MongoDB Applied Design Patterns

Rick Copeland is a senior software engineer with retail analytics firmPredictix, LLC, where he uses SQLAlchemy extensively, primarily for webapplication development. He has been using Python full-time fordevelopment since 2005, in projects as diverse as demand forecastingbusiness web applications, compilers, and hardware synthesis.

Whether you're building a social media site or an internal-use enterprise application, this hands-on guide shows you the connection between MongoDB and the business problems it's designed to solve. You'll learn how to apply MongoDB design patterns to several challenging domains, such as ecommerce, content management, and online gaming. Using Python and JavaScript code examples, you'll discover how MongoDB lets you scale your data model while simplifying the development process.Many businesses launch NoSQL databases without understanding the techniques for using their features most effectively. This book demonstrates the benefits of document embedding, polymorphic schemas, and other MongoDB patterns for tackling specific big data use cases, including: Operational intelligence: Perform real-time analytics of business data Ecommerce: Use MongoDB as a product catalog master or inventory management system Content management: Learn methods for storing content nodes, binary assets, and discussions Online advertising networks: Apply techniques for frequency capping ad impressions, and keyword targeting and bidding Social networking: Learn how to store a complex social graph, modeled after Google+ Online gaming: Provide concurrent access to character and world data for a multiplayer role-playing game

Preface;Audience;Assumptions This Book Makes;Contents of This Book;Conventions Used in This Book;Using Code Examples;Safari® Books Online;How to Contact Us;Acknowledgments;Design Patterns;Chapter 1: To Embed or Reference;1.1 Relational Data Modeling and Normalization;1.2 MongoDB: Who Needs Normalization, Anyway?;1.3 Conclusion;Chapter 2: Polymorphic Schemas;2.1 Polymorphic Schemas to Support Object-Oriented Programming;2.2 Polymorphic Schemas Enable Schema Evolution;2.3 Polymorphic Schemas Support Semi-Structured Domain Data;2.4 Conclusion;Chapter 3: Mimicking Transactional Behavior;3.1 The Relational Approach to Consistency;3.2 Compound Documents;3.3 Using Complex Updates;3.4 Optimistic Update with Compensation;3.5 Conclusion;Use Cases;Chapter 4: Operational Intelligence;4.1 Storing Log Data;4.2 Pre-Aggregated Reports;4.3 Hierarchical Aggregation;Chapter 5: Ecommerce;5.1 Product Catalog;5.2 Category Hierarchy;5.3 Inventory Management;Chapter 6: Content Management Systems;6.1 Metadata and Asset Management;6.2 Storing Comments;Chapter 7: Online Advertising Networks;7.1 Solution Overview;7.2 Design 1: Basic Ad Serving;7.3 Design 2: Adding Frequency Capping;7.4 Design 3: Keyword Targeting;Chapter 8: Social Networking;8.1 Solution Overview;8.2 Schema Design;8.3 Operations;8.4 Sharding;Chapter 9: Online Gaming;9.1 Solution Overview;9.2 Schema Design;9.3 Operations;9.4 Sharding;Afterword;Where Do I Go from Here?;Index;Colophon;