithms are classified into two main categories based on their approach. They are sequential searching and interval searching. Each method has unique characteristics, use cases, and efficiencies. 

Here's a closer look:

1. Sequential Searching

This method involves searching elements in the dataset until the target element is found or the end of the dataset is reached. 

Real-Life Example: Imagine searching for a specific contact in an unsorted phonebook. You'd need to go through the list one name at a time until you find the contact. For smaller datasets, this method works well, but as the dataset grows, it becomes time-consuming.

• Searching in a list of 50 unsorted names may require up to 50 checks in the worst case.
• With 1,000 names, the process could take up to 1,000 checks, showing how performance deteriorates with dataset size.

Also Read: What are Data Structures in C & How to Use Them?

2. Interval Searching

This method works by repeatedly dividing the dataset into smaller parts, significantly reducing the number of comparisons. It requires the dataset to be sorted.

Real-Life Example: Searching for a word in a dictionary uses interval searching. Instead of flipping through every page sequentially, you open the book near the target alphabet, narrowing the search quickly.

• Searching for a name in a sorted list of 1,000 entries might require only 10 comparisons using Binary Search.
• For a dataset with uniformly distributed values, Interpolation Search can predict the element's location, further reducing the number of checks.

Sequential Searching is straightforward but inefficient for large datasets. Interval Searching, while requiring sorted data, is far more efficient for large-scale operations, such as database lookups or file indexing.

If you're finding it tough to break down complex problems efficiently, check out upGrad’s Data Structures & Algorithms free course to strengthen your foundation and start solving challenges with ease. Start today!

Also Read: Time and Space Complexity of Binary Search Explained

Now that you have a grasp of what is searching in data structures, it’s time for you to explore the algorithms and applications of searching in data structures.

Searching in Data Structure: Different Search Algorithms and Their Applications

Searching algorithms are at the core of data retrieval across fields like databases, artificial intelligence (AI), and networking. They help efficiently organize, locate, and retrieve data, enabling systems to perform seamlessly, even with massive datasets.

Here is an insight into the role they play in data retrieval:

• Organize and Locate Data Efficiently: Searching algorithms power systems like Google and Netflix, helping them handle billions of daily user queries while maintaining performance.
• Reduce Processing Delays: Real-time systems, such as GPS navigation or online stock trading platforms, rely on efficient algorithms to minimize delays and provide accurate results.

Also Read: Algorithm Complexity and Data Structure: Types of Time Complexity 

1. Linear Search

Linear searching in data structure finds each element one by one until the target is found or the dataset ends. It’s simple but not ideal for large datasets due to its O(n) time complexity.

Pseudo Code:

def linear_search(array, target):
    for i in range(len(array)):
        if array[i] == target:
            return i
    return -1

Real-Life Example: Imagine searching for a specific book in an unsorted library. You would go shelf by shelf, checking each title until you find the book. For a small library, this works well, but with thousands of books, this method becomes tedious.

Here are few applications of linear search in data structures:

• Searching in unsorted datasets.
• Simple tasks like finding a student's record in a small, unsorted database.

Also Read: Linear Search vs Binary Search: Key Differences Explained Simply | upGrad blog

2.Binary Search

Binary search works by repeatedly dividing a sorted dataset in half to locate the target element. Its O(log n) time complexity makes it highly efficient for large datasets.

Pseudo Code:

def binary_search(array, target):
    low, high = 0, len(array) - 1
    while low <= high:
        mid = (low + high) // 2
        if array[mid] == target:
            return mid
        elif array[mid] < target:
            low = mid + 1
        else:
            high = mid - 1
    return -1

Real-Life Example: Think of looking up a word in a dictionary. Instead of flipping through every page, you open the book near the alphabet of the word, narrowing your search area with each step.

Here are few applications of binary search:

• Searching in sorted phone directories or name lists.
• Retrieving product details from sorted e-commerce databases.

Also Read: Binary Tree in Data Structure: Properties, Types, Representation & Benefits

3. Interpolation Search

Interpolation search improves upon binary search by estimating the position of the target based on its value, making it faster on uniformly distributed datasets. It has a time complexity of O(log log n), which is more efficient than binary search in such cases.

Pseudo Code:

def interpolation_search(arr, target):
    low = 0
    high = len(arr) - 1
    while low <= high and target >= arr[low] and target <= arr[high]:
        pos = low + ((high - low) // (arr[high] - arr[low])) * (target - arr[low])
        if arr[pos] == target:
            return pos
        elif arr[pos] < target:
            low = pos + 1
        else:
            high = pos - 1
    return -1

Real-Life Example: Imagine searching for a specific page number in a book. Instead of flipping through pages one by one or halving the range repeatedly, you can estimate the page number based on the total pages and jump closer to the target.

Here are few applications of interpolation search:

• Indexing in search engines
• File retrieval systems in computer storage
• Searching in large, uniformly distributed datasets

Also Read: Binary Search Algorithm: Function, Benefits, Time & Space Complexity

4. Hashing Search

Hashing uses a hash function to map keys to positions in a hash table, allowing for constant-time average access for storing and retrieving key-value pairs. It provides efficient storage but can encounter issues like hash collisions.

Pseudo Code:

def hash_function(key, size):
    return key % size

def insert(table, key, value):
    index = hash_function(key, len(table))
    table[index] = value

def search(table, key):
    index = hash_function(key, len(table))
    return table.get(index, "Not found")

Real-Life Example: Think of a dictionary, where words are keys, and their definitions are values. Using a hash function, you can quickly locate the definition of any word without searching through the entire dictionary.

Here are few applications of hashing search:

• Storing key-value pairs in hash tables
• Caching in web applications
• Indexing data in databases

Also Read: Create Index in MySQL: MySQL Index Tutorial

5. Depth-First Search (DFS)

Depth-first search (DFS) explores a graph or tree by visiting a node, then recursively visiting its unvisited neighbors before backtracking. It is memory efficient but may not always find the shortest path in graphs.

Pseudo Code:

def dfs(graph, node, visited=None):
    if visited is None:
        visited = set()
    visited.add(node)
    for neighbor in graph[node]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)
    return visited

Real-Life Example: Imagine you’re exploring a maze, and you always go as deep as possible into one path before backtracking to explore other routes. DFS helps you explore all possible paths before backtracking.

Here are few applications of depth-first search:

• Pathfinding in puzzles and games
• Cycle detection in graphs
• Topological sorting in directed acyclic graphs (DAGs)

Also Read: DFS (Depth First Traversal) in Data Structure: What is, Ordering & Applications

6. Breadth-First Search (BFS)

Breadth-First Search (BFS) explores a graph or tree level by level, visiting all neighbors of a node before moving on to the next level. It’s most efficient for finding the shortest path in unweighted graphs and is ideal for problems where the shortest or least-cost path is needed.

Pseudo Code:

from collections import deque

def bfs(graph, start):
    visited = set()  # To keep track of visited nodes
    queue = deque([start])  # Queue for BFS

    while queue:
        node = queue.popleft()
        if node not in visited:
            visited.add(node)
            for neighbor in graph[node]:
                queue.append(neighbor)
    return visited

Real-Life Example: Imagine you’re navigating a city’s road network. If you're looking for the quickest route from your current location to a destination, BFS can explore each intersection level by level, ensuring you find the shortest path to your goal.

Here are few applications of breadth-first search:

• BFS is often used to find the shortest path in puzzles like the "8-puzzle" or mazes.
• BFS helps find the shortest connection between people in a network (like finding the degrees of separation).
• In search engines, BFS is used to crawl web pages level by level.
• BFS is employed in networking algorithms to broadcast messages to all nodes in the network.

Also Read: DFS vs BFS: Difference Between DFS and BFS

Linear search is simple but inefficient for large datasets. In contrast, binary search and interpolation search excel in sorted datasets, providing faster and more reliable results. Binary search is ideal for finding elements in a sorted list by repeatedly dividing the search range in half. 

Interpolation search is even faster for uniformly distributed data, estimating the target's position based on its value. These algorithms significantly improve search efficiency in large datasets, reducing time complexity and making them more scalable.

Also Read: 13 Interesting Data Structure Projects Ideas and Topics For Beginners [2024]

Once you’ve familiarized yourself with the different types of search algorithms, the next step is to apply them in real-world scenarios. Experiment with each algorithm based on your data needs, and analyze how they perform with different data structures. This hands-on practice will help you solidify your understanding and make better decisions when it comes to optimizing your data retrieval processes.

What are the Applications of Searching in Data Structure?

From finding the shortest route on Google Maps to detecting fraudulent transactions in banking, searching algorithms power the systems we rely on daily. They make real-time data retrieval and decision-making possible across industries, ensuring efficiency and precision. 

Let’s dive into some fascinating applications of searching in data structure with real-world examples and stats!

1. Database Management

In databases like Oracle and MySQL, efficient indexing and search algorithms like binary search trees and hash tables enable lightning-fast data retrieval. This is especially crucial for banks like ICICI, which handles over 100 million customer records, ensuring account details are accessible in milliseconds.

Also Read: DBMS Tutorial For Beginners: Everything You Need To Know

2. Artificial Intelligence

AI-driven systems like Google DeepMind’s AlphaGo use advanced search techniques to analyze millions of potential outcomes in real-time. In autonomous vehicles, algorithms such as A* (A-star) optimize decision-making, enabling Tesla cars to analyze 1.8 billion real-time data points daily to navigate roads safely.

Also Read: Top 8 Most Popular Google AI Projects You Should Work On

3. Search Engines

Google's algorithms, including PageRank and other advanced indexing methods, manage more than 8.5 billion searches daily, returning results in under 0.25 seconds. With personalized results based on search history, behavior, and geolocation, Google continues to innovate the way we find information. 

4. Big Data Analysis

Amazon’s search algorithms are integral to their recommendation system, processing data from 350 million products and 1.9 million active sellers to offer personalized suggestions. With the anticipated rise of 175 zettabytes of data by 2025, algorithms like Interpolation Search are crucial for identifying patterns and making real-time business decisions.

Also Read: Big Data Vs Data Analytics: Understanding the Key Differences

5. Cybersecurity

Cisco’s intrusion detection systems monitor 20 billion cyber threats daily, using search algorithms to identify patterns and potential vulnerabilities in network traffic. AI-driven algorithms also enable predictive analytics, allowing organizations to respond to emerging security threats faster and more accurately.

Also Read: Trees in Data Structure: 8 Types of Trees Every Data Scientist Should Know About

After exploring real-life examples of search algorithms, the next step is to dive deeper into optimizing them for your specific use cases. Try implementing these algorithms in your own projects, measure their performance, and explore ways to improve efficiency. 

By experimenting with different datasets and environments, you’ll gain valuable insights that will help you fine-tune your approach and master the art of search in data structures.

Also Read: A Guide to the Types of AI Algorithms and Their Applications

What are the Factors Required for Choosing the Right Search Algorithm? 

Choosing the correct search algorithm depends on the dataset’s characteristics, performance needs, and whether the data is sorted. For example, linear search is great for small, unsorted datasets, while binary search is more efficient for sorted data. 

The table below highlights the key considerations and use cases for various algorithms:

Each algorithm has its strengths and weaknesses. For small datasets, simpler algorithms like linear search are sufficient. For larger or structured datasets, efficient algorithms like binary search or hashing are better suited. By understanding your dataset and requirements, you can optimize data retrieval and system performance effectively.

Also Read: 4 Types of Trees in Data Structures Explained: Properties & Applications

Understanding and choosing the right algorithm is just the first step. To truly excel, gaining practical experience and industry-relevant knowledge is crucial—let’s find out how you can build a successful career in data structures.

How Can upGrad Help You Build a Career in Data Structure?

Understanding different search algorithms and their applications is key to optimizing data retrieval and improving performance in your projects. However, many developers struggle with implementing these algorithms effectively in real-life scenarios, often unsure which method to choose for specific use cases. 

upGrad’s courses offer hands-on experience, helping you master search algorithms and their applications across various domains ensuring you have the practical skills to tackle any challenge. 

In addition to the courses mentioned, here are some more resources to help you further elevate your skills: 

• Artificial Intelligence in the Real World
• Executive Programme in Generative AI for Leaders
• Fundamentals of Deep Learning and Neural Networks

Not sure where to go next in your Python journey? upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today! 

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