Chapter 1, Installing and Starting the Server, is all about starting MongoDB. It will demonstrate how to start the server in the standalone mode, as a replica set, and as a shard, with the provided start up options from the command line or configuration file.

Chapter 2, Command-line Operations and Indexes, has simple recipes to perform CRUD operations in the Mongo shell and create various types of indexes in the shell.

Chapter 3, Programming Language Drivers, discusses about programming language APIs. Though Mongo supports a vast array of languages, we will look at how to use the drivers to connect to the MongoDB server from Java and Python programs only. This chapter also explores the MongoDB wire protocol used for communication between the server and programming language clients.

Chapter 4, Administration, contains many recipes for administration or your MongoDB deployment. This chapter covers a lot of frequently used administrative tasks such as viewing the stats of the collections and database, viewing and killing long-running operations and other replica sets, and sharding-related administration.

Chapter 5, Advanced Operations, is an extension of Chapter 2, Command-line Operations and Indexes. We will look at some of the slightly advanced features such as implementing server-side scripts, geospatial search, GridFS, full text search, and how to integrate MongoDB with an external full text search engine.

Chapter 6, Monitoring and Backups, tells you all about administration and some basic monitoring. However, MongoDB provides a state-of-the-art monitoring and real-time backup service, MongoDB Monitoring Service (MMS). In this chapter, we will look at some recipes around monitoring and backup using MMS.

Chapter 7, Deploying MongoDB on the Cloud, covers recipes that use MongoDB service providers for cloud deployment. We will set up our own MongoDB server on the AWS cloud as well as run MongoDB in Docker containers.

Chapter 8, Integration with Hadoop, covers recipes to integrate MongoDB with Hadoop to use the Hadoop MapReduce API in order to run MapReduce jobs on the data residing in MongoDB data files and write the results to them. We will also see how to use AWS EMR to run our MapReduce jobs on the cloud using Amazon's Hadoop cluster, EMR, with the mongo-hadoop connector.

Chapter 9, Open Source and Proprietary Tools, is about using frameworks and products built around MongoDB to improve a developer's productivity or about simplifying some of the day-to-day jobs using Mongo. Unless explicitly mentioned, the products/frameworks that we will be looking at in this chapter are open source.

Appendix, Concepts for Reference, gives you a bit of additional information on the write concern and read preference for reference.