ListenBrainz community gardening and user statistics

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.

My progress with ListenBrainz slowed, but I am resuming the pace of contributing and advancing on my independent study timeline. This past week, I finished out assigned tasks to discuss contributor-related documentation, like a Code of Conduct, contributor guidelines, and a pull request template. I began research on user statistics and found some already created. I wrote one of my own, but need to learn more about Google BigQuery to advance further.

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Introducing InfluxDB: Time-series database stack

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

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Analyzing Fedora’s impact at FOSDEM and beyond

FOSDEM conference goers in Brussels, Belgium

Conference goers attend the FOSDEM conference in Brussels, Belgium.

Yesterday, Fedora contributor and CommOps team member Bee Padalkar published an article on her blog about measuring the impact of Fedora’s participation at the FOSDEM conference in Europe.

Looking at FOSDEM

In her analysis, Bee looked at people who scanned the FOSDEM badges for 2014, 2015, 2016. Leveraging tools like fedmsg, she was able to draw conclusive evidence of how people who scanned the badge began contributing for the first time or started contributing more than before the conference. The statistics are fascinating and the analysis is comprehensive in how it measures contributions. It’s worth the full time to read how we’re making an impact at conferences!

Looking ahead

The other awesome factor of this is that these kinds of reports are extendable to other events in the world of Fedora. Other Ambassadors can use tools like Fedora Badges and track metrics of how they impact and affect the people they engage with at conferences and hackathons. I’m hoping for us to be able to use these kinds of analytics for the past event at BrickHack 2016 that I helped organize as an Ambassador. Stay tuned for an event report and plenty more on the Community Blog with details about BrickHack.

Read all about it!

Read the full analysis on her blog!