AWS Documentation
Developer Guide (API Version 2009-03-31)
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Welcome
Introduction to Amazon EMR
Overview of Amazon EMR
Architectural Overview of Amazon EMR
Elastic MapReduce Features
Amazon EMR Concepts
Job Flows and Steps
Hadoop and MapReduce
Associated AWS Product Concepts
Using Amazon EMR
Setting Up Your Environment to Run a Job Flow
Creating a Job Flow
How to Create a Streaming Job Flow
How to Create a Job Flow Using Hive
How to Create a Job Flow Using Pig
How to Create a Job Flow Using a Custom JAR
How to Create a Cascading Job Flow
View Job Flow Details
Terminate a Job Flow
Customize a Job Flow
Add Steps to a Job Flow
Add More than 256 Steps to a Job Flow
Bootstrap Actions
Resizing Running Job Flows
Calling Additional Files and Libraries
Using Distributed Cache
Running a Script in a Job Flow
Use Cases
Cascading
Pig
Streaming
Building Binaries Using Amazon EMR
Using Tagging
Protecting a Job Flow from Termination
Lowering Costs with Spot Instances
Choosing What to Launch as Spot Instances
Spot Instance Pricing in Amazon EMR
Availability Zones and Regions
Launching Spot Instances in Job Flows
Changing the Number of Spot Instances in a Job Flow
Troubleshooting Spot Instances
Troubleshooting
Things to Check When Your Amazon EMR Job Flow Fails
Amazon EMR Logging
How to Enable Logging and Debugging
How to Use Log Files
How to Monitor Hadoop on a Master Node
How to Use the Hadoop User Interface
Troubleshooting Tips
Using Ganglia
Initializing Ganglia on a Job Flow
Viewing Ganglia Metrics
Ganglia Reports
Hadoop Metrics in Ganglia
Distributed Copy Using S3DistCp
Exporting, Importing, Querying, and Joining Tables in Amazon DynamoDB Using Amazon EMR
Prerequisites for Integrating Amazon Elastic MapReduce
Step 1: Create a Key Pair
Step 2: Create a Job Flow
Step 3: SSH into the Master Node
Step 4: Set Up a Hive Table to Run Hive Commands
Hive Command Examples for Exporting, Importing, and Querying Data
Optimizing Performance
Monitoring Job Flow Metrics
Using Karmasphere Analytics
Calling the Amazon EMR API
Common Concepts for API Calls
Using SDKs to Call Amazon EMR APIs
Using the AWS SDK for Java to Create an Amazon EMR Job Flow
Generating a Query Request Using AWS Ruby Gems
Using the Java SDK to Sign a Query Request
Using Query Requests to Call Amazon EMR APIs
Why Query Requests Are Signed
Components of a Query Request in Amazon EMR
How to Generate a Signature for a Query Request in Amazon EMR
Environment Configuration
Configuring User Permissions
Using Elastic IP Addresses
Specifying the Amazon EMR AMI Version
Hadoop Configuration
Supported Hadoop Versions
Configuration of hadoop-user-env.sh
Upgrading to Hadoop 0.20
Hadoop Version Behavior
Hadoop 0.20 Streaming Configuration
Hadoop Default Configuration (AMI 1.0)
Hadoop Configuration (AMI 1.0)
HDFS Configuration (AMI 1.0)
Task Configuration (AMI 1.0)
Intermediate Compression (AMI 1.0)
Hadoop Default Configuration (AMI 2.0)
Hadoop Configuration (AMI 2.0)
HDFS Configuration (AMI 2.0)
Task Configuration (AMI 2.0)
Intermediate Compression (AMI 2.0)
Hadoop Memory-Intensive Configuration Settings
File System Configuration
JSON Configuration Files
Multipart Upload
Hadoop Data Compression
Hadoop 0.20.205 Patches
Configuring Hive
Performance Tuning
Running Job Flows on an Amazon VPC
Appendix: Compare Job Flow Types
Appendix: Amazon EMR Resources
Document History
Glossary
Index
AWS Documentation
»
Amazon EMR Documentation
»
Amazon EMR Documentation
»
Using Amazon EMR
»
Troubleshooting
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Troubleshooting
Topics
Things to Check When Your Amazon EMR Job Flow Fails
Amazon EMR Logging
How to Enable Logging and Debugging
How to Use Log Files
How to Monitor Hadoop on a Master Node
How to Use the Hadoop User Interface
Troubleshooting Tips
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