MTV Networks Online Division is leveraging MongoDB as the database platform for its next-generation CMS. The talk will focus on these points: A brief outline of the particular challenges of building a CMS for a large media-focused enterprise company; the selection criteria for the database platform (and how MongoDB fit them); the way the CMS leverages the various features of MongoDB.

This talk will introduce the features of MongoDB by walking through how one can building a simple location-based checkin application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.

Using Morphia as a data persistence bridge makes working with data in Java exceedingly easy with MongoDB. In this session we will take a look at examples of how to customize persistence and common data access patterns like Morphia's Datastore and DAO abstractions. On the more advanced side of things we will discuss life-cycle methods and how to extend Morphia in your own application or framework; these techniques can also be used to help with data migration and schema changes when changing your Java domain objects (Entities).

One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for RDBMSs, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.

With the release of 1.8, MongoDB supports write-ahead journaling of operations to facilitate fast crash recovery and durability in the storage engine. In this session, we'll give an overview of durability with MongoDB, demo journaling, and discuss journaling internals.

Tornado is a framework for building web applications built to scale to thousands of simultaneous standing connections. This scalability is achieved primarily through the non-blocking Tornado web server. Until recently, using MongoDB with Tornado meant using the blocking PyMongo driver. AsyncMongo (created by bit.ly) is an open source library for accessing MongoDB from Tornado applications. This session will introduce both AsyncMongo and the Tornado web framework.

Schema design is a critical step in making sure an application scales well. There are considerations for reads and writes, both with and without sharding. We'll go through a few use cases and examine how difference schemas impact performance.

That's right: you too can learn to read the omens and ensure that your MongoDB deployment stays in tip-top shape. We'll look at memory usage, file sizes, flushing, journaling, and all the special incantations that reveal MongoDB's true inner self. By the end of the talk, you'll have ten concrete steps you can take to address performance degradation before it happens. You'll also get a few tips on application design and pointers on remote monitoring.

When working with PHP and MongoDB you eventually end up asking yourself the question, Should I use an Object Document Mapper or not? In this talk I'll cover the pros and cons of using PHP's native MongoDB driver vs using an ODM. I'll also talk about how some of the more popular PHP MongoDB ODMs work.

B-Trees, Sparse and Covered indexes and how MongoDB uses them still confusing you? In this talk we will dive into the different indexes that are available, how query plans are executed and the internals of index evaluation including understanding of the explain output.

Last August, Dealer.com deployed a new site configuration management system based on MongoDB. We have been running this system in our live production environment for a little over a year now and have continued to learn and refine our solution. This session will look at our experiences with MongoDB. We will start with the process we used to select MongoDB, the project's lifecycle up until the live deployment, and then what we have learned and changed over the last year. We will also take a look at what our plans are moving forward, and some other places that we have deployed MongoDB within our organization. To give an idea of scale, our current system supports configuration files for over 15,000 customer sites with thousands of revisions per site and an on-disk size of almost 200GB.

Receiving data from a source that produces 5-10 GBytes per hour, sustained for 24 hrs, and presenting analysis results as the data streams in has some interesting challenges. We used MongoDB running on Amazon EC2 to house the data, map reduce to analyze it and Django-non-rel to present the results in near-real-time.

Case example presented of how Boundless Learning used MongoDB as a rapid-prototyping technology to developer a robust content management system in a matter of weeks, not months. Topics include browser-to-mongo interaction, rapid migrations, and general strategies.

Deploying and managing a datastore in the cloud is not the most pleasant way to spend the afternoon. Databases are difficult to code to and notoriously resistant to scale. Fortunately, MongoDB provides some silver-lining. Well suited for the cloud, it is easy to learn, adapts to your application and it scales! OpenShift is a free platform-as-a-service that literally turns MongoDB deployments into a check box for Java and PHP apps. Throw auto-scaling, performance monitoring, and log management for your app into the mix and now it's on! In this session you'll learn everything you need to get started with MongoDB in the cloud. Don't have an Amazon EC2 account? No sweat, we've got you covered with 30 hrs of free EC2 time, just come to the session or swing by the booth to get hooked up. Be there or be relational!

MongoDB supports replication for failover and redundancy. In this session we will introduce the basic concepts around replica sets which provide automated failover and recovery of nodes. We'll show you how to set up, configure, and initiate a replica set, and methods for using replication to scale reads. We'll also discuss proper architecture for durability.

MongoDB is synonomous with scale and performance, and, hey, so is cloud computing! It's peanut butter and chocolate all over again as we take at look at why you might consider running MongoDB in the cloud in general and also look at the alpha release of MongoDB on Azure, a collaboration from 10gen and Microsoft.

Art.sy is a fine art search engine that uses the Art Genome Project to infer connections between tens of thousands of works by classic and contemporary artists. After building a prototype with MySQL it became clear that a relational database was poorly suited for storing data in a high dimensional space. We have then adopted MongoDB and are now running it as our only database in production, searching a 560-dimensional genome space in under 1/4s per query using RoR/Mongoid.

Localytics uses MongoDB to process over 100M datapoints every day for their mobile analytics service. They will present a technical deep dive on scaling MongoDB without breaking the bank. This presentation will explore how Localytics optimized their document design, indexes, and EC2 configurations, accounting for MongoDB's internal data structure and concurrency model.

What is Cloud Foundry and how can I use Mongo with it? What is the Spring Data project and how can that simplify my Java applications using Mongo? Thomas Risberg will answer these questions and more. This presentation goes into detail how to configure and code Spring applications that use Mongo and how to deploy them to a local or public Cloud Foundry PaaS.

Video

Come learn about the different ways to backup your single servers, replica sets, and sharded clusters

With MongoDB, you can distribute load across multiple servers using auto-sharding. This session will introduce MongoDB's auto-sharding concepts and how to use them. We'll discuss choosing a shard key, basic architecture concepts, and common usage patterns. We'll close out the session with a few example use cases, including real world sharded deployments.

In this talk we present our experiences building a text analysis system on top of MongoDB. The Interllective, is building research tools to discover conceptually related documents in technical collections (patents, journal articles, etc.). We use sharded MongoDB to scale out our document collections. We discuss experiences performing text clustering, with collection design and indexing strategies for using Hadoop MapReduce over MongoDB. We also discuss building a real-time distributed relevance scoring engine on top of MongoDB. And since our data is too large to fit into memory, we address tradeoffs to consider when scaling using a disk based approach.