Rehashing

Definition

Rehashing is the process of resizing a hash table and redistributing its entries into a new hash table with a different size, usually triggered when the load factor exceeds a certain threshold. This technique is essential to maintain efficient operations within a hash table, such as insertions, deletions, and lookups, by ensuring that the number of collisions remains manageable. As the dataset grows or shrinks, rehashing optimizes the performance of both the hash function and collision resolution techniques.

5 Must Know Facts For Your Next Test

1. Rehashing typically occurs when the load factor exceeds a predefined threshold, often set between 0.7 and 0.9 to balance space efficiency and speed.
2. During rehashing, each entry from the old hash table is re-evaluated with the hash function for placement in the new table, which helps reduce collisions.
3. The time complexity for rehashing is O(n), where n is the number of entries being moved, but this process occurs infrequently compared to standard operations.
4. After rehashing, the new hash table size is usually a prime number or double the size of the previous table to optimize distribution of entries.
5. Properly implementing rehashing can significantly improve the average-case performance of hash table operations by ensuring that they remain close to O(1).

Review Questions

• How does rehashing impact the performance of a hash table during insertions and lookups?
  • Rehashing significantly enhances the performance of a hash table by redistributing its entries into a larger table when the load factor becomes too high. By reducing the number of collisions through a more spacious array, both insertions and lookups can be performed in constant average time complexity, O(1). This makes it crucial for maintaining efficient operations, especially as the dataset grows.
• Discuss how load factor influences when rehashing is initiated in a hash table.
  • The load factor is critical in determining when to initiate rehashing within a hash table. When the load factor reaches a specified threshold, indicating that too many entries are present relative to available slots, rehashing becomes necessary. This ensures that operations remain efficient and that collisions do not degrade performance. A typical load factor threshold might range from 0.7 to 0.9, balancing memory usage and operational speed.
• Evaluate the importance of choosing an appropriate new size for a hash table during rehashing and its effect on collision resolution strategies.
  • Choosing an appropriate new size for a hash table during rehashing is vital as it directly affects how well entries are distributed across the table. If the new size is not carefully selected—commonly as a prime number or double the original size—it can lead to increased collisions and decreased efficiency in retrieval operations. The choice impacts collision resolution strategies since techniques like chaining or open addressing depend heavily on having a well-distributed set of entries to minimize lookup times.

Related terms

Load Factor: A measure that determines how full a hash table is, calculated as the ratio of the number of entries to the number of slots available in the table.

Collision Resolution: Methods employed to handle situations where two keys hash to the same index in a hash table, ensuring that all entries can be stored and accessed correctly.

Hash Function: A function that takes an input (or 'key') and returns an integer, which determines where the input should be placed in the hash table.