Technical and Product News and Insights from Rackspace
This year’s AWS re:Invent was a nonstop, high-powered firehose of exciting new features and products. Native PHP support on Lambda wasn’t one of those features, but the new AWS Lambda runtime API and layers capabilities gives us the ability to build a clean, supportable implementation of PHP on Lambda of our own. In this post, we’ll take a brief look at the overall workflow and runtime lifecycle, and then I will show you one way to build a PHP runtime to start powering your PHP applications on AWS Lambda.
This blog post reviews how to use Amazon Simple Storage Service (S3), as storage for an Oracle® Database backup. Amazon Web Services (AWS) was the first cloud vendor that Oracle partnered with to enable database backup in the cloud. S3 is the main storage offering of AWS.
One of the benefits of containers is the promise of portability. The Docker® mantra is to build, ship, and run. Containers also promise the ability to, with few changes, move from a developer’s laptop to a production environment and, in the same vein, the ability to move from a data center to the cloud or to many clouds. However, adopting containers alone does not guarantee this. At the core, containers are just a better way of packaging your applications. While they ensure a degree of technical compatibility across many clouds, they don’t ensure complete portability by themselves. In this post, we will look at some of the many considerations from the portability lens.
Modern application environments can be complex and include many discrete elements that can all affect the end user’s experience. Because of this, it can be challenging to develop an effective monitoring strategy that allows you to be alerted during potential performance problems and also to use these metrics from a variety of systems to proactively address potential bottlenecks and slow points before they cause end user impact. In this article, we’ll be discussing several best practices for ensuring that your environment is effectively monitored.
I have spent the majority of my career as a Java developer. As a result, I learned to be more productive using an IDE instead of an editor like Vi. Even though Vi is still my editor of choice when I’m in a Linux shell, I don’t believe it’s practical when managing large Java projects.
In Part I of this series, we depicted a fictional scenario for agile development using a simple “Hello World” application composed of just a single UI layer. During this fanciful (albeit contrived) exposition, we glossed over many of the underlying details for the sake of brevity. In this article, we will take a little peek under the covers and explain in more depth how we achieved rapid, automated deployments of immutable application containers to remote test environments.
With this article I begin a series of hands-on developer oriented blog posts that explore OpenStack orchestration using Heat.
To make the most of this article, I recommend that you have an OpenStack installation where you can run the examples I present below. You can use our Rackspace Private Cloud distribution, DevStack, or any other OpenStack distribution that includes Heat.