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This post is part of a series of posts where I contribute to the ListenBrainz project for my independent study at the Rochester Institute of Technology in the fall 2017 semester. For more posts, find them in this tag.
This week is the last week of the fall 2017 semester at RIT. This semester, I spent time with the MetaBrainz community working on ListenBrainz for an independent study. This post explains what I was working on in the last month and reflects back on my original objectives for the independent study.
This post is part of a series of posts where I contribute to the ListenBrainz project for my independent study at the Rochester Institute of Technology in the fall 2017 semester. For more posts, find them in this tag.
Last week moved quickly for me in ListenBrainz. I submitted multiple pull requests and participated in the weekly developer’s meeting on Monday. I was also invited to take part as a mentor for ListenBrainz for the upcoming round of Google Code-In! In addition to my changes and new role as a mentor, I’m researching libraries like D3.js to help build visualizations for music data. Suddenly, everything started moving fast!
This post is part of a series of posts where I contribute to the ListenBrainz project for my independent study at the Rochester Institute of Technology in the fall 2017 semester. For more posts, find them in this tag.
One of the first rites of passage when working on a new project is creating your development environment. It always seems simple, but sometimes there are bumps along the way. The first activity I did to begin contributing to ListenBrainz was create my development environment. I wasn’t successful with the documentation in the README, so I had to play around and work with the project before I was even running it.
The first part of this post details how to set up your own development environment. Then, the second half talks about the solution I came up with and my first contribution back to the project.
A unique opportunity of attending an open source-friendly university is when course credits and working on open source projects collide. This semester, I’m participating in an independent study at the Rochester Institute of Technology where I will contribute to the ListenBrainz project.
Many students take part in independent studies where they work on their own projects. However, in the spirit of open source collaboration, I wanted to contribute to a project that already existed. That way, my work would be helpful to a real-world project where it would have a value even after the end of the semester. Additionally, I wanted a project to help me sharpen my Python skill. And ListenBrainz was a fun, exciting candidate for this.
Article originally published on Opensource.com.
The needs and demands of infrastructure environments changes every year. With time, systems become more complex and involved. But when infrastructure grows and becomes more complex, it’s meaningless if we don’t understand it and what’s happening in our environment. This is why monitoring tools and software are often used in these environments, so operators and administrators see problems and fix them in real-time. But what if we want to predict problems before they happen? Collecting metrics and data about our environment give us a window into how our infrastructure is performing and lets us make predictions based on data. When we know and understand what’s happening, we can prevent problems before they happen.
But how do we collect and store this data? For example, if we want to collect data on the CPU usage of 100 machines every ten seconds, we’re generating a lot of data. On top of that, what if each machine is running fifteen containers? What if you want to generate data about each of those individual containers too? What about by the process? This is where time-series data becomes helpful. Time-series databases store time-series data. But what does that mean? We’ll explain all of this and more and introduce you to InfluxDB, an open source time-series database. By the end of this article, you will understand…
- What time-series data / databases are
- Quick introduction to InfluxDB and the TICK stack
- How to install InfluxDB and other tools
This article was originally published on Opensource.com.
Internet Relay Chat, or IRC, is one of the oldest chat protocols around and still popular in many open source communities. IRC’s best strengths are as a decentralized and open communication method, making it easy for anyone to participate by running a network of their own. There’s also a variety of clients and bots available for IRC. But on the reverse side, usability is a concern. Most common user interfaces for IRC clients or platforms aren’t always intuitive. People from parts of the world with unstable Internet connections are challenged with remaining connected to participate in conversation. Many people have tried addressing this problem before, but none have come as far as Riot.
This article was originally published on the Fedora Magazine.
This is a short series to introduce Kubernetes, what it does, and how to experiment with it on Fedora. This is a beginner-oriented series to help introduce some higher level concepts and give examples of using it on Fedora. In the first post, we covered key concepts in Kubernetes. This second post shows you how to build a single-node Kubernetes deployment on your own computer.
Once you have a better understanding of what the key concepts and terminology in Kubernetes are, getting started is easier. Like many programming tutorials, this tutorial shows you how to build a “Hello World” application and deploy it locally on your computer using Kubernetes. This is a simple tutorial because there aren’t multiple nodes to work with. Instead, the only device we’re using is a single node (a.k.a. your computer). By the end, you’ll see how to deploy a Node.js application into a Kubernetes pod and manage it with a deployment on Fedora.
This article was originally published on the Fedora Magazine.
This article is part of a short series that introduces Kubernetes. This beginner-oriented series covers some higher level concepts and gives examples of using Kubernetes on Fedora.
The information technology world changes daily, and the demands of building scalable infrastructure become more important. Containers aren’t anything new these days, and have various uses and implementations. But what about building scalable, containerized applications? By itself, Docker and other tools don’t quite cut it, as far as building the infrastructure to support containers. How do you deploy, scale, and manage containerized applications in your infrastructure? This is where tools such as Kubernetes comes in. Kubernetes is an open source system that automates deployment, scaling, and management of containerized applications. Kubernetes was originally developed by Google before being donated to the Cloud Native Computing Foundation, a project of the Linux Foundation. This article gives a quick precursor to what Kubernetes is and what some of the buzzwords really mean.
This post was originally published on OpenSource.com.
When people first think of “open source”, their mind probably first goes to code. Something technical that requires an intermediate understanding of computers or programming languages. But open source is a broad concept that goes beyond only binary bits and bytes. Open source projects hold great regard for community participation. The community is a fundamental piece of a successful open source project. For my experience getting involved with open source, I began in the community and worked my way around from there. At the age of fifteen, I was beginning my open source journey and I didn’t even know it.
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