The Art of Deploying a Service Mesh

Service mesh is the next logical step to overcoming security and networking challenges obstructing Kubernetes deployment and container adoption. Check out the benefits of deploying a service mesh here.

With the increased adoption of Microservices, new complexities have emerged for enterprises due to a sheer rise in the number of services. Problems that had to be solved only once for a monolith, such as resiliency, security, compliance, load balancing, monitoring, and observability, now need to be handled for each service in a Microservices architecture.

How to Build a Microservices Architecture With Node.Js to Achieve Scale?

Find out how to build microservices with Node.js.

Introduction

Building real-world applications in the JavaScript language requires dynamic programming, where the size of the JavaScript application grows uncontrollably. New features and updates get released, and you need to fix bugs to maintain the code.

To execute this, new developers need to be added to the project, which becomes complicated. The structure of modules and packages is unable to downsize and simplify the application. To run the application smoothly, it is essential to convert the large, homogeneous structure into small, independent pieces of programs. Such complexities can be resolved effortlessly when JavaScript applications are built on microservices, more so with the Node.js ecosystem.

Microservices Identification Approach

Identifying microservices.Identifying microservices.


Organizations are working to transform their legacy applications to cloud-native architectures to be competitive in the marketplace. Microservices architecture should help in this transformation journey. Microservices is a popular technique or architectural style to structure application as a collection of loosely coupled services.

OpenTracing in NodeJS, GO, Python: What, Why, How?

In previous blogs, we described how to optimize the deployment of applications and utilize guardrails for applications. These guardrails covered:

One additional guardrail in managing your application is to properly implement "Observability". As it turns out, observability is more important than ever because of the shift in application architecture (to a microservices architecture) and increased deployment pace (hourly/weekly vs. quarterly/yearly). Services are dynamically updated and are usually containerized. Hence, the traditional way of adding "monitoring" after the app deployment cannot scale.

CloudBees DevOptics: Value Stream Mapping for Microservices Applications

Applications are not getting simpler and less complex nowadays. As an industry, we are advancing how we architect our systems and deliver services for these applications to become more efficient and effective.

Breaking down your application into microservices can largely decrease complexity of developing and deploying single decoupled components. At the same time the overall system becomes more complex since more components are involved.

Zoomdata Microservices and the Web Application

As we said in our introductory post, we’re going to do several posts about Zoomdata microservices. This one covers a brief overview of microservices and the Zoomdata web application.

Zoomdata Microservices, In General

The Zoomdata platform is architected as a set of loosely-coupled Java microservices. Unlike traditional BI, which is deployed as a monolithic application (or possibly entwined in an old-school enterprise service bus), a microservices architecture allows for:

Your Microservice Architecture Will Collapse

I came across a scientific article published by Science magazine more than 6 years ago, which seemed to me to be exciting to share with you. This article discusses the anticipation of critical transitions, and it has many use cases, including microservice architectures. In this article, we will, therefore, present what a critical transition is, how to discover them in advance, and how to protect yourself against them.

What Is a Critical Transition?

A critical transition is a sudden drop in a system, whether in its production, its ability to evolve, the number of instances it contains, or its performance. This expression can thus be applied to a financial system, a productive system, an ecosystem, and generally to any system with an architecture. And this applies particularly well to microservice architectures (even if no case studies have been done) because the analogy is obvious. We have entities (microservices), connected to each other, which can be heterogeneous or homogeneous.