Are you someone who works with computers and deals with large amounts of data? Do you find it challenging to organize and access this data quickly and efficiently? If so, you're not alone. Many computer professionals struggle with managing data effectively. But don't worry. In this article, we have provided information about which data structure is best. so let's get started!!
Computers are all about Data and instructions to be performed on the data. A data structure is a way of organising the collection of data. Later on, this data can be accessed, queried, or even updated easily and quickly. In other words, a data structure is any data representation and its associated operations. Even an integer or string stored on the computer can be viewed as a simple data structure.
Data Structures are of two types, Linear data structures, and Non-linear data structures. In Linear data structures, elements are arranged in a sequence i.e. one after the other. For example, array stack, queue, etc are linear data structures.
In Non-Linear data structures elements are arranged in a hierarchical order i.e. one element can be connected to multiple elements. Examples of non-linear data structures are graphs, trees, etc.
The Best Data Structure
Data structures are used almost everywhere in our lives. Say for example, while using the browser linked list is used to keep track of previous and next websites, for contacts on your phone HashMap is be used, the stack is used in a calculator to evaluate an expression, etc.
In different scenarios, different data structures are used as per the requirement of the operations to be performed on the data. Data is organized in a way that accessing time of data can be minimized and thus fast results can be obtained.
They are a crucial part of designing efficient software and algorithms. Some of the common operations to be performed on the data include sorting, data exchange, indexing, searching, etc.
There is no the best data structure that can be used in every situation. Depending on the requirement of the situation whichever data structure gives an optimum solution is best and to be chosen. Data structures are chosen that uses less space, and less amount of time, and also, they must not be too complex to implement.
To provide a solution to a problem, it is first analyzed, then the performance goals are determined that must be achieved and then the solution can be presented by selecting the right data structure for the job.
Let us look at each of these data structures in detail:
Arrays
An array is the simplest and most commonly used data structure. It is a fixed-length container containing n elements of the same type. The elements are kept in sequence even in memory. Elements can be accessed using its index starting from 0 to n-1. Thus, it is easier to insert and retrieve information using arrays.
Operations:
- Insert - We can insert elements in an array at any random index which should be less than the size of the array.
- Retrieve - We can retrieve elements of an array, using the index of the elements.
- Remove - We can delete elements present at a particular index.
- Size - As the size is fixed for an array is easy to get the total number of elements in an array.
Use case:
- Arrays are used to build other data structures like a stack, LinkedList, queue, lists, etc.
- Used to elements of same data type.
Stacks
The stack data structure uses the LIFO (Last In First Out) principle to insert and remove elements i.e. the elements entered last will be removed first. It is famous for its UNDO operation. The elements cannot be removed or inserted in the middle position. It maintains a top position variable at which elements can be removed or added.
Operations:
- Insert - Inserts element at the top.
- Delete - Removes an element from the top
- Top element - Returns the value of the top element
- Size - returns the number of elements in the stack.
Use case:
- To evaluate expressions.
- To check for balanced parenthesis.
- In backtrack and recursion.
- Runtime memory management.
Queues
Queue Data Structure follows the FIFO (First In First Out) principle. The elements that are added first are removed first as well. The elements can only be added at the rear end and can be removed from the front end.
Operations:
- Insert - Inserts element at the Rear end.
- Delete - Removes an element from the Front end.
- Top element - Returns the value of the Front element.
- Size - returns the number of elements in the queue.
Use case:
- It is used in CPU and Disk Scheduling
- The queue is used for synchronization.
- It is used in scheduling tasks in day-to-day life as well.
- Circular Queue - In the circular queue, the last element is linked to the first element. It is also used in scheduling and memory management.
- Priority Queue - Priority Queue executes the tasks with the most priority first irrespective of the order in which they are entered. It is also used in CPU scheduling.
- Doubly ended Queue - In a doubly ended queue insertion and removal of elements can be done from both the ends i.e. from the front as well as rear end.
LinkedList
LinkedList data structure not only stores the element but also stores the link to the next element in the list. The data value assigned to the element or node as well as the pointer in the memory address refers to the next node in the list is contained in a list.
Operations:
- Insert element— element can be inserted anywhere in the LinkedList i.e. in front, end, as well as in the middle.
- Delete — Elements can be inserted from anywhere in the LinkedList.
- isEmpty — Returns true if the linked list is empty i.e. head of the LinkedList is null.
Use case:
- LinkedList is used for managing symbol management in a compiler.
- It is also used in switching between applications and programs in the OS (Operating System) as well as switching tabs between in the browser.
- UNDO operation in photoshop, Word, and other applications.
Trees
The tree is a hierarchical data structure where its elements are organized hierarchically. It is a non-linear data structure. A tree node contains the element and the link to the child elements.
Operations:
- Parent - Returns the parent node of the given node. It is null for the root node.
- Child - Returns the child nodes of the given node. Leaf nodes have null child nodes.
- Size - Returns the total number of elements in the tree.
Use case:
- The binary tree is used to store elements in sorted order, which makes searching and retrieval easier.
- Tree data structures have so many types such as Binary Tree, Binary Search Tree, Red-Black trees, B-tree, Weighted-balanced tree, and Heap. It has so many applications in database management.
Graphs
The graph is a non-linear data structure, in which elements are stored in form of a network. It is a set of Vertices and Edges. The pair (x,y) contains two vertices, it indicates the edge between two vertices. Edge may contain weight for weighted graph. Graphs are of two types directed and undirected. Graphs are also weighted and non-weighted.
Operations:
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Add Vertex - to add a vertex to the graph.
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Add Edge - to add an edge between the two vertices of the graph.
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Display Vertex - to display vertices of the graph.
Use case:
- To find the shortest path between two vertices for instance in maps
- To design networks to improve their efficiency.
- Used to depict the flow of computation.
- Mutuals and friend suggestions are determined using graphs.
- In webpages, links from one page to another are also represented by the edge in a graph.
Trie
Trie data structure is efficient for solving string-related problems. It helps in the fast retrieval of search results.
Use case:
- It is used for searching words in dictionaries, providing suggestions in the search engines, etc.
Hash Table
Hashing is used to uniquely identify elements in a table. Thus, hashing makes storage, retrieval, and update easier for a database. Each object is uniquely identified by a unique index called the "key". The object can be searched using this key.
Operations:
- Insert - Elements can be searched using the key value.
- Update - Elements can be updated using key values.
- Remove - We can remove elements using the hashing key.
- Search - We can search for data in the table using the key.
Use case:
- It is used in matching entries in the database.
- It is used to link the file name to the path of the file.
- It is used in storing and matching passwords.
Conclusion
The best data structure for a particular scenario depends on the requirements and goals of the problem that needs to be solved. There are two types of data structures, linear and non-linear, which have various use cases and operations. Arrays, stacks, queues, linked lists, and trees are some of the commonly used data structures, each with its own unique features and functions.
It is important to select a data structure that is efficient in terms of space and time complexity and easy to implement. By analyzing the performance goals, we can select the most appropriate data structure that provides an optimum solution to a given problem.
Frequently Asked Questions
1. What are data structures important in computer programming?
Data structures are important in computer programming as they provide an efficient way to store and organize data in memory. They allow for fast access, searching, and manipulation of data, which is important in designing efficient algorithms and software.
2. How do I choose the right data structure for my problem?
Choosing the right data structure depends on your problem and the operations that need to be performed. For example, if you need to store and retrieve data in a sequential manner, then an array or linked list may be the best choice. However, if the data has a hierarchical structure, then a tree or graph may be more suitable.
3. What are some common operations performed on data structures?
Some common operations performed on data structures include insertion, deletion, searching, sorting, and traversing. The choice of operation depends on the problem being solved.
4. Are there any drawbacks to using certain data structures?
Yes, there can be drawbacks to using certain data structures. For example, they may have a higher memory or computational cost, which could affect the overall performance of an algorithm or some data structures may be more difficult to implement or maintain than others.
