Introduction to Hashing in Data Structures

Efficiency is the ultimate requirement in today’s tech-savvy era, and hashing is a technique that ensures faster access to elements in a database. A hash function is an algorithm that takes a large set of keys or character strings and produces another smaller string of characters. Hash functions are often used in data management, cryptography, and as security measures for storing data. Hashing is observed to be efficient, especially in data structures, as it helps provide and identify particular defined values and ensure their integrity.

Hashing allows a user to transform large, complex data strings into smaller outputs that can be easily identified in data sets. It is a secure way to map a key to a specific index within a hash table. Let’s see how it works in real life and more.

Understanding hashing with real-life examples

The following examples will help you understand the concept and significance of hashing. While not all of these specifically use hashing within computers or data structures, they should provide an example or analogy of how hashing is used: 

In laboratories

Diagnostic facilities and medical laboratories may create distinctive identifiers for patient samples and information. This helps them build a proper record, secure information, and swiftly retrieve data as needed.  

In libraries

A library is a hub of a sizeable collection of books. Therefore, the librarian assigns a unique number to every book in the library. This unique number facilitates quicker access to readers on the bookshelf. 

In schools

The teacher assigns each student a unique roll number to identify them in school quickly. Similarly, the board grants every pupil a unique identification number during the board exam. You use the same number to access your examination results easily. 

What is hashing in data structures?

Hash is a method of breaking your original data into smaller chunks that can be inserted into a database. This allows for better compression, storing data in fewer files and requiring less memory. Hash keys can be calculated from larger amounts of data using a hash function that outputs the original data into smaller pieces to create an index file or table.

Hashing is a key data structure that can assist in indexing and identifying a specific item when data mapping. The hash codes create an index to hold value. This indexing helps speed up the retrieval of data from a database.

Furthermore, the data can be easily stored in hash tables in various formats. The hash tables are specifically built to categorise multiple index numbers. Each value has a unique index number assigned to it in these tables. This makes it easier to locate the items stored under this format using the shorter hashed key than the original value.

Why use hashing in data structures? 

Hashing is a method for reducing the search time of data and keeping it more secure. It helps us find data much faster than our original data structures. 

For example, say a bank holds a client’s name (value 1) and bank account number (value 2). Value 1 and value 2 (the hash key) are put through a hash function to create a string of characters (the hash code) and indexed in a table. To find the client’s name and corresponding bank number, the bank only has to search for the index instead of the original key. This helps keep the client’s information secure and easily accessible.

Hashing components

When using the hash technique, you will come across the following main components: 

Hash table

Hash tables are the locations where a collection of data is stored so that it is easy to find the data when required. This typically makes the searching process of an element significantly efficient.

Hash function 

A hash function is an algorithm that maps a given key into a unique slot index. There are many types of hash functions, depending on the purpose and type of data you are using. 

A good hash function must map all the possible unique slot indexes in order to be considered a good function. It may be used to speed up programs by making the algorithm more efficient or in data compression by reducing the amount of information needed to represent data.

The function is perfect if the hash function can map the key slot index, be easy to compute, have uniform distribution across the hash table, and have minimal collisions.

Handing of collision

The term collision refers to entering a new key that maps to an already present slot in the hash table. Collision-handling techniques help tackle such problems. 

There are two efficient ways to handle collisions that are as follows: 

• Chaining: Chaining is a popular way to handle collisions. The chaining includes the linking of records and the creation of cells that link the various lists of records that use the same hash function value.

• Open addressing: With open addressing, elements are contained in the table, and an empty spot is found for one of the duplicate hash values. So, when you have to hunt for an element, you must examine every table slot until an empty one is found in which to enter the key. 

How does hashing in data structures work?

As stated earlier, hashing assigns every element a unique key. The hash table employs this unique key to find the data in the list. Hash tables are a general-purpose data structure for storing key-value pairs. They use a hashing function to generate an index number for every value in the table.

The following example of hashing in an array will help you comprehend how hashing works:

To make a hash function H(x), let the index be x%10 in an array. If theist of key (the input) is [21,22,23,24 25] and return {1,2 3 4 5}.

Now, let’s consider mapping a list of string keys to a list of string values. For instance, the mapping process uses the capital cities of various countries to save the different information in a table. Thus, we can easily give accurate information on all maps made with our software.

Suppose the hash function is to determine the length of the string minus two. India's hash code is 3 (5-2), so it occupies the third position in the keys array. Delhi takes on the third index.

We will have the following hash table: 

Learn more about hashing with Coursera

Hashing in data structures is a popular technique for storing and recovering data quickly. Its popularity is mainly because it grants optimal results and performs precise searches. 

To learn more about hashing, explore the University of California's Cryptography and Hashing Overview on Coursera. You can also expand your knowledge of data structures with the Data Structures and Algorithms Specialisation, which will help you master algorithmic programming techniques.