MapReduce 2 vs YARN applications - mapreduce

Question

I’m a bit confused about how new MapReduce2 applications should be developed to work with YARN and what happen with the old ones.
I currently have MapReduce1 applications which basically consist in:
Drivers which configure the jobs to be submitted to the cluster (previous JobTracker and now the ResourceManager).
Mappers + Reducers
From one side I see that applications coded in MapReduce1 are compatible in MapReduce2 / YARN, with a few caveats, just recompiling with new CDH5 libraries (I work with Cloudera distribution).
But from other side I see information about writing YARN applications in a different way than MapReduce ones (using YarnClient, ApplicationMaster, etc):
http://hadoop.apache.org/docs/r2.7.0/hadoop-yarn/hadoop-yarn-site/WritingYarnApplications.html
But for me, YARN is just the architecture and how the cluster manage your MR app.
My questions are:
Are YARN applications including MapReduce applications?
Should I write my code like a YARN application, forgetting drivers
and creating Yarn clients, ApplicationMasters and so on?
Can I still develop the client classes with drivers + job settings?
Are MapReduce1 (recompiled with MR2 libraries) jobs managed by YARN
in the same way that YARN applications?
What differences are between MapReduce1 applications and YARN applications regarding the way in which YARN will manage them internally?
Thanks in advance

Answer 1

HADOOP Version 1
The JobTracker is responsible for resource management---managing the slave nodes--- major functions involve
tracking resource consumption/availability
job life-cycle management---scheduling individual tasks of the job, tracking progress, providing fault tolerance for tasks.
Issues with Hadoop v1
JobTracker is responsible for all spawned MR applications, it is a single point of failure---If JobTracker goes down, all applications in the cluster are killed. Moreover, if the cluster has a large number of applications, JobTracker becomes the performance bottleneck, to address the issues of scalability and job management Hadoop v2 was released.
Hadoop v2
The fundamental idea of YARN is to split the two major responsibilities of the Job-Tracker—that is, resource management and job scheduling/monitoring—into separate daemons: a global ResourceManager and a per-application ApplicationMaster (AM). The ResourceManager and per-node slave, the NodeManager (NM), form the new, and generic, operating system for managing applications in a distributed manner.
To interact with the new resourceManagement and Scheduling, A Hadoop YARN mapReduce Application is developed---MRv2 has nothing to do with the mapReduce programming API
Application programmers will see no difference between MRv1 and MRv2, MRv2 is fully backward compatible---Yes a MR application(.jar), can be run on both the frameworks without any change in code.
The Hadoop 2.x already contains the code for MR Client and AppMaster, the programmer just needs to focus on their MapReduce Applications.
MapReduce was previously integrated in Hadoop Core---the only API to interact with data in HDFS. Now In Hadoop v2 it runs as a separate Application, Hadoop v2 allows other application programming frameworks---e.g MPI---to process HDFS data.

Answer 2

Refer to Apache documentation page on YARN architecture and related SE posts:
Hadoop gen1 vs Hadoop gen2
Are YARN applications including MapReduce applications?
YARN support Mapreduce applications. It also runs Spark jobs unlike in Hadoop 1.x.
Should I write my code like a YARN application, forgetting drivers and creating Yarn clients, ApplicationMasters and so on?
Yes. You should forget about all these application components and write your application. Have a look at sample code
Can I still develop the client classes with drivers + job settings? ¿Are MapReduce1 (recompiled with MR2 libraries) jobs managed by YARN in the same way that YARN applications?
Yes. You can do. But look at this compatibility article.
What differences are between MapReduce1 applications and YARN applications regarding the way in which YARN will manage them internally?
Refer to this SE post:
What additional benefit does Yarn bring to the existing map reduce?

Answer 3

YARN is just a cluster manager.
First the applications have to developed for YARN (if not already implemented). Here are few of the applications which are supported on YARN. If you want a new appplications to run on YARN this is the guide.
Then the same MR/Spark/Hama programs can be run on YARN.