I've been a graphite contributor for a while (and still am). It's a great tool for timeseries metrics. Two weeks ago I started working on Graphite-ng: it's somewhere between an early clone/rewrite, a redesign, and an experiment playground, written in Golang. The focus of my work so far is the API web server, which is a functioning prototype, it answers requests like
I.e. it lets you retrieve your timeseries, processed by function pipelines which are setup on the fly based on a spec in your http/rest arguments. Currently it only fetches metrics from text files but I'm working on decent metrics storage as well.
Being active as both a developer and ops person in the professional life, and both an open source developer and packager in my spare time, I noticed some common ground between both worlds, and I think the open source community can learn from the Devops movement which is solving problems in the professional tech world.
For the sake of getting a point across, I'll simplify some things.
Graphite can show events such as code deploys and
puppet changes as vertical markers on your graph.
With the advent of new graphite dashboards and interfaces where we can have popups and annotations to show metadata for each event (by means of client-side rendering),
it's time we have a database to track all events along with categorisation and text descriptions (which can include rich text and hyperlinks).
Graphite is meant for time series (metrics over time), Anthracite aims to be the companion for annotated events.
More precisely, Anthracite aims to be a database of "relevant events" (see further down), for the purpose of enriching monitoring dashboards, as well as allowing visual and numerical analysis of events that have a business impact (for the latter, see "Thoughts on incident nomenclature, severity levels and incident analysis" below)
It has a TCP receiver, a database (sqlite3), a http interface to deliver event data in many formats and a simple web frontend for humans.
Earlier this month we had another iteration of the Monitorama conference, this time in Portland, Oregon.
(photo by obfuscurity)
I submitted a pull request to statsd which adds histogram support.
(refresher: a histogram is [a visualization of] a frequency distribution of data, paraphrasing your data by keeping frequencies for entire classes (ranges of data). histograms - Wikipedia)
It's commonly documented how to plot single histograms, that is a 2D diagram consisting of rectangles whose