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O & #39 - 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--Provided by publisher.
Abstract: MongoDB and Python is a cookbook-style text to help Python programmers work with MongoDB. Read more...

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MongoDB and Python
Niall O'Higgins
Editor
Mike Loukides
Editor
Shawn Wallace

Copyright 2011 Niall O'Higgins

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Preface

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.

Conventions Used in This Book

The following typographical conventions are used in this book:

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Indicates new terms, URLs, email addresses, filenames, and file extensions.

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Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

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Using Code Examples

This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: MongoDB and Python by Niall OHiggins. Copyright 2011 OReilly Media Inc., 978-1-449-31037-0.

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Acknowledgments

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.

Chapter 1. Getting Started
Introduction

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:

  1. MongoDB does not support joins.

  2. MongoDB does not support transactions. It does have some support for atomic operations, however.

  3. 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.

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