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6. Hashing and Hash Tables

Hashing and Hash Tables.

Hashing and Hash Tables​

Hashing is a critical concept in computer science that enables fast data storage and retrieval, which is widely used in applications like databases, caching, and password management. Hashing helps map data (keys) to specific locations (hash values) in a table, called a hash table. Let’s explore the fundamental aspects of hashing and hash tables in this guide.

What is Hashing?​

Hashing is a process of converting a given key (typically a string) into a fixed-size integer value, known as the hash value or hash code, using a mathematical function known as a hash function. These hash values represent the index where the data will be stored in a hash table.

Key Characteristics of Hash Functions​

  1. Deterministic: A hash function always produces the same hash value for the same input.
  2. Non-invertible: It is computationally hard to revert the hash value back to the original input.
  3. Fixed-size output: Hash functions generate fixed-size hash values irrespective of the input size.
  4. Efficient: Hash functions should be quick to compute.

Common Hash Functions​

  1. Simple Linear Congruential Generator (LCG): A basic type of hash function often used for simple random number generation.

  2. Cryptographic Hash Functions:

    • SHA-256: A cryptographic hash function commonly used in security-related applications like password hashing and digital signatures.
    • MD5: Now considered insecure, MD5 was once widely used for data integrity verification.

  3. Division Method: This method involves dividing the key by the size of the hash table and using the remainder as the index.

Hash Collisions​

Hash collision occurs when two different keys produce the same hash value. To handle collisions, several techniques are employed:

  • Chaining: In this method, each cell of the hash table points to a linked list containing all the values that hashed to the same index.
  • Open Addressing: When a collision happens, this method searches for the next available slot based on a probing sequence.

What are Hash Tables?​

A hash table is a data structure that stores key-value pairs. It allows for efficient data retrieval in constant time, O(1), in the best-case scenario. Hash tables are used for a variety of purposes like implementing associative arrays, databases, and caches.

Basic Structure​

A hash table consists of:

  1. Array: The hash table itself is an array where the data is stored.
  2. Hash Function: Converts the key into an index.
  3. Collision Resolution Mechanism: Handles cases where two keys produce the same hash.

Example: Hash Table Implementation in Python​

class HashTable:
    def __init__(self):
        self.table = [None] * 10  # Creates an array of size 10
    
    def _hash_function(self, key):
        return hash(key) % len(self.table)  # Simple hash function using modulus

    def insert(self, key, value):
        index = self._hash_function(key)
        self.table[index] = value
    
    def get(self, key):
        index = self._hash_function(key)
        return self.table[index]

# Example usage
ht = HashTable()
ht.insert("apple", 10)
print(ht.get("apple"))  # Output: 10

Applications of Hash Tables​

  1. Data Storage: Used in databases and file systems for efficient key-value storage and retrieval.
  2. Caches: Used in caching mechanisms to store frequently accessed data.
  3. Symbol Tables in Compilers: Maps variables and functions to memory locations.
  4. Password Storage: Hash tables are used to securely store passwords using cryptographic hash functions.

Best Practices and Performance Considerations​

  1. Choosing a Good Hash Function: A good hash function minimizes collisions and distributes keys uniformly across the table.
  2. Load Factor: The load factor (the ratio of entries to table size) should be kept under control. When the load factor grows too large, performance drops, so rehashing may be needed to resize the table.
  3. Rehashing: When the hash table becomes too full, rehashing involves creating a larger table and transferring all elements to the new table using a new hash function.
  4. Avoiding Poor Collision Resolution: Poorly designed collision resolution techniques can severely impact performance.