What Is Collision in Data Structure?

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Heather Bennett

What Is Collision in Data Structure?

In data structures, collision occurs when two or more elements are mapped to the same location in a hash table. Hash tables are widely used data structures that provide efficient access and retrieval of data. They consist of an array of buckets, where each bucket can hold one or more elements.

Understanding Hashing

To understand collision, let’s first discuss hashing. Hashing is a process that converts an input key into an index within the range of the hash table. The result of this process is called a hash value or hash code.

Hash functions play a vital role in generating these hash values. A good hash function ensures that the distribution of keys throughout the hash table is uniform and minimizes collisions.

Types of Collision

Collision in hashing can be classified into two types:

  • 1. Separate Chaining:
  • In separate chaining, each bucket in the hash table contains a linked list or some other data structure to store multiple elements with the same hash value.

    When a collision occurs, new elements are appended to the linked list at that particular index.

  • 2. Open Addressing:
  • In open addressing, all elements are stored directly within the hash table itself. When a collision occurs, additional probing techniques like linear probing, quadratic probing, or double hashing are used to find an empty slot within the table to accommodate the colliding element.

Handling Collisions

1. Separate Chaining:

In separate chaining, collisions are resolved by storing multiple elements with the same hash value in a linked list or another data structure. This allows for efficient retrieval of elements, even if they collide.

2. Open Addressing:

In open addressing, several techniques can be used to handle collisions:

  • Linear Probing:
  • In linear probing, when a collision occurs, the algorithm checks the next slot in the hash table and continues linearly until an empty slot is found.

  • Quadratic Probing:
  • Quadratic probing uses a quadratic function to determine the next slot to check when a collision occurs. The function increments a counter each time it probes an occupied location.

  • Double Hashing:
  • In double hashing, two hash functions are used to calculate the next possible position of an element in case of a collision. The second hash function helps avoid clustering and reduces collisions.

Collision Resolution Strategies

To minimize collisions and improve the efficiency of hash tables, several strategies can be employed:

  • 1. Choosing a Good Hash Function:
  • A good hash function should provide uniform distribution of keys across the hash table to reduce collisions.

    Load Factor:

    The load factor determines the number of elements stored in relation to the size of the hash table. Maintaining an optimal load factor helps minimize collisions.

  • 3. Resizing and Rehashing:
  • If the number of collisions exceeds a certain threshold, resizing and rehashing can help redistribute elements into a larger hash table, reducing collisions.

Conclusion

Collision is an important concept to understand when working with hash tables. By implementing appropriate collision resolution strategies, such as separate chaining or open addressing, and considering factors like hash function quality, load factor, and resizing, we can effectively handle collisions and ensure efficient data retrieval in our data structures.

Remember to choose the right collision resolution technique based on your specific requirements and the characteristics of your dataset.

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