No matter which programming language you program in if you want to be able to build scalable systems, it is important to learn data structures and algorithms.
Our tutorials on Data structure and algorithms or DSA in short, teach all the basic concepts with examples and code in C, C++, Java, and Python.
DSA - Overview
Data Structure is a systematic way to organize data in order to use it efficiently.
The data structure is a way of organizing and storing data in a computer so that it can be accessed and modified efficiently. Algorithms are a set of steps that perform a specific task. Data structures and algorithms are closely related, as efficient algorithms often depend on good data structures to work with.
The following terms are the foundation terms of a Data structure.
- Interface - Each data structure has an interface. The interface represents the set of operations that a data structure supports. An interface only provides the list of supported operations, the type of parameters they can accept, and the return type of these operations.
- Implementation - The implementation provides the internal representation of a data structure. The implementation also provides the definition of the algorithms used in the operations of the data structure.
Characteristics of a Data Structure
- Correctness - Data structure implementation should implement its interface correctly
- Time Complexity - Running time or the execution time of operations of data the structure must be as small as possible.
- Space Complexity - Memory usage of a data structure operation should be as little as possible.
Need for Data Structure
As applications are getting complex and data-rich, there are three common problems that applications face nowadays.
- Data Search - Consider an inventory of 1 million (10) items in a store. If the application is to search for an item, it has to search an item in 1 million (10) items every time slowing down the search. As data grows, the search will become slower.
- Processor speed - Processor speed although very high, falls limited if the data grows to billion records.
- Multiple requests - As thousands of users can search for data simultaneously on a web server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to the rescue. Data can be organized in a data structure in such a way that all items may not be required to be searched, and the required data can be searched almost instantly.
Execution Time Cases
There are three cases that are usually used to compare various data structures' execution times in a relative manner.- Worst-Case - This is the scenario where a particular data structure operation takes the maximum time it can take. If an operation's worst-case time is fn then this operation will not take more than fn time where fn represents the function of n.
- Average Case - This is the scene depicting the average execution time of an operation of a data structure. If an operation takes fn time in execution, then m operations will take mfn time.
- Best Case - This is the scene depicting the least possible execution time of an operation of a data structure. If an operation takes fn time in execution, then the actual operation may take time as the random number which would be maximum as fn.
Basic Terminology
- Data - Data are values or sets of values.
- Data Item - Data item refers to a single unit of values.
- Group Items -Data items that are divided into sub-items are called Group Items.
- Elementary Items - Data items that cannot be divided are called Elementary Items.
- Attribute and Entity - An entity is that which contains certain attributes or properties, which may be assigned values.
- Entity Set - Entities of similar attributes form an entity set.
- Field - The field is a single elementary unit of information representing an attribute of an entity.
- Record - A record is a collection of field values of a given entity.
- File - The file is a collection of records of the entities in a given entity set.
