Data Structure is a method that provides a proper structure and syntax to store and organize our data so it can be used efficiently. They provide a format for our data so it can be inserted and retrieved in an expected way.
There are two components of data structures:
An interface of data structure deals with the operation related to a particular data structure. Most of the famous data structures, such as Array, List, etc., follow some of the same interfaces. It supports many operations, such as insertion, deletion, traverse, etc.
The implementation of data structure means what algorithm that particular data structure has. Every data structure has its own unique algorithm; that’s what makes it different from others. With its algorithm, we could tell how the data structure will organize the data and how the operations going to perform on it.
Characteristics of a data structure
Data Structure has 3 characteristics:
- Time Complexity
- Space Complexity
The implementation of a data structure must be correct so all its interfaces work accordingly. A small mistake in implementation can make the data structure ineffective.
2. Time Complexity
The time complexity of a data structure defines the time or steps taken by that data structure or program to complete all its operations. Time complexity is a vital asset of any program, and it defines the efficiency of that program; the better the time complexity, the better the efficiency of the program.
Only logic can provide the best time complexity. Further, the time complexity for many data structures and algorithms is pre-defined.
3. Space Complexity
Space complexity defines the memory occupied by a particular data structure; the less memory it occupies, the better space complexity it has.
Why Do We Need Data Structures?
A data structure is a method of organizing data in a particular manner. In the real world, we deal with a huge amount of data, so we need different kinds of data structures to store the data in an organized manner and use it efficiently. There are three main reasons why we need data structures:
- Data Searching
- Processor Speed
- Multiple Requests
1. Data Searching
Real-world problems deal with a huge amount of data. Searching operations are quite slow. If you try to search for a file stored deep down in some directory, the searching process can take 5 to 10 seconds to search that file. Now think of searching a file amongst a hundred million others. It might take 1 to 2 minutes, even by a high-end computer, to find that file.
Here, data structure comes into play! It stores files in an organized manner and can find the requested file in no time, as it knows where the file is located.
2. Processor Speed
With a large number of data, even high-end processor shows limited results. If we have an efficient data structure with an effective algorithm, the load on the processor will decrease, and we will provide impressive results.
3. Multiple Requests
Companies like Google , Facebook, Netflix, etc., have millions of users and get multiple requests. How do they handle all these requests? They simply use effective data structures and algorithms so the request would not throw much load on their server. In the real world, effective data structures and algorithms are the key features of every successive tech business model.
There are three main complexity cases related to the time complexity of an algorithm and data structure.
- Worst Case
- Average Case
- Best Case
1. Worst Case
The worst case of any data structure operation signifies the maximum time taken by that operation. If there are n numbers of data sets in a data structure and the data operation takes n or more than n number of steps or time to complete the operation, then it would be considered the worst case of that operation.
We can represent it with a mathematical function such as f(n) ; it becomes the function of n .
2. Average Case
If the data operations take average steps or time to complete their operations and give proper results, it would be considered as the average case of that data operation.
3. Best Case
If the operations take the least steps or time to calculate the result from a data structure, it would be considered the best case.
Basic Terminology of Data Structure
|Data||Set of values|
|Data Item||A single unit of values|
|Group Items||Group of data items|
|Elementary Items||Atomic data that cannot be divided further|
|Attribute and Entity||Kind of variable in which we can assign values|
|Entity Set||Entities of similar attributes|
|Field||The field is a single elementary unit of information representing an attribute of an entity.|
|Record||Collection of fields|
|File||Collection of records.|
A data structure is a way of organizing data in memory to retrieve and use it efficiently. It makes the processing of data easy and quick. Interface and implementation are two important components of a data structure. An interface implies operations related to a data structure, and implementation refers to the algorithm that a data structure has.
Through this article, we have shed light on the need for a data structure, its components, complexity cases, and basic terminology. We hope the information adds value to your knowledge base.
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