Hashing in C
Introduction
A foundational idea in computer science, hashing is essential to many applications. By mapping key-value pairings, it is a technique that makes it possible to retrieve and store data efficiently. A hash function must be implemented to organize and optimize data structures like hash tables, dictionaries, and caches in C programming.
What is a Hashing in C?
Translating data (keys) to distinct numerical values (hash codes) using the hashing technique in C is possible. It provides effective data storage and retrieval in structures like hash tables, dictionaries, and caches. By reducing collisions, when several keys generate the same hash code, hashing improves speed.
Hash Function in C
Multiple built-in hash function routines are available in C. The DJB2 hash function, which bears Daniel J. Bernstein's name and is one of C's most used hash functions, is used often. To better understand how it was put into practice:
#include<stdio.h> unsigned long djb2_hash(const char *str) { unsigned long hash = 5381; int c; while ((c = *str++)) { hash = ((hash << 5) + hash) + c; // hash * 33 + c } return hash; } int main() { const char *input = "Hello, World!"; unsigned long hashValue = djb2_hash(input); printf("Input: %s ", input); printf("Hash Value: %lu ", hashValue); return 0; }
Output:
Each character in the input string is iterated through by the DJB2 hash algorithm, which increases the hash value by 33 and adds the character's ASCII value. This procedure is repeated until the end of the string is reached, producing an effective and evenly dispersed hash value.
Mechanism of Hashing:
Data (keys) of any size can be converted via the hashing technique into fixed-size values (hash values or hash codes). Hashing is a technique used to quickly store, retrieve, and manipulate data by effectively mapping data to specified areas inside a data structure, such as a hash table. Let's investigate the hashing process in further detail:
1.Hashing Function:
The hashing process is based on a hash function. It creates a hash value from an input (key). The following properties should be present in the hash function:
- Fast Computation: The hash function should be computationally efficient to ensure high-performance hashing.
- Deterministic: The same key should always produce the same hash value.
- Uniform Distribution: To minimize collisions and maximize performance, the hash function should distribute the hash values equally over every aspect of potential hash values.
2. Hash Value:
The result of the hash function is the hash value. It serves as a static illustration of the incoming data. Hash values, used to index and find data inside a data structure, are generally numeric or alphanumeric.
3. Hash Table:
Popular data structures that use hashing to store and retrieve data rapidly include hash tables. It has many places, or buckets, where one or more key-value pairs can be kept in each. The hash value produced by the hash function is utilized as an index to find the slot where the data should be saved or retrieved.
4. Mapping Data to hash table:
The key is given through the hash function, creating a hash value to store data in a hash table. The spot (slot) in the hash table where the data should be kept is then determined using the hash value as an index. Collision resolution strategies are used to tackle the issue where multiple keys result in the same hash value (collision).
5. Retrieving Data From Hash Table:
The key is again run through the hash function, generating the hash value to get data from a hash table. An index is created using the hash value to find the relevant place in the hash table. The desired key-value pair is located by traversing a linked list if chaining is employed for collision resolution. If open addressing is utilized, a search is conducted until the key-value pair is located or a vacant slot is discovered sequentially or via a particular probing approach.
The hashing mechanism uses the idea of hashing functions and hash values to enable effective data storage and retrieval. It offers a method to convert big or complicated keys into fixed-size representations to speed up indexing and lessen the temporal complexity of data operations.
Terminology used in Hashing
- Key: A key in the context of hash functions alludes to the input data that the hash function processes to get the hash result. Any data type, including texts, complex objects, and integers, may be used for the key.
- Hash Function: A hash function produces a fixed-length character string, often a hash value or a hash code using a character or "key" as input.
- Hash Table: Hash table uses a hash function to associate keys with their corresponding values. When storing and locating data items, it uses hash values as indexes to enable effective data retrieval, insertion, and deletion operations.
- Hash Value/Hash Code: The string or number representing the input data as the hash function's output has a defined length. Data retrieval, indexing, and data integrity checks are just a few of the uses for hash values.
- Collision: When two different input values generate the same hash value, this is known as a collision. The unlimited number of possible input values and the finite number of possible hash values make collisions unavoidable. In order to prevent data loss or inefficiency in hash-based data structures, hash functions should properly manage collisions.
- Distribution: How evenly they are distributed throughout the complete gamut of potential hash values is referred to as the distribution of hash values. The consistent distribution of hash values produced by a competent hash function reduces the possibility of collisions and ensures effective data storage and retrieval.
- Load Factor: The load factor is a measure of how many pieces are kept relative to how many buckets or slots there are in hash-based data structures like hash tables. A larger load factor may make collisions more likely and affect the data structure's effectiveness.
- Collision Resolution: When two distinct input values result in the same hash value, collision resolution techniques are utilized to address the collisions. Chaining—using linked lists to hold multiple entries with the same hash value—and open addressing—searching for alternate places in the hash table—are two typical methods for resolving collisions.
- Cryptographic Hash Function: A hash function particularly created for use in cryptography applications is known as a cryptographic hash function. It is useful for jobs like digital signatures, message authentication, and password storage since it has characteristics like collision resistance, preimage resistance, and good data integrity.
Types of Hash Functions
1.Division Function
One of the most fundamental and straightforward hashing operations is division. The hash value is determined by dividing the key's remaining value by the hash table's size. An illustration of its use is provided here:
Program:
#include<stdio.h> unsigned int division_hash(int key, int table_size){ return key % table_size; } int main() { int key = 42; int table_size = 100; unsigned int division_hash_value = division_hash(key, table_size); printf("Division Hash Value: %u ", division_hash_value); return 0; }
Output:
2.Multiplication Hash Function
The hash value is determined via fractional component extraction and multiplication in the multiplication hash algorithm. The key is multiplied by a fixed amount, the fractional component is taken out, and the key is then multiplied by the number of rows in the table. An illustration of its use is provided here:
Program:
#include<stdio.h> unsigned int multiplication_hash(int key, int table_size){ const double A = 0.6180339887; // A constant value (Golden Ratio) double frac = key * A - (int)(key * A); // Extract fractional part return (unsigned int)(frac * table_size); } int main() { int key = 42; int table_size = 100; unsigned int multiplication_hash_value = multiplication_hash(key, table_size); printf("Multiplication Hash Value: %u ", multiplication_hash_value); return 0; }
Output:
3. Mid-Square Hash Function
The key is squared using the mid-square algorithm, and the centerpiece is then taken out and used as the hash value. This approach can offer a more even distribution and is appropriate for keys of different lengths. Here is a C example of how to build the mid-square hash function:
Program:
#includestdio.h> #include<math.h> unsigned int mid_square_hash(int key, int table_size) { unsigned long long squared = (unsigned long long)key * key; int num_digits = floor(log10(squared)) + 1; // Extract the middle portion of the squared value int shift = (num_digits - 1) / 2; unsigned int hash = (squared >> shift) % table_size; return hash; } int main() { int key = 42; int table_size = 100; unsigned int mid_square_hash_value =mid_square_hash(key, table_size); printf("Mid-Square Hash Value:%u ", mid_square_hash_value); return 0; }
Output:
4. Folding Hash Function:
The hash value is determined through folding, which divides the key into smaller pieces and merges them. Working with huge keys calls for the usage of this technique in particular.
Use Cases of Hash Function
a.Data Retrieval:
Hash functions are frequently utilized in data structures like hash tables, which offer constant-time average retrieval, insertion, and deletion operations. Hash functions are appropriate for applications needing quick access to data because they allow for efficient indexing and storing of key-value pairs.
b. Password Storage:
The use of hash functions in password-storing systems is common. Hash functions transform user passwords into hash values and safely store them rather than storing them directly. By not keeping passwords in plain text, authentication uses the hash value of the input password to compare it to the stored hash value, improving security.
c. Data Integrity:
Data integrity can only be ensured by hash functions. You can determine whether data has been altered or tampered with by computing the hash value of a file or communication. It is simple to see any modifications since even a little change in the input will cause a different hash value to be generated.
d. Cryptographic System:
Cryptographic systems' essential building blocks are hash functions. They are utilized in different encryption techniques, message authentication codes, and digital signatures. Due to the one-way function provided by hash functions, it is computationally impossible to deduce the original input from the hash result.
Best Practices and Considerations
1. Collision Resolution
Sometimes the same hash value will be generated by a hash function for distinct inputs, which is known as a collision. Collision resolution methods like chaining or open addressing are used to resolve collisions and guarantee effective data storage and retrieval.
2. Hash Function Selection
The right hash function must be chosen depending on the application. Different hash algorithms have other features such as distribution, speed, and collision resistance. Select the best hash function by examining the needs of your particular use case.
3. Performance Consideration
The effectiveness of your program can be dramatically impacted by how well hash functions execute. When choosing or using a hash function, it is crucial to consider elements like speed, memory utilization, and collision resistance.
Conclusion:
Modern computer science and software development depends heavily on hash functions. They offer effective methods for manipulating, retrieving, and storing data, protecting the confidentiality and integrity of the data. In this post, we looked at the use cases, C's implementation of hash functions, and their intended application. You may use hash functions to improve the effectiveness and security of your apps by being aware of their capabilities and recommended practices.