Book Image

Learning Mongoid

By : Gautam Rege
Book Image

Learning Mongoid

By: Gautam Rege

Overview of this book

Mongoid helps you to leverage the power of schema-less and efficient document-based design, dynamic queries, and atomic modifier operations. Mongoid eases the work of Ruby developers while they are working on complex frameworks. Starting with why and how you should use Mongoid, this book covers the various components of Mongoid. It then delves deeper into the detail of queries and relations, and you will learn some tips and tricks on improving performance. With this book, you will be able to build robust and large-scale web applications with Mongoid and Rails. Starting with the basics, this book introduces you to components such as moped and origin, and how information is managed, learn about the various datatypes, embedded documents, arrays, and hashes. You will learn how a document is stored and manipulated with callbacks, validations, and even atomic updates. This book will then show you the querying mechanism in detail, right from simple to complex queries, and even explains eager loading, lazy evaluation, and chaining of queries. Finally, this book will explain the importance of performance tuning and how to use the right indexes. It also explains MapReduce and the Aggregation Framework.
Table of Contents (14 chapters)
Learning Mongoid
About the Author
About the Reviewers

Good practices and recommendations

Here are some of the best practices that should be followed while working with MongoDB:

  • Use indexing judiciously. Try to keep multikey indexes such that we do not need to duplicate indexes. Database writes are slowed down with lots of indexes, but reads could be fast. We need to strike the right balance.

  • Use the aggregation framework as often as possible. Remember Mongoid caches results for even better performance.

  • MapReduce is a great tool to get faster aggregated results. However, you cannot use it with multiple collections.

  • Know when to use Text indexes, it's fine for really simple searches but not good if you want complex facet queries. If you require complex facet queries, I recommend looking at ElasticSearch.

  • Use the hash index for basic field searches, but not for multikey searches as we have seen.

  • Keep an eye out for slow queries.