Collision Resolution Techniques

Languages like English or Spanish are not understood by computers, users must communicate with computers using a set of languages called programming languages. A computer can be programmed using a variety of language families. Computers are instruments created to address complicated issues, but only when a programming language and programmer are used. Computer software is what powers the various browsers, games, emails, operating systems, and applications. Any problem can be solved creatively through programming.

Computational language is used in programming to provide the computer with a set of instructions for a task. With the right instruction groups and specialized software, any complexity issue can be resolved. There are three fundamental ideas in computer programming design. They are repetition, selection, and sequence. The series is the first essential idea that tells you to execute the instructions in a specific order; choosing the right command comes next. The repetition of the same action, often known as iteration, is the fourth idea. In the creative process of programming, the programmer chooses the appropriate commands to address any issue.

Programming Languages

We require a variety of programming languages because no human language can be understood by computers. Every language has advantages and disadvantages, and some are more appropriate for a given task than others. Many diverse specialists, including software developers, computer system engineers, web designers, app developers, etc., require a programming language to do a variety of jobs; there are many programming languages. More than 50 programming languages are used to perform different tasks and the most commonly used languages are HTML, JAVA, and C- language. 

Computation Thinking

Using four fundamental patterns, computational thinking is a method for solving any problem. If we effectively comprehend and use the four fundamental patterns, computational thinking for programming becomes simple. The first step in effectively understanding an issue is to break it down into smaller components. We can more effectively use other computational thinking components when we divide the problems into smaller, more manageable pieces. The second component in this process is pattern recognition; the problems are reviewed to see if there is any sequence. If there are any patterns, they have been categorized appropriately. If no patterns are found, further simplification of that issue is not necessary. An abstraction or generalization of the issue serves as the third component. When you stand back from the specifics of a problem, you can develop a more general answer that can be useful in a number of different ways. The Algorithm, the fourth and final component, is where problems are incrementally addressed. Making a plan for your solution is crucial. A method for figuring out step-by-step directions on how to tackle any problem is to use an algorithm.

The purpose of collision resolution during insertion is to locate an open location in the hash table when the record’s home position is already taken. Any collision resolution technique may be thought of as creating a series of hash table slots that may or may not contain the record. The key will be in its home position in the first position in the sequence. The collision resolution policy shifts to the following location in the sequence if the home position is already occupied. Another slot needs to be sought if this is also taken, and so on. The probe sequence is a collection of slots that is produced by a probe function that we will refer to as p. This is how insertion operates.

Collision in Hashing

In this, the hash function is used to find the index of the array. The hash value is used to create an index for the key in the hash table. The hash function may return the same hash value for two or more keys. When two or more keys have the same hash value, a collision happens. To handle this collision, we use collision resolution techniques.

Collision Resolution Techniques

There are two types of collision resolution techniques.

• Separate chaining (open hashing)
• Open addressing (closed hashing)

Separate chaining: This method involves making a linked list out of the slot where the collision happened, then adding the new key to the list. Separate chaining is the term used to describe how this connected list of slots resembles a chain. It is more frequently utilized when we are unsure of the number of keys to add or remove.

Time complexity

• Its worst-case complexity for searching is o(n).
• Its worst-case complexity for deletion is o(n).

Advantages of separate chaining

• It is easy to implement.
• The hash table never fills full, so we can add more elements to the chain.
• It is less sensitive to the function of the hashing.

Disadvantages of separate chaining

• In this, the cache performance of chaining is not good.
• Memory wastage is too much in this method.
• It requires more space for element links.

Open addressing: To prevent collisions in the hashing table, open addressing is employed as a collision-resolution technique. No key is kept anywhere else besides the hash table. As a result, the hash table’s size is never equal to or less than the number of keys. Additionally known as closed hashing.

The following techniques are used in open addressing:

• Linear probing
• Quadratic probing
• Double hashing

Linear probing: This involves doing a linear probe for the following slot when a collision occurs and continuing to do so until an empty slot is discovered.
The worst time to search for an element in linear probing is O. The cache performs best with linear probing, but clustering is a concern. This method’s key benefit is that it is simple to calculate.

Disadvantages of linear probing: 

• The main problem is clustering.
• It takes too much time to find an empty slot.

Quadratic probing: When a collision happens in this, we probe for the i2-nd slot in the i^th iteration, continuing to do so until an empty slot is discovered. In comparison to linear probing, quadratic probing has a worse cache performance. Additionally, clustering is less of a concern with quadratic probing.

Double hashing: In this, you employ a different hashing algorithm, and in the i^th iteration, you look for (i * hash 2(x)). The determination of two hash functions requires more time. Although there is no clustering issue, the performance of the cache is relatively poor when using double probing.