Introduction

Implementing a hash table in C++ can seem like a complex task, but mastering the core concepts—hash functions, linked lists, and table structures—can make it manageable. A well-constructed hash table is essential for efficient data retrieval, and understanding how to handle collisions with separate chaining is key to optimizing performance. In this article, we’ll guide you through creating a custom hash table from scratch, covering the necessary operations like inserting, searching, and deleting items, while also touching on critical memory management practices. By the end, you’ll be ready to experiment with various hash functions and collision algorithms to further improve your implementation.

Choosing a Hash Function

So, let’s say you’re setting up a hash table—and just like with any good project, the first step is choosing the right hash function. Think of the hash function as the mastermind behind everything, making sure the keys are spread out evenly across the hash table. The goal here is to keep things neat and tidy, so when you look up, insert, or delete items, it all happens quickly and smoothly.

Now, here’s the thing: if your hash function isn’t up to par, all your keys could end up clumping together like a group of people crowded around the same door during a fire drill. And just like that chaos, collisions in your hash table can cause big performance problems. That’s the last thing you want! A good hash function minimizes these collisions by making sure keys don’t always land in the same spot.

But, for this tutorial, we’re going to shake things up a bit and intentionally use a poor hash function. Why? Because sometimes, seeing problems happen in real time helps you understand the best ways to fix them. We’ll use strings (or character arrays in C) as keys to keep things simple. By using a less efficient function, we can really show you how collisions happen and what to expect when they do.

Here’s our “not-so-great” hash function:

#define CAPACITY 50000 // Size of the HashTable.
unsigned long hash_function(char* str) {
    unsigned long i = 0;
    for (int j = 0; str[j]; j++) {
        i += str[j]; // Sum up the ASCII values of each character.
    }
    return i % CAPACITY; // Return the result within the bounds of the table.
}

What’s happening here? Well, this function is simply going through each character in the string, grabbing its ASCII value, and adding them up. Pretty straightforward, right? But here’s the twist: the sum is then divided by the capacity of the table (50000 in this case) to make sure the final hash value stays within the table’s limits.

Okay, here’s where it gets fun. When you test this hash function with different strings, you’ll see that some strings—like “Hel” and “Cau”—end up with the same hash value. How is that possible? Well, it turns out that the sum of the ASCII values for both strings happens to be the same. Let’s break it down:

• “Hel”: 72 (H) + 101 (e) + 108 (l) = 281
• “Cau”: 67 (C) + 97 (a) + 117 (u) = 281

As you can see, both strings add up to 281. So when they go through our hash function, they end up with the same hash value. This means both strings land in the same spot in the hash table, and bam—collision.

This situation—where two different keys end up with the same hash value—is called a collision, and it’s something you definitely want to handle carefully. Collisions can lead to all kinds of headaches if not properly managed. But for now, we’ve let one happen on purpose so you can see how important it is to have a solid hash function to avoid these situations in the first place.

Finally, a little word of caution: always make sure the hash value fits within the capacity of the table. If the calculated index goes beyond the table’s limits, the program might try to access a memory location that doesn’t exist, leading to all sorts of chaos—like errors or weird behavior. So, be sure to validate the index before moving forward.

Now that you know how collisions can sneak up on you, you’re all set to start fixing that hash function and make it more efficient. The goal is to strike the right balance between performance and accuracy!

Hash Table Visualization

Defining the Hash Table Data Structures

Imagine you’re working in a busy office, and your job is to keep track of everything—from client names to project details. You need a system that helps you organize all this information so you can grab whatever you need, whenever you need it. This is where the hash table comes in. It’s like a super-efficient filing system that stores information as key-value pairs—think of each “key” as a client’s name and each “value” as that client’s project details. With this system, you can instantly pull up all the info you need just by knowing the key.

Now, let’s dive into the technical side and see how we set this up. To store everything properly, we need to define the structure of the individual key-value pairs. In C++, we start by creating the building blocks for these pairs. Each pair needs a key and a value, and each one will be stored in a “bucket” inside the hash table.

// Defines the HashTable item.
typedef struct Ht_item {
    char* key;   // Key is a string that uniquely identifies the item.
    char* value; // Value is the associated data for that key.
} Ht_item;

This small snippet creates the Ht_item structure, which represents a single entry in the hash table. Each item has a key (a string that acts as the identifier) and a value (another string that holds the actual data related to that key). The key will be hashed, determining where the item gets placed in the hash table, and the value contains all the info you need about that key.

But we can’t stop there—we need to define the hash table itself that holds these items. You see, the hash table is essentially just an array of pointers, each pointing to an individual Ht_item . This is where it gets a bit tricky—it’s a “double pointer” structure. The array in the hash table holds the addresses (pointers) to the actual key-value pairs.

Let’s define the HashTable structure:

// Defines the HashTable.
typedef struct HashTable {
    // Contains an array of pointers to items.
    Ht_item** items; // Array of pointers to Ht_item.
    int size;        // The size of the hash table (how many slots it contains).
    int count;       // The number of items currently stored in the hash table.
} HashTable;

Now, we have a HashTable structure with three key parts:

• items: This is the array of pointers that will point to each Ht_item .
• size: This tells you how big your hash table is—basically, how many slots it has to store items.
• count: This tracks how many items are actually in the hash table at any given time.

So, every time you add or remove an item, count changes to reflect the number of key-value pairs currently in your table. And size is your constant reminder of how much space is available. If you hit the limit (i.e., when count equals size ), you’ll need to resize the hash table to make room for more items, which is a crucial part of keeping things efficient.

To make sure the hash table stays in good shape, you need to keep a close eye on these fields. size tells us the overall capacity, and count tells us how many pairs are actually in the table. If you’re thinking about adding a new pair but the table is full, you’ll need to consider resizing or rehashing—basically, making a bigger table to avoid collisions.

With these foundations in place, the next step is to implement the functions that manage everything in the hash table. These functions will handle insertions, searches, and deletions, ensuring everything happens smoothly and efficiently, just like any good office system should.

Creating the Hash Table and Hash Table Items

Let’s take a journey into the world of hash tables, where we’ll create and manage key-value pairs like digital detectives hunting down data. So, we’re getting into the fun part—defining functions that will allocate memory and create hash table items. The idea here is that these items—each containing a key-value pair—will be dynamically allocated. This gives us the flexibility to grow or shrink the table as needed, managing memory efficiently while keeping everything organized.

Imagine, you’re about to create the perfect hash table item. First, we allocate memory for the Ht_item structure. This structure will hold both the key and value for each entry. It’s like a small container where we store a unique key (think of it as an identifier) and its corresponding value (which is the actual data related to that key). We also need to allocate memory for the key and value strings, making sure there’s space for everything, including the special “null-terminating” character that marks the end of a string in C++.

Here’s how we do it in code:

Ht_item* create_item(char* key, char* value) { // Creates a pointer to a new HashTable item.
    Ht_item* item = (Ht_item*) malloc(sizeof(Ht_item)); // Allocates memory for the item.
    item->key = (char*) malloc(strlen(key) + 1); // Allocates memory for the key.
    item->value = (char*) malloc(strlen(value) + 1); // Allocates memory for the value.
    strcpy(item->key, key); // Copies the key into the allocated memory.
    strcpy(item->value, value); // Copies the value into the allocated memory.
    return item; // Returns the pointer to the newly created item.
}

What’s happening here? The create_item function allocates memory for a new Ht_item and then allocates memory for the key and value strings. We use malloc to set aside space for these strings, including the extra byte needed for that null-terminator. Once everything is allocated, we use strcpy to copy the actual data (key and value) into the memory locations, and we return a pointer to this freshly created item.

Now, let’s think bigger. We need a place to store all these items. Enter the hash table! This is where we store all our key-value pairs, neatly organized in an array. But, here’s the thing: the hash table is a bit like a warehouse that uses pointers to keep track of where each item is located. It’s a “double-pointer” setup—an array of pointers to Ht_item objects.

We can create the hash table like this:

HashTable* create_table(int size) { // Creates a new HashTable.
    HashTable* table = (HashTable*) malloc(sizeof(HashTable)); // Allocates memory for the hash table structure.
    table->size = size; // Sets the size of the hash table.
    table->count = 0; // Initializes the item count to zero.
    table->items = (Ht_item**) calloc(table->size, sizeof(Ht_item*)); // Allocates memory for the items array.
    // Initializes all table items to NULL, indicating that they are empty.
    for (int i = 0; i < table->size; i++) {
        table->items[i] = NULL;
    }
    return table; // Returns the pointer to the newly created table.
}

This function allocates memory for the hash table itself, sets its size, and initializes an array for the items. We use calloc to ensure that every pointer in the items array starts out as NULL, meaning no items are stored there yet. The size tells us how many slots we have to work with, and count keeps track of how many items are currently inside the table.

Of course, we have to be responsible with our memory. Once we’re done with the hash table, we want to make sure we clean up and free up all that allocated memory. For that, we’ll create functions that handle the cleanup process—one for freeing individual items, and one for freeing the table itself.

Here’s the code to free up an Ht_item:

void free_item(Ht_item* item) { // Frees an item.
    free(item->key); // Frees the memory allocated for the key.
    free(item->value); // Frees the memory allocated for the value.
    free(item); // Frees the memory allocated for the Ht_item structure itself.
}

We’re just cleaning up the mess here! We free the memory allocated for both the key and value, then we free the Ht_item structure itself. This ensures we don’t leave any dangling references to memory we no longer need.

Next, we free the HashTable:

void free_table(HashTable* table) { // Frees the table.
    for (int i = 0; i < table->size; i++) {
        Ht_item* item = table->items[i];
        if (item != NULL) {
            free_item(item); // Frees the individual item.
        }
    }
    free(table->items); // Frees the array of pointers to items.
    free(table); // Frees the hash table structure itself.
}

Here, we loop through each item in the hash table, freeing each one if it exists. Once the individual items are gone, we free the array that held the pointers to those items, and finally, we free the hash table structure itself. Clean-up done!

But wait—what if you want to check what’s inside your hash table? Maybe you’re debugging or just curious about the contents. That’s where a print_table function comes in handy:

void print_table(HashTable* table) {
    printf(“\nHash Table\n——————-\n”);
    for (int i = 0; i < table->size; i++) {
        if (table->items[i]) {
            printf(“Index:%d, Key:%s, Value:%s\n”, i, table->items[i]->key, table->items[i]->value);
        }
    }
    printf(“——————-\n\n”);
}

This function prints out the contents of the table, showing each index, key, and value for any item stored in the hash table. It’s a great tool for visualizing how the table looks and checking whether everything is working as expected.

With these pieces in place, you’ve got the foundations of a solid hash table system. You can now insert, search, and delete items, knowing that you’ve got memory management under control and can clean up when you’re done. It’s a bit like setting up a well-organized library where every book (or item) has its own place and can be found in an instant. Ready for the next chapter?

Hash Table Data Structure

Inserting into the Hash Table

Let’s take a journey through the process of inserting data into a hash table. Picture it like a library where each book has a unique ID (the key), and the book’s information (the value) is carefully stored. But here’s the catch: finding a book in the library should be fast, so we need a system that gets us to the right shelf (index) in a snap. This is where hash functions come into play. So, how do we insert a new book into this library system? We use a function called ht_insert() , and it does all the magic.

Here’s the deal— ht_insert() takes a pointer to the hash table, a key, and a value as its parameters. Then it figures out where the new item should go, ensuring everything stays organized and that the hash table remains error-free. But let’s break it down, step by step, so you can understand exactly how this happens.

Step 1: Create the Item

First, we create the item we want to insert. This is where the magic starts—allocating memory for the Ht_item structure that will store the key and value. It’s like preparing a brand new book with a title and content ready to go. We’ll use the create_item() function for this:

Ht_item* item = create_item(key, value); // Creates the item based on the key-value pair.

Step 2: Compute the Index

Now that we have our book (the item), we need to figure out where to place it in our library (the hash table). This is done by computing an index using the hash function. It’s like having a special system that converts each book’s title (the key) into a shelf number (the index). Here’s how we do it:

int index = hash_function(key); // Computes the index based on the hash function.

The hash function takes the key and magically computes an index within the size of our hash table, making sure it lands in the right spot.

Step 3: Check for Index Availability

At this point, we’ve got our index, but now we have to check if the spot is already taken. Is the shelf empty, or is there already a book there? If the index is empty (i.e., NULL ), then we can directly place the new item there. But if the shelf is already occupied, then we’re looking at a collision. Let’s check for that:

Ht_item* current_item = table->items[index]; // Get the current item at the index.
if (current_item == NULL) {
    if (table->count == table->size) {
        printf(“Insert Error: Hash Table is full\n”);
        free_item(item); // Free memory for the item before returning.
        return;
    }
    table->items[index] = item; // Insert the item directly at the computed index.
    table->count++; // Increment the item count.
}

Step 4: Handle the Scenario Where the Key Already Exists

Okay, now let’s say the index isn’t empty—there’s already a book at that shelf. But what if the book we want to insert has the same title as the existing one? It happens! When this occurs, we update the existing book with the new content (value). This ensures that the most recent information is always stored. Here’s how we handle it:

else {
    // Scenario 1: The key already exists at the index.
    if (strcmp(current_item->key, key) == 0) {
        strcpy(table->items[index]->value, value); // Update the value.
        return; // Exit after updating the value.
    }
}

Step 5: Handle Collisions

But what happens when the shelf is occupied by a book with a different title? This is what we call a collision. Two different keys are being hashed to the same index. And here’s the tricky part: we need a solution. One way to resolve a collision is by using separate chaining, which stores collided items in a linked list at the same index. So, if we encounter a collision, we handle it with a special function:

void handle_collision(HashTable* table, Ht_item* item) {
    // Collision handling logic goes here.
}

Now, let’s see how everything fits together in the ht_insert() function:

void ht_insert(HashTable* table, char* key, char* value) {
    Ht_item* item = create_item(key, value); // Create the item.
    int index = hash_function(key); // Compute the index.
    Ht_item* current_item = table->items[index]; // Get the current item at the index.     if (current_item == NULL) {
        if (table->count == table->size) {
            printf(“Insert Error: Hash Table is full\n”);
            free_item(item);
            return;
        }
        table->items[index] = item;
        table->count++;
    } else {
        if (strcmp(current_item->key, key) == 0) {
            strcpy(table->items[index]->value, value);
            return;
        } else {
            handle_collision(table, item);
            return;
        }
    }
}

And there you have it! The ht_insert() function can now handle both inserting new items and updating existing ones. If a collision happens, it calls the handle_collision() function, which you can extend to handle different collision resolution strategies, like chaining or open addressing.

This way, your hash table stays efficient, always able to store and retrieve your precious data, no matter how big it grows. Isn’t that cool?

Hash Table Data Structure

Searching for Items in the Hash Table

Imagine you’re on a treasure hunt, and your goal is to find the specific treasure (or in our case, a key) hidden in a giant chest (the hash table). Your job is to make sure you find the exact treasure you’re looking for without wasting time digging through everything. That’s where the ht_search() function comes in, acting like your trusty map to find the exact spot in the chest where the treasure is hidden. Let’s walk through how we can search for an item inside a hash table using this handy function.

First Step: Getting the Right Map

Before you can start your hunt, you need a map to tell you where to look, right? In the case of a hash table, the map is created using the hash function. The function takes the key (the treasure you’re looking for) and computes a specific index where this key might be located in the table. It’s like the treasure map pointing you directly to the correct drawer in the chest.

Here’s how we do it in code:

char* ht_search(HashTable* table, char* key) {
    int index = hash_function(key);  // Computes the index for the key.
    Ht_item* item = table->items[index];  // Retrieves the item at that index.
}

Step 2: Checking if the Item is There

Once you have the right map (index), you need to check if the treasure is actually there, right? If there’s no item at that location, the map is useless. So, we need to check if there’s an item at the calculated index.

if (item != NULL) {
    if (strcmp(item->key, key) == 0)
        return item->value;
}

If there is an item at that index, the next step is checking if it’s the right treasure. We compare the key of the item at that spot with the one we’re looking for. If it matches, bingo! We found our treasure (or value).

Step 3: Handling Missing Treasures

Now, if you get to a spot where the chest is empty, or if the treasure isn’t the one you’re looking for, we need a plan. What happens when the key isn’t found? Well, we don’t want to return an empty treasure chest, so the function returns NULL . This means the treasure just isn’t there.

return NULL;  // If the key isn’t found, we return NULL.

Displaying the Search Results

Now that we’ve got the treasure hunt sorted, let’s make it easier for you to keep track of whether you’ve found your treasure or not. To do this, we add a little helper function called print_search() . It’s like a reporter that comes in after the search, giving you an update on what happened. Did you find the treasure, or are you still looking? Let’s look at how it works:

void print_search(HashTable* table, char* key) {
    char* val;
    if ((val = ht_search(table, key)) == NULL) {
        printf(“Key:%s does not exist\n”, key);  // If the key isn’t found, print a message.
        return;
    } else {
        printf(“Key:%s, Value:%s\n”, key, val);  // If found, print the key and value.
    }
}

The print_search() function does a quick check using the ht_search() function. If the treasure (key) is there, it proudly displays the key and its associated value. If not, it informs you that the treasure is missing, giving you closure on your search.

And that’s it! With these two functions, ht_search() and print_search() , you’ve got a fast and reliable way to find, or not find, treasures in your hash table. Whether the key exists or not, you’ll always know exactly where you stand, making it easier to debug and ensure your hash table is working like a charm.

Hash Table Overview

Handling Collisions

Alright, picture this: you’ve got a hash table set up, and everything is running smoothly. You’re using your hash function to map keys to specific spots in the table. But then, something unexpected happens: two keys hash to the same spot! It’s like showing up to a party where two people are trying to claim the same chair. This, my friend, is what we call a collision.

Now, don’t panic. Just like at that party, we have a way to handle things without chaos. We use a technique called Separate Chaining, which is like creating a separate little table for each person who shows up at the same time, so no one has to fight for the seat. In the world of hash tables, this means we’re using linked lists to store multiple items at the same index, instead of overwriting anything.

Step One: Setting Up the Linked List

When a collision happens, we need a linked list to manage it. Think of the list as a chain, where each link is a person sitting in the same spot (in this case, each link holds an item in the hash table). The idea is to keep everything organized and efficient.

typedef struct LinkedList {
    Ht_item* item; // The item stored at this node.
    struct LinkedList* next; // A pointer to the next item in the chain.
} LinkedList;

Each LinkedList node holds an item (which is the key-value pair) and a pointer to the next node. So, in the event of a collision, we just add another node to the list. It’s like adding another person to that table!

Step Two: Allocating Memory for the List

Now, before we can start adding people (items) to our table, we need to make sure there’s enough room. This means we need a function to allocate memory for each new linked list node.

LinkedList* allocate_list() {
    // Allocates memory for a LinkedList node.
    LinkedList* list = (LinkedList*) malloc(sizeof(LinkedList));
    return list;
}

Each time we run this function, we’re creating a new linked list node that will hold an item from the hash table.

Step Three: Adding Items to the List

Now, imagine it’s time to add a new guest to the party. The linked list will either be empty, or someone is already there. So, we check if the list is empty, and if so, we make the new item the head of the list. If there are already people there, we just add the new person to the end of the line.

LinkedList* linkedlist_insert(LinkedList* list, Ht_item* item) {
    if (!list) {
        // Create a new node if the list is empty.
        LinkedList* head = allocate_list();
        head->item = item;
        head->next = NULL;
        list = head;
        return list;
    } else if (list->next == NULL) {
        // Add the new item to the list if only one item is there.
        LinkedList* node = allocate_list();
        node->item = item;
        node->next = NULL;
        list->next = node;
        return list;
    }    LinkedList* temp = list;
    while (temp->next) {
        temp = temp->next;
    }
    LinkedList* node = allocate_list();
    node->item = item;
    node->next = NULL;
    temp->next = node;
    return list;
}

This function checks if the list is empty, and if not, it walks down the list until it finds the end, where it adds the new item.

Step Four: Removing Items

But sometimes, someone has to leave the party, right? In this case, we need to remove an item from the linked list. The linkedlist_remove() function takes care of that by adjusting the pointers and freeing the memory used by the item.

Ht_item* linkedlist_remove(LinkedList* list) {
    if (!list) return NULL;
    if (!list->next) return NULL;    LinkedList* node = list->next;
    LinkedList* temp = list;
    temp->next = NULL;
    list = node;
    Ht_item* it = temp->item;
    free(temp->item->key);
    free(temp->item->value);
    free(temp->item);
    free(temp);
    return it;
}

This function removes the head of the list and properly frees the memory associated with it.

Step Five: Cleaning Up

Once we’re done with the hash table and no longer need it, we need to clean up. That’s where the free_linkedlist() function comes in. It goes through the list, freeing every node and its associated memory.

void free_linkedlist(LinkedList* list) {
    LinkedList* temp = list;
    while (list) {
        temp = list;
        list = list->next;
        free(temp->item->key);
        free(temp->item->value);
        free(temp->item);
        free(temp);
    }
}

This function ensures that we don’t leave any lingering data behind when we’re finished using the linked list.

Adding Overflow Buckets to the Hash Table

Now that we’ve got the linked list all set up, it’s time to integrate it with the hash table itself. Each index in the hash table will get its own linked list (or overflow bucket). These overflow buckets are the perfect place to store any collided items, ensuring that no data is lost.

typedef struct HashTable HashTable;
struct HashTable {
    Ht_item** items; // Array of pointers to items.
    LinkedList** overflow_buckets; // Array of pointers to linked lists for overflow.
    int size; // Size of the hash table.
    int count; // Number of items currently in the hash table.
};

Creating and Deleting Overflow Buckets

We need functions to create and delete these overflow buckets. The create_overflow_buckets() function creates the array of linked lists, while free_overflow_buckets() cleans up the memory when we’re done.

LinkedList** create_overflow_buckets(HashTable* table) {
    LinkedList** buckets = (LinkedList**) calloc(table->size, sizeof(LinkedList*));
    for (int i = 0; i < table->size; i++) {
        buckets[i] = NULL;
    }
    return buckets;
}void free_overflow_buckets(HashTable* table) {
    LinkedList** buckets = table->overflow_buckets;
    for (int i = 0; i < table->size; i++) {
        free_linkedlist(buckets[i]);
    }
    free(buckets);
}

These functions allocate and free memory for the overflow buckets, ensuring that they’re handled properly.

Handling Collisions during Insertions

Finally, when a collision happens during an insert operation, we use the handle_collision() function to add the new item to the appropriate linked list.

void handle_collision(HashTable* table, unsigned long index, Ht_item* item) {
    LinkedList* head = table->overflow_buckets[index];
    if (head == NULL) {
        head = allocate_list();
        head->item = item;
        table->overflow_buckets[index] = head;
        return;
    } else {
        table->overflow_buckets[index] = linkedlist_insert(head, item);
        return;
    }
}

Updating the Search Method to Use Overflow Buckets

When searching for an item, we need to check not just the main table but also the overflow buckets. So we update the ht_search() method to account for this.

char* ht_search(HashTable* table, char* key) {
    int index = hash_function(key);
    Ht_item* item = table->items[index];
    LinkedList* head = table->overflow_buckets[index];    if (item != NULL) {
        if (strcmp(item->key, key) == 0) {
            return item->value;
        }
        if (head == NULL) {
            return NULL;
        }
        item = head->item;
        head = head->next;
    }
    return NULL;
}

With this, you now have a hash table that handles collisions with style, using linked lists to store multiple items at the same index without losing any data. The table can grow, shrink, and remain efficient no matter how many collisions come its way. The beauty of Separate Chaining lies in its simplicity and effectiveness, especially when you’re dealing with hash tables of varying sizes and capacities.

Hash Table Visualization

Deleting from the Hash Table

Imagine you’re at a library, and you’ve just checked out a book—let’s call it “The Hash Table Handbook.” Everything’s going great until you realize that you need to return the book, but wait—what if someone else has borrowed it, and it’s been stacked on top of several others? That’s pretty much what happens in a hash table when you try to delete an item, and there’s a collision. But don’t worry! We’ve got a way to handle it smoothly. So, let’s dive in. The task here is to delete an item from the hash table. Now, the process might sound a bit tricky at first, especially with collisions to think about. But really, it’s a lot like finding the right book in that stack of other books. You’ve got to know where to look and how to handle things when the books pile up at the same spot.

Here’s how we do it:

void ht_delete(HashTable* table, char* key) {
    int index = hash_function(key);   // Calculate the index using the hash function.
    Ht_item* item = table->items[index];   // Retrieve the item at the computed index.
    LinkedList* head = table->overflow_buckets[index];   // Get the linked list at the overflow bucket.</p>
<p>    // If there is no item at the computed index, the key doesn’t exist, so return immediately.
    if (item == NULL) {
        return;   // Key not found.
    } else {
        // If no collision chain exists (i.e., no linked list), proceed with deletion.
        if (head == NULL && strcmp(item->key, key) == 0) {
            table->items[index] = NULL;   // Set the table index to NULL.
            free_item(item);   // Free the allocated memory for the item.
            table->count–;   // Decrement the item count.
            return;   // Item deleted. 
        } else if (head != NULL) {
            // If a collision chain exists (i.e., there is a linked list at the index),
            // we need to check if the item is part of the chain.
        if (strcmp(item->key, key) == 0) {
            free_item(item);   // Free the memory of the current item.
            LinkedList* node = head;   // Store the head node.
            head = head->next;   // Set the head of the list to the next node.
            node->next = NULL;   // Set the next pointer of the node to NULL.
            table->items[index] = create_item(node->item->key, node->item->value);   // Replace the item at the table index with the new head item.
            free_linkedlist(node);   // Free the old head node.
            table->overflow_buckets[index] = head;   // Update the overflow bucket.
            return;   // Item deleted from collision chain.
        }
        // If the item is not the first element in the chain, traverse the list to find the matching item.
        LinkedList* curr = head;
        LinkedList* prev = NULL;
        while (curr) {
            if (strcmp(curr->item->key, key) == 0) {
            if (prev == NULL) {
                // If the item is the first element in the chain, we need to free the entire chain.
                free_linkedlist(head);
                table->overflow_buckets[index] = NULL;   // Set the overflow bucket to NULL.
                return;   // Item deleted from chain.
            }
            else {
                // If the item is somewhere in the middle or end of the chain, update the previous node.
                prev->next = curr->next;
                curr->next = NULL;
                free_linkedlist(curr);
                table->overflow_buckets[index] = head;   // Update the overflow bucket.
                return;   // Item deleted from chain.
        }
        }
        curr = curr->next;   // Move to the next node in the chain.
        prev = curr;   // Update the previous node.
    }
    }
}

The Magic Behind the Scenes

Now, let’s break down how this all works:

• Computing the Index: First, we calculate the index in the hash table using the hash function. The hash function takes the key, runs it through a set of algorithms, and gives us a specific index where we should look. Think of it like finding the right shelf in the library for your book.
• Item Retrieval: After we get that index, we retrieve the item at that spot in the table. If there’s no item at that spot (maybe it was already borrowed, so to speak), we can just exit the function without doing anything. Simple!
• No Collision? No Problem!: If there’s no linked list (collision chain) at the index, and the key matches the item’s key, we can just delete the item. It’s like pulling that one book from the shelf and returning it to the library—it’s gone, and everything’s back in order. We simply set the index to NULL and free the memory.
• Handling Collisions: Here’s where it gets interesting. If there’s already a collision, things are a bit more complicated. Instead of just removing the item and moving on, we need to check if the item is part of a linked list (or a “chain” of items). The first item in the chain is easy—just remove it and make the next item the head of the list. If the item isn’t at the start, we have to walk through the list until we find it, remove it, and adjust the links in the chain.
• Freeing Memory: The last thing we need to do is make sure we don’t leave anything behind. We free the memory allocated for the item and any linked list nodes that were involved in the deletion. No leftovers here!

Edge Cases Handled Like a Pro:

• Key Not Found: If the key doesn’t exist in the table, we don’t make any changes—just exit. Simple.
• Collision Handling: If multiple items hash to the same spot, we make sure to go through the linked list and find the right one to delete. This ensures we don’t accidentally mess up other items in the chain.
• First, Middle, or Last in the Chain?: Whether the item you want to remove is at the beginning, middle, or end of the chain, we handle it appropriately. You won’t run into any issues when deleting items from a linked list!

So, with this ht_delete() function, you’ve got a solid method for removing items from the hash table, whether they’re standing alone or tangled up in a collision chain. It keeps everything nice and tidy, ensuring your hash table remains functional and memory is properly managed.

Understanding Hashing in Data Structures

Conclusion

In conclusion, mastering the implementation of a hash table in C++ involves understanding key concepts like hash functions, linked lists, and efficient collision handling. By following the steps outlined in this article, you now know how to create a custom hash table with basic operations such as inserting, searching, and deleting items. Moreover, with techniques like separate chaining, collisions can be managed effectively, ensuring your hash table remains efficient. Memory management, including freeing allocated memory, is also a crucial part of maintaining optimal performance. Moving forward, experimenting with different hash functions and collision resolution algorithms can further enhance your hash table’s functionality. By continuing to explore these concepts, you’ll be better prepared to build more efficient data structures in your C++ projects.

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