In my previous series of articles about installing OpenStack from source, we installed keystone as a standalone application. This technique has been deprecated in the Kilo release in favor of running the keystone server, as a web server gateway interface (wsgi) service behind the Apache server. This allows for each Apache thread to start its own separate keystone thread. Example configuration files are available in the httpd directory in the cloned keystone repo. This article is a how to setup both keystone and the horizon dashboard to run under Nginx.
This is the second in a series of posts written by the Repose Ninja on Duty. Special thanks to Jim Baker: author of the Fireside project, Jython core contributor, and integral braniac in the effort to support the WSGI specification through Servlet technologies.
If you have ever had the pleasure of evaluating Repose, you may have noticed that, while it provides an incredibly powerful foundation, it is missing that one all-important feature that you need. While the Repose team does its best to handle all common and reasonable use-cases, there are an infinite number of problems for which Repose is a solution. Therefore, it is impossible to predict and develop features to solve every problem. Luckily, Repose is built on a pluggable architecture that any developer can leverage to solve the problem of the day.
In this post, I will expand upon the previous post in this series by diving deeper into the Repose extensibility model and explaining how Repose plans to make that model more developer friendly in the future.
You may have noticed our Developer Portal looks different. This portal provides you with everything you need to build powerful, scalable apps using our Software Developer Kits (SDKs), which are built on open-source platforms and take advantage of our extensive APIs. We have redesigned Developer.Rackspace.com.
Managing infrastructure and database technology has grown at Rackspace and our list of supported technologies in the data umbrella has grown tremendously.
My name is David Grier and I am a product engineer at Rackspace. I concentrate most of my time on Cassandra, Hadoop and related components in the Big Data ecosystem.
We are proud to announce our partnership with Datastax, with whom we are providing a managed DataStax Enterprise (DSE) solution. This article is a high level view of that managed solution and how we are providing it to our customers.
Rackspace Private Cloud (RPC) powered by OpenStack has done a great job incorporating and enabling many of the great capabilities natively found within Cinder. With RPC, you gain the ability to leverage either Cinder nodes (commodity hardware using ephemeral storage exposing that storage as Block storage to your cloud) or to connect your OpenStack cloud directly to a shared storage solution via Cinder integration drivers. This is where our friends at NetApp come into play. Rackspace and NetApp have formed a unique relationship to improve the Cinder shared storage capability within OpenStack. These two teams worked together to create a repeatable, approved, and tested process to integrate NetApp storage solutions into Rackspace Private Cloud footprints within a Rackspace datacenter or at the customer's datacenter.
Federation support in OpenStack has been greatly improved in the Kilo release, and, while there are still some rough edges, the feature is now usable, if you don't mind adding a little elbow grease. This article focuses on one specific configuration called Keystone-to-Keystone federation (or K2K), in which users of an OpenStack cloud use their credentials to access services in another OpenStack cloud, with both clouds using Keystone as their identity services.
Modern browsers have APIs called
find one or more elements matching a CSS selector. I'm assuming basic
familiarity with CSS selectors: how you select elements, classes and ids. If
you haven't used them, the Mozilla Developer Network has an excellent
Welcome to part 2 of Jenkins Post-build Plugin development tutorial. This post talks in detail about the plugin project entities and their interactions with each other. To brush up on the previous post, please visit Jenkins Post-build Plugin Part 1.
Wes McKinney started working on Pandas in 2008. Since then, Pandas has become one of the most popular and useful software components for the data scientist. For good reason; using Python, Pandas and iPython/Jupyter notebooks makes it simple and quick to perform analysis on various datasets.
In this post, we perform some basic analysis on the City of Baltimore employee salary data from data.gov, but this technique can be used on a wide variety of data sets very easily.
Pandas and Jupyter notebooks make this work quick. It may be surprising to see where the money goes!
This is the first part of a two-part tutorial series to develop a Jenkins plugin (specifically, Jenkins post-build plugin). Jenkins is a very popular continuous-integration tool and with the small amount of (scattered) information present out there, it is really hard for a beginner to dive in this amazing area of extending it - Plugins!