Before getting into the nuts and bolts of the load balancing architecture itself, it’s important to understand the (typical) multiple tiers of an E-Commerce application framework:
Keep in mind that, top to bottom, the environment will be asymmetrical from a load perspective. For example, a single web server will typically be capable of 2-3x the number of concurrent connections as a single application server; heavily dependent on cache density - higher density will shift more load up into the web tier. Caching will be a subject for a later discussion, but at a glance should account for 80+ percent of content served. With room for variance, the majority of successful architectures achieve this metric and those that struggle tend to miss. This is not to say, of course, that a lower density will necessarily have difficulties. In addition to relocating load away from application servers, a higher cache density opens an opportunity for external services, such as Akamai CDN, to absorb load ahead of ever reaching the environment.
MongoDB is, among other things, a document-oriented NoSQL database. This means that it deviates from the traditional, relational model to present a flexible, horizontally scaling model for data management and organization.
MongoDB integrates with Adobe Experience Manager (AEM) by means of the crx3mongo runmode and JVM options: -Doak.mongo.uri and -Doak.mongo.db
Primarily MongoDB provides an alternate HA configuration to the older CRX cluster configuration. In reality, the architecture is more similar to a shared catalog on NFS or to NetApp than true clustering. The authors and publishers using MongoDB are not necessarily aware of each other.
If you are an OpenStack contributor, you likely rely on DevStack for most of your work. DevStack is, and has been for a long time, the de-facto platform that contributors use for development, testing, and reviews. In this article, I want to introduce you to a project I’m a contributor to, called openstack-ansible. For the last few months, I have been using this project as an alternative to DevStack for OpenStack upstream development, and the experience has been very positive.
This is the first of a two-part series that demonstrates a pain-free solution a developer could use to transition code from laptop to production. The fictional deployment scenario depicted in this post is one method that can significantly reduce operational overhead on the developer. This series will make use of technologies such as Git, Docker, Elastic Beanstalk, and other standard tools.
Container technology is evolving at a very rapid pace. The purpose of the webinar talk in this post is to describe the current state of container technologies within the OpenStack Ecosystem. Topics we will cover include:
Architecting applications for a cloud environment usually means treating each cloud server as ephemeral. If you destroy the cloud server, the data is destroyed with it. But, you still need a way to persist data. Cloud block storage has typically been that solution. Attach cloud block storage to a cloud server, save your data within that cloud block device, and when/if the cloud server is destroyed, your data persists and can be re-attached to another cloud server.
The IPython/Jupyter notebook is a wonderful environment for computations, prose, plots, and interactive widgets that you can share with collaborators. People use the notebook all over the place across many varied languages. It gets used by data scientists, researchers, analysts, developers, and people in between.