Apache Hadoop 2.2.0

Apache > Hadoop

Wiki | SVN  | Last Published: 2013-10-07  | Version: 2.2.0

General

• Overview
• Single Node Setup
• Cluster Setup
• Hadoop Commands Reference
• File System Shell
• Hadoop Compatibility

Common

• CLI Mini Cluster
• Native Libraries
• Superusers
• Service Level Authorization
• HTTP Authentication

HDFS

• HDFS User Guide
• High Availability With QJM
• High Availability With NFS
• Federation
• HDFS Snapshots
• HDFS Architecture
• Edits Viewer
• Image Viewer
• Permissions and HDFS
• Quotas and HDFS
• HFTP
• C API libhdfs
• WebHDFS REST API
• HttpFS Gateway
• Short Circuit Local Reads

MapReduce

• Compatibilty between Hadoop 1.x and Hadoop 2.x
• Encrypted Shuffle
• Pluggable Shuffle/Sort

YARN

• YARN Architecture
• Writing YARN Applications
• Capacity Scheduler
• Fair Scheduler
• Web Application Proxy
• YARN Commands

YARN REST APIs

• Introduction
• Resource Manager
• Node Manager
• MR Application Master
• History Server

Auth

• Overview
• Examples
• Configuration
• Building

Reference

• Release Notes
• API docs
• Common CHANGES.txt
• HDFS CHANGES.txt
• MapReduce CHANGES.txt

Configuration

• core-default.xml
• hdfs-default.xml
• mapred-default.xml
• yarn-default.xml
• Deprecated Properties

Apache Hadoop 2.2.0

Apache Hadoop 2.2.0 consists of significant improvements over the previous stable release (hadoop-1.x).

Here is a short overview of the improvments to both HDFS and MapReduce.

• HDFS Federation

  In order to scale the name service horizontally, federation uses multiple independent Namenodes/Namespaces. The Namenodes are federated, that is, the Namenodes are independent and don't require coordination with each other. The datanodes are used as common storage for blocks by all the Namenodes. Each datanode registers with all the Namenodes in the cluster. Datanodes send periodic heartbeats and block reports and handles commands from the Namenodes.

  More details are available in the HDFS Federation document.

• MapReduce NextGen aka YARN aka MRv2

  The new architecture introduced in hadoop-0.23, divides the two major functions of the JobTracker: resource management and job life-cycle management into separate components.

  The new ResourceManager manages the global assignment of compute resources to applications and the per-application ApplicationMaster manages the application???s scheduling and coordination.

  An application is either a single job in the sense of classic MapReduce jobs or a DAG of such jobs.

  The ResourceManager and per-machine NodeManager daemon, which manages the user processes on that machine, form the computation fabric.

  The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.

  More details are available in the YARN document.

Getting Started

The Hadoop documentation includes the information you need to get started using Hadoop. Begin with the Single Node Setup which shows you how to set up a single-node Hadoop installation. Then move on to the Cluster Setup to learn how to set up a multi-node Hadoop installation.

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