In this blog post, we will explore a queue-based implementation of a hash table in the C++ programming language. Hash tables are widely used data structures that provide efficient storage and retrieval of key-value pairs. By combining the principles of queues and hash functions, we can create a powerful and efficient implementation of a hash table.

Table of Contents

1. Introduction
2. Hash Table Basics
3. Queue-based Implementation
4. Code Example
5. Conclusion

Introduction

Hash tables, also known as hash maps or dictionaries, are data structures that store key-value pairs in a table-like fashion. They utilize a hash function to map keys to their corresponding values, enabling fast retrieval.

Traditionally, hash tables are implemented using arrays, which offer constant-time access to elements. However, in scenarios where the number of elements is unknown or large, it becomes challenging to determine the optimal size of the array.

Hash Table Basics

The basic idea behind a hash table is to use a hash function to convert a key into an index that corresponds to a location in an array. Each location in the array is known as a bucket, and it can store one or more key-value pairs.

To handle collisions, where multiple keys map to the same index, we use separate chaining or linear probing. In separate chaining, each bucket contains a linked list of key-value pairs. In linear probing, if a collision occurs, we simply search for the next available location in the array.

Queue-based Implementation

Instead of using an array, our queue-based implementation of a hash table will use a queue data structure to store the key-value pairs.

Here’s a high-level overview of the implementation:

1. We initialize a fixed-size array of queues, where the size of the array is determined based on the expected number of keys.
2. We define a hash function that takes a key as input and returns an index within the range of the array.
3. To insert a key-value pair into the hash table, we compute the hash of the key and enqueue the key-value pair into the corresponding queue.
4. To search for a key, we compute the hash of the key and search for it in the corresponding queue.
5. To delete a key, we compute the hash of the key and dequeue it from the corresponding queue.

Code Example

Here’s an example implementation of a queue-based hash table in C++:

#include <iostream>
#include <queue>

const int TABLE_SIZE = 10; // Determined based on expected number of keys

class HashTable {
private:
    std::queue<std::pair<int, std::string>> table[TABLE_SIZE];

public:
    int hash(int key) {
        return key % TABLE_SIZE;
    }

    void insert(int key, const std::string& value) {
        int index = hash(key);
        table[index].push({key, value});
    }

    std::string search(int key) {
        int index = hash(key);
        while (!table[index].empty()) {
            auto pair = table[index].front();
            if (pair.first == key) {
                return pair.second;
            }
            table[index].pop();
        }
        return "Key not found";
    }

    void remove(int key) {
        int index = hash(key);
        while (!table[index].empty()) {
            auto pair = table[index].front();
            if (pair.first == key) {
                table[index].pop();
                return;
            }
            table[index].pop();
        }
    }
};

int main() {
    HashTable hashTable;
    hashTable.insert(1, "John");
    hashTable.insert(2, "Jane");
    hashTable.insert(3, "Bob");

    std::cout << hashTable.search(2) << std::endl; // Output: "Jane"

    hashTable.remove(2);
    std::cout << hashTable.search(2) << std::endl; // Output: "Key not found"

    return 0;
}

In the above code example, we define a HashTable class that uses an array of queues table to store key-value pairs. The hash function calculates the index based on the key, and the insert, search, and remove functions operate on the respective queues.

Conclusion

In this blog post, we explored a queue-based implementation of a hash table in C++. By using queues instead of arrays, we can handle collisions efficiently while providing constant-time operations for insertion, search, and deletion.

The presented code example provides a basic foundation for further customization and enhancement. Being familiar with different implementations and variations of hash tables can be valuable when working on various software engineering projects.

#programming #cpp