How do You Handle Collisions in Hash Tables

Collisions occur in hash tables when two or more keys hash to the same index, resulting in multiple keys attempting to occupy the same slot or bucket in the hash table. Handling collisions is a crucial aspect of hash table implementation to ensure efficient retrieval and storage of key-value pairs. There are several common techniques to handle collisions in hash tables:

 

1. Separate Chaining:

   • In separate chaining, each slot or bucket in the hash table is associated with a linked list or another data structure (such as an array or tree).
   • When a collision occurs, the colliding key-value pair is inserted into the linked list or data structure associated with the corresponding slot.
   • Retrieval involves hashing the key to find the appropriate slot and then searching through the associated data structure to find the desired key-value pair.
   • Separate chaining is simple to implement and handles collisions effectively, but it may require additional memory overhead due to the storage of pointers or references.

2. Open Addressing:

   • In open addressing, collisions are resolved by finding an alternative empty slot within the hash table itself.
   • Various probing techniques are used to determine the next available slot when a collision occurs, such as linear probing, quadratic probing, and double hashing.
   • Linear probing involves searching for the next available slot by incrementing the index linearly until an empty slot is found.
   • Quadratic probing uses a quadratic function to probe for an empty slot, which helps reduce clustering.
   • Double hashing involves applying a secondary hash function to calculate the step size for probing, offering better distribution of keys.
   • Open addressing typically results in better cache performance and reduced memory overhead compared to separate chaining but may suffer from clustering and degraded performance under high load factors.

3. Robin Hood Hashing:

   • Robin Hood hashing is a variant of open addressing that aims to reduce clustering and improve performance.
   • When a collision occurs, Robin Hood hashing attempts to move the colliding key-value pair closer to the ideal slot (i.e., the slot where the key would ideally hash to) by swapping with keys that are further away from their ideal slots.
   • This redistribution of keys helps balance the distribution and reduces the average search time.

4. Cuckoo Hashing:

   • Cuckoo hashing is another open addressing technique that uses multiple hash functions and two hash tables to handle collisions.
   • When a collision occurs, the colliding key-value pair is moved to an alternative slot in the other hash table, which may trigger a cascade of displacements.
   • Cuckoo hashing guarantees constant-time lookups in the worst case and can achieve high performance under certain conditions.

 

The choice of collision resolution technique depends on factors such as the expected load factor, memory constraints, performance requirements, and the characteristics of the key distribution. Each technique has its advantages and trade-offs, and the most suitable approach should be selected based on the specific use case.

 

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