At LogicMonitor, we deal primarily with large quantities of time series data. Our backend infrastructure processes billions of metrics, events, and configurations daily. In previous blogs, we discussed our transition from monolith to microservice. We also explained why we chose Quarkus as our microservices framework for our Java-based microservices. In this blog we will cover.

Microservices, also known as microservices architecture, refers to a method of designing and developing software systems. Microservice architecture is becoming increasingly popular as developers create larger and more advanced apps. The goal is to help enterprises become more Agile, especially as they adopt a culture of continuous testing. Here are the basic features of microservices.

The pandemic has accelerated digital business initiatives. Ultimately, it’s the application that delivers new capabilities to customers and employees; but transformation occurs at several layers to support those applications. At VMware, we see our customers navigating three transformations, each of which supports digital business and app modernization at different levels.

Microservice architecture is a software design pattern in which we write applications by combining several small programs. These programs, which are called microservices, work together for a common goal. For some teams, it takes a lot less time and effort to write several small applications than a single large one.

With almost every blog you read about monitoring, troubleshooting, or more recently, the observability of modern application stacks, you’ve probably read a statement saying that complexity is growing as a demand for more elasticity increases which makes management of these applications increasingly difficult. This blog will be no exception, but there’s a good reason for that: we just enabled the first Sumo Logic customers with powerful new tools to tackle these exact challenges.

This article was originally published on InfoQ at December 3rd 2020. If you’ve migrated from a monolith to a microservices architecture you probably experienced it: Modern systems today are far more complex to monitor. Microservices combined with containerized deployment results in highly dynamic systems with many moving parts across multiple layers.

As microservices gain in popularity, containers have become a hot topic for developers. But how do they differ from virtual machines? Will containers replace virtual machines? And when should you choose containers over virtual machines? When it comes to defining virtual machines, the name says it all – machines (servers or desktops) that have been virtualized.

Breaking down larger, monolithic software, services, and applications into microservices has become a standard practice for developers. While this solves many issues, it also creates new ones. Architectures composed of microservices create their own unique challenges. In this article, we are going to break down some of the most common. More specifically, we are going to assess how observability-based solutions can overcome many of these obstacles.

Distributed tracing allows you to track the execution of your user's transactions by following them between applications’ microservices. It provides easy to understand visualizations of transactional lifespan with the ability to pinpoint any slowdowns and errors in response to microservices. During my presentation at Illuminate, I shared that we extended the Sumo Logic platform to cover application performance use cases.