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From Apache Wiki <wikidi...@apache.org>
Subject [Hadoop Wiki] Update of "NextGenMapReduce" by Arun C Murthy
Date Tue, 22 Mar 2011 06:32:35 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Hadoop Wiki" for change notification.

The "NextGenMapReduce" page has been changed by Arun C Murthy.
http://wiki.apache.org/hadoop/NextGenMapReduce?action=diff&rev1=1&rev2=2

--------------------------------------------------

  This wiki tracks development of [[https://issues.apache.org/jira/browse/MAPREDUCE-279|Next
Generation Apache Hadoop MapReduce]] (jira: MAPREDUCE-279).
  
+ === Overview ===
+ The fundamental idea of the re-architecture is to divide the two major functions of the
'''JobTracker''', resource management and job scheduling/monitoring, 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 classic MapReduce jobs or a DAG
of such  jobs. The ResourceManager and per-machine NodeManager server, 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.
+ 
+ The ResourceManager supports hierarchical application queues and  those queues can be guaranteed
a percentage of the cluster resources. It  is pure scheduler in the sense that it performs
no monitoring or  tracking of status for the application. Also, it offers no guarantees on
 restarting failed tasks either due to application failure or hardware  failures.
+ 
+ The ResourceManager performs its scheduling function based the  resource requirements of
the applications; each application has multiple  resource request types that represent the
resources required for  containers. The resource requests include memory, CPU, disk, network
 etc. Note that this is a significant change from the current model of  fixed-type slots in
Hadoop MapReduce, which leads to significant  negative impact on cluster utilization. The
ResourceManager has a  scheduler policy plug-in, which is responsible for partitioning the
 cluster resources among various queues, applications etc. Scheduler  plug-ins can be based,
for e.g., on the current CapacityScheduler and  FairScheduler.
+ 
+ The ResourceManager has two main components:
+ 
+  * Scheduler - The core scheduler which allocates resources to applications based on the
chosen policy (capacity guarantees, fairness etc.)
+ 
+  * ApplicationsManager - The component of the ResourceManager which is responsible for accepting
job-submissions, negotiating the first container for running the appropriate ApplicationMaster
and provides service for restarting the ApplicationMaster container on failure.
+ 
+ The NodeManager is the per-machine framework agent who is responsible  for launching the
applications’ containers, monitoring their resource  usage (cpu, memory, disk, network)
and reporting the same to the  Scheduler.
+ 
+ The per-application ApplicationMaster has the responsibility of  negotiating appropriate
resource containers from the Scheduler,  launching tasks, tracking their status & monitoring
for progress,  and handling task-failures.
+ 
+ === Source & Documentation ===
  The source for the first-cut is available in the [[http://svn.apache.org/viewvc/hadoop/mapreduce/branches/MR-279/|MR-279]]
branch in Apache Hadoop MapReduce:
  
  {{{
- 
  $ svn co http://svn.apache.org/hadoop/mapreduce/branches/MR-279/
  }}}
  We are currently in the process of adding design/implementation documentation, but some
links for current reference:
@@ -14, +31 @@

   * http://developer.yahoo.com/blogs/hadoop/posts/2011/02/mapreduce-nextgen/
   * http://developer.yahoo.com/blogs/hadoop/posts/2011/02/mapreduce-nextgen-scheduler/
  
+ === Development Process ===
+ Everyone is welcome to contribute, we'd love that! Just be aware we'll be moving fast. Thus,
you'll need to watch the branch. We plan to use more of mapreduce-dev@ and hadoop wiki and
less of jira to coordinate. We'll send out email statuses to allow everyone to track, maybe
even use the wiki to maintain todo lists. We'll learn as it goes, make changes to the dev
process as appropriate.Please shout out if you are interested in specific areas to let others
know; they could be anything - development, code-reviews, build, docs, tests etc. The horses
for specific courses as you contribute:
+ 
+  * MapReduce ApplicationMaster - Sharad & Vinod
+   * Availability - Sharad
+ 
+  * RM - Arun & Mahadev
+   * Scheduler - Arun & Mahadev
+   * ApplicationsManager & Availability - Mahadev
+ 
+  * NodeManager - Chris & Vinod
+ 
+  * Security - Vinod
+ 

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