hadoop/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-site/src/site/apt/YARN.apt.vm
2012-08-08 05:22:27 +00:00

78 lines
3.7 KiB
Plaintext

~~ 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.