IT professionals love their metaphors. From “pets vs. cattle” to “post mortems” to “fog computing” and beyond, practitioners tend to use analogies to shape the way they think about complex technical topics. Here’s another analogy: Log looms.

Observability has gained a lot of momentum and is now rightly a central component of the microservices landscape: It’s an important part of the cloud native world where you may have many microservices deployed on a production Kubernetes cluster, and a need to monitor these microservices keeps rising. In production, quickly finding failures and fixing them is crucial. As the name suggests, observability plays an important role in this failure discovery.

Companies across industries are under tremendous pressure to develop and deploy IT applications and services faster and with far greater efficiency. Traditional enterprise application development falls short since it is not efficient and speedy. IT and business leaders are keen to take advantage of cloud computing as it offers businesses cost savings, scalability at the touch of a button, and flexibility to respond quickly to change.

Find out about the challenges of implementing a microservice architecture, in light of different communication types between microservices, and see how pub-sub message transmission can mitigate or even eliminate these challenges.

Modern software development increasingly relies on distributed, service-based architectural patterns to achieve scalability, reliability, and rapid build, test, and release cycles. Two of the most popular service-based approaches are service-oriented architecture (SOA) and microservices. In this article, we will examine both approaches to identify their similarities and differences as well as some use cases for each.

Jaeger is a popular open-source tool used for distributed tracing in a microservice architecture. In a microservice architecture, a user request or transaction can travel across hundreds of services before serving what a user wants. Distributed tracing helps to track the performance of a transaction across multiple services. Before we deep dive into how Jaeger accomplishes distributed tracing for microservices-based architecture, let's take a short detour to understand distributed tracing.

Modern microservices-based applications bring with them a new set of challenges when it comes to operating at scale across multiple clouds. While the goal of most modernization projects is to increase the velocity at which business features are created, with this increased speed comes the need for a highly flexible, microservices-based architecture. The result is that the architectural convenience created on day 1 by developers turns into a challenge for site reliability engineers (SREs) on day 2.

“How am I supposed to debug this?" Just imagine: Late Friday, you are about to shut down your laptop and … an issue comes up. Warnings, alerts, red colors. Everything that we, developers, hate the most. The architect decided to develop that system based on microservices. Hundreds of them! You, as a developer, think why? Why does the architect hate me so much? And then, the main question of the moment: How am I supposed to debug this?

The software design is perhaps the most important aspect that directly influences the ability to scale up, workload performance, the availability of the software, and the longevity of the software itself. It is also important to understand that traditional monolithic designs are still usable and widely used to fulfil many everyday goals. However, now we have a different problem. With the rapid growth of digital services , virtualization services, and an increasing dependency on cloud-based services