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You’ve probably heard that MongoDB uses Memory Mapped Files and supports Journaling. But how exactly does it all fit together? In this advanced internals talk, we’ll dive into the kernel of MongoDB and look at how the databases uses these functions to persist your data. Expect low level database internals, memory hierarchies, and durability models.

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Last bugs are finished, testing is complete, business is ready. What do you do next? In this talk we will cover the topics to ensure that you are prepared for a successful launch of your MongoDB based product, including: Machine Sizing - How much CPU, Memory, Disk should I use for my MongoDB? Backup / Restore procedures - What are my options and what do I need to do? Load Testing and Capacity Planning - How much resource is my MongoDB going to use? When do I need to add replicas and shards? Monitoring - What should I be watching and how do I know if things are running correctly?

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This is an overview of the MongoDB architecture: the storage engine for bson and btrees, the query evaluator, the replication design, and the sharding design. The talk is more broad than deep, and it is intended to give an effective overview of the mongodb design decisions and implementation strategies.

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

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There’s lots of talk in the NoSQL space about using the “right tool for the right job”. In this talk, Dwight Merriman, co-founder of 10gen and the MongoDB project will talk about the history of MongoDB, going back to his years of building high performance systems at DoubleClick through to today. Dwight will also cover the design decisions that went into creating MongoDB and the use cases it’s particularly well suited to.

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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. Understandably, this begets good questions: Are foreign keys permissible, or is it better to represent one-to-many relations withing a single document? Are join tables necessary, or is there another technique for building out many-to-many relationships? What level of denormalization is appropriate? How do my data modeling decisions affect the efficiency of updates and queries? In this session, we'll answer these questions and more, provide a number of data modeling rules of thumb, and discuss the tradeoffs of various data modeling strategies.

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This talk goes over various performance tuning techniques used in real world examples from our various implementations of MongoDB at Shutterfly. We will cover various techniques including usage of the profiler, query tuning, monitoring for performance, data-modeling, data locality. I will also discuss our implementation of Facebook Flashcache for MongoDB.

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With over 1400 mongo instances deployed we have learned much about how and what to monitor, how to triage incidents and we have developed a set of in-house tools to help us with diagnosis and forensic analysis of day to day issues. We will share some insights we have gained in a year of building & maintaining a reliable and scalable mongo service for a variety of Disney products.

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This talk describes the life-cycle of a query, starting from when it arrives at mongod:27017, and moves through the query parser, query optimizer, query evaluator, touches the journaling subsystem and the storage engine, then returns a cursor to the result set. This talk focuses on the non-sharded configuration.

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Two very different MongoDB implementations; both with substantial outcomes. 1) Scaling MongoDB to calculate football team picks, and ranking in realtime, as the results unfold. 2) Leveraging MongoDB's flexible schema to reduce complexity and speed performance for XML data feeds. In both of these examples, we will review the architecture deployed on EC2, and discuss how MongoDB was a crucial component of the optimal solution.

Today's real-time world leaves nothing to wait. With more data to process, and less time at hand, pairing MongoDB with Fusion-io flash memory solutions is a winning combination. Meet a couple of customers – Kontera and Aggregate Knowledge – and learn how you can harness the power of MongoDB along with the speed of NAND flash memory today!

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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.

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With over 180,000 projects and over 2 million users, SourceForge has tons of data about people developing and downloading open source projects. Until recently, however, that data didn't translate into usable information, so Zarkov was born. Zarkov is system that captures user events, logs them to a MongoDB collection, and aggregates them into useful data about user behavior and project statistics. This talk will discuss the components of Zarkov, including its use of Gevent asynchronous programming, ZeroMQ sockets, and the pymongo/bson driver.

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This talk described how Wordnik is taking advantage of the cloud-readiness of MongoDB to improve uptime and reduce costs against it's huge and dynamic English language corpus.

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A brief overview of building and deploying applications using MongoDB Replica Sets on Windows Azure followed by an interactive session to discuss recommended configurations and tips (and tricks).

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Consumer social web is seeing lot of momentum and traction around various NoSQL solutions, including MongoDB. Enterprise world is not lagging too much behind this time around. A number of Enterprise Social Software (aka ESS or E2.0) systems are embracing MongoDB, node.js and other alternative stacks as a replacement for their traditional J2EE architectures. Biren Gandhi, an early architect at Facebook and Zynga will share his recent experiences with MongoDB as part of Cisco's Enterprise Collaboration Software - Quad.

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MongoDB supports a wide range of indexing options to enable fast querying of your data. In this talk we’ll cover how indexing works, the various indexing options, and cover use cases where each might be useful.

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MongoDB has been designed for versatility, but the techniques you might use to build, say, an analytics engine or a hierarchical data store might not be obvious. In this talk, we'll learn about MongoDB in practice by looking at four hypothetical application designs (based on real-world designs, of course). Topics to be covered include schema design, indexing, transactions (gasp!), trees, what's fast, and what's not. Sprinkled with tips, tricks, shoots, ladders, and trap doors, you're guaranteed to learn something new in this interdisciplinary talk.

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Foursquare is a service that makes the real world easier to use. Our production datastore, built on MongoDB, has scaled to more than 15 millon users, more than a billion check-ins, and millions of check-ins per day. This talk will discuss the systems and procedures we use to maintain uptime and performance when running MongoDB in the dynamic, virtualized environment of Amazon EC2.

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Drop in on this technical talk with OpenShift Evangelist and all-around Open Source guy Mark Atwood. In this talk you'll be able to follow along on your own laptop as we deploy a Twitter-clone Python app built using the Bottle web framework with a MongoDB back-end. Nothing complicated, nothing convoluted. We'll use Git to push code and OpenShift to host our app and MongoDB. We'll take a crack at how to preform snapshots (backups and restores) and effectively tail MongoDB logs while we are at it. If you don't have one, maybe even two MongoDB apps running on the cloud by the end of the talk, without having to pull out your credit card, you're doing it wrong. MongoDB on OpenShift is Big and it's FREE!

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Technological innovation - faster computers, more efficient solar cells, more compact energy storage - is often enabled by materials advances. Yet, it takes an average of 18 years to move new materials discoveries from lab to market. This is largely because materials designers operate with very little information and must painstakingly tweak new materials in the lab. Computational materials science is now powerful enough that it can predict many properties of materials before those materials are ever synthesized in the lab. By scaling materials computations over supercomputing clusters, we have computed some properties of over 80,000 materials and screened 25,000 of these for Li-ion batteries. The Materials Project is making these materials and their properties available to scientists around the world through a sophisticated web interface. MongoDB is at the core of the Materials Project architecture. It is used to schedule and track quantum mechanical calculations of materials properties on supercomputers, to store and search the results of these computations, and to perform advanced analytics on the computed materials properties.

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Administering a MongoDB cluster is a lot like administering an RDBMS. While MongoDB introduces new concepts to the administrator, much of what you already know about database administration will apply to MonogDB. In this session we’ll review how to administer and deploy MongoDB starting from the basics and covering the best practices and procedures.

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Schema design is a critical step in making sure an application scales well. Schema design will impact various performance critical aspects of your deployment including index size, working set size, disk IOPS, query/update efficiency and data transfer rates. We’ll use real world use cases to examine how schema design impacts performance and what you can do to make your design rock solid.

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In any fast growing application, emergencies happen. And when something goes wrong with our production system, it can be stressful and painful. We’re often forced to diagnose and recover from failures with limited information and on a strict time budget. In this talk, we’ll review four true stories of MongoDB emergencies including how we detected something was wrong, how we diagnosed the problem, and the corrective action taken to get the app back online.

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Desperate for COBOL support for MongoDB but the community isn’t responding? Well, you can build your own driver! There are currently dozens of drivers for MongoDB in most modern languages. In this talk, we’ll cover the driver architecture, how they connect to replica sets and handle failover, and how they serialize BSON. We'll also discuss the wire protocol and the cool features therein.

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This talk will provide an overview of the PaaS (Platform as a Service) landscape, and will describe the Cloud Foundry open source PaaS, with its multi-framework, multi-service, multi-cloud model. Cloud Foundry allows developers to provision apps in Java/Spring, Ruby/Rails, Ruby/Sinatra, Javascript/Node, and leverage services like MySQL, MongoDB, Reddis, Postgres and RabbitMQ. It can be used as a public PaaS on CloudFoundry.com and other service providers (ActiveState, AppFog), to create your own private cloud, or on your laptop using the Micro Cloud Foundry VM. Micro Cloud Foundry is a very easy way for developers to start working on their application using their framework of choice and MongoDB, without the need to setup a development environment, and your app is one command line away (vmc push) from deployment to cloudfoundry.com.

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Amazon Web Services offers one of the most common deployment platforms for MongoDB. Whether running development or test machines, or a full MongoDB deployment, AWS is up to the task. In this talk we’ll cover the details of how to best use the tools and services from AWS to run MongoDB.

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After outgrowing an existing MQ solution we turned to Mongo. With just a few lines of code we were able to replace our old MQ cluster with a sharded Mongo deployment. In addition to the sharding ability, Mongo has options that allow us to tailor durability to the level we require. We are currently successfully processing millions of messages per day. This talk will feature side-by-side examples in both Python and the Mongo Shell.

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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.

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This talk goes over various patterns, techniques and best practices around developing webscale applications using MongoDB. We will cover various techniques including a DSL like schema definition tool, benefits of using data abstraction API, and other best practices we have learned.

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At Loggly we use Mongo as a repository of statistics and summary information for time series log data. Loggly collects and stores metrics into Mongo including the size and count of log events, and the results of saved searches run against our customized fulltext search engine based on SOLR. These metrics are used to drive graphs, reports, dashboards and exciting visualizations for our users. This talk will also discuss how we tied Mongo into our infrastructure, how we use Mongo for stats rollups, and how we expose that data to end users via our REST APIs.

Being able to define MongoDB services and intelligently relate them to applications can help you create the most powerful yet flexible system imaginable. Join Juan Negron and Mark Baker from Canonical, the company behind Ubuntu and learn how service orchestration technology Juju can be used to define, deploy and manage sophisticated implementations of MongoDB and technologies such as Tomcat and Node.js.

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Sharding allows you to distribute load across multiple servers and keep your data balanced across those servers. This session will review MongoDB’s support for auto-sharding, including an architectural overview, usage patterns, as well as a few example use cases of real world sharded deployments.

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X.commerce has a big vision. Actually, a really really big vision. We're going to revolutionize commerce for consumers, merchants and developers. Consumers get an exciting, seamless and personalized experience. Merchants get access to more capabilities and analytics, enabling them to expand their businesses and deliver more to their customers. Developers get a open, standards-based platform that gives them rich commerce-focused capabilities, cloud-based deployment and reach to merchants globally. We recognized early in the conception of the technical strategy for X.commerce that realization of our vision would demand that we adopt a fresh set of principles and practices. You can see this reflected in our go-to-market strategy, our engineering processes, our architecture and our technology selections. This presentation will discuss how X.commerce has made strategic technical decisions to align with key open source projects that have maximum momentum in their respective domains. We'll dig deepest on how MongoDB enables our distributed, cloud-based architecture, but also touch on our use of OpenStack, Cloud Foundry, RabbitMQ, Hadoop and an open-source continuous delivery pipeline. We'll also spend a few minutes on a key component of X.commerce - Magento - which has been committed to open source since its inception.

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An often cited strength of Node.js and MongoDB is that it’s “easy to get started”. Ease of use, access, integration and interoperability are first class citizens right up there with performance, scalability and security. Open source also aids in these. Jason will be discussing examples and the importance of this in developer adoption.

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CustomInk has been using MongoDB in production for over a year. In this talk I'll detail some of the reasons we decided to go with MongoDB, the challenges we faced bringing it into the organization, how we're using it in production today, lessons learned, and some of our future plans. The talk will also describe some of the operational considerations for putting MongoDB into production: how do we deploy, operate, and monitor MongoDB. During this talk I'll also describe CustomInk's involvement in the MongoDB community.

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Monster is Stripe's in-house framework for producing and consuming events. Whenever a user logs in, whenever a payment is received, whenever a cron job runs, an event is logged into our MongoDB event store. These events update aggregate totals, feed into fraud algorithms, and can be analyzed as a changelog of the system. In this talk, we'll discuss how MongoDB's unique features make it easy to implement Monster.

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We're working on a new aggregation framework for MongoDB that will introduce a new aggregation system that will make it a lot easier to do simple tasks like counting, averaging, and finding minima or maxima while grouping by keys in a collection. The new aggregation features are not a replacement for map-reduce, but will make it possible to do a number of things much more easily, without having to resort to the big hammer that is map-reduce. After introducing the syntax and usage patterns for the new aggregation system, we will give some demonstrations of aggregation using the new system.

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MongoDB has been built for high availability, but achieving high availability isn't always as simple as deploying a replica set. This talk will detail key replica set semantics and deployment architectures, helping you to build your own robust MongoDB clusters.

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Using some clever functions within MongoDB you can implement various algorithms to create trainable learning systems within your app's database. You can apply these algorithms to systems such as spam filtering, content auto-tagging, social analytics, and other classification applications. Why build these systems using MongoDB? Other than the performance benefits of Mongo's aggregation systems, you can simplify your workflow, and improve the portability of your business logic. Finally, MongoDB offers many tools that your language or toolkit of choice may not.

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This talk will introduce MoPub, our data needs, and the challenges faced as we grew from 50M request / day to 1 billion / requests per day. We now use MongoDB for realtime stats (lots of counters!), our budgeting system, and our user store.

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Ok, so you’ve launched your development sandbox, love MongoDB, and are now thinking about how you want to handle your production environment. Learn all sorts of tips and tricks in this practical session on MongoDB operations by leading cloud database hosting provider MongoLab. We at MongoLab provide database hosting on EC2, Rackspace and Joyent for thousands of applications powered by MongoDB. In this session we will share with you some of the best practices we have developed, and help you avoid some of the pitfalls common with running production MongoDB deployments. This talk will cover the basics, such as VM selection, OS and disk configuration as well as more advanced topics such as clustering, VM migrations/upgrades, backup strategies and monitoring, with special emphasis on running MongoDB in the cloud. Don’t miss this informative session that will help you operate MongoDB like a pro!