Hashing in the data structure is used to quickly identify a specific value within a given array. It creates a unique hash code for each element in the array and then stores the hash code instead of the actual element. This allows for quick lookup when searching for a specific value, as well as easy identification of any duplicates. Hashing in the data structure is a technique that is used to quickly identify a specific value within a given array. It works by creating a unique hash code for each element in the array and then stores the hash code in lieu of the actual element. This allows for quick look-up when searching for a specific value, as well as easy identification of any duplicates.
The data structure's hash function validates the imported file using a hash value. You may quicken the process by using the item's hash key. It improves search efficiency and retrieval effectiveness. This is a straightforward method for defining hashing in a data structure. Hashing is an important tool to have in your arsenal when constructing data structures and can be used in many different ways. The data structure's hash function validates the imported file by using a hash value. You may quicken the process by using the item's hash key. It improves search efficiency and retrieval effectiveness. This is a straightforward method for defining hashing in a data structure. Hashing is an important tool to have in your arsenal when constructing data structures and can be used in many different ways.
However, you'll need to spend a lot of time searching through the full list to find that particular number. Not only is this manual scanning technique time-consuming, but it is also ineffective. You may speed up the search and locate the number by hashing the data structure. To make things easier for you, KnowledgeHut brings comprehensive skill-oriented online courses to learn the nitty gritty of data science and programming language. Learn Python Programming for big data systems to enhance your hands-on experience.
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What is Hashing?
Hashing involves changing one value into another based on a specified key or string of characters. The original string is often represented with a smaller, fixed-length value or key, which makes it simpler to locate or use.
Implementing hash tables is the most well-liked use of hashing. A list that may be accessed by using a hash table's index contains key and value pairs. The hash function helps map the keys to the table size since key and value pairs are infinite. The value for a given element is then changed to a hash value.
A hash value, or simply a hash, is a value generated by a mathematical hashing algorithm. A good hash uses a one-way hashing algorithm to prevent the hash from being converted back into its original key.
Use cases of Hashing
1. Data Retrieval
Utilizing algorithms or functions, hashing converts object data into a useful integer value. Once these things have been located on such object data map, queries can be filtered using a hash.
For instance, developers store data, such as a customer record, as key and value pairs in hash tables. Hash codes are then mapped to integers of a predetermined size, while keys are used to identify data and are input to the hash function.
2. Digital Signatures
In addition to allowing quick data retrieval, hashing aids in the encryption and decryption of digital signatures that are used to verify message senders and recipients. In this case, the digital signature is changed by a hash function before the hashed value or a message digest, and the signature is transmitted separately to the recipient.
When a message is received, the same hash function uses the signature to create the message digest, which is then compared to the message digest that was transmitted to make sure they are identical. The hash function indexes the initial value or key and makes data linked to a particular value or key that is obtained accessible in a one-way hashing operation.
What is Hashing in Data Structure?
Hashing in data structure pertains to a method of breaking up a huge amount of data into smaller tables. Also known as the message digest function, it is a method for distinguishing one distinct object from a group of related ones. By condensing the original input strings and data assets to short alphanumeric hash keys, developers and programmers can reduce both time and file space.
Hashing assists in focusing a search for a particular item on a data map. Hash codes create an index to hold values in this scenario. Therefore, hashing is employed to index and retrieve data from a database since it speeds up the process; finding an item using a smaller hashed key than just its original value is much simpler. is employed in this situation to index and retrieve data from a database since it speeds up the process; it is much simpler to find an item using a smaller hashed key than just its original value.
Hash tables are used to store the data in an array format. There is a unique index number for each value within the array. Hash tables create these distinct index numbers for each value stored in an array format using a method that they use, called the hash technique.
Some real-life examples of hashing in data structure include -
- In Libraries - A library contains an endless supply of books. The librarian assigns each book a unique number. This distinctive number aids in locating the exact position of the books on the bookshelf.
- In Schools - Each student in a class is assigned a unique roll number for easy identification. The school authority, later on, uses the unique roll number to retrieve relevant information about the particular student.
- In diagnostic centers and laboratories - Medical laboratories use unique serial numbers to identify and distinguish patient samples and patient information.
- Security Purposes - One must take precautions to ensure that the account does not end up in the wrong hands when they first visit a website that requests authentication through "Sign Up" and where they submit the login information to access the personal accounts. As a result, the database stores the entered password as a hash.
How does Hashing in Data Structure Work?
When it comes to data structures, hashing is a technique used to store and retrieve data in a database. It is fundamental to many data structures, such as hash tables and hash trees. Hashing involves mapping data to a unique value, called a hash code. The hash code is then used to index into an array, where the data is stored. To retrieve the data, the hash code is simply re-computed and used to index into the array.
Hashing is an efficient way to store and retrieve data in a data structure because it avoids the need for comparisons between elements. It also allows duplicate values to be stored in the same structure without causing collisions. Hashes are typically generated using a hashing algorithm, which takes an input and produces a hash code. There are many different hashing algorithms, but they all share the same basic principle: map data to a value that can be used to index into an array. Some popular hashing algorithms include SHA-1, MD5, and Murmur Hash. Hashing is a fundamental technique that is used in many different applications. It is particularly useful for storing and retrieving data from large collections. is a fundamental technique that is used in many different applications. It is particularly useful for storing and retrieving data from large collections.
In a nutshell, hashing works by mapping data to a hash code, which is then stored in a hash table. When data needs to be retrieved, the hash code is used to look up the data in the hash table. Hash tables are often used for quick lookup data, such as in caches and databases. One key advantage of hashing is that it can store data of any type, including integers, strings, and objects. Furthermore, hashing is relatively simple to implement and efficient when done correctly. As a result, hashing is frequently used in computer programming. is frequently used in computer programming.
Let's look at a hashing example to get a better understanding of how it works -
For example, let's say we want to map a list of string keys to a list of string values to grasp the concept better. Map the capital cities of countries, for example. Let's say we wish to save the information from the map's Table 1.
Now, assuming that the hash function is just to determine the length of the string, the hash table will look like this -
|Position (hash = key length)||Key||Value|
As Italy's hash code (length) is 5, we placed Italy in the 5th position in the Keys array and Rome on the fifth index… and so on.
What is the 'Key' in Hashing?
Hashing Key is the raw data that has to be hashed in a hash table. The hashing algorithm carries out a function to translate the hash key into the hash value. The outcome of feeding the hash key through the hashing algorithm is what is known as the hash value.
Hash Key = Key Value % Number of Slots in the Table
The different types of hashing keys are -
- Public key - Public key often termed 'asymmetric' key, is a type of key only used for data encryption. The mechanism is relatively slower because the public key is an open key. The common uses of public keys include the functions of cryptography, the transfer of bitcoins, and securing online sessions. However, Public key functions along with a set of private keys, and thus, the overall security is not compromised.
- Private key - The private key is employed in both encryption and decryption. Each party that sends or receives sensitive information that has been encrypted shares a key. Due to the fact that both parties share it, the private key is also referred to as "symmetric". A private key is typically a long, impossible-to-guess string of bits generated at random or artificially random.
- SSH public key - SSH uses both a public and a private key. SSH is a set of keys that can be used to authenticate and decrypt a communication sent from a distance. Both the distant servers and the stakeholders have access to the public key.
What are Hash Function and Hash Table?
What is a Hash Table?
The hash table is an associative data structure that stores data as associated keys. An array format is used to store hash table data, where each value is assigned its unique index. By knowing the index of the desired data, we can access it very quickly.
As a result, inserting and searching data is very fast, regardless of data size. In a Hash Table, elements are stored in an array, and an index is generated using hashing techniques in a data structure.
What is Hash Function?
A key is transformed into a hash key through a fixed process in hash functions. Hash values are length-restricted values that can be derived from a key. Despite the fact that the hash value is usually less than the original, the original sequence of characters is still reflected in the hash value. Following the transfer of the digital signature, the recipient receives both the hash value and the digital signature. A comparison is made between the hash value generated by the receiver and the one received along with the message using the same hash algorithm. Messages are sent without error if their hash values match exactly.
The characteristics of the hash function are -
- There is no limit to the length of a message
- Message digests are generated with a fixed length
- A message digest can be computed quickly (and easily)
- Message digests cannot be generated from the hash - they are irreversible
- Message values change dramatically when small changes are made
- The hash is collision-free because two different messages can't result in the same hash value
Some important characteristics of the hash function are described below -
- Collision free - A hash function is collision-free because it contains two key features. The function should, first and foremost, transfer equally likely inputs to all feasible outputs. Second, the function must be deterministic, guaranteeing that it will always yield the same results when given the same input. A collision-free hash function ensures that no two input hash maps out the same output hash.
- Property to be hidden - A hash function is used to map data of arbitrary size to fixed-size data. One key characteristic of a good hash function is that it should be hard to guess the input value from its output. In other words, it should be difficult to find two different inputs that produce the same output. The output should be evenly distributed across all possible values. This ensures that every possible input will have a unique output and that the outputs will be spread evenly throughout the range of possible values.
- Puzzle friendly – A hash function is ought to be suitable for puzzles. The choice of an input that yields a predetermined result ought to be challenging. As a result, it is best to choose the input from a range that is as diverse as feasible.
Types of Hash functions
Many hash functions use alphanumeric or numeric keys. The main hash functions cover -
- Division Method.
- Mid Square Method.
- Folding Method.
- Multiplication Method.
Let's examine these methods in more detail.
1. Division Method
The division method is the simplest and easiest method used to generate a hash value. In this hash function, the value of k is divided by M and uses the remainder as obtained.
Formula - h(K) = k mod M
(where k = key value and M = the size of the hash table)
- This method works well for any value of M
- The division approach is extremely quick because it only calls for one operation.
- This may lead to poor performance as consecutive keys are mapped to consecutive hash values in the hash table
- There are situations when choosing the value of M requires particular caution.
- k = 1320
- M = 11
- h (1320) = 1320 mod 11
- = 0
2. Mid Square Method
The steps involved in computing this hash method include the following -
- Squaring the value of k ( like k*k)
- Extract the hash value from the middle r digits.
Formula - h(K) = h(k x k)
(where k = key value )
- Since most or all of the key value's digits contribute to the outcome, this strategy performs well. The middle digits of the squared result are produced by a process in which all of the essential digits participate.
- The top or bottom digits of the original key value do not predominate in the outcome.
- One of this method's constraints is the size of the key; if the key is large, its square will have twice as many digits.
- Chance of repeated collisions.
Let's take the hash table with 200 memory locations and r = 2, as decided on the size of the mapping in the table.
- k = 50
- k = k x k
- = 50 x 50
- = 2500
- h(50) = 50
3. Folding Method
There are two steps in this method -
- The key-value k should be divided into a specific number of parts, such as k1, k2, k3,..., kn, each having the very same number of digits aside from the final component, which may have fewer digits than the remaining parts.
- Add each component separately. The last carry, if any, is disregarded to determine the hash value.
Formula - k = k1, k2, k3, k4, ….., kn
s = k1+ k2 + k3 + k4 +….+ kn
(Where, s = addition of the parts of key k)
- Breaks up the key value into precise equal-sized segments for an easy hash value
- Independent of distribution in a hash table
- Sometimes inefficient if there are too many collisions
- k = 54321
- k1 = 54 ; k2 = 32 ; k3 = 1
- s = k1 + k2 + k3
- = 54 + 32+ 1
- = 87
- h (k) = 87
4. Multiplication Method
Steps to follow -
- Pick up a constant value A (where 0 < A < 1)
- Multiply A with the key value
- Take the fractional part of kA
- Take the result of the previous step and multiply it by the size of the hash table, M.
Formula - h(K) = floor (M (kA mod 1))
(Where, M = size of the hash table, k = key value and A = constant value)
- It may be applied to any number between 0 and 1, albeit some numbers tend to produce better results than others.
- When the table size is a power of two, the multiplication technique is typically appropriate since multiplication hashing makes it possible to compute the index by key quickly.
- k = 1234
- A = 0.35784
- M = 100
- h (1234) = floor [ 100(1234 x 0.35784 mod 1)]
- = floor [ 100 ( 441.57456 mod 1)]
- = floor [100 ( 0. 57456)]
- = floor [ 57.456]
- = 57
- h (1234) = 57
Choosing a good hash function
- Creating an effective hash function that distributes the added item's index value evenly across the database is important.
- Quick and easier to compute according to the requirements.
- An approach to successfully resolve collisions in hash tables is essential for generating an index for a key whose hash index corresponds to an existing spot.
What is a Hash Collision?
A hash collision happens when the same hash value is produced for two different input values by a hash algorithm. But it's important to point out that collisions aren't a problem; they're a fundamental aspect of hashing algorithms.
Collisions occur because different hashing techniques in data structure convert every input into a fixed-length code, regardless of its length. Since there are an endless number of inputs and a limited number of outputs, the hashing algorithms will eventually produce repeating hashes.
Types of Hashing in Data Structure
There are two primary hashing techniques in a data structure.
- Open hashing / separate chaining / closed addressing
- Closed hashing / open addressing
1. Open Hashing / Separate Chaining
Separate chaining is the most used collision hashing technique in data structures that uses a lined list. Any two or more components that meet at the same point are chained together to form a single-linked list known as a chain. Every linked list member that hashes is chained to the same position here. Also known as closed addressing, open hashing is used to avoid any hash collisions, using an array of linked lists in order to resolve the collision.
- In a simple hash function, h(key) = key mod table size.
- Let us take a simple hash table with table size = 6
- Sequence keys = 50, 600, 74, 113, 14, 99
(As collision occurs after inserting keys 74 and 14, they are added to the chain)
Advantages of Open hashing -
- Easy and basic to implement
- As there are a lot of empty spaces in the hash table, we can add more keys to the table.
- Comparatively less sensitive to different load factors
- Mostly used when unsure about the amount and frequency of the keys to be implemented in the hash table.
- Wastage of space
- With a long chain, the search time is increased
- Poor cache performance as compared to closed hashing.
2. Closed hashing (Open addressing)
Open addressing stores all entry records within the array itself, as opposed to linked lists. The phrase 'open addressing' refers to the notion that the hash value of an item does not identify its location or address. In order to insert a new entry, the array is first checked before computing the hash index of the hashed value, starting with the hashed index. If the space at the hashed index is empty, the entry value is inserted there; otherwise, some probing sequences are used until an empty slot is found.
The procedure used to navigate through entries is known as the probe sequence. You can vary the time between succeeding entry slots or probes in different probe sequences.
In Closed hashing, there are three techniques that are used to resolve the collision:
Linear probing involves systematically checking the hash table from its very beginning. A different site is searched if the one received is already occupied. In linear probing, the interval between the probes is usually fixed (generally, to a value of 1).
- The formula for linear probing: index = key % hashTableSize
- The hash(n) is the index computed using a hash function, and T is the table size.
- If slot index = ( hash(n) % T) is full, then the next slot index is calculated by adding 1 ((hash(n) + 1) % T).
The sequence goes as -
- index = ( hash(n) % T)
- (hash(n) + 1) % T
- (hash(n) + 2) % T
- (hash(n) + 3) % T … and so on.
- For a hash table, Table Size = 20
- Keys = 3,2,46,6,11,13,53,12,70,90
|SL. NO||KEY||HASH||INDEX||INDEX |
(AFTER LINEAR PROBING)
The only distinction between linear and quadratic probing is the space between succeeding probes or entry slots. When a hashed index slot for an entry record is already taken, you must start traversing until you discover an open slot. The spacing between slots is calculated by adding each subsequent value of any arbitrary polynomial in the initial hashed index.
The formula for quadratic probing: index = index % hashTableSize
- The hash(n) is the index computed using a hash function, and T is the table size.
- If slot index = ( hash(n) % T) is full, then the next slot index is calculated by adding 1 (hash(n) + 1 x 1) % T
The sequence goes as -
- index = ( hash(n) % T)
- (hash(n) + 1 x 1) % T
- (hash(n) + 2 x 2) % T
- (hash(n) + 3 x 3) % T … and so on
- For a hash table, Table Size = 7
- Keys = 22,30,50
|SL. NO||KEY||HASH||INDEX||INDEX |
(AFTER QUADRATIC PROBING)
|5||50||50%7||1||1(1+2 x 2)|
It is another hash function that determines the intervals between probes. An optimized method of reducing clustering is double hashing. An additional hash function is used to calculate the increments for the probing sequence.
The formula for double hashing - (first hash(key) + i * secondHash(key)) % size of the table
The sequence goes as follows -
- index = hash(x) % S
- (hash(x) + 1*hash2(x)) % S
- (hash(x) + 2*hash2(x)) % S
- (hash(x) + 3*hash2(x)) % S … an so on
- For a hash table, Table Size = 7
- Keys = 27,43,98,72
|SL. NO||KEY||HASH||INDEX||INDEX |
(AFTER DOUBLE HASHING)
|2||92||92%7||6||[h1(92) + i * (h2(92)] % 7|
= [6 + 1 * (1 + 92 % 5)] % 7
= 9 % 7
|5||72||72%7||2||[h1(72) + i * (h2(72)] % 7|
= [2 + 1 * (1 + 72 % 5)] % 7
= 5 % 7
When compared to any other data structure, hashing provides a more secure and adjustable method of retrieving data. Arrays and lists can be searched more quickly in hashing, meaning you can find the index of the required item in no time with just a few operations using this method. As a result, hashing plays a crucial role in information security by ensuring that messages are not altered during transmission. As well as encoding and decoding advanced marks and digital signatures, it provides security for sensitive data.
Hashing is mostly used for database recovery since using a small hash key to find anything is quicker than using the original value. The information can also be determined if indistinguishable without opening or comparing them.
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