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Hashing Technique.md

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A hash table is a data structure that stores key-value pairs and uses hashing to efficiently map and retrieve values based on keys. It’s widely used for tasks where fast data lookup, insertion, and deletion are essential, such as in databases, caches, and associative arrays.

Key Concepts

  1. Hashing:

    • Hashing is the process of converting a key (like a string or a number) into an index, or "hash code," that represents the position where the value associated with that key should be stored in the hash table.
    • A hash function is used to perform this transformation. It takes a key as input and outputs a fixed-size integer, typically the index within the table.

  2. Buckets:

    • The hash code generated by the hash function determines the "bucket" (an index in the array) where the key-value pair is stored. The hash table typically consists of an array where each position (bucket) can hold data.
    • When retrieving data, the hash code allows the hash table to go directly to the appropriate bucket, making lookups very fast (average time complexity of O(1)).

  3. Collision Handling:

    • Collisions occur when two different keys produce the same hash code and therefore map to the same bucket. A well-designed hash function minimizes collisions, but they can’t be entirely avoided.
    • Common methods to handle collisions:
      • Separate Chaining: Each bucket holds a linked list (or another data structure), where multiple entries can be stored. If there’s a collision, the new entry is added to this list.
      • Open Addressing: If a collision occurs, the hash table looks for the next available bucket (using probing techniques like linear or quadratic probing) and places the item there.
      • Tree-based Buckets: In some implementations (e.g., Java’s HashMap after a certain threshold), the linked list in a bucket converts to a balanced tree, improving lookup times in cases of high collision.

  4. Load Factor and Resizing:

    • The load factor is the ratio of the number of elements to the total capacity of the hash table. A high load factor means more entries are stored, increasing the chance of collisions.
    • When the load factor exceeds a certain threshold (often 0.75), the hash table will resize by creating a new, larger array and rehashing all existing elements into it. This maintains efficient lookup times by reducing the load on each bucket.

Advantages of Hash Tables

  • Fast Lookups and Inserts: On average, hash tables provide O(1) time complexity for insertions, deletions, and lookups, making them one of the most efficient data structures for these operations.
  • Efficient Memory Usage: Hash tables dynamically resize, balancing memory usage and performance.

Limitations of Hash Tables

  • Not Ordered: Entries in a hash table aren’t stored in any specific order, making it less suitable for ordered data requirements.
  • Collision Management: While collision handling methods help, excessive collisions can degrade performance.
  • Space Complexity: Hash tables might use more memory than simpler data structures, especially if they grow in size frequently.

Summary

A hash table is a powerful data structure for storing and retrieving data quickly by leveraging hash codes to locate entries directly. It’s widely used in areas where fast data access is crucial, balancing performance with dynamic memory management.