- Hadoop Beginner's Guide
- Table of Contents
- Hadoop Beginner's Guide
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. What It's All About
- 2. Getting Hadoop Up and Running
- Hadoop on a local Ubuntu host
- Time for action – checking the prerequisites
- Time for action – downloading Hadoop
- Time for action – setting up SSH
- Time for action – using Hadoop to calculate Pi
- Time for action – configuring the pseudo-distributed mode
- Time for action – changing the base HDFS directory
- Time for action – formatting the NameNode
- Time for action – starting Hadoop
- Time for action – using HDFS
- Time for action – WordCount, the Hello World of MapReduce
- Using Elastic MapReduce
- Time for action – WordCount on EMR using the management console
- Comparison of local versus EMR Hadoop
- Summary
- 3. Understanding MapReduce
- Key/value pairs
- The Hadoop Java API for MapReduce
- Writing MapReduce programs
- Time for action – setting up the classpath
- Time for action – implementing WordCount
- Time for action – building a JAR file
- Time for action – running WordCount on a local Hadoop cluster
- Time for action – running WordCount on EMR
- Time for action – WordCount the easy way
- Walking through a run of WordCount
- Startup
- Splitting the input
- Task assignment
- Task startup
- Ongoing JobTracker monitoring
- Mapper input
- Mapper execution
- Mapper output and reduce input
- Partitioning
- The optional partition function
- Reducer input
- Reducer execution
- Reducer output
- Shutdown
- That's all there is to it!
- Apart from the combiner…maybe
- Time for action – WordCount with a combiner
- Time for action – fixing WordCount to work with a combiner
- Hadoop-specific data types
- Time for action – using the Writable wrapper classes
- Input/output
- Summary
- 4. Developing MapReduce Programs
- Using languages other than Java with Hadoop
- Time for action – implementing WordCount using Streaming
- Analyzing a large dataset
- Time for action – summarizing the UFO data
- Time for action – summarizing the shape data
- Time for action – correlating of sighting duration to UFO shape
- Time for action – performing the shape/time analysis from the command line
- Time for action – using ChainMapper for field validation/analysis
- Time for action – using the Distributed Cache to improve location output
- Counters, status, and other output
- Time for action – creating counters, task states, and writing log output
- Summary
- 5. Advanced MapReduce Techniques
- Simple, advanced, and in-between
- Joins
- Time for action – reduce-side join using MultipleInputs
- Graph algorithms
- Time for action – representing the graph
- Time for action – creating the source code
- Time for action – the first run
- Time for action – the second run
- Time for action – the third run
- Time for action – the fourth and last run
- Using language-independent data structures
- Time for action – getting and installing Avro
- Time for action – defining the schema
- Time for action – creating the source Avro data with Ruby
- Time for action – consuming the Avro data with Java
- Time for action – generating shape summaries in MapReduce
- Time for action – examining the output data with Ruby
- Time for action – examining the output data with Java
- Summary
- 6. When Things Break
- Failure
- Time for action – killing a DataNode process
- Time for action – the replication factor in action
- Time for action – intentionally causing missing blocks
- Time for action – killing a TaskTracker process
- Time for action – killing the JobTracker
- Time for action – killing the NameNode process
- What just happened?
- Starting a replacement NameNode
- The role of the NameNode in more detail
- File systems, files, blocks, and nodes
- The single most important piece of data in the cluster – fsimage
- DataNode startup
- Safe mode
- SecondaryNameNode
- So what to do when the NameNode process has a critical failure?
- BackupNode/CheckpointNode and NameNode HA
- Hardware failure
- Host failure
- Host corruption
- The risk of correlated failures
- Task failure due to software
- What just happened?
- Time for action – causing task failure
- Time for action – handling dirty data by using skip mode
- Summary
- 7. Keeping Things Running
- A note on EMR
- Hadoop configuration properties
- Time for action – browsing default properties
- Setting up a cluster
- Time for action – examining the default rack configuration
- Time for action – adding a rack awareness script
- Cluster access control
- Time for action – demonstrating the default security
- Managing the NameNode
- Time for action – adding an additional fsimage location
- Time for action – swapping to a new NameNode host
- Managing HDFS
- MapReduce management
- Time for action – changing job priorities and killing a job
- Scaling
- Summary
- 8. A Relational View on Data with Hive
- Overview of Hive
- Setting up Hive
- Time for action – installing Hive
- Using Hive
- Time for action – creating a table for the UFO data
- Time for action – inserting the UFO data
- Time for action – validating the table
- Time for action – redefining the table with the correct column separator
- Time for action – creating a table from an existing file
- Time for action – performing a join
- Time for action – using views
- Time for action – exporting query output
- Time for action – making a partitioned UFO sighting table
- Time for action – adding a new User Defined Function (UDF)
- Hive on Amazon Web Services
- Time for action – running UFO analysis on EMR
- Summary
- 9. Working with Relational Databases
- Common data paths
- Setting up MySQL
- Time for action – installing and setting up MySQL
- Time for action – configuring MySQL to allow remote connections
- Time for action – setting up the employee database
- Getting data into Hadoop
- Time for action – downloading and configuring Sqoop
- Time for action – exporting data from MySQL to HDFS
- Time for action – exporting data from MySQL into Hive
- Time for action – a more selective import
- Time for action – using a type mapping
- Time for action – importing data from a raw query
- Getting data out of Hadoop
- Time for action – importing data from Hadoop into MySQL
- Time for action – importing Hive data into MySQL
- Time for action – fixing the mapping and re-running the export
- AWS considerations
- Summary
- 10. Data Collection with Flume
- A note about AWS
- Data data everywhere...
- Time for action – getting web server data into Hadoop
- Introducing Apache Flume
- Time for action – installing and configuring Flume
- Time for action – capturing network traffic in a log file
- Time for action – logging to the console
- Time for action – capturing the output of a command to a flat file
- Time for action – capturing a remote file in a local flat file
- Time for action – writing network traffic onto HDFS
- Time for action – adding timestamps
- Time for action – multi level Flume networks
- Time for action – writing to multiple sinks
- The bigger picture
- Summary
- 11. Where to Go Next
- A. Pop Quiz Answers
- Index
We will now turn to Hadoop in the cloud, the Elastic MapReduce service offered by Amazon Web Services. There are multiple ways to access EMR, but for now we will focus on the provided web console to contrast a full point-and-click approach to Hadoop with the previous command-line-driven examples.
Before using Elastic MapReduce, we need to set up an Amazon Web Services account and register it with the necessary services.
Amazon has integrated their general accounts with AWS, meaning that if you already have an account for any of the Amazon retail websites, this is the only account you will need to use AWS services.
Note that AWS services have a cost; you will need an active credit card associated with the account to which charges can be made.
If you require a new Amazon account, go to http://aws.amazon.com, select create a new AWS account, and follow the prompts. Amazon has added a free tier for some services, so you may find that in the early days of testing and exploration you are keeping many of your activities within the non-charged tier. The scope of the free tier has been expanding, so make sure you know for what you will and won't be charged.
Once you have an Amazon account, you will need to register it for use with the required AWS services, that is, Simple Storage Service (S3), Elastic Compute Cloud (EC2), and Elastic MapReduce (EMR). There is no cost for simply signing up to any AWS service; the process just makes the service available to your account.
Go to the S3, EC2, and EMR pages linked from http://aws.amazon.com and click on the Sign up button on each page; then follow the prompts.
Note
Caution! This costs real money!
Before going any further, it is critical to understand that use of AWS services will incur charges that will appear on the credit card associated with your Amazon account. Most of the charges are quite small and increase with the amount of infrastructure consumed; storing 10 GB of data in S3 costs 10 times more than for 1 GB, and running 20 EC2 instances costs 20 times as much as a single one. There are tiered cost models, so the actual costs tend to have smaller marginal increases at higher levels. But you should read carefully through the pricing sections for each service before using any of them. Note also that currently data transfer out of AWS services, such as EC2 and S3, is chargeable but data transfer between services is not. This means it is often most cost-effective to carefully design your use of AWS to keep data within AWS through as much of the data processing as possible.
-
No Comment