jd:/dev/blog

Finally! Four months ago we pushed the Gnocchi 3.1 release and here we are now, release the 4th major version of that timeseries database.

I've always been frustrated by the GitHub workflow. A while back I wrote how Gerrit workflow was superior to GitHub pull-request system. But it seems that GitHub listened and they improved the pull-request system these last years to include reviews, and different workflow implementation, e.g. requiring continuous integration tests to pass before merging a patch.

The first OpenStack Summit of 2017 was last week, in Boston, MA, USA. I was able to attend as I've been selected to give 3 talks, to help for a hands-on and to animate an on-boarding session. This made sure I was a bit busy every day, which was good.

Three years have passed since I started working on Gnocchi. It's amazing to gaze at the path we wandered on.

A couple of years ago, I wrote about the Python retrying library. This library was designed to retry the execution of a task when a failure occurred.

As I wrote a few weeks ago in my post about Gnocchi 3.1 being released, one of the new feature available in this version it the S3 driver. Today I would like to show you how easy it is to use it and store millions of metrics into the simple, durable and massively scalable object storage provided by Amazon Web Services.

Knowing that collectd is a daemon that collects system and applications metrics and that Gnocchi is a scalable timeseries database, it sounds like a good idea to combine them together. Cheery on the cake: you can easily draw charts using Grafana.

Last week-end, I was in Brussels, Belgium for the 2017 edition of the FOSDEM, one of the greatest open source developer conference.

It's always difficult to know when to release, and we really wanted to do it earlier. But it seems that each week more awesome work was being done in Gnocchi, so we kept delaying it while having no pressure to push it out.

My day-to-day activities are still evolving around the Python programming language, as I continue working on the OpenStack project as part of my job at Red Hat. OpenStack is still the biggest Python project out there, and attract a lot of Python hackers.

Those last few years, however, things have taken a different turn for me when I made the choice with my team to rework the telemetry stack architecture. We decided to make a point of making it scale way beyond what has been done in the project so far.