Hashing and Indices, Differences, which is better.

 

Hashing and Indices

 

Differences between Hashing and Indices:

 

1.  Definition:

   -  Hashing:  Hashing is a technique that uses a hash function to map data into a fixed-size hash code,           which is then used as an address to store or retrieve the data.

   -  Indices:  Indices are data structures that provide a fast and efficient way to locate specific rows or           records in a database table, often through the use of sorted lists or tree structures.

 

2.  Data Structure:

   -  Hashing:  Utilizes hash tables or similar structures where data is stored at locations determined by           the hash code.

   -  Indices:  Can be implemented using various data structures such as B-trees, binary trees, or simple           sorted lists.

 

3.  Uniqueness:

   -  Hashing:  The hash code generated by the hash function is expected to be unique for different                 inputs, but collisions (different inputs producing the same hash code) can occur.

   -  Indices:  Entries in an index are generally unique, ensuring a one-to-one mapping with the actual            data.

 

4.  Search Mechanism:

   -  Hashing:  Involves a direct lookup based on the hash code, providing constant time complexity for         searches in an ideal scenario.

   -  Indices:  Typically involve a more structured search, such as binary search in a sorted index or tree         traversal in a B-tree index.

 

5.  Handling Collisions:

   -  Hashing:  Collisions may occur, and strategies like chaining or open addressing are employed to             manage situations where multiple data elements hash to the same location.

   -  Indices:  Collisions are less common, as indices are often designed to ensure unique entries. In the           case of primary keys, for example, duplicates are not allowed.

 

 Why Hashing Might Be Preferred Over Indices:

 

1.  Faster Retrieval in Certain Cases:

   - Hashing can provide faster data retrieval in scenarios where direct access to data based on a                    calculated hash code is feasible. This is especially true for lookups on unique keys.

 

2.  Constant Time Complexity:

   - In an ideal situation without collisions, hashing offers constant time complexity for search                       operations, making it efficient for large datasets.

 

3.  Simplicity:

   - Hashing can be simpler to implement and manage in certain cases, especially for scenarios where            direct address calculation is straightforward.

 

4.  Better for Equality Searches:

   - Hashing may be more efficient for equality searches (finding an exact match) compared to certain types of indices, especially when dealing with large datasets.

 

5.  Space Efficiency:

   - Hashing can be more space-efficient, as it typically requires fewer storage structures than complex indices like B-trees.