Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly.
Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.
Main goals for Atlas were to build a system that provided:
Also Atlas supports Stack Language for complex data queries in a URL-friendly format. It is loosely based on the RPN expressions supported by Tobias Oetiker‘s rrdtool. The following is an example of a stack language expression:
nf.cluster,discovery,:eq,(,nf.zone,),:by
This example pushes two strings nf.cluster and discovery onto the stack and then executes the command :eq. The equal command pops two strings from the stack and pushes a query object onto the stack. The behavior can be described by the stack effect String:key String:value – Query. We then push a list of tag keys to the stack and execute the command :by to group the results.
More on Stack Language : https://github.com/Netflix/atlas/wiki/Stack-Language
Quick start : https://github.com/Netflix/atlas/wiki/Getting-Started
There are few things as valuable to a business as well-designed software. Organizations today rely…
The cryptocurrency industry is being reshaped by the fusion of blockchain technology and artificial intelligence…
Introduction Artificial Intelligence (AI) has also found its relevance in graphic design and is quickly…
Imagine a world where the brilliance of Artificial Intelligence (AI) meets the unbreakable security of…
In today’s fast-paced digital landscape, automation is not just a luxury but a necessity for…
The world of casino gaming has leveraged the emerging technology advancements to create immersive and…
This website uses cookies.