What is Hashing in Data Structure? Explore Hashing Techniques, Benefits, Limitations, and More

Hashing in data structure maps data to fixed-size values using a hash function, allowing fast storage and quick access. You see it in search operations, indexing, caching, and secure verification systems. It cuts lookup time, avoids full scans, and keeps data organized through direct addressing. This approach powers databases, compilers, file systems, and many real-world applications where fast retrieval is essential.

In this guide, you’ll read more about the basics, hashing techniques, collision handling methods, benefits, limitations, and common use cases.

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What Is Hashing in Data Structure?

Think of a library with thousands of books. If you had to search shelf by shelf, it would take a long time. But if every book had a unique code that pointed you directly to its exact spot, you could find it in seconds.
That is the basic idea behind hashing in data structure. It gives every piece of data a special value that tells the system where it should be stored or found.
Hashing in DSA is a method of converting input data into a fixed-size value called a hash value. A hash function performs this conversion. When you store something, the hash value decides the location. When you search for it later, the same hash value helps you jump straight to the right spot.

Here’s a simple flowchart explaining the core concept of hashing:
 
 

Core Components of Hashing in Data Structures

When we define hashing in data structures, it is also important to know about the core components.

Let’s explore the components of hashing in detail now.

1. Key 

A key is the piece of data you want to store and retrieve. It can be a number, text, or any unique identifier. Hashing in DSA uses this key as the starting point for generating a hash code. The system applies a hash function to the key so it can decide exactly where the data should be placed.

When you use meaningful keys, locating or updating entries becomes much easier. A key must remain consistent across operations so that the same input always produces the same code.

2. Hash Function

The hash function converts each key into a numeric output called a hash code. Hashing in Data Structures relies on this step because the hash code decides where the data will be stored in the table. A well-designed hash function distributes keys evenly across the hash table and reduces collisions. 

Simple versions might use modulo arithmetic (key % tableSize), while advanced ones use more complex math or encryption-based formulas. 

A good hashing function in data structure should do the following things:

3. Hash Table

A hash table is an array or similar structure that reserves spots for each hash code. The table’s size depends on factors like expected data volume and desired efficiency. Each index in the table points to a location where data is kept. 

Here’s a quick flowchart on how hash tables work:

When a new key arrives, the hash function determines which slot the data should occupy. If a collision occurs, the system uses hashing collision resolution techniques — like chaining or open addressing — to store items without discarding anything.

You can also check out upGrad’s free tutorial, Data Structures and Algorithms. Explore categories and algorithms. Master all the key operations in data handling and decipher DSA like never before. 

Also Read: Hash Tables and Hash Maps in Python

Working of Hashing in Data Structures

Each component in hashing works together to keep data access quick and predictable. The key represents the data you want to store. The hash function turns that key into a code that points to a specific place. The hash table stores the value so the system can reach it instantly. When you define hashing in data structure, this is the core idea: convert a key into a location and reach it without scanning everything.
This setup supports fast insertion, retrieval, and deletion. It works well in large databases, indexing systems, caching, and any situation where quick access to information is important.

Here’s an example to help you understand how hashing works in data structures:

Here’s a step-by-step guide on how hashing in DSA works:

1. Receive the Key: You have a distinct identifier, such as a student ID, product code, or string, that needs storage.
2. Apply the Hash Function: The key is passed into the function, which calculates a number representing the key’s position in the table.
3. Obtain the Hash Code: The function outputs an integer (hash code) that stays the same each time you use the same key.
4. Place Data in the Hash Table: You use the hash code as an index. Data goes into the corresponding slot, ready for quick access.
5. Handle Collisions if Needed: When two different keys yield the same code, the system stores them without overwriting anything, often through methods like linking items in a list or probing the next free slot.

Let’s understand this better through two examples that illustrate how hashing in Data Structure works.

Example 1: Numeric Hashing

Suppose you have three numbers: 14, 19, and 23, along with a simple hash function h(key) = key mod   5. 

The table below shows how each key maps to a slot in a hash table of size 5 (indexes 0 through 4):

You place 14 at index 4, but 19 also maps to index 4, which causes a collision. The last key, 23, goes to index 3 with no issue. Collisions get handled through methods like chaining or open addressing, which you’ll learn more about later.

Example 2: String Hashing

When hashing a string like "CAT," you might add the ASCII values of each character (C=67, A=65, T=84) for a total of 216. If you apply 216 % 10, you get 6 as the final index. 

Below is a table for three strings:

Each string generates an index in the table, although collisions can still occur if different strings produce the same final value. The main advantage is that hashing can point you directly to each item's spot instead of forcing you to check every element in the table.

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Also Read: Sorting in Data Structure: Categories & Types [With Examples]

Hashing Techniques in Data Structure

Hash functions follow different approaches, or hashing techniques, to turn keys into compact codes. While all hashing techniques in a data structure aim to minimize collisions, they handle numeric or string inputs in unique ways.

• Some focus on simple arithmetic
• Others rely on more complex formulas

Below is a closer look at widely used hashing techniques, complete with examples to show how keys transform into hash codes.

1. Division Method

The division method is one of the simplest types of hashing in data structure. You take a key and divide it by the size of the hash table, then use the remainder as the index. This approach is quick because it involves a single division operation, but picking the right table size is important to reduce collisions.

How Does It Work?

• Formula: h(key) = key mod   tableSize
• Choice of tableSize: A prime number or a value unrelated to your data patterns helps spread out entries more evenly.

Let’s understand this through an example: Suppose you have keys [13, 27, 18, 42] and a hash table of size 10:

You place each key at its remainder index. Even if different keys produce the same remainder, collisions can still happen, so you must have a plan to handle them (for example, chaining or open addressing).

2. Mid Square Method

The mid-square method squares the key and extracts the middle digits to form the index. This hashing technique can work well if the squared values vary enough to reduce collisions.

How Does It Work?

1. Square the key
2. Extract a set number of middle digits from the result
3. Convert those digits into a valid index (often through a final modulo operation)

Example: Use a table size of 100 for clarity. Let’s take the key ‘56’:

• Square 56 to get 3136.
• Choose the two middle digits, which are 13.
• Store the data at index 13 in the table.

If the key is 123, then:

1. 123 * 123 = 15129
2. Middle digits could be 12 (from 15129).
3. You store the data at index 12 in the table.

This approach distributes keys better than a basic division if the squared numbers vary enough. However, repeated patterns can still result in collisions.

This hashing technique in data structure taps into more of the key's digits by squaring it, often spreading values. However, if certain patterns repeat, collisions can still occur.

3. Folding Method

In folding, you divide the key into equal parts and add those parts together to get the final index. The last segment may be shorter if the key length doesn’t divide evenly. This method works well when keys have multiple segments, like phone numbers or identification codes. In hashing in data structure, folding is a simple way to break down large keys and convert them into manageable values for quick storage and lookup.

How Does It Work?

1. Break the key into fixed-size chunks.
2. Sum the numeric values of those chunks.
3. If needed, take the sum modulo the table size to get the final index.

Example: Take a phone number-like key, such as 123456, and split it into two-digit segments: 12, 34, 56. 

• Sum: 12+34+56=102
• Index: If the table size is 10, then 102 mod  10 = 2

If the key had odd digits, the last chunk might have fewer digits. You still add it to the sum.

4. Multiplication Method

The multiplication method multiplies the key by a constant between 0 and 1, then uses the fractional part of the result to compute the index. This approach aims to spread keys more evenly than simple division. In hashing in data structure, this method is popular because it reduces patterns that can cluster keys in the same area.

How it works

• Pick a constant A between 0 and 1 (many hashing techniques in data structure use the golden ratio value of about 0.618).
• Multiply the key by A.
• Take the fractional part of the result.
• Multiply that fraction by the table size to get the index.

Example
Key = 45, A = 0.7, table size = 10
45 × 0.7 = 31.5
Fractional part = 0.5
Index = 0.5 × 10 = 5

The multiplication method can still produce collisions, but it generally distributes data more smoothly than basic division.

5. Universal Hashing

Universal hashing is one of those hashing techniques in data structure that picks a random function from a set of possible options. This makes it harder for any adversary or pattern to consistently cause collisions. It can be more secure for certain applications, though it can add complexity.

How Does It Work?

1. Maintain a family of functions (e.g., different multipliers or constants).
2. Pick one at random when the system starts or on demand.
3. Apply that chosen function to each key to compute the index.

Example: Say we keep a set of arithmetic functions like the ones listed below:

• h1(key) = (a1 * key + b1) % tableSize
• h2(key) = (a2 * key + b2) % tableSize

We pick one function at run time. Since it’s random, a worst-case scenario for one function may not apply to the next one.

6. Cryptographic Hashing

Cryptographic hashing techniques are commonly used for passwords, digital checks, and data verification, but they can also support hashing techniques in data structures when stronger collision resistance is required. These functions create fixed outputs that are extremely hard to reverse, making them useful when accuracy and integrity matter.

How it works

• The process is one way. You cannot rebuild the original input from the hash.
• Collision resistance is much stronger, so finding two keys with the same output is difficult.
• Popular options include MD5, SHA-1, and SHA-256.

Example
If you apply SHA-256 to the word “apple,” you get a long hexadecimal output of 64 characters. This method costs more computing power than simple arithmetic-based approaches, but it provides stronger protection and very stable distribution.

Also Read: What is MD5 Algorithm? How Does it Work?

Implementation of Hashing in Different Programming Languages

Many programming languages offer built-in structures that use a hashing function in data structure operations to store and retrieve values quickly. These structures take a key, compute a hash code internally, and place the value in a table without you handling the process manually. This is one of the reasons hashing in DSA is widely used in real projects.
Below are quick Python, Java, and C++ examples that show how each language manages key–value pairs.

Python Hash Method

Python’s built-in dictionary uses a hash function on each key. 

• When you insert an item, Python decides the index based on that hash code.
• Collisions are resolved automatically. 

Below is a short code snippet that stores and retrieves data.

def demo_python_dict():
    # Create a dictionary
    phonebook = {
        "Asha": 12345,
        "Rahul": 67890
    }
    
    # Insert a new key-value pair
    phonebook["Meera"] = 55555
    
    # Retrieve values
    print("Asha's number:", phonebook["Asha"])
    print("Rahul's number:", phonebook["Rahul"])
    print("Meera's number:", phonebook["Meera"])

if __name__ == "__main__":
    demo_python_dict()

Output:

Asha's number: 12345
Rahul's number: 67890
Meera's number: 55555

Python’s internal hash function generates an index for each name. When you call phonebook["Asha"], the hashing function in data structure logic helps Python use that hash code to jump directly to the stored value. This gives you constant average-time access without scanning the entire table.

Java Hash Method

Java provides the HashMap class, which maps keys to values using hashing. It automatically resizes the table when the load factor becomes high and handles collisions by linking entries in buckets.

import java.util.HashMap;

public class DemoHashMap {
    public static void main(String[] args) {
        HashMap<String, Integer> phonebook = new HashMap<>();
        
        // Insert entries
        phonebook.put("Asha", 12345);
        phonebook.put("Rahul", 67890);
        
        // Insert a new one
        phonebook.put("Meera", 55555);
        
        // Retrieve values
        System.out.println("Asha's number: " + phonebook.get("Asha"));
        System.out.println("Rahul's number: " + phonebook.get("Rahul"));
        System.out.println("Meera's number: " + phonebook.get("Meera"));
    }
}

Output:

Asha's number: 12345
Rahul's number: 67890
Meera's number: 55555

When you add a key, HashMap turns that key into a hash code and places the data in the corresponding bucket. If two keys end up in the same bucket, Java uses a linked list or tree to differentiate them internally.

Also Read: What is Hashtable in Java? Explained with Examples

C++ Hash Method

C++ provides an unordered_map that relies on a hashing function in data structure operations. When you insert a key, it generates a hash code and finds the right bucket to place the value. Collisions are usually handled through chaining, allowing multiple entries to occupy the same bucket safely.

#include <iostream>
#include <unordered_map>
#include <string>

int main() {
    std::unordered_map<std::string, int> phonebook;
    
    // Insert entries
    phonebook["Asha"] = 12345;
    phonebook["Rahul"] = 67890;
    
    // Insert a new one
    phonebook["Meera"] = 55555;
    
    // Retrieve values
    std::cout << "Asha's number: " << phonebook["Asha"] << std::endl;
    std::cout << "Rahul's number: " << phonebook["Rahul"] << std::endl;
    std::cout << "Meera's number: " << phonebook["Meera"] << std::endl;

    return 0;
}

Output:

Asha's number: 12345
Rahul's number: 67890
Meera's number: 55555

unordered_map calculates a hash code for each name. When you call phonebook["Asha"], it looks up the code and jumps to the correct spot. This method keeps data retrieval at a constant average time.

Also Read: Types of Data Structures in Python That Every Coder Should Know! 

Importance of Hashing For Fast Data Retrieval

Hashing makes large-scale lookups feel instant. You can store thousands or even millions of records and still reach the item you want in constant average time. Many data-driven systems rely on this hashing technique for real-time performance, whether it’s checking passwords or searching entries in a phone directory. 

The idea is straightforward: convert each key into a numeric code, then use that code to jump directly to the data’s location. You skip repetitive scans through entire lists, which boosts speed dramatically. By cutting down the overhead of sequential searches, hashing keeps data operations efficient, even as your collection expands.

Below are some key reasons why hashing offers strong benefits for data retrieval:

• Constant Average-Time Access: Most hash-based lookups complete in O(1) time, letting you insert, delete, or search data without scanning everything in memory.
• Scalability With Large Datasets: You can scale up your storage while keeping the same time complexity, which allows systems to grow without a big performance hit.
• Reduced Overhead: You avoid the heavy comparison loops that many other data structures need. This lightens the load on your processor and speeds up responses.
• Flexibility for Different Types of Data: Numeric inputs, strings, and other complex keys all map quickly to hash codes, which makes hashing suitable for a broad range of use cases.
• Efficient Collision Handling: Hashing techniques include collision resolution methods like chaining or open addressing to store new entries without losing existing ones.

Understanding Collisions and Their Handling

Collisions happen when two different keys map to the same index in a hash table. In hashing in data structure, you work with a limited number of slots, but the space of possible keys is usually much larger. Because of this mismatch, two inputs can produce the same hash code, leading both items to compete for the same position. This is normal, since no hashing function in data structure can guarantee a unique output for every possible key.

A strong hash function, a suitable table size, and balanced load factors reduce collisions, but they cannot eliminate them fully. That’s why hashing techniques in data structure focus heavily on how to manage collisions safely without losing data.

Common reasons collisions occur:
 

• Limited hash table range: When the table has fewer slots than the potential key space.
• Weak hash function: A poor function may cluster unrelated keys.
• Patterns in data: Inputs with similar structures can trigger repeated results.

Collision resolution is a core part of what is hashing in data structure, because storing and retrieving values reliably depends on handling these conflicts correctly.

1. Separate Chaining (Open Hashing)

Separate chaining stores multiple values at the same index by linking them in a small list. Instead of overwriting data, each slot points to a chain of entries. This is one of the most common hashing techniques in data structure with example use cases in dictionaries, symbol tables, and indexing systems.

How it works:

• Each index points to a linked list.
• When two keys collide, the new key is added to that list.
• Searching involves walking the list until the key is found or the chain ends.

Separate chaining is easy to implement and scales well, although long chains can slow lookups.

2. Open Addressing (Closed Hashing)

Open addressing keeps all elements inside the main array. When a collision occurs, the system probes other slots until it finds an empty one. This approach is common in hashing in DSA because it keeps memory contiguous.

A good example of hashing here is understanding the different probing strategies:

Linear Probing
Moves step-by-step to the next index until it finds an empty slot. Simple but prone to clustering.

Quadratic Probing
Jumps wider using square increments like 1, 4, 9, and 16. This reduces consecutive collisions.

Double Hashing
Uses a second hash function to determine how far to jump during each probe. This often spreads keys more evenly.

How linear probing works (example):

• key1 and key2 both hash to index 3.
• key1 goes into index 3.
• key2 finds index 3 full, tries index 4, then index 5, and so on until it finds an empty space.
• Lookup follows the same path, checking each location in the probe sequence.

These methods form the foundation of hashing techniques in data structure. 

Implementing a Custom Hash Table

A custom hash table gives you full control over how data gets stored and how collisions are resolved. You can pick a hash function, decide on the table size, and choose whether to use chaining or open addressing. 

Before you dive into the intricate details, here’s a quick flowchart on implementing custom hash tables:

Below is a step-by-step approach, followed by an example in pseudo-code.

Step 1. Choose a Hash Function

Select a function that spreads your keys. A simple choice is the division method:

index = key % tableSize

If you need a better distribution, you can choose more advanced approaches (mid-square, multiplication). Consider data patterns before deciding.

Step 2. Decide on Table Size

A table that is too small leads to excessive collisions.  Depending on the function, a prime number or power of two is common. Ensure you keep an eye on the load factor — if too many items fill up your table, your performance may decline.

Step 3. Select a Collision Resolution Method

• Separate Chaining: Each slot holds a linked list for any keys that share the same index.
• Open Addressing: You store each item directly in the array. If collisions occur, you probe for the next available slot. Methods include linear probing, quadratic probing, and double hashing.

Step 4. Implement Basic Operations

1. Insert:
   • Compute the index using the hash function.
   • If the slot is empty, place the item there.
   • If it’s occupied, use your chosen collision strategy (e.g., add to the linked list for chaining, or probe until you find an open slot).
2. Search:
   • Compute the index from the key.
   • Check that slot (or linked list/probed slots) until you find the key or confirm it doesn’t exist.
3. Delete:
   • Compute the index.
   • Remove the key if present.
   • In open addressing, you might mark the slot as a “deleted” placeholder to keep the search chain intact.

Step 5. Manage the Load Factor and Rehashing

• The load factor is (number of stored items) / (tableSize).
• If it exceeds a certain threshold (commonly 0.7 or 0.75), consider doubling the table size and rehash all keys into the new slots. This prevents a buildup of collisions over time.

Example: Custom Hash Table (Using Chaining)

Below is a short pseudo-code showing how you might implement insertion and search using separate chaining.

Initialize array hashTable of size N
For i in [0..N-1]:
    hashTable[i] = EmptyLinkedList()

function hashFunction(key):
    return key % N

function insert(key, value):
    index = hashFunction(key)
    // Insert at the head or tail of the linked list
    hashTable[index].addNode(key, value)

function search(key):
    index = hashFunction(key)
    // Traverse linked list at hashTable[index]
    for node in hashTable[index]:
        if node.key == key:
            return node.value
    return null // Not found

How Does It Work?

1. Initialization: You create an array of linked lists, each corresponding to a slot.
2. hashFunction: A simple modulo operation.
3. Insert: You compute the index, then add the new entry to the corresponding list. If more than one key maps to the same index, those keys line up in that list.
4. Search: You compute the index, then walk through the list until you find the matching key or reach the end.

What About Collision Handling?
Here, collisions occur when more than one key lands on the same index. The linked list approach stores them all in that slot. This keeps the insert logic simple, although searches can take longer if the list grows too big. Balancing the table size and keeping an eye on the load factor help maintain a fast average case.

Advantages and Disadvantages of Hashing in Data Structures

Below is a clear comparison of the advantages and disadvantages of Hashing:

Advantages of Hashing

• Constant average-time access for insertion, deletion, and search
• Efficient memory usage with fixed-size tables
• Simple to implement, even for beginners
• Works with many key types, including numbers, strings, and objects
• Stable performance with proper load-factor management
• Supported by built-in structures like Python dict or Java HashMap

Disadvantages of Hashing

• Collisions still occur, adding overhead
• No natural ordering for range queries or sorted results
• Rehashing can be expensive during resizing
• Poor hash functions reduce performance and cluster keys
• Choosing an initial table size can be difficult
• Some methods are vulnerable to predictable patterns

Comparison Table

Hashing in data structure remains one of the most effective ways to manage large collections when your main focus is speed and direct access. Despite its limitations, it stays a core technique in DSA because of its balance between simplicity and performance.

Real-World Applications of Hashing

Hashing in DSA underpins everything from password validation in online services to database indexing at large scale. By converting keys into fixed-size outputs, hashing allows systems to locate, verify, or compare data in constant average time, even when volumes soar.

Below is a quick overview of how various industries and technologies rely on hashing, along with an example of hashing to make the idea clearer:

1. Fast Data Retrieval in Databases
Problem: Large databases take time to search.
How Hashing Helps: Hash-based indexing converts search keys into hash values that point directly to the right record.
Why It Matters: Databases return results almost instantly, which is crucial for e-commerce, finance, and analytics.

2. Secure Password Storage
Problem: Storing plain passwords puts users at risk.
How Hashing Helps: Passwords are converted into irreversible hash strings. Adding salt makes each hash unique.
Why It Matters: Even if attackers access the database, they can’t easily recover real passwords.

3. Message and File Integrity
Problem: Files can be corrupted or tampered with during transfer.
How Hashing Helps: Functions like MD5 and SHA-256 create a digest of the data. Any change produces a completely different hash.
Why It Matters: Used in secure messaging, software updates, and blockchain verification.

4. Optimized Caching Systems
Problem: Repeated data requests slow down apps and servers.
How Hashing Helps: Caches store frequently accessed data using hash-based lookups.
Why It Matters: Reduces latency and improves performance for busy platforms like social media and news sites.

5. Blockchain Integrity
Problem: Decentralized systems must ensure that stored data cannot be altered.
How Hashing Helps: Every block contains the hash of the previous one. Any change breaks the chain.
Why It Matters: Hashing keeps blockchain tamper-proof and trustworthy.

6. Symbol Tables in Compilers
Problem: Compilers need quick access to variable and function names.
How Hashing Helps: Symbol tables use hash tables to store and retrieve identifiers.
Why It Matters: Speeds up compilation and reduces memory usage.

7. File Verification and Download Safety
Problem: Downloads may be corrupted or manipulated.
How Hashing Helps: Comparing the file’s hash with the original confirms integrity.
Why It Matters: Common in software distribution and open-source downloads.

8. Load Balancing in Distributed Systems
Problem: Servers must share traffic evenly.
How Hashing Helps: Consistent hashing spreads requests across servers and adapts smoothly when nodes change.
Why It Matters: Supports high availability in cloud systems and microservices.

Hashing vs Encryption and Performance Factors

Hashing and encryption both transform data, but they solve different problems. Hashing in DSA creates a fixed output used for integrity checks or fast lookups. Encryption protects data by converting it into a form that can be reversed only with the right key. Understanding this difference is important when designing secure systems or choosing the right method for storage.

Hashing vs Encryption

Load Factor and Rehashing

A hash table performs well when collisions stay low. The load factor helps track this. It measures how full the table is.
If it rises too high, collisions increase and lookups slow down.

• Formula: items stored ÷ table size
• Typical threshold: around 0.7
• Purpose: shows when performance is degrading

When the load factor crosses the limit, the table needs rehashing.
Rehashing creates a larger table and recomputes positions for every key so they spread out again.

• Trigger: high load factor
• Process: build a bigger table, reassign all keys
• Result: fewer collisions and faster operations

Both concepts work together. The load factor warns you of rising collisions, and rehashing restores speed by redistributing data across more space.

How Can upGrad Help You?

It's clear that hashing in data structure is a cornerstone of efficient data retrieval and a fundamental concept for any serious programmer. By converting a key into an index, it allows for near-instantaneous access to data, making it the powerhouse behind dictionaries, hash maps, and database indexing.

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• Post Graduate Certificate in Data Science & AI (Executive)
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