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

MapReduce and the aggregation framework

MapReduce is a concept: we map data into multiple independent tasks, process the temporary results, and reduce the results in parallel. Basically, we spawn many parallel tasks to mappers. These mappers (which can be threads, processes, or servers, among others) process a specific dataset and spew out results to the reducers. As the reducers keep getting information, they update the final results with this data. This is basically the divide and conquer process.

Nothing explains this better than an example! Suppose we want to show the statistical count of authors by the first letter of their name; it is a good case for using MapReduce. We want to see information as follows:

   Authors starting with "a": 1020
   Authors starting with "b": 477
   Authors starting with "c": 719
   Authors starting with "d": 586
   Authors starting with "e": 678

First, let's create many authors in our database. For this, we shall use the faker gem so that we can generate nice...