Double hashing is a collision resolution technique used in hash tables. It works by using two hash functions to compute two different hash values for a given key. The first hash function is used to compute the initial hash value, and the second hash function is used to compute the step size for the probing sequence.
Double hashing has the ability to have a low collision rate, as it uses two hash functions to compute the hash value and the step size. This means that the probability of a collision occurring is lower than in other collision resolution techniques such as linear probing or quadratic probing.
However, double hashing has a few drawbacks. First, it requires the use of two hash functions, which can increase the computational complexity of the insertion and search operations. Second, it requires a good choice of hash functions to achieve good performance. If the hash functions are not well-designed, the collision rate may still be high.
Advantages of Double hashing
- The advantage of Double hashing is that it is one of the best forms of probing, producing a uniform distribution of records throughout a hash table.
- This technique does not yield any clusters.
- It is one of the effective methods for resolving collisions.
Double hashing can be done using :
(hash1(key) + i * hash2(key)) % TABLE_SIZE
Here hash1() and hash2() are hash functions and TABLE_SIZE
is size of hash table.
(We repeat by increasing i when collision occurs)
Method 1: First hash function is typically hash1(key) = key % TABLE_SIZE
A popular second hash function is hash2(key) = PRIME – (key % PRIME) where PRIME is a prime smaller than the TABLE_SIZE.
A good second Hash function is:
- It must never evaluate to zero
- Just make sure that all cells can be probed
Below is the implementation of the above approach:
Status of hash table after initial insertions : -1, 66, -1, -1, -1, -1, 123, -1, -1, 87, -1, 115, 12, Search operation after insertion : 12 present 115 present Status of hash table after deleting elements : -1, -2, -1, -1, -1, -1, -2, -1, -1, -2, -1, 115, 12,
- Insertion: O(n)
- Search: O(n)
- Deletion: O(n)
Auxiliary Space: O(size of the hash table).
Here is an Easy implementation of Double Hashing in Python.
Note: It’s written in python3.
Entered key: 4 at index 4 Entered key: 11 at index 1 Entered key: 29 at index 0 Entered key: 1 at index 3 Entered key: 5 at index 2 The Hash List After Entering Elements 29 11 5 1 4
- Best Case: O(1), When there is no collision and the element is placed at the first Hash index then the time complexity of Double Hashing is O(1).
- Worst Case: O(n), When the list is full of elements and the new element is added then the time complexity of Double Hashing is O(n).
Auxiliary Space: The space complexity of Double Hashing is O(n) as we need to create a hash list of size equal to the table size.