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Progetti Apache
Apache Hadoop YARN

Apache Hadoop YARN


informazioni generali

The Architectural Center of Enterprise Hadoop

Part of the core Hadoop project, YARN is the architectural center of Hadoop that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform, unlocking an entirely new approach to analytics.

YARN is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize a modern data architecture.

What YARN Does

YARN is the prerequisite for Enterprise Hadoop, providing resource management and a central platform to deliver consistent operations, security, and data governance tools across Hadoop clusters.

YARN also extends the power of Hadoop to incumbent and new technologies found within the data center so that they can take advantage of cost effective, linear-scale storage and processing. It provides ISVs and developers a consistent framework for writing data access applications that run IN Hadoop.

YARN Applications Diagram

As its architectural center, YARN enhances a Hadoop compute cluster in the following ways:

Caratteristica Descrizione
    • YARN allows multiple access engines (either open-source or proprietary) to use Hadoop as the common standard for batch, interactive and real-time engines that can simultaneously access the same data set.

Multi-tenant data processing improves an enterprise’s return on its Hadoop investments.

Cluster utilization
    YARN’s dynamic allocation of cluster resources improves utilization over more static MapReduce rules used in early versions of Hadoop
    Data center processing power continues to rapidly expand. YARN’s ResourceManager focuses exclusively on scheduling and keeps pace as clusters expand to thousands of nodes managing petabytes of data.
    Existing MapReduce applications developed for Hadoop 1 can run YARN without any disruption to existing processes that already work

How YARN Works

YARN’s original purpose was 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
  • a per-application Container running on a NodeManager

The ResourceManager and the NodeManager formed the new generic system for managing applications in a distributed manner. The ResourceManager is the ultimate authority that arbitrates resources among all applications in the system. The ApplicationMaster is a framework-specific entity that negotiates resources from the ResourceManager and works 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 applications running in the cluster, according to constraints such as queue capacities and user limits. The scheduler schedules based on the resource requirements of each application.

Each ApplicationMaster has responsibility for 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.

Il NodeManager è lo slave per computer responsabile dell'avvio dei contenitori di applicazioni, del monitoraggio del loro utilizzo delle risorse (CPU, memoria, disco, rete) e della relativa segnalazione al ResourceManager.

Hortonworks Focus for YARN

YARN is the central point of investment for Hortonworks within the Apache community. In fact, YARN was originally proposed (MR-279) and architected by one of our founders, Arun Murthy. Our engineers have been working within the Hadoop community to deliver and improve YARN for years. It has matured to become the solid, reliable architectural center of Hadoop and is a foundational component.

While relied upon by thousands, YARN can always be improved, especially with new engines emerging to interact with Hadoop data. To this end, Hortonworks has laid out the following investment themes for this foundational technology.

Focus Planned Enhancements
Reliable Operations
  • Support for rolling upgrades: upgrade a YARN cluster without down time
  • Work-preserving restarts
  • Support NodeGroup layer topology
Scheduling and Isolation
  • Admin node labels: allow applications to request specific nodes for scheduling tasks
  • YARN admission planner: allow users to reserve future capacity for applications
Applications on YARN
  • Support Docker for packaging applications to run on YARN
  • Store application timeline data in secure clusters

Recent Progress in YARN


  • Capacity scheduler preemption
  • Security for Timeline Server
  • Resource Manager application submit/kill REST API
Apache Hadoop Version Prior Enhancements
  • Timeline Server for per-framework application timeline server
  • Generic application history service
  • Resource manager high availability (HA)
  • Run MapReduce framework via the distributed cache


Hadoop in the Press

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