Book Image

MongoDB Administrator???s Guide

By : Cyrus Dasadia
Book Image

MongoDB Administrator???s Guide

By: Cyrus Dasadia

Overview of this book

MongoDB is a high-performance and feature-rich NoSQL database that forms the backbone of the systems that power many different organizations. Packed with many features that have become essential for many different types of software professional and incredibly easy to use, this cookbook contains more than 100 recipes to address the everyday challenges of working with MongoDB. Starting with database configuration, you will understand the indexing aspects of MongoDB. The book also includes practical recipes on how you can optimize your database query performance, perform diagnostics, and query debugging. You will also learn how to implement the core administration tasks required for high-availability and scalability, achieved through replica sets and sharding, respectively. You will also implement server security concepts such as authentication, user management, role-based access models, and TLS configuration. You will also learn how to back up and recover your database efficiently and monitor server performance. By the end of this book, you will have all the information you need—along with tips, tricks, and best practices—to implement a high-performance MongoDB solution.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Creating a partial index


Partial indexes were introduced recently, in MongoDB Version 3.2. A partial index is slightly similar to sparse index but with the added advantage of being able to use expressions ($eq, $gt, and so on) and operators ($and).

Getting ready

For this recipe, load the sample dataset and create an index on the city field, as described in the Creating an index recipe.

How to do it...

  1. Check the total number of documents in our collection and number of documents without the language field:
db.mockdata.count()

The preceding command should return 100000:

db.mockdata.find({language: {$eq:null}}).count()

The preceding command should return 12704.

  1. Create a sparse index on the document:
> db.mockdata.createIndex(
 {first_name:1},
 {partialFilterExpression: { language: {$exists: true}}}
)

This should give you output similar to this:

{
    "createdCollectionAutomatically" : false,
    "numIndexesBefore" : 1,
    "numIndexesAfter" : 2,
    "ok" : 1
}
  1. Confirm that the index was created:
db.mockdata...