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Apache Hadoop YARN – Application Master (AM)


2 April 2014

Requirements’ Origin

  1. Scalability
  2. Multi-tenancy
  3. Serviceability
  4. Locality Awareness
  5. High Cluster Utilization
  6. Reliability/Availability
  7. Secure and Auditable Operation
  8. Support for Programming Model Diversity
  9. Flexible Resource Model
  10. Backward compatibility

Application Master (AM)

An application may be a static set of processes, a logical description of work, or even a long-running service. The ApplicationMaster is the process that coordinates the application’s execution in the cluster, but it itself is run in the cluster just like any other container. A component of the RM negotiates for the container to spawn this bootstrap process.

The AM periodically heartbeats to the RM to affirm its liveness and to update the record of its demand. After building a model of its requirements, the AM encodes its preferences and constraints in a heartbeat message to the RM. In response to subsequent heartbeats, the AM will receive a container lease on bundles of resources bound to a particular node in the cluster. Based on the containers it receives from the RM, the AM may update its execution plan to accommodate perceived abundance or scarcity. In contrast to some resource models, the allocations to an application are late binding: the process spawned is not bound to the request, but to the lease. The conditions that caused the AM to issue the request may not remain true when it receives its resources, but the semantics of the container are fungible and framework-specific [R3,R8,R10]. The AM will also update its resource asks to the RM as the containers it receives affect both its present and future requirements.

By way of illustration, the MapReduce AM optimizes for locality among map tasks with identical resource requirements. When running on HDFS, each block of input data is replicated on k machines. When the AM receives a container, it matches it against the set of pending map tasks, selecting a task with input data close to the container. If the AM decides to run a map task mi in the container, then the hosts storing replicas of mi’s input data are less desirable. The AM will update its request to diminish the weight on the other k − 1 hosts. This relationship between hosts remains opaque to the RM; similarly, if mi fails, the AM is responsible for updating its demand to compensate. In the case of MapReduce, note that some services offered by the Hadoop JobTracker— such as job progress over RPC, a web interface to status, access to MapReduce-specific, historical data—are no longer part of the YARN architecture. These services are either provided by ApplicationMasters or by framework daemons.

Since the RM does not interpret the container status, the AM determines the semantics of the success or failure of the container exit status reported by NMs through the RM. Since the AM is itself a container running in a cluster of unreliable hardware, it should be resilient to failure. YARN provides some support for recovery, but because fault tolerance and application semantics are so closely intertwined, much of the burden falls on the AM.


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