Using Python with CouchDB

LinuxPlanet has a tutorial on their site for using CouchDB with Python. Admittingly, the tutorial is light on installation and start-up details. However, they more than make up for it with their descriptions of CouchDB's usage of MapReduce and views.

Read the whole tutorial here

Python Datastructures Backed by Redis

Will Larson has penned some thoughts about extending python data structures like dictionaries and lists to Redis.

Read more: Python Datastructures Backed by Redis

From MySQL to MongoDB at Sluggy.com

Brendan W. McAdams from Sluggy Freelance and Evil Monkey Labs has an excellent presentation about their migration from MySQL to MongoDB.

Sluggy.com started using MySQL along with PHP for their publishing platform in 2002. Migrated to MongoDB (and MongoKit) in August of 2009.

Key points about the migration:

• Completely eliminated the need for physical hardware, as a result Sluggy.com migrated to virtual hosting.
• Average system load is 0.05 on a 2GB slice
• MongoDB uses 1% CPU on average
• Switchover took 2 minutes (ran data conversion script, deployed new code tag, bounced webserver / pylons app)
• No downtime in any way attributable to MongoDB since go live (Can’t say the same for MySQL)

Lessons learned while using MongoDB:

• MongoDB’s MMAP system gives you a “free” MRU cache. Done right and simple; caching on MongoDB is durable, light and fast.
• Flexible schemas are good.
• Wasting your time mapping data back and forth between your presentation layer & RDBMS is not just tedious - it’s error prone.
• The more you can put in memory, the less you beat on your disks. Especially important on virtual hosting: Be a Good Neighbor
• MongoDB is very good at automatically memory caching frequently used data, reducing the amount of code you need to write 

Reasons for using MongoDB:

• Dynamic Querying
• Flexibility
• CouchDB’s approach appeared obtuse and rather unPythonic
• Tools like MongoKit allowed for easy replacement of existing MySQL ORM code with something almost identical
• FAST
• Great Support & Community Available
• Easy access - talking to developers NOT support staff. One official development company behind MongoDB.

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.