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Hadoop >> mail # dev >> Adding Elasticity to Hadoop MapReduce


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Re: Adding Elasticity to Hadoop MapReduce
Hello Arun and all,
         I think current hadoop have a good capability of scale out but not so good at scale in. As its design for dedicated cluster and machines, there is not too much attention for "scale in" capability in a long time. However, I noticed that there are more and more users to deploy hadoop clusters in Cloud (ec2, eucalyptus, etc.) or shared infrastructures(vmware, xen) that "scale in" capability can contribute to save resource utilization for other clusters or applications. The current "scale in" solution (as you proposed in previous mail) have some significant drawbacks:
         1. It doesn't use a formal way to handle scale-in case but rather a temporary workaround base on a disaster recovery mechanism.
         2. It is not convenient, Hadoop admin have to manually kill datanode one by one(in fact, maximum to be N(replica number) -1 each time to avoid possible data loss) and wait replica back To shrink a cluster from 1000 nodes to 500 nodes, how much time and effort it could be?
         3. It is not efficient as it is not well planned. Let's say both node A, B and C should be eliminated from cluster. At first, A and B will be eliminated from cluster ( suppose N =3), and it is possible that C can get some replicas for block in A or B. This problem is serious if big shrink happens.
         Thus, I think it is necessary to have a good discussion to let hadoop have this cool "elastic" features. Here I am volunteer for proposing one possible solution and welcome better solutions:
         1. We can think of breaking out the assumption of coexist of Datanode and TaskTracker on one machine and let some machines only have task node. I think network traffic inside a rack is not so expensive, but you may say that it waste some local I/O resource for machines only with task node. Hey, don't look at these machines as dedicated resource for this hadoop cluster. They can be used by other clusters and application(so they should be eliminated at some time). To this cluster, these machines are better than nothing, right?
          2. The percentage of machines with only task node in whole cluster is a "elastic" factor for this cluster. Take a example, if this cluster want to be scalable between "500"-"1000", the elastic factor could be 1/2, and it should have 500 normal machines with both data and task nodes and another 500 machines with task node only.
          3. Elastic factor can be configured by hadoop admin and non-dedicated machines in this cluster can be marked through some script like what have been done in rack-awareness.
          4. One command is provided to hadoop admin to shrink the cluster to the target size directly. Some policy can be applied here for waiting or not waiting task completed. If target size is smaller than elastic factor * current size, some data node will be killed too but in a well planned way.
          My 2 cents.

Thanks,

Junping
________________________________
From: Arun C Murthy <[EMAIL PROTECTED]>
To: [EMAIL PROTECTED]
Sent: Thursday, September 15, 2011 5:24 AM
Subject: Re: Adding Elasticity to Hadoop MapReduce
On Sep 14, 2011, at 1:27 PM, Bharath Ravi wrote:

> Hi all,
>
> I'm a newcomer to Hadoop development, and I'm planning to work on an idea
> that I wanted to run by the dev community.
>
> My apologies if this is not the right place to post this.
>
> Amazon has an "Elastic MapReduce" Service (
> http://aws.amazon.com/elasticmapreduce/) that runs on Hadoop.
> The service allows dynamic/runtime changes in resource allocation: more
> specifically, varying the number of
> compute nodes that a job is running on.
>
> I was wondering if such a facility could be added to the publicly available
> Hadoop MapReduce.

From a long while  you can bring up either DataNodes or TaskTrackers and point them (via config) to the NameNode/JobTracker and they will be part of the cluster.

Similarly you can just kill the DataNode or TaskTracker and the respective masters will deal with their loss.

Arun
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB