The data structure is the process to arrange the data in the memory location. The data structure is important for processing, accessing, storing, organizing, and retrieving data from a memory location. Data structure works with synchronization with the algorithm. Data Structure algorithm is used to solve the problems using data.
The data structure is one of the most important aspects of computer science. The knowledge of data structure is a must for doing programming and is a must known thing for every programmer. If you want to make your career in IT or programming, then you must know the concept of data structure to crack the interview.
There are different types of data structures for different problems. For example, If you want to search for a word in the dictionary, you don’t go from one page to another checking all the words. Instead, you do a search alphabetically. This is a type of binary search.
Types of data structure and data type:
There are two types of data structures:
- Primitive data structure
- Non-primitive data structure
Primitive data structure: Primitive data structures are primitive data types and these are defined by the programming language. Examples of the primitive data structures are integer, float, string, and pointers
Non-primitive data structure: These are those data structures that are not defined by the programming language. They are derived from the primitive data structure and create by programmers themselves. Non-primitive data structures are also called object references or reference variables as they reference a memory location where the data is stored.
Non-primitive data structures are further classified into two parts.
- Linear data structure
- Non-linear data
Linear data structure:
In a linear data structure, all elements are arranged in a linear or sequential manner in which elements are attached to their previous and next adjacent. In a linear data structure, there is only one level, so that you can traverse the elements in a single go. These data structures are very easy to implement because of the linear arrangement of the memory location. The example of linear data structures is an array, stack, linked list, queue, etc.
Non-Linear data structure:
A data structure in which memory location is not arranged in a linear or sequential manner is called a non-linear data structure. In the non-linear data structure, there is more than one level, so that they can not be traversed in a single run. Non-linear data structures are complex in comparison to linear data structures. A non-linear data structure is efficient and uses the memory location efficiently in comparison to the linear data structure. The example of non-linear data structures is graphs and trees.
What are the needs of data structure?
As the network and applications are getting complex and dada getting rich, so, the data structure is needed to solve the complex problems. The data structure is designed to perform the complex data problem. There are three common problems that applications are facing these days, which are:
1. Data Search: Let us consider there is millions of items are stored in a system. If the application is to search for a specific item from these millions of items, then it has done a search from one to a million (106) items. It will be a very slow process and it becomes slower as the data grows.
2. Processor speed: However the processor speed being very high, but when the data grows to a very high complex, its speed falls limited.
3. Multiple requests: As at a time, thousands of users can search for data on a web server. This will leads to the fall down of the server.
A data structure is needed to solve these problems. In a Data Structure, data can be organized in a way that the desired item can be searched in a faster way.
What are the foundation terms in Data Structure:
Interface: An interface in Data Structure represents the set of operations that it supports. Each data structure has an interface that provides the list of supported operations and parameters they accept and return.
Implementation: In a Data Structure, Implementation provides the internal representation and definition of the algorithms that are used in the operation of data structure.
Complexities in Data Structure:
Time Complexity − It is the data structure complexity for the time that states that execution or running time of an operation in data structure must be as small as possible.
Space Complexity −This is the complexity for space in a data structure which states that the memory usage in an operation of data structure should be as little as possible.
Execution Time cases in Data Structure:
There are three-time cases that are compared to the various execution time of Data Structure:
Worst Case: This is the case where the operation in a data structure takes the maximum time to execute. It is called the worst-case time. If the worst-case time of a data structure operation is f(n) then this operation will not take more than f(n) time to execute an operation. f(n) represents the function of n.
Average Case: This is the case where an operation in a data structure takes the average time to execute. If it takes f(n) time to execute an operation, then m operations will take f(n) time to execute an operation.
Best Case: This is the case where an operation in a data structure will take the least possible time to execute. If an operation takes f(n) time to execute, then the actual operation will take a shorter or equal time than f(n) to execute the operation.
