Yarn

Yarn and MapReduce v2
©2014 Zaloni, Inc. All Rights Reserved.

Agenda
 What is YARN?
 Why YARN ?
 Components of YARN
 Architecture
 API in MRv2
 Gain with MRv2
 Failure in MRv2

So What YARN is really
???

YARN INTRODUCTION
YARN – (Yet another resource negotiator)
And is responsible for
•Cluster resource management
•Scheduling
Various applications may run on YARN- MapReduce is just a
choice.

YARN INTRODUCTION
HADOOP 1.0
MapReduce
(cluster resource management
& data processing)
HDFS
(redundant, reliable storage)
HADOOP 2.0
YARN
MapReduce
(data processing)
Others
(data processing)
(cluster resource management)
HDFS2
(redundant, reliable storage)
Single Use System
Batch Apps
Multi Purpose Platform
Batch, Interactive, Online, Streaming, …

YARN INTRODUCTION
Store ALL DATA in one place…Interact with
Applications Run Natively IN Hadoop
YARN (Cluster Resource Management)
HDFS2 (Redundant, Reliable Storage)
BATCH
(MapReduce)
INTERACTIVE
(Tez)
STREAMING
(Storm, S4,…)
GRAPH
(Giraph)
IN-MEMORY
(Spark)
HPC MPI
(OpenMPI)
ONLINE
(HBase)
OTHER
(Search)
(Weave…)
that data in MULTIPLE WAYS

Why YARN and MRv2 ???

Hadoop MapReduce Classic
JobTracker
Manages cluster resources
and job scheduling
TaskTracker
Per-node agent
Manage tasks

MRv1 Limitations
• Scalability – JT limits horizontal scaling
• Cluster utilization- Fixed sized slots degrade the cluster
utilization
• Availability – when JT dies, jobs must restart
• Upgradability – must stop jobs to upgrade JT
• Hardwired – JT only supports MapReduce

ResourceManager
JobTracker
ApplicationMaster

Architecture

YARN Components
So, What was Developed
• Resource Manager
• Node Manager
• Application Master
• Container

• Manages the global assignment of
compute resources to applications.
• A pure Scheduler
• No monitoring, tracking status of
application
Resource Manager (RM)

• Each client/application may
request multiple resources
– Memory
– Network
– Cpu
– Disk ..
• This is a significant change from
static Mapper / Reducer model
Resource Manager (RM)

Application Master
• A per – application ApplicationMaster
(AM) that manages the application’s life
cycle (scheduling and coordination).
• An application is either a single job in the
classic MapReduce jobs or a DAG of such
jobs.

• Application Master has
the responsibility of
– negotiating appropriate resource
containers from the Scheduler
– launching tasks
– tracking their status
– monitoring for progress
– handling task-failures.
Application Master

NodeManager : per-machine
framework agent
– responsible for launching the
applications’ containers,
monitoring their resource usage
(cpu, memory, disk, network)
and reporting the same to the
Scheduler.
Node Manager

– Basic unit of allocation monitoring their
resource usage (cpu, memory, disk,
network)
– Fine-grained resource allocation across
multiple resource types (memory, cpu,
disk, network, gpu etc.)
– Replaces the fixed map/reduce slots
Container

Lifecycle of a job
Here you are
Do work!
I need resources!
Client
Resource
Manager
App
Master
Submit
OK
Done?
No
Done?
No
Done?
Yes
Go
Containers
Done
Done
Node
Managers
Start containers
Here you are

Lifecycle of a job

Job execution on MRv2
1. Client submits MapReduce job by interacting with Job objects;
2. Job’s code interacts with Resource Manager to acquire application meta-data,
such as application id
3. Job’s code moves all the job related resources to HDFS to make them
available for the rest of the job
4. Job’s code submits the application to Resource Manager
5. Resource Manager chooses a Node Manager with available resources and
requests a container for MRAppMaster
6. Node Manager allocates container for MRAppMaster; MRAppMaster will
execute and coordinate MapReduce job

Job execution on MRv2
7. MRAppMaster grabs required resource from HDFS copied there in step 3
8. MRAppMaster negotiates with Resource Manager for available resources;
Resource Manager will select Node Manager that has the most resources
9. MRAppMaster tells selected NodeManager to start Map and Reduce tasks
10.NodeManager creates YarnChild containers that will coordinate and run tasks
11.YarnChild acquires job resources from HDFS that will be required to execute
Map and Reduce tasks
12.YarnChild executes Map and Reduce tasks

YARN – Resource Allocation & Usage
ResourceRequest
–Fine-grained resource ask to the ResourceManager
–Ask for a specific amount of resources (memory, cpu etc.) on a
specific machine or rack
–Use special value of * for resource name for any machine
© Hortonworks Inc. 2013
ResourceRequest
priority
resourceName
capability
numContainers

ResourceRequest
priority capability resourceName numContainers
0 <2gb, 1 core>
host01 1
rack0 1
* 1
1 <4gb, 1 core> * 1

Container
–The basic unit of allocation in YARN
–The result of the ResourceRequest provided by
ResourceManager to the ApplicationMaster
–A specific amount of resources (cpu, memory etc.) on a specific
machine Container
containerId
resourceName
capability
tokens

ContainerLaunchContext
– The context provided by ApplicationMaster to NodeManager to launch the
Container
– Complete specification for a process
– LocalResource used to specify container binary and dependencies
– NodeManager responsible for downloading from shared namespace
(typically HDFS)
ContainerLaunchContext
container
commands
environment
localResources
LocalResource
uri
type

YARN - ApplicationMaster
ApplicationMaster
–ApplicationSubmissionContext is the complete
specification of the ApplicationMaster, provided by Client
–ResourceManager responsible for allocating and launching
ApplicationMaster container
ApplicationSubmissionContext
resourceRequest
containerLaunchContext
appName
queue

API in MRv2
ClientRMProtocol (Client—RM ) :
This is the protocol for a client to communicate with the RM to launch a
new application (i.e. an AM), check on the status of the application or kill
the application.
AMRMProtocol (AM—RM) :
This is the protocol used by the AM to register/unregister itself with the
RM, as well as to request resources from the RM Scheduler to run its
tasks.
ContainerManager (AM – NM) :
This is the protocol used by the AM to communicate with the NM to start
or stop containers and to get status updates on its containers.

ResourceManager
YARN Application API – ClientRM
NodeManager NodeManager NodeManager NodeManager
NodeManager NodeManager NodeManager
NodeManager
Client2
Application Request:
YarnClient.createApplication
Submit
Application:YarnClient.submitApplication
1
2
Scheduler

YARN Application API – AMRM
ResourceManager
AMRMClient.allocate
Container
Scheduler
AM
unregisterApplicationMaster
registerApplicationMaster
4
1
2
3

YARN Application API – ContainerManager
ResourceManager
Scheduler
Container 1.1
AMNMClient.startContainer
AM 1
AMNMClient.getContainerStatus

Gain with New Architecture
• RM and Job manager segregated
• The Hadoop MapReduce
JobTracker spends a very significant
portion of time and effort managing
the life cycle of applications
• Scalability
• Availability
• Wire-compatibility
• Innovation & Agility
• Cluster Utilization
• Support for programming
paradigms other than
MapReduce

• ResourceManage
– Uses ZooKeeper for fail-over.
– When primary fails, secondary can
quickly start using the state stored in
ZK
• Application Master
– MapReduce ApplicationMaster can
recover from failures by restoring
itself with the help of checkpoint.
• Scalability
• Availability
MapReduce

• MRv2 uses wire-compatible
protocols to allow different
versions of servers and clients to
communicate with each other.
• Rolling upgrades for the cluster in
future.
• Scalability
• Availability
MapReduce

• New framework is generic.
– Different versions of MR running in
parallel
– End users can upgrade to MR
versions on their own schedule
• Scalability
• Availability
MapReduce

• MRv2 uses a general concept of a
resource for scheduling and allocating to
individual applications.
• Better cluster utilization
• Scalability
• Availability
MapReduce

Store all data in one place and use
them for more than one application
• Scalability
• Availability
MapReduce

Failures in YARN
For MapReduce programs running on YARN, we need to consider the
failure of any of the following entities: the task, the application master, the
node manager, and the resource manager.
• Task fail :Failure of the running task is similar to the classic case. Runtime
exceptions , sudden exits of the JVM ,timed out tasks are marked as failed.
a task is marked as failed after four attempts (set by mapreduce.map
.maxattempts for map tasks and mapreduce.reduce.maxsttempts for
reducer tasks).

Failures in YARN
• NodeManager fail: If a node manager fails, then it will stop sending
heartbeats to the resource manager, and the node manager will be
removed from the resource manager’s pool of available nodes.
• Node managers may be blacklisted if the number of failures for the
application is high. Blacklisting is done by the application master, and for
MapReduce the application master will try to reschedule tasks on different
nodes if more than three tasks fail on a node manager. The threshold may
be set with mapreduce.job.maxtaskfai lures.per.tracker.

Failures in YARN
• Application master fail: An application master sends periodic
heartbeats to the resource manager, and in the event of application master
failure, the resource manager will detect the failure and start a new instance
of the master running in a new container (managed by a node manager)
• ResourceManager fail : Most critical as this failure can shut down
the whole process. Eliminated by checkpoints, or standby node (HA ).

Yarn

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