Hashing in Data Structure is a fundamental concept that serves as the backbone for efficient data retrieval and storage mechanisms. Essentially, it involves converting a large or complex key into a smaller, fixed-size value, which is then used as an index to store the actual data in a hash table. This process significantly reduces the time it takes to find an item, as it allows direct access to the data instead of going through each element sequentially. My experience with hashing has been nothing short of transformative, enabling me to handle large datasets with ease and improve the performance of applications dramatically. 

From my early days in the field to my current projects, I’ve seen firsthand how hashing can streamline data processing and storage. The beauty of hashing lies in its simplicity and efficiency, making it a vital tool for anyone working with data structures. In this article, I’ll delve into more in-depth Hashing in Data Structure, exploring its functions and techniques and providing examples. Whether you’re just starting out or looking to deepen your understanding of data structures, I’m excited to share my insights and experiences to help you grasp the power of hashing. 

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This blog will give you a deeper understanding of the hash method, hash tables, and linear probing with examples.

What is Hashing in Data Structure? 

Hashing in the data structure is a technique of mapping a large chunk of data into small tables using a hashing function. It is also known as the message digest function. It is a technique that uniquely identifies a specific item from a collection of similar items.

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It uses hash tables to store the data in an array format. Each value in the array has been assigned a unique index number. Hash tables use a technique to generate these unique index numbers for each value stored in an array format. This technique is called the hash technique.

You only need to find the index of the desired item, rather than finding the data. With indexing, you can quickly scan the entire list and retrieve the item you wish. Indexing also helps in inserting operations when you need to insert data at a specific location. No matter how big or small the table is, you can update and retrieve data within seconds.

The hash table is basically the array of elements and the hash techniques of search are performed on a part of the item i.e. key. Each key has been mapped to a number, the range remains from 0 to table size 1

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Types of hashing in data structure is a two-step process.

1. The hash function converts the item into a small integer or hash value. This integer is used as an index to store the original data.
2. It stores the data in a hash table. You can use a hash key to locate data quickly.

Examples of Hashing in Data Structure

The following are real-life examples of hashing in the data structure:

• In schools, the teacher assigns a unique roll number to each student. Later, the teacher uses that roll number to retrieve information about that student.
• A library has an infinite number of books. The librarian assigns a unique number to each book. This unique number helps in identifying the position of the books on the bookshelf.

Checkout: Sorting in Data Structure

Hash Function

The hash function in a data structure maps the arbitrary size of data to fixed-sized data. It returns the following values:  a small integer value (also known as hash value), hash codes, and hash sums. The hashing techniques in the data structure are very interesting, such as: 

hash = hashfunc(key)

index = hash % array_size

The hash function must satisfy the following requirements:

• A good hash function is easy to compute.
• A good hash function never gets stuck in clustering and distributes keys evenly across the hash table.
• A good hash function avoids collision when two elements or items get assigned to the same hash value.

One of the hashing techniques of using a hash function is used for data integrity. If using a hash function one change in a message will create a different hash. 

The three characteristics of the hash function in the data structure are:

1. Collision free
2. Property to be hidden
3. Puzzle friendly

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Hash Table

Hashing in data structure uses hash tables to store the key-value pairs. The hash table then uses the hash function to generate an index. Hashing uses this unique index to perform insert, update, and search operations.

It can be defined as a bucket where the data are stored in an array format. These data have their own index value. If the index values are known then the process of accessing the data is quicker.

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How does Hashing in Data Structure Works?

In hashing, the hashing function maps strings or numbers to a small integer value. Hash tables retrieve the item from the list using a hashing function. The objective of hashing technique is to distribute the data evenly across an array. Hashing assigns all the elements a unique key. The hash table uses this key to access the data in the list.

Hash table stores the data in a key-value pair. The key acts as an input to the hashing function. Hashing function then generates a unique index number for each value stored. The index number keeps the value that corresponds to that key. The hash function returns a small integer value as an output. The output of the hashing function is called the hash value.

Let us understand hashing in a data structure with an example. Imagine you need to store some items (arranged in a key-value pair) inside a hash table with 30 cells.

The values are: (3,21) (1,72) (40,36) (5,30) (11,44) (15,33) (18,12) (16,80) (38,99)

The hash table will look like the following:

The process of taking any size of data and then converting that into smaller data value which can be named as hash value. This hash alue can be used in an index accessible in hash table. This process define hashing in data structure.

Also Read: Types of Data Structures in Python

Collision Resolution Techniques

Hashing in data structure falls into a collision if two keys are assigned the same index number in the hash table. The collision creates a problem because each index in a hash table is supposed to store only one value. Hashing in data structure uses several collision resolution techniques to manage the performance of a hash table.

It is a process of finding an alternate location. The collision resolution techniques can be named as-

1. Open Hashing (Separate Chaining)
2. Closed Hashing (Open Addressing)

• Linear Probing
• Quadratic Probing
• Double Hashing

Linear Probing

Hashing in data structure results in an array index that is already occupied to store a value. In such a case, hashing performs a search operation and probes linearly for the next empty cell.

Linear probing in hash techniques is known to be the easiest way to resolve any collisions in hash tables. A sequential search can be performed to find any collision that occurred.

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Linear Probing Example

Imagine you have been asked to store some items inside a hash table of size 30. The items are already sorted in a key-value pair format. The values given are: (3,21) (1,72) (63,36) (5,30) (11,44) (15,33) (18,12) (16,80) (46,99).

The hash(n) is the index computed using a hash function and T is the table size. If slot index = ( hash(n) % T) is full, then we look for the next slot index by adding 1 ((hash(n) + 1) % T). If (hash(n) + 1) % T is also full, then we try (hash(n) + 2) % T. If (hash(n) + 2) % T is also full, then we try (hash(n) + 3) % T.

The hash table will look like the following:

Double Hashing

The double hashing technique uses two hash functions. The second hash function comes into use when the first function causes a collision. It provides an offset index to store the value.

The formula for the double hashing technique is as follows:

(firstHash(key) + i * secondHash(key)) % sizeOfTable

Where i is the offset value. This offset value keeps incremented until it finds an empty slot.

For example, you have two hash functions: h1 and h2. You must perform the following steps to find an empty slot:

1. Verify if hash1(key) is empty. If yes, then store the value on this slot.
2. If hash1(key) is not empty, then find another slot using hash2(key).
3. Verify if hash1(key) + hash2(key) is empty. If yes, then store the value on this slot.
4. Keep incrementing the counter and repeat with hash1(key)+2hash2(key), hash1(key)+3hash2(key), and so on, until it finds an empty slot.

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Double Hashing Example

Imagine you need to store some items inside a hash table of size 20. The values given are: (16, 8, 63, 9, 27, 37, 48, 5, 69, 34, 1).

h1(n)=n%20

h2(n)=n%13

n h(n, i) = (h1 (n) + ih2(n)) mod 20

n	h(n,i) = (h’(n) + i2) %20
16	I = 0, h(n,0) = 16
8	I = 0, h(n,0) = 8
63	I = 0, h(n,0) = 3
9	I = 0, h(n,0) = 9
27	I = 0, h(n,0) = 7
37	I = 0, h(n,0) = 17
48	I = 0, h(n,0) = 8 I = 0, h(n,1) = 9 I = 0, h(n,2) = 12
5	I = 0, h(n,0) = 5
69	I = 0, h(n,0) = 9 I = 0, h(n,1) = 10
34	I = 0, h(n,0) = 14
1	I = 0, h(n,0) = 1

Conclusion

Hashing in Data Structure is a pivotal concept that underscores the efficiency and effectiveness of data retrieval and storage in computer science. Through the exploration of hash functions, hash tables, and the operational mechanics of hashing, we’ve uncovered how this technique streamlines processes and ensures rapid access to data. Moreover, the discussion on collision resolution techniques, such as linear probing and double hashing, illuminates the adaptability and resilience of hashing in managing data conflicts. For mid-career professionals, mastering these aspects of hashing is not merely an academic exercise but a practical necessity. The ability to implement and optimize hashing algorithms can significantly impact the performance of software applications. As we continue to navigate the complexities of data structures, let the principles and examples outlined herein serve as a robust foundation for enhancing your computational problem-solving skills. Remember, the power of Hashing in Data Structure lies in its ability to transform abstract data into a structured, accessible format, paving the way for innovative solutions in the digital age.

You can try out the example to strengthen your data structure knowledge. If you are curious to know more about data structure, check out the upGrad Executive PG Programme in Full Stack Development course. This course is designed for working professionals and offers rigorous training and job placement with top companies.