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

Querying with indexed fields

Indexing increases the query performance tremendously but causes some overheads in write operations. We will learn in great detail about the various types of indexes in the next chapter. When we index fields, it's important to maintain the right order. For example, for the Book model, if we will always search by the title, and then maybe by the author and published date, it makes sense to create a compound index.

class Book

  index({title: 1, author: 1, published_date: 1}

So, if we now try to search for books and the title is present, it will always use the BTreeCursor indexed search that is faster. However, if the title is not used in searches, the BasicCursor function is relatively slower.


BTreeCursor, as the name suggests, uses a Binary Tree for storing the index. This will change our index searches complexity to O(log2n), where n is the number of values that are indexed. When no index is specified on the field, the search is linear O(n). By default...