MAPREDUCE-3322. Added a better index.html and an brief overview of YARN architecture.

git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1195764 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Arun Murthy 2011-11-01 02:00:58 +00:00
parent 68fa208b1c
commit d36f847b87
8 changed files with 123 additions and 14 deletions

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@ -455,6 +455,9 @@ Release 0.23.0 - Unreleased
MAPREDUCE-3275. Added documentation for AM WebApp Proxy. (Robert Evans via
acmurthy)
MAPREDUCE-3322. Added a better index.html and an brief overview of YARN
architecture. (acmurthy)
OPTIMIZATIONS
MAPREDUCE-2026. Make JobTracker.getJobCounters() and

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@ -22,7 +22,6 @@ Hadoop MapReduce Next Generation - Capacity Scheduler
%{toc|section=1|fromDepth=0}
* {Purpose}
This document describes the <<<CapacityScheduler>>>, a pluggable scheduler

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@ -20,6 +20,8 @@ Hadoop MapReduce Next Generation - Setting up a Single Node Cluster.
\[ {{{./index.html}Go Back}} \]
%{toc|section=1|fromDepth=0}
* Mapreduce Tarball
You should be able to obtain the MapReduce tarball from the release.

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@ -21,7 +21,7 @@ Hadoop MapReduce Next Generation - Writing YARN Applications
\[ {{{./index.html}Go Back}} \]
%{toc|section=1|fromDepth=1}
%{toc|section=1|fromDepth=0}
* Purpose

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@ -0,0 +1,77 @@
~~ Licensed under the Apache License, Version 2.0 (the "License");
~~ you may not use this file except in compliance with the License.
~~ You may obtain a copy of the License at
~~
~~ http://www.apache.org/licenses/LICENSE-2.0
~~
~~ Unless required by applicable law or agreed to in writing, software
~~ distributed under the License is distributed on an "AS IS" BASIS,
~~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~~ See the License for the specific language governing permissions and
~~ limitations under the License. See accompanying LICENSE file.
---
YARN
---
---
${maven.build.timestamp}
Apache Hadoop NextGen MapReduce (YARN)
MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have,
what we call, MapReduce 2.0 (MRv2) or YARN.
The fundamental idea of MRv2 is to split up the two major functionalities of
the JobTracker, resource management and job scheduling/monitoring, into
separate daemons. The idea is to have a global ResourceManager (<RM>) and
per-application ApplicationMaster (<AM>). An application is either a single
job in the classical sense of Map-Reduce jobs or a DAG of jobs.
The ResourceManager and per-node slave, the NodeManager (<NM>), form the
data-computation framework. The ResourceManager is the ultimate authority that
arbitrates resources among all the applications in the system.
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.
[./yarn_architecture.gif] MapReduce NextGen Architecture
The ResourceManager has two main components: Scheduler and
ApplicationsManager.
The Scheduler is responsible for allocating resources to the various running
applications subject to familiar constraints of capacities, queues etc. The
Scheduler is pure scheduler in the sense that it performs no monitoring or
tracking of status for the application. Also, it offers no guarantees about
restarting failed tasks either due to application failure or hardware
failures. The Scheduler performs its scheduling function based the resource
requirements of the applications; it does so based on the abstract notion of
a resource <Container> which incorporates elements such as memory, cpu, disk,
network etc. In the first version, only <<<memory>>> is supported.
The Scheduler has a pluggable policy plug-in, which is responsible for
partitioning the cluster resources among the various queues, applications etc.
The current Map-Reduce schedulers such as the CapacityScheduler and the
FairScheduler would be some examples of the plug-in.
The CapacityScheduler supports <<<hierarchical queues>>> to allow for more
predictable sharing of cluster resources
The ApplicationsManager is responsible for accepting job-submissions,
negotiating the first container for executing the application specific
ApplicationMaster and provides the service for restarting the
ApplicationMaster container on failure.
The NodeManager is the per-machine framework agent who is responsible for
containers, monitoring their resource usage (cpu, memory, disk, network) and
reporting the same to the ResourceManager/Scheduler.
The per-application ApplicationMaster has the responsibility of negotiating
appropriate resource containers from the Scheduler, tracking their status and
monitoring for progress.
MRV2 maintains <<API compatibility>> with previous stable release
(hadoop-0.20.205). This means that all Map-Reduce jobs should still run
unchanged on top of MRv2 with just a recompile.

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@ -11,14 +11,32 @@
~~ limitations under the License. See accompanying LICENSE file.
---
Hadoop MapReduce Next Generation ${project.version}
Apache Hadoop 0.23
---
---
${maven.build.timestamp}
Hadoop MapReduce Next Generation
Apache Hadoop 0.23
* Architecture
Apache Hadoop 0.23 consists of significant improvements over the previous
stable release (hadoop-0.20.205).
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 {{{./Federation.html}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
@ -32,22 +50,32 @@ Hadoop MapReduce Next Generation
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.
user processes on that machine, form the computation fabric.
* User Documentation
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.html}YARN}}
document.
* Release Documentation
* {{{./SingleCluster.html}Setting up a Single Node Cluster}}
* {{{./ClusterSetup.html}Setting up a full-fledged Hadoop Cluster}}
* {{{./apidocs/index.html}JavaDocs}}
* {{{./CapacityScheduler.html}Capacity Scheduler}}
* {{{./Federation.html}HDFS Federation feature description, configuration and
management}}
* {{{./YARN.html}NextGen MapReduce}}
* User Documentation
* {{{./WritingYarnApplications.html}Writing Yarn Applications}}
* {{{./CapacityScheduler.html}Capacity Scheduler}}
* {{{./apidocs/index.html}JavaDocs}}
* {{{./Federation.html}HDFS Federation feature description, configuration and management}}

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See the License for the specific language governing permissions and
limitations under the License. See accompanying LICENSE file.
-->
<project name="Hadoop MapReduce Next Gen">
<project name="Apache Hadoop 0.23">
<skin>
<groupId>org.apache.maven.skins</groupId>