How do you use hashing to improve the performance of data structures?

2 Hashing benefits

One of the main benefits of hashing is that it can speed up the search, insert, and delete operations on data structures. For example, if you have an array of data items, and you want to find a specific item, you would have to scan the entire array, which takes O(n) time, where n is the number of items. However, if you use hashing, you can store the items in an array based on their hashes, and then use the hash function to find the index of the item in the array, which takes O(1) time, or constant time. Similarly, you can use hashing to insert or delete items from the array in constant time, by computing their hashes and updating the array accordingly.

3 Hashing challenges

One of the main challenges of hashing is that it can cause collisions, which occur when two different data items have the same hash. Collisions can reduce the performance of hashing, as they can create conflicts and require extra steps to resolve them. For example, if you have an array of data items, and two items have the same hash, you would have to store them in the same index of the array, which can create a problem when you want to search, insert, or delete them. To avoid or handle collisions, you need to use a good hash function that minimizes the chances of collisions, and a collision resolution technique that resolves them efficiently.

4 Hashing techniques

To implement hashing in data structures, there are various techniques available. Direct addressing uses the data item itself as the hash, and stores it in an array at the corresponding index. Although this is simple and fast, it requires a lot of space and only handles integers as data items. The division method divides the data item by a prime number and takes the remainder as the hash, making it easy and uniform. However, collisions can occur if the data items have common factors with the prime number. The multiplication method multiplies the data item by a constant fraction and takes the fractional part as the hash, making it fast and random. But, collisions may happen if the constant fraction is not well chosen. Lastly, universal hashing uses a family of hash functions that are randomly selected for each data item; this is secure and adaptable but can be slow and complex.

5 Collision resolution

Resolving collisions in hashing can be done through a variety of techniques. Chaining is a simple and flexible option, but it can increase the space and time complexity of hashing if the linked lists are long or unbalanced. Open addressing is space-efficient and easy to implement, yet it can reduce the performance of hashing if the array is full or has many collisions. Double hashing, on the other hand, is fast and uniform, but requires a good choice of hash functions and can be complex to implement.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?