Hashing refers to the process of generating a fixed-size output from an input of

variable size using the mathematical formulas known as hash functions. This
technique determines an index or location for the storage of an item in a data
structure.

Need for Hash data structure

The amount of data on the internet is growing exponentially every day, making it
difficult to store it all effectively. In day-to-day programming, this amount of data
might not be that big, but still, it needs to be stored, accessed, and processed easily
and efficiently. A very common data structure that is used for such a purpose is the
Array data structure.
Now the question arises if Array was already there, what was the need for a new
data structure! The answer to this is in the word “efficiency“. Though storing in Array
takes O(1) time, searching in it takes at least O(log n) time. This time appears to be
small, but for a large data set, it can cause a lot of problems and this, in turn, makes
the Array data structure inefficient.
So now we are looking for a data structure that can store the data and search in it in
constant time, i.e. in O(1) time. This is how Hashing data structure came into play.
With the introduction of the Hash data structure, it is now possible to easily store
data in constant time and retrieve them in constant time as well.
Components of Hashing
There are majorly three components of hashing:
 1. Key: A Key can be anything string or integer which is fed as input in the hash
function the technique that determines an index or location for storage of an
item in a data structure.
2. Hash Function: The hash function receives the input key and returns the index
of an element in an array called a hash table. The index is known as the hash
index.
3. Hash Table: Hash table is a data structure that maps keys to values using a
special function called a hash function. Hash stores the data in an associative
manner in an array where each data value has its own unique index.

Components of Hashing
What is Collision?
The hashing process generates a small number for a big key, so there is a possibility
that two keys could produce the same value. The situation where the newly inserted
key maps to an already occupied, and it must be handled using some collision
handling technology.
Collision in Hashing

Advantages of Hashing in Data Structures

 Key-value support: Hashing is ideal for implementing key-value data
structures.
 Fast data retrieval: Hashing allows for quick access to elements with constant-
time complexity.
 Efficiency: Insertion, deletion, and searching operations are highly efficient.
 Memory usage reduction: Hashing requires less memory as it allocates a fixed
space for storing elements.
 Scalability: Hashing performs well with large data sets, maintaining constant
access time.
 Security and encryption: Hashing is essential for secure data storage and
integrity verification.
How does hashing work?
Hashing involves three components:
 Input. The data entered into the algorithm is called input. This data can have
any length and format. For instance, an input could be a music file or a paper.
In hashing, every piece of input data is used to produce a single output.
 Hash function. The central part of the hashing process is the hash function.
This function takes the input data and applies a series of mathematical
operations to it, resulting in a fixed-length string of characters. The hash
 function ensures that even a small change in the input data produces a
significantly different hash value.
 Hash output. Unlike the input, the hashing process's output or hash value has
a set length. It's challenging to determine the length of the original input
because outputs have a set length, which contributes to an overall boost in
security. A hash value is a string of characters and numbers that a hacker
might not be able to read, keeping a person's information private. As each
hash value is distinct, hash values are also frequently referred to
as fingerprints.
Benefits of hashing
Hashing has applications in various fields such as cryptography, computer science
and data management. Some common uses and benefits of hashing include the
following:
 Data integrity. Hashing is commonly used to ensure data integrity. By
generating a hash value for an amount of data, such as a file or message, a
user can later compare it with the hash value of the received data to verify if
any changes or corruption occurred during transmission.
 Efficient data retrieval. Hashing enables efficient data retrieval in hash tables,
especially when dealing with large data sets. It uses functions or algorithms to
map object data to a representative integer value. A hash can then be used to
narrow down searches when locating these items on that object data map.
For example, in hash tables, developers store data -- perhaps a customer
record -- in the form of key and value pairs. The key identifies the data and
operates as an input to the hashing function, while the hash code or the
integer is then mapped to a fixed size. Typically functions supported by hash
tables include insert (key, value), get (key) and delete (key).
 Digital signatures. In addition to enabling rapid data retrieval, hashing helps
encrypt and decrypt digital signatures used to authenticate message senders
and receivers. In this scenario, a hash function transforms the digital signature
before both the hashed value -- known as a message digest -- and the
signature are sent in separate transmissions to the receiver. Upon receipt, the
same hash function derives the message digest from the signature, which is
then compared with the transmitted message digest to ensure both are the
same. In a one-way hashing operation, the hash function indexes the original
value or key and enables access to data associated with a specific value or key
that's retrieved.
 Password storage. Hashing is widely used for secure password storage.
Instead of storing passwords in plain text, they're hashed and stored as hash
values. This adds an extra layer of security so even if the hash values are
compromised, it's computationally infeasible to reverse-engineer the original
passwords.
 Fast searching. Hashing algorithms are designed to organize data into easily
searchable buckets. This makes searching for specific data faster compared to
other data structures. Hashing is particularly useful in applications that require
rapid search results, such as databases and search engines.
 Efficient caching. Hash tables are commonly used to
configure caching systems. By using hash values as keys, data can be quickly
retrieved from cache memory, reducing the need to access slower storage
systems. This improves overall system performance and response times.
 Cryptographic applications. Hashing plays a crucial role in various
cryptographic algorithms. Cryptographic hash functions are used to generate
digital signatures, authenticate messages and ensure data integrity and
authenticity. Hashing algorithms such as Secure Hash Algorithm 2, or SH-2, are
widely used in cryptographic applications.
 Space efficiency. Hashing enables efficient use of storage space. Hash values
are typically shorter than the original data, making them more compact and
easier to store. This is especially beneficial when dealing with large data sets
or limited storage resources.
 Blockchain technology. Hashing is widely used in blockchain, especially in
cryptocurrencies such as Bitcoin. Blockchain is a digital ledger that stores
transactional data and each new record is called a block. Since all participants
in a blockchain have access to identical data, ensuring the integrity of previous
transactions is critical. This is when hashing comes into play, as it ensures the
integrity and immutability of data stored in blocks.
 Data compression. By employing coding algorithms such as the Huffman
coding algorithm, which is a lossless compression algorithm, hashing can be
used to encode data efficiently.
 Database management. When dealing with large data sets, combing through
multiple entries to obtain the necessary data can be intimidating. Hashing
offers an alternative by letting users search for data records using a search key
and a hash function rather than an index structure. Hash files organize data
into buckets, each of which can hold numerous records. The basic role of hash
 functions is to map search keys to the exact location of a record within a given
bucket.

This illustrates the process of converting key values into indexes.

Disadvantages of hashing
While hashing offers several benefits, it also has certain drawbacks and limitations,
including the following:
 Risk of collisions. Hashing can sometimes suffer from collisions, which occur
when two different inputs produce the same hash value. Collisions can lead to
decreased performance and increased lookup time, especially if the number
of collisions is high. Techniques such as chaining and open addressing can be
used to handle collisions, but they can introduce additional complexity. For
example, the cache performance of chaining isn't always the best, as keys use
a linked list.
 Non-reversible. Since hash functions are intended to be one-way functions,
reversing the process and getting the original input data isn't computationally
viable. This could be a drawback if reverse lookup is necessary.
 Limited sorting. Hashing isn't ideal if data needs to be sorted in a specific
order. While hash tables are designed for efficient lookup and retrieval, they
don't provide inherent support for sorting operations. If sorting is a
requirement, other data structures such as balanced search trees might be
worth considering.
 Space overhead. To store the hash values and the related data, hashing
typically requires more storage space. This space overhead can be substantial
when working with big data sets and can be a cause for concern when storage
resources are limited.
  Key dependency. Hashing relies on the uniqueness of keys to ensure efficient
data retrieval. If the keys aren't unique, collisions can occur more frequently,
leading to performance degradation. It's important to carefully choose or
design keys to minimize the likelihood of collisions.
 Difficulty in setting up. Configuring a hash table or a hashing algorithm can be
more complex compared to other data structures. Handling collisions, resizing
the hash table and ensuring efficient performance requires careful
consideration and planning and can make hashing challenging to set up.
What is hashing in data structure?
Hashing is used in data structures to efficiently store and retrieve data. The Dewey
Decimal System, which enables books to be organized and stored based on their
subject matter, has worked well in libraries for many years and the underlying
concept works just as well in computer science. Software engineers can save both file
space and time by shrinking the original data assets and input strings to short
alphanumeric hash keys.
When someone is looking for an item on a data map, hashing narrows down the
search. In this scenario, hash codes generate an index to store values. Here, hashing
is used to index and retrieve information from a database because it helps accelerate
the process. It's much easier to find an item using its shorter hashed key than its
original value.
What is hashing in cybersecurity?
Many encryption algorithms are used to enhance cybersecurity, including MD5, SHA-
256, SHA-512 and Bcrypt. Each algorithm has unique qualities and levels of security
and the application's specific requirements determine which algorithm is used.
Hashed strings and inputs are meaningless to hackers without a decryption key. For
example, if hackers breach a database and find data such as "John Doe, Social
Security number 273-76-1989," they can immediately use that information for their
nefarious activities. However, a hashed value such as "a87b3" is useless for threat
actors unless they have a key to decipher it. As such, hashing secures passwords
stored in a database.
What is hashing in cryptography?
The primary purpose of hashing in cryptography is to provide a unique and
irreversible representation of data. Cryptography uses multiple hash functions to
secure data.
The MD5 hashing algorithm and how it works in cryptography.
Some of the most popular cryptographic hashes include the following:
 SHA-2.
 SHA-3.
 The series of message-digest hash functions: MD2, MD4, MD5 and MD6.
Message-digest hash functions such as MD2, MD4 and MD5 hash digital signatures.
Once hashed, the signature is transformed into a shorter value called a message
digest.
SHA is a standard algorithm used to create a larger 160-bit message digest. While it's
similar to MD4 as well as good at database storage and retrieval, this isn't the best
approach for cryptographic or error-checking purposes. SHA-2 is used to create a
larger 224-bit message digest. SHA-3 is SHA-2's successor.
Best Practices for Hashing
Okay, now we know what hashing is, its role in computer science, and how to tackle
those pesky collisions. But how do we make sure we're doing it right? Well, here are
some best practices for hashing that you should remember:
1. Choose a Good Hash Function: Remember, the right hash function is like a
great party host. It ensures everyone gets a unique spot and keeps collisions
to a minimum. So, choose wisely!
2. Consider the Load Factor: Load factor is the ratio of the number of elements
to the total size of the table. It's like making sure there's enough cake for all
 your party guests. If the load factor gets too high, it might be time to resize
your hash table.
3. Use Appropriate Collision Resolution: Choose the collision resolution
technique that fits your data and requirements. Not all parties are the same,
after all!
4. Remember Security: Hashing can be a powerful tool for securing data. But
make sure you're using the right techniques, like cryptographic hashing, to
keep your data safe and sound.
These are just some of the best practices for hashing in computer science education.
By following these, you'll be well on your way to becoming a hashing pro! But don't
stop here. The world of hashing is vast and ever-evolving, so there's always more to
learn.
Real-world Applications of Hashing
Now that we've chatted about the best practices for hashing, let's look at where all
this comes into play in the real world. You may be thinking, "Where would I use
hashing in computer science education?" Well, here are a few examples:
1. Data Retrieval: Think about a library. How do they keep track of all those
books? They could use a hash table, where the book's title is hashed to a
specific location on the shelf. That way, finding your favorite book becomes a
breeze.
2. Password Verification: Ever wonder how websites check your password
without actually knowing what it is? That's the magic of hashing! When you
set your password, it's hashed and stored. When you log in, your password is
hashed again, and if the hashes match, you're in!
3. Cache Memory: Your computer's cache memory uses hashing to quickly find
data. It's like your computer's own personal library, and hashing is the
librarian.
4. Database Indexing: Databases use hashing to speed up data retrieval. It's like
being able to find exactly what you're looking for in a warehouse in seconds.
And there are many more applications of hashing in computer science education. It's
a tool that's as versatile as it is powerful. So, next time you're using a computer,
remember: there's probably some hashing going on behind the scenes.