Hashing is an important data structure designed to efficiently store and find data in an array. For example, if you have a list of 20000 numbers and you're given a number to search for in that list, you would scan each number in the list until you find a match. The hash function in the data structure is used to verify files imported from other sources. A hash key for an item can be used to speed up the process. It increases the efficiency of retrieval and optimizes the search. This is how we can simply give a hashing definition in data structure. It can take a significant amount of time to search through an entire list to locate a specific number. Manually scanning documents can be time-consuming and inefficient.

 

What is Hashing?

Hashing is a technique for mapping a large chunk of data into small tables using a hashing function. It is also known as the message digest function. It is a technique that uniquely identifies a specific item from a collection of similar items. Hash tables are a type of data storage that uses an array format. Each value in the array is assigned a unique index number. Hash tables use a technique called the hash technique to generate these unique index numbers. You only need to find the index of the desired item. With indexing, you can quickly scan the entire list, making it easy to find the item you're looking for. Indexing also helps with inserting operations when you need to insert data at a specific location. With indexing, you can quickly scan the entire list, making it easy to find the item you're looking for. The hash table is a collection of elements, and the hash search technique is performed on a part of the item, called the key. Each key is mapped to a number, with a range from 0 to the table size of 1.

 

What is Re-Hashing?

We’ll now look into the concept of Re-Hashing. We had a look at the concept of Hashing as this lays the foundation for Re-Hashing.

Rehashing is the process of recalculating the hash code of already stored entries and moving them to a bigger size hashmap when the number of elements in the map reaches the maximum threshold value. The computational complexity of this particular concept is O(1). The process of rehashing is when the hashcode of previously stored entries (Key-Value pairs) is recalculated in order to shift them to a larger size hashmap. This happens when the number of elements in the hash map reaches the maximum threshold value. According to the Java specification, a good load factor value is 75, while HashMap's default beginning capacity is 16. When the number of elements hits or exceeds 0.75 times the capacity, the complexity rises. To combat this, the array's size is doubled, and all the values are hashed again and saved in the new double-sized array. This is done to simplify things and keep the focus on the important things while keeping the load factor low.

 

Re-Hashing is always measured in terms of Load Factor.

 

What is the Load Factor?

We’ll have a brief look into what Load Factor is.

The load factor is a measure that helps us decide when to increase the HashMap or Hashtable capacity to maintain the get() and put() operation complexity at O(1). The default value of the load factor of HashMap is 0.75 (75% of the map size). In short, we can say that the load factor decides when to increase the number of buckets to store the key-value pairs. The hypothesis behind the use of Load Factor is that using a larger load factor will result in lower space consumption, but there will be more lookups required. If you use a smaller load factor, there will be more space consumed compared to the number of elements required.

 

Why Re-Hashing?

If we want to keep our HashMap efficient, we need to occasionally rehash it. This is because when we add new key-value pairs, the load factor rises and the complexity of the HashMap grows. By rehashing, we can disperse the items across the map, lowering the load factor and the complexity. This way, search() and insert() will always have a constant time complexity of O(1). If the load factor increases, we need to rehash. This happens when we insert key-value pairs into the map, which also increases the time complexity. Normally, the time complexity of HashMap is O(1). However, to reduce the time complexity and load factor of the HashMap, we use rehashing.

Re-Hashing is used when the following conditions arise:

i) When half of the table is filled.

ii) As the operation of Insertion fails.

iii) When the value of the Load Factor goes beyond the Threshold.

 

How does the concept of Re-Hashing work?

i) After you add a new entry to the map, be sure to check the load factor.

ii) If it's more than the predefined value (or the default value of 0.75 if none is specified), you'll need to rehash.

iii) This means making a new bucket array that's double the size of the previous one.

iv) Then, go through each element in the old bucket array and call insert() on it to add it to the new, larger bucket array.

 

Conclusion

In this article, we have discussed the concept of Hashing, followed by the concepts of Re-Hashing, including the working of Re-Hashing. Re-Hashing is an important concept of Data Structures and Algorithms. Why a need for DSA always? DSA plays a vital role in equipping candidates to ace interviews to get placed in a Product-Based organization. A candidate needs to be well prepared to get placed. For this, there is SkillSlash, which is an Online Project-Based Learning Platform. They provide a 1:1 training session. They make the candidates industry-ready by making them train on live-projects, for which they get a desired experience letter directly from that organization. Skillslash also offers Data Science Course In Delhi and Data science course in Nagpur with guaranteed job assurance. Contact the support team to know more.