Embedded Files
Hadoop YARN
Hadoop YARN
A next-generation framework for Hadoop data processing.
Apache™ Hadoop® YARN is a sub-project of Hadoop at the Apache Software Foundation introduced in Hadoop 2.0 that separates the resource management and processing components. YARN was born of a need to enable a broader array of interaction patterns for data stored in HDFS beyond MapReduce. The YARN-based architecture of Hadoop 2.0 provides a more general processing platform that is not constrained to MapReduce.
As part of Hadoop 2.0, YARN takes the resource management capabilities that were in MapReduce and packages them so they can be used by new engines. This also streamlines MapReduce to do what it does best, process data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management. Many organizations are already building applications on YARN in order to bring them IN to Hadoop.
When enterprise data is made available in HDFS, it is important to have multiple ways to process that data. With Hadoop 2.0 and YARN organizations can use Hadoop for streaming, interactive and a world of other Hadoop based applications.
What YARN Does
What YARN Does
YARN enhances the power of a Hadoop compute cluster in the following ways:
Scalability. The processing power in data centers continues to grow quickly. Because YARN ResourceManager focuses exclusively on scheduling, it can manage those larger clusters much more easily.
Compatibility with MapReduce. Existing MapReduce applications and users can run on top of YARN without disruption to their existing processes.
Improved cluster utilization. The ResourceManager is a pure scheduler that optimizes cluster utilization according to criteria such as capacity guarantees, fairness, and SLAs. Also, unlike before, there are no named map and reduce slots, which helps to better utilize cluster resources.
Support for workloads other than MapReduce. Additional programming models such as graph processing and iterative modeling are now possible for data processing. These added models allow enterprises to realize near real-time processing and increased ROI on their Hadoop investments.
Agility. With MapReduce becoming a user-land library, it can evolve independently of the underlying resource manager layer and in a much more agile manner.
How YARN Works
How YARN Works
The fundamental idea of YARN is to split up the two major responsibilities of the JobTracker/TaskTracker into separate entities:
a global ResourceManager
a per-application ApplicationMaster.
a per-node slave NodeManager and
a per-application Container running on a NodeManager
The ResourceManager and the NodeManager form the new, and generic, system for managing applications in a distributed manner. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system. The per-application ApplicationMaster is a framework-specific entity and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the component tasks.\ The ResourceManager has a scheduler, which is responsible for allocating resources to the various running applications, according to constraints such as queue capacities, user-limits etc. The scheduler performs its scheduling function based on the resource requirements of the applications. The NodeManager is the per-machine slave, which is responsible for launching the applications’ containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager. Each ApplicationMaster has the responsibility of negotiating appropriate resource containers from the scheduler, tracking their status, and monitoring their progress. From the system perspective, the ApplicationMaster runs as a normal container. For more technical information on YARN, you may enjoy our series of blog posts on the topic :
Page updated
Google Sites
Report abuse