Rehashing

Definition

Rehashing is the process of changing the hash table size and recalculating the hash values for existing entries when the load factor exceeds a certain threshold. This technique helps maintain efficient data retrieval and storage by reducing collisions and ensuring that the hash table can accommodate more entries. By redistributing the data across a larger table, rehashing optimizes performance and keeps operations like insertion, deletion, and search efficient.

5 Must Know Facts For Your Next Test

1. Rehashing typically occurs when the load factor of a hash table exceeds a predefined limit, often around 0.7 to 0.75, indicating that it's becoming too full.
2. When rehashing takes place, a new larger array is created, and all existing entries are rehashed and inserted into this new array based on their new hash values.
3. The performance improvement from rehashing can significantly reduce the average time complexity of operations like search, insert, and delete from O(n) to O(1) under ideal conditions.
4. Rehashing can be an expensive operation since it requires iterating through all existing entries and recalculating their positions in the new array.
5. Dynamic resizing through rehashing ensures that a hash table remains efficient over time as more elements are added or removed, thus maintaining optimal performance.

Review Questions

• How does rehashing improve the efficiency of a hash table?
  • Rehashing improves efficiency by resizing the hash table when the load factor exceeds a specific threshold. This resizing allows for more available slots in the table, which reduces collisions and helps distribute entries more evenly. By recalculating the hash values for existing entries, rehashing ensures that data retrieval and storage operations remain efficient, maintaining average time complexities close to O(1).
• What factors should be considered when determining when to perform rehashing in a hash table?
  • When deciding to perform rehashing, it's crucial to monitor the load factor closely, which indicates how full the hash table is. A commonly accepted threshold is around 0.7 to 0.75; if exceeded, it triggers the need for rehashing. Additionally, considerations include the cost of rehashing compared to potential performance gains and how frequently data is being added or removed from the hash table, as this influences overall efficiency.
• Evaluate the impact of rehashing on both time complexity and memory usage in hash tables over extended operations.
  • Rehashing has a significant impact on both time complexity and memory usage during extended operations. While individual operations like insertions may become O(1) after rehashing due to reduced collisions, the actual process of rehashing can temporarily increase time complexity to O(n) as all existing entries must be processed. Memory usage also increases since a larger array must be allocated during rehashing. However, in a dynamic context with frequent insertions or deletions, these costs are justified as they help maintain long-term efficiency and performance stability.

Related terms

Hash Function: A mathematical function that converts an input (or 'key') into a fixed-size string of bytes, typically producing a unique hash code used to index data in a hash table.

Collision: An event that occurs when two different keys hash to the same index in a hash table, requiring additional strategies to manage the conflict.

Load Factor: A measure of how full a hash table is, calculated as the ratio of the number of entries to the total number of slots; it helps determine when rehashing should occur.