Applications, Advantages and Disadvantages of Hash Data Structure

Introduction :

Imagine a giant library where every book is stored in a specific shelf, but instead of searching through endless rows of shelves, you have a magical map that tells you exactly which shelf your book is on. That’s exactly what a Hash data structure does for your data!

Hash data structures are a fundamental building block of computer science and are used in a wide range of applications such as databases, caches, and programming languages. They are a way to map data of any type, called keys, to a specific location in memory called a bucket. These data structures are incredibly fast and efficient, making them a great choice for large and complex data sets.

Whether you’re building a database, a cache, or a programming language, Hash data structures are like a superpower for your data. They allow you to perform basic operations like insertions, deletions, and lookups in the blink of an eye, and they’re the reason why your favorite apps and websites run so smoothly.

A hash data structure is a type of data structure that allows for efficient insertion, deletion, and retrieval of elements. It is often used to implement associative arrays or mappings, which are data structures that allow you to store a collection of key-value pairs.

In a hash data structure, elements are stored in an array, and each element is associated with a unique key. To store an element in a hash, a hash function is applied to the key to generate an index into the array where the element will be stored. The hash function should be designed such that it distributes the elements evenly across the array, minimizing collisions where multiple elements are assigned to the same index.

When retrieving an element from a hash, the hash function is again applied to the key to find the index where the element is stored. If there are no collisions, the element can be retrieved in constant time, O(1). However, if there are collisions, multiple elements may be assigned to the same index, and a search must be performed to find the correct element.

To handle collisions, there are several strategies that can be used, such as chaining, where each index in the array stores a linked list of elements that have collided, or open addressing, where collisions are resolved by searching for the next available index in the array.

Hash data structures have many applications in computer science, including implementing symbol tables, caches, and databases. They are especially useful in situations where fast retrieval of elements is important, and where the number of elements to be stored may be large.

Collision Resolution: Collision resolution in hash can be done by two methods: 

• Open addressing and 
• Closed addressing. 

Open Addressing: Open addressing collision resolution technique involves generating a location for storing or searching the data called probe. It can be done in the following ways:

• Linear Probing: If there is a collision at i then we use the hash function – H(k, i ) = [H'(k) + i ] % m
  where, i is the index, m is the size of hash table H( k, i ) and H'( k ) are hash functions.
• Quadratic Probing: If there is a collision at i then we use the hash function –  H(k, i ) = [H'(k) + c₁ * i + c₂ * i² ] % m
  where, i is the index, m is the size of hash table H(k, i ) and H'( k ) are hash functions, c₁ and c₂ are constants.
• Double Hashing: If there is a collision at i then we use the hash function – H(k, i ) = [H₁(k, i) + i * H₂(k) ] % m
  where, i is the index, m is the size of hash table H(k, i ), H₁( k) = k % m and H₂(k) = k % m’ are hash functions.

Closed Addressing:

Closed addressing collision resolution technique involves chaining. Chaining in the hashing involves both array and linked list. In this method, we generate a probe with the help of the hash function and link the keys to the respective index one after the other in the same index. Hence, resolving the collision.

Applications of Hash:

• Hash is used in databases for indexing.
• Hash is used in disk based data structures.
• In some programming languages like Python, JavaScript hash is used to implement objects. 
• Hash tables are commonly used to implement caching systems
• Used in various cryptographic algorithms.
• Hash tables are used to implement various data structures.
• Hash tables are used in load balancing algorithms 
• Databases: Hashes are commonly used in databases to store and retrieve records quickly. For example, a database might use a hash to index records by a unique identifier such as a social security number or customer ID.
• Caches: Hashes are used in caches to quickly look up frequently accessed data. A cache might use a hash to store recently accessed data, with the keys being the data itself and the values being the time it was accessed or other metadata.
• Symbol tables: Hashes are used in symbol tables to store key-value pairs representing identifiers and their corresponding attributes. For example, a compiler might use a hash to store the names of variables and their types.
• Cryptography: Hashes are used in cryptography to create digital signatures, verify the integrity of data, and store passwords securely. Hash functions are designed such that it is difficult to reconstruct the original data from the hash, making them useful for verifying the authenticity of data.
• Distributed systems: Hashes are used in distributed systems to assign work to different nodes or servers. For example, a load balancer might use a hash to distribute incoming requests to different servers based on the request URL or other criteria.
• File systems: Hashes are used in file systems to quickly locate files or data blocks. For example, a file system might use a hash to store the locations of files on a disk, with the keys being the file names and the values being the disk locations.

Real-Time Applications of Hash:

• Hash is used for cache mapping for fast access of the data.
• Hash can be used for password verification.
• Hash is used in cryptography as a message digest.

Applications of Hash::

• Hash provides better synchronization than other data structures.
• Hash tables are more efficient than search trees or other data structures.
• Hash provides constant time for searching, insertion and deletion operations on average.
• Hash tables are space-efficient.
• Most Hash table implementation can automatically resize itself.
•  Hash tables are easy to use.
• Hash tables offer a high-speed data retrieval and manipulation.
• Fast lookup: Hashes provide fast lookup times for elements, often in constant time O(1), because they use a hash function to map keys to array indices. This makes them ideal for applications that require quick access to data.
• Efficient insertion and deletion: Hashes are efficient at inserting and deleting elements because they only need to update one array index for each operation. In contrast, linked lists or arrays require shifting elements around when inserting or deleting elements.
• Space efficiency: Hashes use space efficiently because they only store the key-value pairs and the array to hold them. This can be more efficient than other data structures such as trees, which require additional memory to store pointers.
• Flexibility: Hashes can be used to store any type of data, including strings, numbers, and objects. They can also be used for a wide variety of applications, from simple lookups to complex data structures such as databases and caches.
• Collision resolution: Hashes have built-in collision resolution mechanisms to handle cases where two or more keys map to the same array index. This ensures that all elements are stored and retrieved correctly.

Disadvantages of Hash:

• Hash is inefficient when there are many collisions.
• Hash collisions are practically not be avoided for large set of possible keys.
• Hash does not allow null values. 
• Hash tables have a limited capacity and will eventually fill up.
• Hash tables can be complex to implement.
• Hash tables do not maintain the order of elements, which makes it difficult to retrieve elements in a specific order.