MongoDB's powerful built-in geospatial queries are one of the big reasons

One particularly neat feature of mongoose is the chainable query builder API. This API provides the ability to build up MongoDB queries with helper methods, rather than via a JSON object.

Mongoose 4.5 introduces a new API for populating documents. The new

Mongoose 4.5.0 introduces the ability to handle errors in middleware. This lets

Mongoose 4.5.0 is just around the corner (current ETA is June 3),

Getting data into and out of MongoDB is a pain. Although MongoDB 3.0 and 3.2

2015 was a big year for mongoose. Mongoose 4.0 was released, the first major

In last week's article, you

MongoDB 3.2.0 was released earlier this week. It includes some exciting

Discriminators are a powerful yet unfortunately poorly documented

The NBA Finals just concluded last week, and LeBron James officially has

Mongoose 3.9.7 has just been released. While I did say that 3.9.6 would be the

In case you haven't come across Petko Petkov's post on injection attacks against MongoDB and NodeJS yet, its definitely worth a careful read. In this article, he explains a pretty simple exploit that I suspect affects a fair number of applications, including some that I've implemented.

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.

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.

In last week's blog post, I showed you how to install all of the basic tools that you need to get up and running with the MEAN Stack. Didn't catch that one and need help getting started with the MEAN Stack? You can find everything you need in Introduction to the MEAN Stack, Part One.

I've received several emails asking for instructions on how to set up a basic MEAN stack app. I'm going to take it one step further and give you guys a two-part post that will walk you through creating your first MEAN stack app- from installing the tools to actually writing the code. In Part One we'll go through the setup and installation process. Next in Part Two we'll walk through the steps for building a very simple to-do list. Part One consists of seven steps, although only the first two are are strictly necessary.

If you're familiar with Ruby on Rails and are using MongoDB to build a NodeJS app, you might miss some slick ActiveRecord features, such as declarative validation. Diving into most of the basic tutorials out there, you'll find that many basic web development tasks are more work than you like. For example, if we borrow the style of, a route that pulls a document by its ID will look something like this:

If you've ever tried to build any kind of website, odds are you've had to create some way of validating and saving input from a form. Back in the bad old days this used to be a huge pain, because there were no good frameworks to help get the job done right. The three primary pain points that you have to deal with when trying to validate a form without the aid of a framework are:

This post was featured as a guest blog post for MongoDB on April 30th 2013, which can be found here