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A Brief Guide To Hashing In Data Structure: Definition, Features, & More

UserSienna Kelly time30 September,2022

Data structures are integral to information systems. Together with algorithms, they form the foundations of simple information retrieval systems as well as the most potent AI superclusters.

Arrays, Linked Lists, Stacks & Queues, Trees, Graphs—there are different types of data structures, each with its quirks, strengths, and applications. And hashing & hash tables are among the most effective & efficient data structure types, with a wide array of software development & IT applications.

So, what is hashing in data structure? How does it work? Find out everything & learn more about hash tables and hashing through this simple & intuitive write-up.

Data Structure & Hashing: A Brief Introduction

Any significant information source, such as a database, can be visualized as a table. There can be multiple fields in any particular table, each signifying a particular aspect or feature of the information stored.

  • Consider a telephone book with multiple fields, such as names, addresses, and phone numbers. So, if you are searching for a piece of particular information, then you use a particular field to search for it. The fields point to the information you are looking for.
  • Similarly, an account in Gmail has a specific user_id and password. Users use the ID and password to log in & access all the data stored in their Google Workspace.  

In the above examples, you did not need to know everything about the information you needed to retrieve or access, nor did you need to know the content of each & every field. Instead, the particular field that you use to access the information is known as the key. The key is a particular aspect of the whole information, the portion used to locate the information in its entirety in a data structure.

Keys play an essential role in hashing and hash tables. They are used to find particular information and must be unique to avoid confusion or misrepresentation—the benefits of hashing stem from the fact that it offers a highly time-effective way to access information.

But what exactly is hashing in computer science?

Hashes and Hashing Function

In hashing, hash tables are generally implemented using arrays (the simplest type of data structure), but entries or nodes of information are not stored consecutively. Instead, data is stored in different locations using their hashing values and generated using a particular function called the hashing function. The keys are modified using hashing functions to generate a hash value, which points to the node's location.

If you are using arrays to implement the hashing table, then the hash points to a specific array index that points to the memory space containing the information.

  • The output value computed by the hashing function using the key is often called the hashed key or the hash key value. The hash key value indicates the location where the data should be stored.
  • Entries are generally not sequential but scattered across the array.
  • Different types of hashes/hashing techniques/hashing methods are employed, with some of the most prominent ones being the division, mid-square, folding, and multiplication methods.
  • The average time complexity of hashing tables for insertion, searching, & deletion is O(1); that is, the time taken to search for any element in the table is constant. This is because hashing functions take a constant time to generate hashes ……

Performance Parameters of Hashing

Three key factors affect the performance of any hashing operation and the associated hashing table. They are:

  1. The Hash Function Employed

The function should run fast and be able to process keys to distribute all the data evenly across the data structure. At the same time, the hashes it generates must be able to minimize the number of probable collisions.  

  • The Collision Resolution Method

The hashing process must adopt an effective strategy for quick resolutions of any collision. The most common methods employed are the open addressing technique, which stores keys/entries in different positions and the separate chaining technique, which chains numerous keys/entries at the same position.

  • The Table Size

The table and the underlying data structure utilized are other crucial factors. Too much size may lead to memory wastage, and too low memory can lead to more collisions & lead to rehashing. The table size must fit the hash function and typically be a prime number.

Before moving on, make sure that you have got your jargon clear.

  • The raw data is the hashing key.
  • The hashing key is used to convert the hash key into the hash value.
  • The hashing algorithm generates the hash value to determine the location or the hash bucket.
  • Key-Value Pairs in hashing are pairs of the hashing key (raw data) and the hashing value (the location index).

How does hashing work? Well, you are employing hashing whenever you use a hashing function to determine the location of any data in a data structure. So, how do we use hashing in data structure?

The following hashing algorithm example can elucidate things better.

A Simple Example

Use the hash functions ‘key mod 7’ (the division method), and insert the following sequence of keys in a simple hash table.

50, 700, 76, 85, 92, 73, 101

Employ the separate chaining technique to resolve collision.

Solution:

The given sequence of steps for inserting entries in the hash table includes:

Step 1:

  • The hashing function is key mod 7, the keys being the values to be entered.
  • There are 7 entries, so the number of buckets in the structure MUST BE MORE than 7.
  • Develop an empty hash table with more than 7 entries.

In the division method, the dividend is the key value, while the divisor is the size of the hash table. Finally, the hash value or location index is the remainder of the process.

Step 2:

  • Insert all the values/keys using the hashing function (mod 7) to determine the location index.
  • The first key to be inserted is 50. 50 mod 7 is 1(the remainder is 1), so 50 goes at index 1, that is, the second slot if you use an array.
  • The hash buckets of the other values are then determined using the mod function.
  • 700 goes at index 0, 76 goes at 6, 85 shares the same bucket as 50 (since its remainder is also 1) and so does 92. 73 goes into index 3, and so does 101.
  • The separate chaining method implements linked lists to prevent any collision in hash buckets.  

Source: https://www.kdkce.edu.in/pdf/YDC-4IT-ADS-Hashing%20Techniques.pdf 

Collision Resolution in Hashing

Collisions occur when the hashes generated by the hashing function point to the exact location in the hashing table. Collision resolution strategies are employed to resolve such situations.

The preceding example uses the separate chaining collision resolution or handling method to resolve collisions.

It involves inserting elements sharing a hashing bucket into a linked list implemented as the array used as the hashing table. And that's the fundamental idea behind the separate chaining method. It is one of the most commonly used techniques for handling collisions. When multiple elements share the same slot index, they are inserted into singly linked lists called chains.

The following image showcases the separate chaining technique.

Well, that about wraps up this write-up. Hope this was an informative read for everyone.

Hashing is a prominent part of data structures and algorithms. An online course is one of the best ways to learn more about hashing methods & techniques, what hashing is in Java, C, C++, Python, etc.  

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