The MongoDB NoSQL Database Blog

Recently, I attended both Pycon UK and Pycon Ireland to talk about the lessons I have learnt while maintaining mongoengine. The conferences were both excellent and surprisingly different. Pycon UK had quite an “unconference” feel, with some exciting sprint rooms - I wish I had more time as by all reports the educational jam was inspirational. Pycon Ireland in contrast felt more slick with booths from DemonWare, Amazon and Facebook. If you can, I’d advise going to both conferences as they really complement each other.

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Today we are releasing updated versions of most of the officially supported MongoDB drivers with new error checking and reporting defaults. See below for more information on these changes, and check your driver docs for specifics.

Over the past several years, it’s become evident that MongoDB’s previous default behavior (where write messages did not wait for a return code from the server by default) wasn’t intuitive and has caused confusion for MongoDB users. We want to rectify that with minimal disruption to the MongoDB apps already in production.

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Storage-viz is a suite of web-based visualizers and new experimental database commands that may help you understand how MongoDB utilizes storage and organizes btrees. Storage-viz is now available in the MongoDB Nightly builds.

When a MongoDB collection is created, an on-disk extent is allocated to store the documents. Each time a newly created or updated document cannot fit into the existing collection’s extents, a new extent is created. Each document occupies a contiguous storage area - a record - in one of the collection’s extents. Storage-viz’ experimental storageDetails command extracts information about how the disk storage is used and the web-based visualizer generates an easy-to-read graphical representation. Storage-viz also showcases which parts of the collection’s extents are currently in RAM [NOTE: the visualizer doesn’t display how much memory is available].

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The Los Angeles MongoDB User Group was founded in the summer of 2011 and, thanks to the leadership of Joe Devon, has grown to over 400 members in the past year. Joe Devon, a long-time member of the Silicon Beach tech community, shares his insights from a year of working with the MongoDB community in Los Angeles. 

You’re a User Group Veteran. How did you get started organizing user groups?

I was living in NY, with several meetups to choose from every night. Then moved to Los Angeles, where there were none. In the words of Cal Evans, if you don’t know who your local organizer is, you’re looking at him :) 

So I started a bunch of meetups, told people to gather at Panera Bread once a month in one big, joint set of meetups…. And couldn’t get 10 people to show. But after awhile, there were some regulars. Some of whom agreed to take over a group here, a group there. Fast forward to today and there’s a ton of tech meetups in Los Angeles, with sometimes 100-200 people showing up on good nights. 

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Today, I’m excited to announce the launch of Precog for MongoDB, a release that bundles all of the really cool Precog technology into a free package that anyone can download and deploy on their existing MongoDB database.

Precog is a data science platform that lets developers and data scientists do advanced analytics and statistics using Quirrel, the “R for big data” language. You can analyze data programmatically with a REST API (or client library) or interactively with Labcoat, an easy-to-use HTML5 application built on the REST API. We provide a cloud-hosted version of Precog, but we’ve known for a long time that we were going to bring a standalone version of our data science Precog to some NoSQL database.

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This is part 3 in a series, which will focus on the data modeling aspect of working with document databases. The previous parts are also available for reading: Part 1: Getting Started, and Part 2: Queries and Indexes.

Although there are plenty of existing articles, presentations and webcasts about modeling your data to take advantage of a document database, this post is taking a slightly PHP-centric position as a part of this series. This information is useful to anyone though, regardless of their chosen programming language.

We’re going to use two different scenarios to look at data modeling in the document world, chosen as common examples to illustrate differences in implementation between relational and document databases:

• Blog. Your garden variety of data, covering posts, comments and tags
• Private Sale / E-Commerce. Taking a look at needs for orders, users and products

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• Motor Installation Instructions by  a. jesse jiryu davis
• Interview and Hacking Session with Steven Chin, by Trisha Gee
• Big Data for the Rest of us at Strata NYC by Steve Francia
• Managing Pull Requests for the MongoDB PHP Driver by Derick Rethans
• Writing Reactive Webapps with ReactiveMongo and Play!, pt. 2 by Stéphane Godbillon
• Monitoring the MongoDB Shard Balancer Status, by David Mytton
• Build a Backbone/Brunch/Chaplin Backend with Python, Flask and MongoDB by Thomas Sileo
• How to Build MongoDB with SSL for Linux, by David Golden
• Optimize Node.js and MongoDB in Every Way Possible, by Life by Experimentation
• MongoDB Aggregation Framework and SQL Side-by-Side, by Francois Zaninotto 
• Optimizing MongoDB Compound Indexes, by a. jesse jiryu davis
• MongoDB Performance at Gilt, by Laura Nolan

• MongoDB 2.2.1
• MongoDB on Windows Azure 0.6
• MongoDB 2.3.0 unstable
• MongoDB Java Driver 2.9.3
• C#/.NET Driver 1.6.1