- MongoDB Cookbook Second Edition
- Table of Contents
- MongoDB Cookbook Second Edition
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Installing and Starting the Server
- Introduction
- Installing single node 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 in the Mongo shell with JavaScript
- Connecting to a single node using a Java client
- Connecting to a single node using a Python client
- Starting multiple instances as part of a replica set
- Connecting to the replica set in the shell to query and insert data
- Connecting to the replica set to query and insert data from a Java client
- Connecting to the replica set to query and insert data using a Python client
- Starting a simple sharded environment of two shards
- Connecting to a shard in the shell and performing operations
- 2. Command-line Operations and Indexes
- Introduction
- Creating test data
- Performing simple querying, projections, and pagination from Mongo shell
- Updating and deleting data from the shell
- Creating index and viewing plans of queries
- Creating a background and foreground index in the shell
- 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
- Introduction
- Executing query and insert operations with PyMongo
- Executing update and delete operations using PyMongo
- Implementing aggregation in Mongo using PyMongo
- Executing MapReduce in Mongo using PyMongo
- Executing query and insert operations using a Java client
- Executing update and delete operations using a Java client
- Implementing aggregation in Mongo using a Java client
- Executing MapReduce in Mongo using a Java client
- 4. Administration
- Introduction
- Renaming a collection
- Viewing collection stats
- Viewing database stats
- Manually padding a document
- The mongostat and mongotop utilities
- Getting current executing operations and killing them
- Using profiler to profile operations
- Setting up users in Mongo
- Interprocess security in Mongo
- Modifying collection behavior using the collMod command
- Setting up MongoDB as a windows service
- Replica set configurations
- Stepping down as primary 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 non-sharded collections
- Manual split and migration of chunks
- Domain-driven sharding using tags
- Exploring the config database in a sharded setup
- 5. Advanced Operations
- Introduction
- Atomic find and modify operations
- Implementing atomic counters in Mongo
- Implementing server-side scripts
- Creating and tailing a capped collection cursors in MongoDB
- Converting a normal collection to a capped collection
- Storing binary data in Mongo
- Storing large data in Mongo using GridFS
- Storing data to GridFS from Java client
- Storing data to GridFS from Python client
- Implementing triggers in Mongo using oplog
- Flat plane 2D geospatial queries in Mongo using geospatial indexes
- Spherical indexes and GeoJSON compliant data in Mongo
- Implementing full text search in Mongo
- Integrating MongoDB for full text search with Elasticsearch
- 6. Monitoring and Backups
- Introduction
- Signing up for MMS and setting up an MMS monitoring agent
- Managing users and groups in MMS console
- Monitoring instances and setting up alerts on MMS
- Setting up monitoring alerts in MMS
- Back up and restore data in Mongo using out-of-the-box tools
- Configuring MMS Backup service
- Managing backups in MMS Backup service
- 7. Deploying MongoDB on the Cloud
- 8. Integration with Hadoop
- 9. Open Source and Proprietary Tools
- A. Concepts for Reference
- Index
In this chapter, we will cover the following recipes:
- 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
Hadoop is a well-known open source software to process large datasets. It also has an API for the MapReduce programming model, which is widely used. Nearly all the big data solutions have some sort of support to integrate them with Hadoop in order to use its MapReduce framework. MongoDB has a connector as well that integrates with Hadoop and lets us write MapReduce jobs using the Hadoop MapReduce API, process the data residing in the MongoDB/MongoDB dumps, and write the result to the MongoDB/MongoDB dump files. In this chapter, we will look at some recipes about the basic MongoDB and Hadoop integration.
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