Niall O’Higgins - MongoDB and Python
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Learn how to leverage MongoDB with your Python applications, using the hands-on recipes in this book. You get complete code samples for tasks such as making fast geo queries for location-based apps, efficiently indexing your user documents for social-graph lookups, and many other scenarios.
This guide explains the basics of the document-oriented database and shows you how to set up a Python environment with it. Learn how to read and write to MongoDB, apply idiomatic MongoDB and Python patterns, and use the database with several popular Python web frameworks. Youll discover how to model your data, write effective queries, and avoid concurrency problems such as race conditions and deadlocks.
The recipes will help you:
- Learn how to use the rich MongoDB query language
- Maintain data integrity in replicated/distributed MongoDB environments
- Use embedding to efficiently model your data without joins...
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Ive been building production database-driven applications for about 10 years. Ive worked with most of the usual relational databases (MSSQL Server, MySQL, PostgreSQL) and with some very interesting nonrelational databases (Freebase.coms Graphd/MQL, Berkeley DB, MongoDB). MongoDB is at this point the system I enjoy working with the most, and choose for most projects. It sits somewhere at a crossroads between the performance and pragmatism of a relational system and the flexibility and expressiveness of a semantic web database. It has been central to my success in building some quite complicated systems in a short period of time.
I hope that after reading this book you will find MongoDB to be a pleasant database to work with, and one which doesnt get in the way between you and the application you wish to build.
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I would like to thank Ariel Backenroth, Aseem Mohanty and Eugene Ciurana for giving detailed feedback on the first draft of this book. I would also like to thank the OReilly team for making it a great pleasure to write the book. Of course, thanks to all the people at 10gen without whom MongoDB would not exist and this book would not have been possible.
First released in 2009, MongoDB is relatively new on the database scene compared to contemporary giants like Oracle which trace their first releases to the 1970s. As a document-oriented database generally grouped into the NoSQL category, it stands out among distributed key value stores, Amazon Dynamo clones and Google BigTable reimplementations. With a focus on rich operator support and high performance Online Transaction Processing (OLTP), MongoDB is in many ways closer to MySQL than to batch-oriented databases like HBase.
The key differences between MongoDBs document-oriented approach and a traditional relational database are:
MongoDB does not support joins.
MongoDB does not support transactions. It does have some support for atomic operations, however.
MongoDB schemas are flexible. Not all documents in a collection must adhere to the same schema.
1 and 2 are a direct result of the huge difficulties in making these features scale across a large distributed system while maintaining acceptable performance. They are tradeoffs made in order to allow for horizontal scalability. Although MongoDB lacks joins, it does introduce some alternative capabilites, e.g. embedding, which can be used to solve many of the same data modeling problems as joins. Of course, even if embedding doesnt quite work, you can always perform your join in application code, by making multiple queries.
The lack of transactions can be painful at times, but fortunately MongoDB supports a fairly decent set of atomic operations. From the basic atomic increment and decrement operators to the richer findAndModify, which is essentially an atomic read-modify-write operator.
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