Sessions at MongoSV 2011 with video on Friday 9th December

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.

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.

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.

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.

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.

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).

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.

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.

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!

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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!