Book Image

MongoDB Cookbook - Second Edition - Second Edition

By : Amol Nayak
Book Image

MongoDB Cookbook - Second Edition - Second Edition

By: Amol Nayak

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 – it’s easy to see why it’s the most popular NoSQL database on the market. Packed with many features that have become essential for many different types of software professionals and incredibly easy to use, this cookbook contains many solutions to the everyday challenges of MongoDB, as well as guidance on effective techniques to extend your skills and capabilities. This book starts with how to initialize the server in three different modes with various configurations. You will then be introduced to programming language drivers in both Java and Python. A new feature in MongoDB 3 is that you can connect to a single node using Python, set to make MongoDB even more popular with anyone working with Python. You will then learn a range of further topics including advanced query operations, monitoring and backup using MMS, as well as some very useful administration recipes including SCRAM-SHA-1 Authentication. Beyond that, you will also find recipes on cloud deployment, including guidance on how to work with Docker containers alongside MongoDB, integrating the database with Hadoop, and tips for improving developer productivity. Created as both an accessible tutorial and an easy to use resource, on hand whenever you need to solve a problem, MongoDB Cookbook will help you handle everything from administration to automation with MongoDB more effectively than ever before.
Table of Contents (17 chapters)
MongoDB Cookbook Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Running MapReduce jobs on Hadoop using streaming


In our previous recipe, we implemented a simple MapReduce job using the Java API of Hadoop. The use case was the same as what we did in the recipes in Chapter 3, Programming Language Drivers where we implemented MapReduce using the Mongo client APIs in Python and Java. In this recipe, we will use Hadoop streaming to implement MapReduce jobs.

The concept of streaming works on communication using stdin and stdout. You can get more information on Hadoop streaming and how it works at http://hadoop.apache.org/docs/r1.2.1/streaming.html.

Getting ready…

Refer to the Executing our first sample MapReduce job using the mongo-hadoop connector recipe in this chapter to see how to set up Hadoop for development purposes and build the mongo-hadoop project using Gradle. As far as the Python libraries are concerned, we will be installing the required library from the source; however, you can use pip (Python's package manager) to set up if you do not wish to build...