Thesis Reading Notes-yarn: Architecture of next generation Apache hadoop mapreduceframework

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Author: Liu Xuhui Raymond reprinted. Please indicate the source

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Target question=


The next-generation hadoop framework supports hadoop clusters with more than 10,000 nodes and more flexible programming models.


Core Ideology=


Fixed programming models and single-point resource scheduling and task management methods make hadoop 1.0 applications increasingly show its limitations in terms of model and scale.


Yarn adopts a two-level distributed resource scheduling and task management framework. It supports modular task scheduling components and custom task management modules, to adapt to a variety of programming models and the increasing cluster size.


Yarn schedules resources and tasks in container units, and the schedulable resource type is memory (long-term targets include CPU, disk, and Io ), by allocating and sharing resources among various task management frameworks to improve cluster utilization, the overall idea is very close to mesos.




The main components of yarn include:


One global RM (ResourceManager), one am (applicationmaster) for each job)
Each node has one nm (nodemanager)



Rm is further divided into the scheduling module (sched) and Application Management Module (applications ).
The scheduling module is responsible for allocating resources among jobs, and the application management module is responsible for listening to the client to create job requests and starting the PER
Job AM


After the application management module starts am, am takes over the management work after its own job. Am is responsible for negotiating with the scheduling module to obtain the resources required for running the job, create a task process for the required resources through nm, and monitor the completion of the task.


From the perspective of the communication protocols between AM and RM, the scheduling interface for resources has been simplified to a list of the container configurations, quantities, and locations required by AM. Therefore, it has great versatility. Of course, because the scheduling module simply schedules resources based on job requirements and priorities, without considering the details and execution of any task, this results in loss of information that can be used as the basis for scheduling. Taking mapreduce as an example, information related to mapsplit is unknown to the scheduling module. Locality and other requirements need to be guaranteed by AM.


Related research and projects=


Mesos's problem and overall thinking are very similar to yarn. The same two-level resource scheduling can be modularized. The specific computing framework is responsible for second-level resource scheduling. The isolated resource management methods and similar task execution methods are used. However, in terms of resource level-1 scheduling, mesos adopts the push method, while yarn adopts the pull method. mesos claims to make interfaces simpler and more universal, yarn's pull approach seems more flexible. But from the API perspective, I personally understand that am still needs to obtain the status of global resources before making scheduling requests, and may have to pay a higher communication cost?


Facebook's corona is also developed for hadoop and basically integrates the job in mapreduce1.0
Tracker is split in the unit of job. Similarly, pull is used to dispatch data to the central cluster module.
Manager requests resources. However, scope is approximately smaller than yarn. The purpose of visual testing is to solve the cluster scale problem by means of distribution, while yarn also hopes to flexibly adapt to different computing frameworks.

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