Sunday, September 10, 2023

Understanding the SOLID Principles: Building Better Software

Introduction:

In the world of software development, creating scalable, maintainable, and robust applications is crucial. To achieve this, developers follow various best practices, and one of the most prominent set of principles are the SOLID principles. These principles were introduced by Robert C. Martin to guide developers in writing high-quality, flexible, and extensible code. In this blog, we will delve into each of the five SOLID principles and understand how they contribute to building better software.

Single Responsibility Principle (SRP):

The Single Responsibility Principle states that a class should have only one and only one reason to change. In other words, a class should have a single responsibility and should focus on doing just one thing. By adhering to this principle, we prevent a class from becoming bloated and tightly coupled with other classes. Each class becomes more manageable, easier to understand, and maintain.
Imagine a scenario where you have a class responsible for both handling user authentication and processing file operations. In case of changes to either of these functionalities, you'll need to modify the same class, which could lead to unintended consequences. Instead, we can have separate classes for authentication and file operations, each adhering to the SRP.

Open/Closed Principle (OCP):

The Open/Closed Principle emphasizes that software entities (classes, modules, functions, etc.) should be open for extension but closed for modification. In simpler terms, once a module is written and tested, it should not be modified to add new features. Instead, we should extend its behavior by creating new modules that build upon the existing ones.
By following the OCP, we promote code reusability and reduce the risk of introducing bugs when modifying existing code. One common way to implement this principle is by using interfaces or abstract classes to define behavior that can be extended by concrete implementations.

Liskov Substitution Principle (LSP):

The Liskov Substitution Principle emphasizes that objects of a superclass should be replaceable with objects of its subclasses without affecting the correctness of the program. In other words, any derived class should be able to substitute the base class without altering the desirable properties of the system.
This principle ensures that inheritance hierarchies are well-designed and that subclasses don't introduce unexpected behaviors that might break the code. Violating the LSP can lead to code that is hard to reason about and maintain, as the assumptions made for the base class might not hold true for its subclasses.

Interface Segregation Principle (ISP):

The Interface Segregation Principle advises that a class should not be forced to implement interfaces it does not use. Instead of creating large, monolithic interfaces, we should segregate interfaces into smaller, more focused ones, tailored to the specific needs of implementing classes.
By following the ISP, we prevent unnecessary dependencies and ensure that classes are not burdened with irrelevant functionality. Clients can then choose to use only the interfaces that are relevant to their requirements.

Dependency Inversion Principle (DIP):

The Dependency Inversion Principle suggests that high-level modules should not depend on low-level modules but both should depend on abstractions. Additionally, abstractions should not depend on details; instead, details should depend on abstractions.
This principle encourages the use of dependency injection, allowing the decoupling of components and making the code more flexible and testable. By depending on abstractions, we can easily swap implementations without affecting the overall functionality of the system.

Conclusion:

The SOLID principles provide developers with a set of guidelines that promote the creation of maintainable, flexible, and scalable software. By adhering to these principles, developers can build code that is easier to understand, modify, and extend. Embracing SOLID not only leads to better software architecture but also fosters collaboration within development teams and improves the overall software development process. So, the next time you start a new project or refactor existing code, remember to apply the SOLID principles for a cleaner and more robust solution. Happy coding!

Tuesday, February 7, 2023

Difference between JavaScript and TypeScript | JavaScript Vs TypeScript

 In this article, we will see the difference between JavaScript and TypeScript. These are the 2 most widely used scripting languages.



JavaScript

  • JavaScript is a scripting language with first-class functions to create dynamic web pages.
  • JavaScript is loosely typed. In JavaScript, only dynamic typing is supported, but not static typing.
  • JavaScript is a simple language that optimizes code for compatibility, easy to read and write.
  • JavaScript does not need a compilation.
  • JavaScript is only a scripting language that gives functionality to the pages. There is no support for interfaces.
  • JavaScript does not require annotation.
  • JavaScript does not have a feature for prototyping.
  • In JavaScript, errors are detected only at the run time, since it is an interpreted language.
  • JavaScript is ideally used for web applications, mobile and desktop applications, and game development.
  • JavaScript runs directly on the browser and supports cross-platform, cross browsers.

TypeScript

  • TypeScript is a powerful object-oriented language as a superset to JavaScript, with generic and JS features to overcome the complexities of JS.
  • TypeScript is strongly typed. TypeScript supports both static and dynamic typing.
  • TypeScript is a powerful object-oriented language as a superset to JavaScript, with generic and JS features to overcome the complexities of JS.
  • TypeScript is strongly typed. TypeScript supports both static and dynamic typing.
  • TypeScript requires the need for annotation to get the full advantage of TypeScript features.
  • The prototyping feature is available in TypeScript language.
  • Errors are detected or highlighted in the early development stage in TypeScript.
  • TypeScript is a more front-end-oriented language with rich IDE support. It is best suited for complex applications and also any JS applications as well.
  • TypeScript does not run directly on the browser.

Wednesday, November 2, 2022

Difference between Redis and MongoDB | Redis Vs MongoDB

There are many NoSQL databases that have different. In this article, we will see the difference between two of the NoSQL databases - Redis and MongoDB.


Redis:

  • Redis is written in ANSI and C languages.
  • The primary database model for Redis is Key-Value Store.
  • Redis is In-memory storage with on-disk persistence
  • Redis supports data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs, and geospatial indexes. Hence, have partial predefined data types.
  • Redis supports secondary indexes with RediSearch module only.
  • Redis supports both Master-Slave Replication and Master-Master Replication.
  • The Server-side scripting in Redis is through Lua.
  • Server operating systems for Redis are BDS, Linux, OS X and Windows.
  • Only Hash Sharding is supported in Redis.
  • Manual failover is required if you need to promote a replica in another data centre to master.

MongoDB:

  • MongoDB is written in C++, Go, JavaScript, and Python languages.
  • The primary database model for MongoDB is Document Store.
  • MongoDB is On-disk storage by default.(In-memory storage engine is available with MongoDB Enterprise Edition.)
  • MongoDB supports data types are string, integer, double, decimal, boolean, date, object_id, geospatial. Hence, have predefined data types.
  • MongoDB supports secondary indexes without any restrictions.
  • MongoDB supports only Master-Slave Replication.
  • The Server-side scripting in MongoDB is through JavaScript.
  • Server operating systems for MongoDB are Solaris, Linux, OS X and Windows.
  • Sharding(Partition data) by range, hash, or zone is supported in MongoDB.
  • Automatic failover with replica set elections.

Wednesday, October 26, 2022

Difference between ActiveMQ and RabbitMQ | ActiveMQ Vs RabbitMQ

ActiveMQ and RabbitMQ are two of the widely used message queues used in the software industry.

In this article, we will be the differences between the two technologies.


ActiveMQ:

  • ActiveMQ is used in enterprise projects to store multiple instances and supports clustering environments.
  • ActiveMQ is programmed in Java
  • ActiveMQ is developed by the Apache Software Foundation
  • ActiveMQ supports C, C#, Haxe, Node.js, Perl, Racket, Python, and Ruby on Rails.
  • OpenWire, Stomp, WSIF, WS, AUTO, AMQP and MQTT are the applied protocols in ActiveMQ.
  • It can be implemented with two brokers along with P2P architecture.
  • It is a multi-protocol-supported message broker.
  • PUB-SUB and message queues are available message patterns.

RabbitMQ:

  • RabbitMQ is a message broker which is executed in low-level AMQP protocol and acts as an intermediator between two applications in the communication process.
  • RabbitMQ is written in Erlang or OTP language.
  • RabbitMQ is developed by Pivotal Software under the Mozilla Public License.
  • RabbitMQ supports multiple languages such as Java, Ruby, Python, PHP, Perl Rust, Go, JavaScript, C, and C++.
  • HTTP, MQTT, STOMP, and AMQP are the implemented protocols in RabbitMQ
  • One broker is required.
  • RabbitMQ works on the management plugin.
  • It has general message patterns such as Message Queue, PUB-SUB and RPC, and Routing.


Sunday, October 16, 2022

Difference between Apache Kafka and Apache Flume | Kafka Vs Flume

Apache Kafka and Apache Flume are two of the most widely used technologies for data ingestion and centralized data storage respectively

In this article, we will be looking at the difference between Apache Kafka and Apache Flume.


Apache Kafka:
  • Apache Kafka is a distributed data system.
  • Kafka is optimized for ingesting and processing streaming data in real time.
  • Kafka basically works as a pull model.
  • Kafka is easy to scale.
  • Kafka is a fault-tolerant, efficient, and scalable messaging system.
  • Kafka supports automatic recovery if resilient to node failure.
  • Kafka runs as a cluster that handles the incoming high-volume data streams in real time.
  • Kafka will treat each topic partition as an ordered set of messages.

Apache Flume:
  • Apache Flume is an available, reliable, and distributed system.
  • Flume is efficiently collecting, aggregating, and moving large amounts of log data from many different sources to a centralized data store.
  • Flume basically works as a push model
  • Flume is not scalable in comparison with Kafka.
  • Flume is specially designed for Hadoop.
  • You will lose events in the channel in case of flume-agent failure.
  • Flume is a tool to collect log data from distributed web servers.
  • Flume can take in streaming data from multiple sources for storage and analysis which use in Hadoop.

Sunday, September 25, 2022

Difference between SQL and NoSQL databases | SQL Vs NoSQL DB

 SQL stands for Structured Query Language where as NoSQL stands for Not Only SQL.

In this article, we are going o look at the difference between two databases - SQL and NoSQL.



SQL:

  • SQL databases are primarily called as Relational Databases (RDBMS).
  • SQL databases are table-based
  • SQL databases have fixed or static or predefined schema.
  • SQL databases follow the ACID property
  • SQL databases are Vertically Scalable.
  • SQL databases are better for multi-row transactions
  • SQL databases are best suited for complex queries
  • Examples of SQL databases are MySQL, PostgreSQL, Oracle, MS-SQL Server, etc.

NoSQL:

  • NoSQL databases are primarily called as non-relational or distributed database. 
  • NoSQL databases are either key-value pairs, document-based, graph databases or wide-column stores. 
  • NoSQL databases have dynamic schema.
  • NoSQL  databases follow the CAP Theorem
  • NoSQL databases are Horizontally scalable.
  • NoSQL is better for unstructured data like documents or JSON.
  • NoSQL  databases are not so good for complex queries
  • Examples of NoSQL databases are MongoDB, GraphQL, HBase, Neo4j, Cassandra, etc


Sunday, September 18, 2022

Difference between Java and C++ | Java Vs C++

Java and C++ are two of the most widely used languages in the software world. There are many similarities between these 2 languages, but they also have a few differences. Let's look at the difference between Java and C++.



Java

  • Java is platform-independent. It is based on the concept of Write Once Run Anywhere.
  • Java is also an object-oriented language
  • Java uses both compiler and interpreter.
  • Java is mainly used for application programming.
  • Java has built-in thread support.
  • Java doesn't support multiple inheritance through class. It can be achieved by using interfaces in Java
  • Java supports call-by-value.
  • Java does not support features like operator overloading, Goto statements, structures, pointers, unions, etc.
  • Java has no virtual keyword. We can override all non-static methods by default
  • Java supports unsigned right shift >>> operator
  • Java supports documentation comments. (/** ... */)

C++

  • C++ is platform dependent. It is based on the concept of Write Once Compile Anywhere.
  • C++ is both procedural as well as an object-oriented language
  • C++ uses only compiler.
  • C++ is mainly used for system programming.
  • C++ doesn't have built-in support for threads.
  • C++ supports multiple inheritance.
  • C++ supports both call-by-value and call-by-reference.
  • C++ supports features like operator overloading, Goto statements, structures, pointers, unions, etc.
  • C++ supports virtual keyword used to decide whether or not to override a function.
  • C++ doesn't support >>> operator.
  • C++ doesn't support documentation comments.

Understanding the SOLID Principles: Building Better Software

Introduction: In the world of software development, creating scalable, maintainable, and robust applications is crucial. To achieve this, de...