Hashing in Data Structure: Function, Techniques

Hashing is a useful data structure for effectively retrieving and saving data in an array. For example, if you have a list of 20000 numbers and you've given one of them a number to look up, you'll scan each one until you discover a match. It will take a large amount of time for you to search through the full list for that precise number. This manual scanning method is not only time-consuming but also inefficient. You may narrow down the search and find the number in seconds using hashing in the data structure.

With examples, this blog will provide a greater grasp of the hash method, hash tables, and linear probing.

What is Hashing in Data Structure?

The use of a hashing function to map a large piece of data into little tables is known as hashing in the data structure. The message digest function is another name for it. It's a strategy for distinguishing a single object from a group of similar items.

The data is stored in an array format using hash tables. A unique index number has been assigned to each value in the array. For each value stored in an array format, hash tables use a technique to produce these unique index numbers. The hash approach is the name for this method.

Rather than finding the data, you simply need to locate the index of the required object. You can swiftly scan the full list and retrieve the item you want with indexing. When you need to put data at a certain location, indexing also comes in handy. You can update and retrieve data in seconds, no matter how big or tiny the table is.

In a data structure, hashing is a two-step procedure.

The hash function returns a tiny integer or hash value for the item. The original data is stored using this integer as an index.

It keeps the information in a hash table. A hash key can be used to rapidly locate data.

Examples of Hashing in Data Structure

The following are some real-world data structure hashing examples:

In schools, each student is assigned a unique roll number by the teacher. Later, the teacher will utilize that roll number to look up information on that particular kid.

There are no limits to the number of books that can be found in a library. Each book is given a unique number by the librarian. This one-of-a-kind number assists in locating the books on the bookcase.

Hash Function

A data structure's hash function converts data of any size to data of a specific size. It returns a tiny integer number (sometimes known as a hash value), as well as hash codes and hash sums.

hash = hashfunc(key)

index = hash % array_size

The following requirements must be met by the has function:

• It is simple to compute a suitable hash function.

• A good hash function avoids clustering and equally distributes keys over the hash table.

• When two components or items are assigned to the same hash value, a good hash function prevents collision.

Hash Table

In a data structure, hash tables are used to hold key-value pairs. The hash function is then used to build an index for the hash table. Insert, update, and search activities are all performed with the help of this one-of-a-kind index.

How does Hashing in Data Structure Works?

Hashing is a function that converts texts or integers into a tiny integer value. Hash tables use a hashing function to recover an item from a list. The goal of the hashing technique is to uniformly distribute data over an array. Hashing assigns a unique key to each element. This key is used by the hash table to access the data in the list.

The data is stored in a key-value pair in a hash table. The hashing function takes the key as an input. For each value recorded, the hash function generates a unique index number. The value that corresponds to that key is stored in the index number. As an output, the hash function returns a tiny integer number. The hash value is the result of the hashing function.

Collision Resolution Techniques

When two keys in a hash table are assigned the same index number, a collision occurs. Because each index in a hash table is designed to store just one item, the collision causes a problem. To manage the efficiency of a hash table, hashing in data structures employs many collision resolution approaches.

Linear Probing

In a data structure, hashing produces an array index that is already being used to store a value. Hashing executes a search operation and probes linearly for the next empty cell in this situation.

Double Hashing

Two hash functions are used in the double hashing technique. When the first hash function creates a collision, the second hash function is used. It stores the value using an offset index.

The formula for the double hashing technique is as follows:

(firstHash(key) + i * secondHash(key)) % sizeOfTable

Where i is the offset value. This offset value keeps incremented until it finds an empty slot.

Conclusion

Although double hashing has a significant computational cost, it finds the next available slot faster than linear probing. The examples included in this article are solely for illustration. You can change the statements above to meet your own needs. We learned about the notion of hashing in data structures in this blog.