What is Hashing: Types and Advantages

Hashing is a significant data structure that solves the problem of efficiently identifying and storing data in the array. In data structure, hashing helps in narrowing down search and find number within seconds.

Hashing is the process of converting input data of any size into a fixed-size value, usually for the purpose of fast data retrieval. Hash functions are used to map the input data (often called a “key”) to a fixed-size string of bytes.

Table of Contents

• What is Hashing?
• How does hashing work?
• Operations in hashing
• Types
• Components of hashing
• Advantages of Hashing in Data Structures
• Disadvantages of Hashing
• Conclusion

What is Hashing?

Hashing is a process of converting a given key into another value. It is one of the most common methods for implementing hash tables that store key/value pairs in the form of a list where elements can be accessed using an index. It is a method to represent dictionaries for large datasets that allow lookups, retrieval, and updating operations to occur in constant time. Hashing quickly identifies a specific value within a given array. Then, it creates a unique hash code for every element in the array and stores the hash code in place of the actual element. 

How does hashing work?

In a balanced binary search tree, when we try to insert, delete or search any element. In such a case, the time complexity for the same is 0 (logn). Hashing is used when applications want to perform the same operation in an optimized way. In hashing, every operation can be performed in 0(1) in constant time. In hashing, one value changes into another based on a specified key or string of characters. An original string is represented with a smaller, fixed-length value, making it simpler to use or locate. 

Operations in Hashing

Some of the operations in hashing include:

• Delete: This hashing operation is used for deleting a particular key-value pair from the hash table.
• HashTable: Through this hashing operation, a new hash table is created.
• Get: This table is used for searching for a key inside a hash table and returning the value associated with the key.
• Put: This hashing operation is used for inserting a new key-value pair inside of the hash table.
• DeleteHashTable: This table is used for deleting the hash table.

Types of Hashing Algorithm

Different  types of hashing algorithms used include the following:

1. CR32: This represents the cyclic redundancy check (CRC) which is an error-detection code that detects any accidental changes to the data. When the same data string is encoded using CR32, it will result in the same hash output. It is also used as a hash algorithm for the purpose of file integrity checks. Except for Zip files and FTP servers, CR32 is rarely used. 
2. MD5: This hash function encodes string of information and encodes it into 128-bit fingerprint. It is used as a checksum for verifying data integrity. MD5 is the world’s most used algorithm that tends to suffer from extensive hash collision
3. SHA2 (Secure Hash Algorithm 2): It is a cryptographic hash function that includes significant changes from its predecessor. It uses different shift amounts and additive constants where their structures are virtually identical with only differences in the number of rounds. 

Components of hashing

The following are the components of Hashing:

1. Hash Function

It is a function that converts a given big phone number to a smaller practical integer value. The mapped integer value is used as index in the hash table. Hash function transforms a given key into a unique slot index. When every key is mapped into a unique slot index, hash function is known as a perfect hash function. It is important to create a hash function using which the number of collections are limited to a minimum.

2. Hash Table

Also known as a Hash map, it is a data structure that implants an associative array or dictionary. A hash table is an abstract data type that maps the keys to values. Hash function is used by the hash table for computing an index called hash code into the array of buckets from which desired value can be determined.

3. Collision Handling

The situation in which a newly inserted key maps to already occupied slots in the hash table is known as collision. It must be handled using the collision handling technique, including open addressing and chaining. 

In chaining, each hash table cell points to a linked list of records with the same function value. It required an additional memory outside the table. Regarding open addressing, every element is stored in the hash table itself. Every table entry consists of either an NIL or a record. While searching for an element, table slots are examined individually until the required element is identified or not found.

Advantages of Hashing in Data Structures

The following are the advantages of Hashing:

1. It is used for comparing two files for equality. Calculated hash values of these files help owners compare documents word-to-word without opening. The file owners can immediately know that the files are different. 
2. Hashing verifies the file’s integrity once it is transferred from one place to another in a file backup program. Users can compare the hash value of both files to ensure that transferred files are not corrupted.
3. Hash values can identify differences in files even when an encrypted file is designed in a manner to disallow change in file size.

Disadvantages of Hashing in Data Structures

The following points highlight the disadvantages of hashing in data structures:

1. Multiple keys can hash to the same index, leading to collisions. This requires additional mechanisms like chaining or open addressing to handle these conflicts, which can impact performance.
2. Hash tables are not ideal for operations like finding the smallest or largest key, or iterating through elements in sorted order. Other data structures like trees or heaps might be more suitable for these tasks.
3. If a weak or poorly designed hash function is used, it can be susceptible to attacks like collisions, rainbow table attacks, and preimage attacks, compromising data integrity and security.
4. As the number of elements in a hash table grows, it might become necessary to resize the underlying data structure. This process can be computationally expensive and disrupt performance.
5. The order of elements in a hash table is not predictable, as it depends on the hash function and potential collisions. This can be a disadvantage if maintaining a specific order is crucial.

Conclusion

Thus, Hashing is essential for efficient data management and security. It utilizes algorithms to convert diverse data into a fixed-size string of characters, facilitating swift data retrieval and ensuring data integrity.