Node.js and MongoDB

MongoDB
July 14, 2010 | Updated: January 20, 2017
#Releases

Visit the more recent post, Getting Started with VMware Cloud Foundry, MongoDB, and Node.js. Listen to the recorded Node.js Panel Discussion webinar.

Node.js is turning out to be a framework of choice for building real-time applications of all kinds, from analytics systems to chat servers to location-based tracking services. If you’re still new to Node, check out Simon Willison’s excellent introductory post. If you’re already using Node, you probably need a database, and you just might have considered using MongoDB.

The rationale is certainly there. Working with Node’s JavaScript means that MongoDB documents get their most natural representation – as JSON – right in the application layer. There’s also significant continuity between your application and the MongoDB shell, since the shell is essentially a JavaScript interpreter, so you don’t have to change languages when moving from application to database.

Node.js MongodB Driver

Especially impressive to us at 10gen has been the community support for Node.js and MongoDB. First, there’s Christian Kvalheim’s excellent mongodb-node-native project, a non-blocking MongoDB driver implemented entirely in JavaScript using Node.js’s system libraries. The project is a pretty close port of the MongoDB Ruby driver, making for an easy transition for those already used to the 10gen-supported drivers. If you’re just starting, there’s a helpful mongodb-node-native mailing list.

Hummingbird

Need a real-world example? Check out Hummingbird, Michael Nutt’s real-time analytics app. It’s built on top of MongoDB using Node.js and the mongodb-node-native driver. Hummingbird, which is used in production at Gilt Groupe, brings together an impressive array of technologies; it uses the express.js Node.js app framework and sports a responsive interface with the help of web sockets. Definitely worth checking out.

Mongoose

Of course, one of the admitted difficulties in working with Node.js is dealing with deep callback structures. If this poses a problem, or if you happen to want a richer data modeling library, then Mongoose is the answer. Created by Learnboost, Mongoose sits atop mongodb-node-native, providing a nice API for modeling your application.

Node Knockout

All of this just to show that the MongoDB/Node.js ecosystem thrives. If you need a good excuse to jump into Node.js or MongoDB development, be sure to check out next month's Node Knockout. It’s a weekend app competition for teams up to four, and registration is now open. 

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Blog Contest Winners!

We’re pleased to announce the winner’s of the MongoDB blogging contest ! Grand Prize Why (and How) I Replaced Amazon SQS with MongoDB  - Matt Insler Runners Up Running MongoDb on Microsoft Windows Azure with CloudDrive - Simon Green Using MongoDB for CodeIgniter Logs - Tom Schlick Integrating MongoDB with Spring Batch  - Baruch Sadogursky The winners should contact meghan@10gen.com to claim their prizes. You check out all the awesome entries at mongodb.slinkset.com Thanks to everyone who submitted!

June 8, 2010
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Empower Modern App Developers with Document Databases

Across industries, business success depends on a company’s ability to deliver new digital experiences through software. The speed at which a company can develop and deploy a new application with innovative features is a direct lever on business outcomes. Given the vital role developers play in the success of your business, it stands to reason that equipping them with the tools to maximize their productivity is in your best interest. Unfortunately, many organizations are unaware of the tax they’re placing on their development teams by using a relational database. While the relational database has been a bedrock for data-driven applications for 50 years, it was developed in an era before the internet and is a poor fit as the foundation of today’s web and mobile applications. Document databases, which have emerged over the past decade, have cemented themselves as the most popular and widely used alternative to the tabular model found in traditional relational databases. Document databases have become so powerful that even relational databases are trying to emulate them. Built around JavaScript Object Notation (JSON)–like documents, document databases are intuitive for developers to use. Instead of the rigid row-and-column structure of the relational model, document databases map documents directly to objects in code, which is how coders naturally think of and work with data. Let’s break down the key advantages to document databases in building modern applications. We’ll see why the document model’s flexibility eliminates the complex intergroup dependencies that have traditionally slowed developers down. The limitations of the relational database model Relational databases add complexity to a developer’s workload, severely hampering the velocity of work. The rigid row-and-column structure creates a mismatch between the way developers think of code and data, and how they need to store it. Additionally, while the relational model was fine in an age when most applications used a small pre-set of attributes such as last names, ZIP codes, and state abbreviations, the majority of data collected by organizations today is rich in structure. We have given names and sometimes preferred names. We have unique attributes that are relevant to only some of us: For example, people with PhDs have dissertation topics, sports lovers have favorite sports, and our families come in every conceivable shape and size. This richly structured data reflects how we actually think about the real world, and it’s very difficult to flatten, store, analyze, or query using rows and columns. With relational databases, developers can feel stuck in quicksand with changes to their applications requiring them to carefully collaborate with experts like database administrators (DBAs) who help them translate their schemas and queries to underlying relational data models to ensure that indexing strategies are appropriately employed. The layer of indirection increases cognitive load, is hard to reason about, and slows everything down. More than half of application changes require database schema modifications. Those database modifications take longer to complete than the application changes they are designed to support. You can quickly see why these complicated efforts severely slow the delivery of new software features into production. Enabling development of modern apps with document databases With the birth of the internet and the proliferation of mobile and web apps, developers’ roles evolved. The emergence of robust development frameworks, which abstracted away underlying complexity, and the rise of DevOps led organizations to consolidate developer functions. The new generation of full-stack developers wanted databases that better addressed their applications’ requirements and their ways of working with data. The founders of MongoDB recognized a need for a modern database solution while at the adtech giant DoubleClick in 2007. They still were unable to scale to the 400,000 transactions per second the business required due to the constraints of the relational model. These challenges inspired them to create a new, modern, general-purpose database. This database could address the shortcomings of the relational data model and offer a solution that developers actually wanted to use. The result was a horizontally scalable, document-based NoSQL database called MongoDB. The document database model in general, and MongoDB more specifically, addresses the limitations of relational databases in several notable ways: Intuitive data model: The documents at the center of document databases have a universal data format. JSON is a lightweight, language-independent, and human-readable format that has become a widely used standard for storing and exchanging data. These documents map directly to data structures in popular programming languages so there is no need for the additional mapping layer often used with relational databases. Because data that is accessed together is stored together, there is less code to write; developers don’t need to decompose data across tables or run joins. Flexible schema: These JSON-like documents are flexible. Each document can have its own fields, and there’s no need to pre-define the schema in the database. It can be modified at any time. That flexibility enhances developer agility. Meeting user expectations while simplifying application architectures The most innovative applications we use in our daily lives — think Netflix and Instagram — have raised user expectations for what every application should be. Today we expect applications to be: Highly responsive Able to deliver relevant information Optimized for mobile devices Secure Powered by real-time insights Continuously improved Meeting those expectations can be extremely challenging, especially for developers using relational databases. A typical data infrastructure built around a legacy relational database can trap your development team in overly complex and siloed architectures. Document databases, on the other hand, can simplify application architectures. Documents are a superset of all other data models, so developers can store and work with a variety of data types. Development teams can accommodate most of their use cases in a single data model and database The document data model can help your developers overcome the limitations of the relational model while improving their productivity and velocity. Allowing them to minimize the undifferentiated work of maintaining their infrastructure and to focus on meeting demanding user expectations. As a result, they can deliver better, more innovative applications faster than before. Click here to read the original article published on The New Stack.

June 5, 2023