Microservices vs Distributed System

Microservices and distributed systems are two important architectures in modern software development. Though both focus on scalability and resilience, they diverge in principles and execution. This article provides a concise overview, helping readers grasp the differences and choose the right approach.

What are Microservices?

Microservice is a small, loosely coupled distributed service. Each microservice is designed to perform a specific business function and can be developed, deployed, and scaled independently.

• It allows you to take a large application and decompose or break it into easily manageable small components with narrowly defined responsibilities.
• It is considered the building block of modern applications. Microservices can be written in a variety of programming languages, and frameworks, and each service acts as a mini-application on its own.

What are Distributed Systems?

Distributed systems are a type of computer architecture where components or nodes of the system are located on different networked computers, communicating and coordinating their actions through message passing.

• Unlike traditional centralized systems, distributed systems spread data processing and storage tasks across multiple machines, enabling them to work collaboratively to achieve common goals.
• These systems often exhibit characteristics such as decentralization, concurrency, fault tolerance, and scalability. Examples of distributed systems include cloud computing platforms, peer-to-peer networks, and distributed databases.

Microservices vs. Distributed system

Below are the differences between Microservices and Distributed systems:

Use Cases of Microservices and Distributed Systems

Below are the use cases of Mircroservices and Distributed Systems

Use Cases of Microservices:

• E-commerce Platforms: Each service can process a limited part of the operations, like stock management, user control, or order completion, respectively.
• Content Management Systems: Functions responsible for creation, storage, and delivery of content can be individually built and developed as and when necessary.
• Finance Applications: The work of the core steps, which can be done independently, can be implemented.

Use Cases of Distributed Systems:

• Big Data Processing: The two major paradigms in distributed computing are MapReduce and wide-area data networks. Apache Hadoop and Spark often play this role, relying on clusters of machines to compute vast data sets.
• IoT (Internet of Things): A distributed program ensures connectivity and coordination within different types of Internet of Things devices, sensors, and gateways.
• Blockchain Networks: Blockchain networks are distributed systems that, in their basic structure, allow for decentralized agreement about transactions and their verification.