Hash Tables: A Deep Dive into C#

Imagine you have a big toy box. You want to store your toys in an organized way, so you decide to put similar toys together. For example, all your cars go in one section, all your dolls in another, and all your blocks in a third.

That's kind of how a hash table works!

Hash tables are a fundamental data structure used to store key-value pairs. They offer efficient insertion, deletion, and retrieval operations, making them a powerful tool for various applications.

using System.Collections;

Hashtable hashtable = new Hashtable();
hashtable.Add("key1", "value1");
hashtable.Add(2, "value2"); // Key can be any object type
string value1 = (string)hashtable["key1"];
int value2 = (int)hashtable[2];
hashtable.Remove("key1");

Implementing Hash Tables in C#


Hashtable phonebook = new Hashtable();
phonebook.Add("Alice", "123-456-7890");
phonebook.Add("Bob", "987-654-3210");

Console.WriteLine(phonebook["Alice"]); // Output: 123-456-7890
phonebook.Remove("Bob");
foreach (DictionaryEntry entry in phonebook)
{
    Console.WriteLine("{0}: {1}", entry.Key, entry.Value);
}

Example Usage:

HashTable<string, int> ht = new HashTable<string, int>(10);
ht.Add("apple", 10);
ht.Add("banana", 20);
ht.Add("cherry", 30);

Console.WriteLine(ht.Get("banana")); // Output: 20

ht.Remove("apple");

// ...

How Hash Tables Work?

Hash Function

A hash function is like a magical box that takes an input (a key) and produces a fixed-size output (a hash code). This output is used to determine where to store the key-value pair in the hash table. The goal is to distribute the keys evenly across the table to minimize collisions.

Think of it like this:

  • Input: Your favorite book's title.

  • Hash Function: A magical machine that crunches the title and spits out a number.

  • Output: A specific shelf number in a library.

The hash function ensures that different books (keys) are placed on different shelves (buckets) as much as possible, making it easy to find a specific book later.

Collision Handling

Sometimes, two different keys might produce the same hash code, leading to a collision. To handle this, hash tables use two main techniques:

Separate Chaining:

  • Each bucket in the hash table is a linked list.

  • When a collision occurs, the new key-value pair is simply added to the end of the linked list at that bucket.

public class HashTable<TKey, TValue>
{
    private LinkedList<KeyValuePair<TKey, TValue>>[] table; // Array of linked lists to store key-value pairs
    // ... other members

    public void Add(TKey key, TValue value)
    {
        int index = GetHashCode(key); // Calculate the hash code
        table[index].AddLast(new KeyValuePair<TKey, TValue>(key, value)); // Add the key-value pair to the end of the linked list at the calculated index
    }
}

Open Addressing: When a collision occurs, the algorithm probes for the next available slot in the array. Different probing techniques exist:

  • Linear Probing: The algorithm probes sequentially, starting from the collision index.

  • Quadratic Probing: The algorithm probes using a quadratic function of the original hash value.

  • Double Hashing: The algorithm uses a second hash function to determine the step size for probing.

public class HashTable<TKey, TValue>
{
    private TValue[] table; // Array to store values directly

    public void Add(TKey key, TValue value)
    {
        int index = GetHashCode(key); // Calculate the hash code
        while (table[index] != null) // Check if the slot is occupied
        {
            index = (index + 1) % table.Length; // Linear probing: move to the next index
        }
        table[index] = value; // Assign the value to the empty slot
    }
}

In simpler terms:

  • Separate Chaining: Imagine a library where multiple books can be placed on the same shelf.

  • Open Addressing: Imagine a library where you have to find an empty shelf for a new book, even if it means moving some books around.

Choosing the Right Technique:

The choice between separate chaining and open addressing depends on various factors:

  • Expected load factor: The ratio of the number of elements to the size of the hash table.

  • Desired performance characteristics: Prioritizing speed or memory usage.

  • Distribution of hash values: A good hash function can minimize collisions.

In many cases, separate chaining is a good default choice, especially when the load factor is relatively low. However, for high load factors or specific performance requirements, open addressing techniques may be more suitable.

Key Points:

  • Hash Function: A good hash function distributes keys evenly across the array to minimize collisions.

  • Collision Handling: Separate chaining is a simple and effective technique, but open addressing can also be used.

  • Load Factor: The load factor is the ratio of the number of elements to the size of the hash table. A high load factor can lead to performance degradation.

  • Resizing: As the number of elements in the hash table grows, it may be necessary to resize the underlying array to maintain good performance.

Remember to choose an appropriate hash function and consider the load factor to optimize performance.

Hash Tables are a powerful tool for efficient data storage and retrieval. By understanding their underlying principles and implementation techniques, you can leverage them effectively in your C# applications.

Note: While HashTables are still supported, it's generally recommended to use the more type-safe Dictionary<TKey, TValue> class for modern C# development.

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