Two weeks ago marked a big milestone: mongoose 3.9.0 was released. Be warned, mongoose's versioning practice is that even numbered branches are stable and odd are unstable. While all our tests check out on 3.9.0, I would recommend sticking to 3.8.x releases in production for now. 3.9.0 was mongoose's first unstable release since October 2013. While the changes in 3.9.0 were relatively minor, they open the door to getting some interesting features into 4.0. Here are some of the high-level features I think should make it in to 4.0:
I have an important announcement to make: over the last couple weeks I've been taking over maintaining mongoose, the popular MongoDB/NodeJS ODM. I have some very big shoes to fill, Aaron Heckmann has done an extraordinary job building mongoose into an indispensable part of the NodeJS ecosystem. As an avid user of mongoose over the last two years, I look forward to continuing mongoose's storied tradition of making dealing with data elegant and fun. However, mongoose isn't perfect, and I'm already looking forward to the next major stable release, 4.0.0. Suggestions are most welcome, but please be patient, I'm still trying to catch up on the backlog of issues and pull requests.
From a performance perspective as well as a developer productivity perspective, MongoDB really shines when you only need to load one document to display a particular page. A traditional hard drive only needs one sequential read to load a single MongoDB document, which limits your performance overhead. In addition, much like how Nas says life is simple because all he needs is one mic, grouping all the data for a single page into one document makes understanding and debugging the page much simpler.
MongoDB shipped the newest stable version of its server, 2.6.0, this week. This new release is massive: there were about 4000 commits between 2.4 and 2.6. Unsurprisingly, the release notes are a pretty dense read and don't quite convey how cool some of these new features are. To remedy that, I'll dedicate a couple posts to putting on my NodeJS web developer hat and exploring interesting use cases for new features in 2.6. The first feature I'll dig in to is text search, or, in layman's terms, Google for your MongoDB documents.
As much as I love geeking out about basketball stats, I want to put a MongoDB data set out there that's a bit more app-friendly: the USDA SR25 nutrient database. You can download this data set from my S3 bucket here, and plug it into your MongoDB instance using mongorestore. I'm very meticulous about nutrition and have, at times, kept a food journal, but sites like FitDay and DailyBurn have far too much spam and are far too poorly designed to be a viable option. With this data set, I plan on putting together an open source web-based food journal in the near future. However, I encourage you to use this data set to build your own apps.
While math and computer science have been lumped together for about as long as the latter has existed, there's a lot of backlash recently toward the idea that a solid math background is integral to being a good developer. The relationship between the two was something that I struggled to grasp as an undergraduate in Computer Science. The relationship between math and CS isn't as direct as, say, math and physics, or even philosophy and CS. However, taking a rigorous pure math course as an undergraduate will help you significantly, whether you choose to be an ivory tower academic, a developer for the latest hip startup out of Silicon Valley, or an engineer for a big NYC bank.
If you're an avid podcast listener and online courseware consumer like I am, odds are you've gotten frustrated with how long it takes to listen to a single lecture. An hour-long podcast on Bulletproof Executive? 20 minutes listening to a TEDTalk from a HackDesign lesson? No offense to these awesome content creators, but ain't nobody got time for that.
When you are looking to run analytics on large and complex data sets, you might instinctively reach for Hadoop. However, if your data's in MongoDB, using the Hadoop connector seems like overkill if your data fits on your laptop. Luckily, MongoDB's built-in aggregation framework offers a quick solution for running sophisticated analytics right from your MongoDB instance without needing any extra setup.
