Book Image

MongoDB Cookbook

By : Amol Nayak
Book Image

MongoDB Cookbook

By: Amol Nayak

Overview of this book

<p>MongoDB is a high-performance and feature-rich NoSQL database that forms the backbone of numerous complex development systems. You will certainly find the MongoDB solution you are searching for in this book.</p> <p>Starting with how to initialize the server in three different modes with various configurations, you will then learn a variety of skills including the basics of advanced query operations and features in MongoDB and monitoring and backup using MMS. From there, you can delve into recipes on cloud deployment, integration with Hadoop, and improving developer productivity. By the end of this book, you will have a clear idea about how to design, develop, and deploy MongoDB.</p>

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MongoDB Cookbook
Amol Nayak
Nov, 2014 | 388 pages
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Table of Contents (17 chapters)
MongoDB Cookbook
MongoDB Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Installing and Starting the MongoDB Server
Installing and Starting the MongoDB Server
Introduction
Single node installation of MongoDB
Starting a single node instance using command-line options
Single node installation of MongoDB with options from the config file
Connecting to a single node from the Mongo shell with a preloaded JavaScript
Connecting to a single node from a Java client
Starting multiple instances as part of a replica set
Connecting to the replica set from the shell to query and insert data
Connecting to the replica set to query and insert data from a Java client
Starting a simple sharded environment of two shards
Connecting to a shard from the Mongo shell and performing operations
2
Command-line Operations and Indexes
Command-line Operations and Indexes
Creating test data
Performing simple querying, projections, and pagination from the Mongo shell
Updating and deleting data from the shell
Creating an index and viewing plans of queries
Background and foreground index creation from the shell
Creating unique indexes on collection and deleting the existing duplicate data automatically
Creating and understanding sparse indexes
Expiring documents after a fixed interval using the TTL index
Expiring documents at a given time using the TTL index
3
Programming Language Drivers
Programming Language Drivers
Introduction
Installing PyMongo
Executing query and insert operations using PyMongo
Executing update and delete operations using PyMongo
Aggregation in Mongo using PyMongo
MapReduce in Mongo using PyMongo
Executing query and insert operations using a Java client
Executing update and delete operations using a Java client
Aggregation in Mongo using a Java client
MapReduce in Mongo using a Java client
4
Administration
Administration
Renaming a collection
Viewing collection stats
Viewing database stats
Disabling the preallocation of data files
Manually padding a document
Understanding the mongostat and mongotop utilities
Estimating the working set
Viewing and killing the currently executing operations
Using profiler to profile operations
Setting up users in MongoDB
Understanding interprocess security in MongoDB
Modifying collection behavior using the collMod command
Setting up MongoDB as a Windows Service
Configuring a replica set
Stepping down as a primary instance from the replica set
Exploring the local database of a replica set
Understanding and analyzing oplogs
Building tagged replica sets
Configuring the default shard for nonsharded collections
Manually splitting and migrating chunks
Performing domain-driven sharding using tags
Exploring the config database in a sharded setup
5
Advanced Operations
Advanced Operations
Introduction
Atomic find and modify operations
Implementing atomic counters in MongoDB
Implementing server-side scripts
Creating and tailing capped collection cursors in MongoDB
Converting a normal collection to a capped collection
Storing binary data in MongoDB
Storing large data in MongoDB using GridFS
Storing data to GridFS from a Java client
Storing data to GridFS from a Python client
Implementing triggers in MongoDB using oplog
Executing flat plane (2D) geospatial queries in Mongo using geospatial indexes
Spherical indexes and GeoJSON-compliant data in MongoDB
Implementing a full-text search in MongoDB
Integrating MongoDB with Elasticsearch for a full-text search
6
Monitoring and Backups
Monitoring and Backups
Introduction
Signing up for MMS and setting up the MMS monitoring agent
Managing users and groups on the MMS console
Monitoring MongoDB instances on MMS
Setting up monitoring alerts on MMS
Backing up and restoring data in Mongo using out-of-the box tools
Configuring the MMS backup service
Managing backups in the MMS backup service
7
Cloud Deployment on MongoDB
Cloud Deployment on MongoDB
Introduction
Setting up and managing the MongoLab account
Setting up a sandbox MongoDB instance on MongoLab
Performing operations on MongoDB from MongoLab GUI
Setting up MongoDB on Amazon EC2 using the MongoDB AMI
Setting up MongoDB on Amazon EC2 without using the MongoDB AMI
8
Integration with Hadoop
Integration with Hadoop
Introduction
Executing our first sample MapReduce job using the mongo-hadoop connector
Writing our first Hadoop MapReduce job
Running MapReduce jobs on Hadoop using streaming
Running a MapReduce job on Amazon EMR
9
Open Source and Proprietary Tools
Open Source and Proprietary Tools
Introduction
Developing using spring-data-mongodb
Accessing MongoDB using Java Persistence API
Accessing MongoDB over REST
Installing the GUI-based client, MongoVUE, for MongoDB
Concepts for Reference
Concepts for Reference
Write concern and its significance
Read preference for querying
Index
Index

Running a MapReduce job on Amazon EMR

This recipe involves running the MapReduce job on the cloud using AWS. You will need an AWS account in order to proceed. Register to AWS at http://aws.amazon.com/.We will see how to run a MapReduce job on the cloud using Amazon Elastic MapReduce (EMR). Amazon EMR is a managed MapReduce service provided by Amazon on the cloud. For more details, refer to https://aws.amazon.com/elasticmapreduce/. Amazon EMR requires the data, binaries/jars, and so on to be present in the S3 bucket that it processes. It then writes the results back to the S3 bucket. Amazon Simple Storage Service (S3) is another service by AWS for data storage on the cloud. For more details on Amazon S3, refer to http://aws.amazon.com/s3/. Though we will use the mongo-hadoop connector, an interesting fact is that we won't require a MongoDB instance to be up and running. We will use the MongoDB data dump stored in an S3 bucket and use it for our data analysis. The MapReduce program will run...

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