Robin Hood Hash Table

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Introduction

In the realm of data structures, hash tables are a fundamental component of many algorithms and systems. However, as the size of datasets grows, the performance of traditional hash tables can degrade significantly. This is where the Robin Hood hash table comes into play, offering a more efficient and balanced approach to storing and retrieving data. In this article, we will delve into the implementation of a Robin Hood hash table in C23, exploring its performance benefits and memory optimization techniques.

What is a Robin Hood Hash Table?

A Robin Hood hash table is a type of hash table that uses a combination of linear probing and quadratic probing to resolve collisions. Unlike traditional hash tables, which use linear probing to resolve collisions, Robin Hood hash tables use a more complex probing strategy to minimize the number of collisions and reduce the likelihood of clustering. This approach leads to improved performance and reduced memory usage.

C23 Implementation

Our implementation of the Robin Hood hash table in C23 is designed to be efficient, scalable, and easy to use. The code is written in a modular fashion, with each component responsible for a specific aspect of the hash table's functionality. This approach allows for easy modification and extension of the code, making it ideal for large-scale applications.

Hash Table Structure

typedef struct robin_hood_hash_table {
    size_t size;
    size_t capacity;
    void** keys;
    void** values;
    size_t* indices;
} robin_hood_hash_table;

The robin_hood_hash_table structure represents the core of our implementation, containing the following members:

  • size: The current size of the hash table.
  • capacity: The maximum capacity of the hash table.
  • keys: An array of pointers to the keys stored in the hash table.
  • values: An array of pointers to the values stored in the hash table.
  • indices: An array of indices used to resolve collisions.

Hash Function

size_t robin_hood_hash(const void* key, size_t capacity) {
    size_t hash = 0;
    const uint8_t* bytes = (const uint8_t*)key;
    for (size_t i = 0; i < capacity; i++) {
        hash = (hash + bytes[i]) % capacity;
    }
    return hash;
}

The robin_hood_hash function calculates the hash value of a given key using a simple modulo operation. This function is used to determine the initial index of a key in the hash table.

Insertion

int robin_hood_insert(robin_hood_hash_table* table, const void* key, const void* value) {
    size_t index = robin_hood_hash(key, table->capacity);
    size_t i = 0;
    while (table->indices[index] != 0) {
        if (table->indices[index] == i) {
            // Collision detected, use quadratic probing to resolve
            index = (index + (i * i)) % table->capacity;
            i++;
        } else {
            // Key already exists, update value
            table->values[table->indices[index]] = value;
            0;
        }
    }
    // Insert new key-value pair
    table->keys[index] = key;
    table->values[index] = value;
    table->indices[index] = i;
    return 1;
}

The robin_hood_insert function inserts a new key-value pair into the hash table. If a collision is detected, the function uses quadratic probing to resolve the collision and find an available slot.

Search

int robin_hood_search(robin_hood_hash_table* table, const void* key, void** value) {
    size_t index = robin_hood_hash(key, table->capacity);
    size_t i = 0;
    while (table->indices[index] != 0) {
        if (table->indices[index] == i) {
            // Collision detected, use quadratic probing to resolve
            index = (index + (i * i)) % table->capacity;
            i++;
        } else if (table->keys[index] == key) {
            // Key found, return value
            *value = table->values[index];
            return 1;
        }
    }
    // Key not found
    return 0;
}

The robin_hood_search function searches for a given key in the hash table. If the key is found, the function returns the corresponding value.

Deletion

int robin_hood_delete(robin_hood_hash_table* table, const void* key) {
    size_t index = robin_hood_hash(key, table->capacity);
    size_t i = 0;
    while (table->indices[index] != 0) {
        if (table->indices[index] == i) {
            // Collision detected, use quadratic probing to resolve
            index = (index + (i * i)) % table->capacity;
            i++;
        } else if (table->keys[index] == key) {
            // Key found, remove key-value pair
            table->keys[index] = NULL;
            table->values[index] = NULL;
            table->indices[index] = 0;
            return 1;
        }
    }
    // Key not found
    return 0;
}

The robin_hood_delete function removes a key-value pair from the hash table. If the key is found, the function removes the key-value pair and updates the indices array.

Performance Benefits

The Robin Hood hash table offers several performance benefits over traditional hash tables:

  • Improved collision resolution: The use of quadratic probing reduces the likelihood of clustering and minimizes the number of collisions.
  • Reduced memory usage: The hash table's ability to store keys and values in a compact format reduces memory usage.
  • Faster search and insertion: The use of a more efficient probing strategy and the ability to store keys and values in a compact format reduce the time complexity of search and insertion operations.

Memory Optimization

The Robin Hood hash table is designed to optimize memory usage by storing keys and values in a compact format. This approach reduces memory usage and improves performance.

Compact Key-Value Storage

typedef struct robin_hood_key_value {
    void* key;
    void* value;
} robin_hood_key_value;

The robin_hood_key_value structure represents a compact key-value pair, containing the key and value as a single unit.

Compact Array Storage

typedef struct robin_hood_array {
    robin_hood_key_value* values;
    size_t size;
    size_t capacity;
} robin_hood_array;

The robin_hood_array structure represents a compact array of key-value pairs, containing the values and their corresponding indices.

Conclusion

In conclusion, the Robin Hood hash table offers a more efficient and balanced approach to storing and retrieving data. Its use of quadratic probing and compact key-value storage reduces the likelihood of clustering and minimizes memory usage. The C23 implementation of the Robin Hood hash table provides a high-performance and scalable solution for large-scale applications.

Future Work

Future work on the Robin Hood hash table includes:

  • Improving collision resolution: Further optimizing the probing strategy to reduce the likelihood of clustering.
  • Enhancing memory optimization: Exploring new techniques to reduce memory usage and improve performance.
  • Supporting additional data structures: Integrating the Robin Hood hash table with other data structures to create a comprehensive library.

Introduction

The Robin Hood hash table is a high-performance data structure designed to store and retrieve data efficiently. In this article, we will answer some of the most frequently asked questions about the Robin Hood hash table, covering its implementation, performance benefits, and memory optimization techniques.

Q: What is the Robin Hood hash table?

A: The Robin Hood hash table is a type of hash table that uses a combination of linear probing and quadratic probing to resolve collisions. This approach leads to improved performance and reduced memory usage.

Q: How does the Robin Hood hash table resolve collisions?

A: The Robin Hood hash table uses quadratic probing to resolve collisions. When a collision is detected, the function uses a quadratic probing strategy to find an available slot in the hash table.

Q: What are the performance benefits of the Robin Hood hash table?

A: The Robin Hood hash table offers several performance benefits, including:

  • Improved collision resolution: The use of quadratic probing reduces the likelihood of clustering and minimizes the number of collisions.
  • Reduced memory usage: The hash table's ability to store keys and values in a compact format reduces memory usage.
  • Faster search and insertion: The use of a more efficient probing strategy and the ability to store keys and values in a compact format reduce the time complexity of search and insertion operations.

Q: How does the Robin Hood hash table optimize memory usage?

A: The Robin Hood hash table optimizes memory usage by storing keys and values in a compact format. This approach reduces memory usage and improves performance.

Q: What is the compact key-value storage in the Robin Hood hash table?

A: The compact key-value storage in the Robin Hood hash table is represented by the robin_hood_key_value structure, which contains the key and value as a single unit.

Q: What is the compact array storage in the Robin Hood hash table?

A: The compact array storage in the Robin Hood hash table is represented by the robin_hood_array structure, which contains the values and their corresponding indices.

Q: How does the Robin Hood hash table handle key-value pairs with different sizes?

A: The Robin Hood hash table handles key-value pairs with different sizes by using a compact storage format that can accommodate keys and values of varying sizes.

Q: Can the Robin Hood hash table be used in large-scale applications?

A: Yes, the Robin Hood hash table can be used in large-scale applications due to its high-performance and scalable design.

Q: What are the future work directions for the Robin Hood hash table?

A: Future work on the Robin Hood hash table includes:

  • Improving collision resolution: Further optimizing the probing strategy to reduce the likelihood of clustering.
  • Enhancing memory optimization: Exploring new techniques to reduce memory usage and improve performance.
  • Supporting additional data structures: Integrating the Robin Hood hash table with other data structures to create a comprehensive library.

Conclusion

In conclusion, Robin Hood hash table is a high-performance data structure designed to store and retrieve data efficiently. Its use of quadratic probing and compact key-value storage reduces the likelihood of clustering and minimizes memory usage. By continuing to improve and expand the Robin Hood hash table, we can create a more efficient and scalable solution for storing and retrieving data in large-scale applications.

Additional Resources

For more information on the Robin Hood hash table, please refer to the following resources:

  • Implementation: The C23 implementation of the Robin Hood hash table is available on GitHub.
  • Documentation: The documentation for the Robin Hood hash table is available on GitHub.
  • Benchmarks: The benchmarks for the Robin Hood hash table are available on GitHub.

By exploring these resources, you can gain a deeper understanding of the Robin Hood hash table and its applications in large-scale data storage and retrieval.