Articles

How Scrum Helps Developers Build Technical Skills and Flexible Architecture

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Developers who tried to build at least one product for an end-user know how many things are essential in a product: thoughtful UX, friendly UI, good performance and stability, security and data consistency, logging and maintenance, etc.
Multiply this to the number of platforms that you have to support. Add marketings and licensing, client support and bug reports, new feature requests, and competitive product pressure.

It’s hard to track everything in one head, and it is even harder to be perfect at every job. That’s why we work in teams. That’s why we use project management processes.

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Observability 101: Terminology and Concepts

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When I first started following Charity on Twitter back in early 2019, I was quickly overwhelmed by the new words and concepts she was discussing. I liked the results she described: faster debugging, less alert fatigue, happier users. Those are all things I wanted for my team! But I was hung up on these big polysyllabic words, which stopped me from taking those first steps toward improving our own observability.

This post is my attempt to help orient folks who want to learn more about observability but maybe feel overwhelmed or intimidated by the vocabulary list, like I did. My goal is to get everyone on the same page about what these words mean so that we can focus on leveraging the tools and ideas to build better software and deliver more value! 

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Logging As a Last Resort

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Motivation

In software development one often finds themselves investigating issues. Depending on the type of the application, various sources of informations can be available: 

  • screenshots
  • verbal description of the problem
  • metrics
  • logs (application, framework, container, application server, OS, …)
  • thread and heap dumps

In an ideal world, the exact inputs that caused an issue and the code that failed would be immediately available. In a typical case though, it can take hours of digging through logs and code to understand what happened. Would it not be nice to avoid that?

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EFK Stack on Kubernetes (Part 2)

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This is the final part of our Kubernetes logging series. In case you missed part 1, you can find it here. In this tutorial, we will learn about configuring Filebeat to run as a DaemonSet in our Kubernetes cluster in order to ship logs to the Elasticsearch backend. We are using Filebeat instead of FluentD or FluentBit because it is an extremely lightweight utility and has a first-class support for Kubernetes. It is best for production-level setups.

Deployment Architecture

Filebeat will run as a DaemonSet in our Kubernetes cluster. It will be:

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What Is the Best Java Debugger?

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Many people might think this a simple question; I am not one of them. I feel that in the modern world of development, there are too many factors to pick a single tool for debugging any language, let alone Java.

In The Beginning

Let’s take a step back and look at where we started with debugging, and while I am not going to get into the history of debugging, we should look at some of the basic tools used for debugging Java, aside from logging and system-out.

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Unit Testing Log Messages Made Easy

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As a Java Developer, we need to cover a lot of scenarios to ensure the quality of our software and catch bugs as soon as possible when introducing a new code. For 99% of all my use cases AssertJ, Junit, Mockito, and Wiremock are sufficient enough do cover the test cases. But for the other use cases, like unit testing info, debug or warn log messages, these frameworks don’t help you out. There is also no other framework that can provide an easy to use method to capture log messages.

The answer which the community provided works well, but it is a lot of boilerplate code to just assert your log events. Even I faced the same trouble and so I wanted to make it easier for myself and share it with you! So the LogCaptor library came into life.

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