MongoDB Crash Recovery

In Kristina Chodorow, software engineer at 10gen, latest post she walks us through several recovery scenarios that could occur while using MongoDB. Of course, a straight list would boring... but choose your own adventure, how could you resist.

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Automated Partitioning, Sharding and Failover in MongoDB

After significant time in development, sharding and replica sets have been incorporated into MongoDB with the 1.6.0 release. Why is this important?

Sharding will allow us to let MongoDB handle distribution of data across any number of nodes to maximise use of disk space and dynamically load balance queries. Replica sets provides automated failover and redundancy so you can be sure your data exists on any number of servers across multiple data centres.

This is definitely good news for MongoDB users. However, how do you set up replica sets and sharding? David Mytton, of Boxed Ice, has written a great tutorial, complete with videos on how to set up both replica sets and sharding on a MongoDB cluster.

Read more: Automating partitioning, sharding and failover with MongoDB

Learning to Relax - CouchDB for beginners

In his presentation, Alan Hoffman, Co-Founder of Cloudant (hosted CouchDB) gives us an introduction to CouchDB. So what is CouchDB?

• Apache Project
• Written in Erlang
• Schema-free document database management system (like MongoDB)
• Robust, Concurrent and Fault-Tolerant
• RESTful JSON API
• Custom Persistent Views using MapReduce
• Bi-directional Increment Replication

So a couple of tidbits about CouchDB that I found interesting.

• Each document has a revision and under certain replication environments conflicts can occur
• Documents can have binary attachments
• Unlike some other RDBMS systems there is nothing to repair if the server crashes, just restart the server and you are back up and online
• Replication can be performed with one-click
• Multiple replication setups are available: Master-Slave, Master-Master and Robust Multi-Master
• Does not support ad-hoc queries, this is by design
• 1.0 is just around the corner

CouchDB Lounge is an open source proxy-based partitioning and clustering solution. It uses a combinatino of a smart and dumb proxy to add partitioning and cluster to CouchDB. Another option, which is being developed by Cloudant, is Open Cloudant which essentially tries to duplicate ring clustering like what is found in Amazon's Dynamo. According the slides this solution is "Coming soon to github near you" so keep a look out.

High Performance Scalable Data Stores

Rick Catell has written a paper that I think is a very good comparison of the various NoSQL data stores. In it he discusses the various types of data stores available, i.e. key-value, document and column. For each data store he provides a description of the various implementations available, such as, Voldemort, Redis and Scalaris.

Data stores discussed in the paper:

Key/Value: Voldemort, Riak, Redis, Scalaris, Tokyo Tyrant and Enhanced Memcached
Document: SimpleDB,  CouchDB and MongoDB
Column: HBase, HyperTable and Cassandra

At a minimum the paper allows the reader to get a basic understanding of each data store.

Read more: High Performance Scalable Data Stores

Implementing MongoDB at shutterfly

Kenny Gorman, Data Architect at shutterfly shows us how some NoSQL data stores are ready for primetime. In this case it's MongoDB. This presentation discusses how shutterfly started to make the transition from a traditional Oracle RDBMS to MongoDB. The results, 500% improvement in cost, 900% improvement in performance and shards on demand. Pretty impressive.

Couple of interesting stats about shutterfly:

• 20TB of RDBMS storage
• 10000 ex/sec
• 6 Billion photos
• Adding 400TB a month

Key lessons learned:

• Keep it simple
• Data Modeling
• Walk before you run
• Use Jira for MongoDB issues
• There is life after Larry



Schema Design with MongoDB

If you ask most developers to design a schema for a traditional relational database they would have no problem doing so. However, introduce a document oriented database like MongoDB and they might struggle. Fortunately, Dwight Merriman, CEO of 10gen, has come to our rescue. In his presentation he walks through a couple of different use cases and describes techniques to handle those use cases.

Zero to Mongo in 60 Hours

Ryan Angilly, a Senior Developer from MyPunchbowl.com and self proclaimed "pretty awesome dude" brings us our next presentation about how MyPunchbowl.com integrated MongoDB into their software stack and into production in 60 hours.

Why did they choose MongoDB? Ryan lists six strengths of MongoDB:

1. Easy to get running
2. Open Source
3. Support in multiple (computer) languages. Prototype in Ruby, move to Java if necessary
4. Very active development
5. Full featured
6. Great ecosystem

Ryan does a good job of describing the various stages of development, testing and deployment. Finally, Ryan discusses where they are at 200 days later and what tripped them up during the process.

Java Development with MongoDB

James Williams, a Software Engineer at BT/Ribbit, demonstrates how to use Java with MongoDB. In addition, James introduces us to Morphia an open source Apache 2 licensed library that:

• Brings Hibernate/JPA paradigms to MongoDB
• Allows annotating of POJOs to make converting them between MongoDB and Java very easy
• Supports DAO abstractions
• Offers type-safe query support
• Compatible with GWT, Guice, Spring and DI frameworks

Flexible Event Logging - Analyzing Funnels, Retention and Viral Spread with MongoDB

his presentation is by Paul Gebheim from Justin.tv. In the presentation Paul discusses how MongoDB helped answer create:

A general framework for create, deploying and analyzing A/B tests in terms of Funnels, Virality and Retention

In addition, the solution should be flexible, queryable, scalable and easy to work with. So how do you do it? With Python, Map/Reduce and MongoDB. Paul walks through the use cases and shows how justin.tv uses this recipe to create the framework.

MongoDB in the wild

Often times you read papers and posts about why someone is choosing a particular NoSQL database over another. Almost invariably you see the feature matrix describing why their choice was the best. It is usually hit or miss whether those same folks come back to tell us the results. Well in this particular case we are in luck. David Mytton from BoxedIce has taken the time to post their experience with MongoDB  in production and answers the important question, would they pick MongoDB again?

Read More: Notes from a production MongoDB deployment