Various load balancing techniques used in Hash table to ensure efficient access time

Load balancing refers to the process of distributing workloads evenly across multiple servers, nodes, or other resources to ensure optimal resource utilization, maximize output, minimize response time, and avoid overload of any single resource. Load balancing helps to improve the reliability and scalability of applications and systems, as well as reduce downtime and downtime-related costs.

In the context of hash tables, load balancing refers to the process of evenly distributing keys across the available buckets in the hash table to ensure efficient access time. Load balancing is critical in hash tables because when keys are distributed unevenly, some buckets may become overflowing while others remain empty, resulting in longer access times and decreased performance.

Load-balancing techniques used in hash tables to ensure efficient access time are:

• Separate Chaining: In this technique, each bucket in the hash table is implemented as a linked list or another data structure, such as a tree. When there are multiple keys that hash to the same index, they are stored in the same bucket and can be searched for using the linked list or another data structure.
  • Advantages:
    • It can handle an arbitrary number of collisions.
    • Simple to implement
  • Disadvantages:
    • Requires a separate data structure for each bucket and can have poor cache locality
      
• Open Addressing: In this technique, all keys are stored in the same array, and when there are multiple keys that hash to the same index, a probing sequence is used to find the next available slot for the key. Common probing sequences include linear probing and quadratic probing.
  • Advantages:
    • Better cache locality
    • Requires only one data structure
  • Disadvantages:
    • suffer from the clustering and requires a good hash function
      
• Double Hashing: In this technique, two hash functions are used instead of one. The first hash function calculates the initial index for a key, and the second hash function is used to calculate the next index for the key if the first indExplanationex is already occupied.
  • Advantages:
    • Can avoid clustering better than linear or quadratic probing
  • Disadvantages:
    • Requires two hash functions and can suffer from clustering if the second hash function is poorly chosen
      
• Rehashing: In this technique, the hash table is resized and the keys are rehashed to new indices when the load factor (the number of keys in the hash table divided by the number of buckets) becomes too large.
  • Advantages:
    • Dynamically adjusts the size of the hash table to maintain a low load factor and minimize collisions
  • Disadvantages:
    • Can be costly in terms of time and memory usage and can cause cache invalidation
      
• Cuckoo Hashing: In this technique, each key is stored in one of two hash tables, and when there is a collision, the key is moved to the other hash table. This helps to ensure that the hash table remains balanced and that the time complexity for key access remains constant on average.
  • Advantages:
    • Achieves constant time complexity for key access on average.
    • Can handle an arbitrary number of collisions without using a separate data structure
  • Disadvantages:
    • Can suffer from pathological key distributions that cause an infinite loop and is more complex to implement.

Conclusion:

Separate Chaining is the most commonly used technique for load balancing in hash tables because it is simple and flexible and can handle many collisions. Other techniques, such as Open Addressing and Double Hashing, can save space but require a good hash function and are susceptible to clustering. Rehashing adjusts the size of the hash table dynamically to minimize collisions, but it can be slow. Cuckoo Hashing achieves fast key access, but it is more difficult to implement and suffers from certain key types.