Custom development for Drupal is not for the faint of heart. If you are a developer with little concern for maintainability, reusability, readability, or extensibility, then you may not struggle - there are many, many ways to approach any particular problem. However, if you're like me and have to carefully consider these core concepts before you proceed, then creating Drupal modules to solve a particular business problem requires a lot information at hand. Personally, I think the design patterns used in Drupal are conceptually approachable for even a fresh Computer Science graduate.
MongoDB might appears as a bit foreign to people who are used to relational databases like Oracle and MySQL. In fact the first time I used it it reminded me of Berkeley DB in the past. Drupal was originally developed using relational databases so during installation you will have to create either SQLLite, PostresSQL or MySQL etc. Because of this, it appears that MongoDB doesn't really fit into Drupal right? In fact there is a already MongoDB module in the works that allows Drupal connects to MongoDB.
This is the first of a four part series on Optimization. We’ll begin by exploring optimization in Drupal as a general overview of the topic, and then I’ll zoom into each aspect (PHP, MySQL, and Server) later in the series. Just to clarify our strategy for approaching this topic, I won’t be reiterating optimization tips and tricks, i.e. “6 Best Practices to Optimization”, that you can find online. The goal is to offer insights on the problem, and give recommendations.
My last blog post demonstrated the ability to retrieve and set values using the Entity Metadata Wrapper. Today, I’m going to follow that up by showing how to create and set values for field collections as well as taxonomy terms.
Creating a field collection on the fly is done by using the “entity_create” function available from the entity module. Once the entity field collection object is created, associating that object to the node is done by using the "setHostEntity" function and passing the node you wish to associate to.
Nowadays, Big Data solutions are very popular and widely used. Hadoop is the open source leader, but other solutions exist, like Google Big Query. They allow enterprises to find answers to questions they wouldn't think to ask before. This can result in insights that lead to new product ideas or help identify ways to improve operational efficiencies. Here are 5 very effective use-case examples: