What role does yarn architecture play in hadoop ecosystem

YARN (Yes Another Resource Negotiator) is a key component in the Hadoop ecosystem. It plays the role of resource management and job scheduling in the entire Hadoop cluster. The following are the main roles of the YARN architecture in the Hadoop ecosystem:

1. Resource management: YARN is responsible for the unified management and allocation of computing resources in the cluster. It divides the resources of the whole cluster into multiple containers, and each container can be a combination of CPU, memory and other resources. YARN monitors the use of cluster resources through the Global Resource Manager and dynamically allocates resources according to job requirements.
2. Job scheduling: YARN provides an application manager (ApplicationMaster) to coordinate and manage various applications running on the cluster. Each application has its own ApplicationMaster, which is responsible for interacting with the Resource Manager and requesting the required resources from it. At the same time, the ApplicationMaster is also responsible for splitting jobs into multiple tasks and assigning them to the Node Manager for execution.
3. Multi tenant support: YARN supports multi tenant resource isolation and sharing. By assigning independent container resources to each application, YARN can ensure that different applications are isolated from each other and avoid interference with each other. This allows different departments or teams to run their own applications on the same cluster, improving resource utilization and cluster diversity.
4. Elastic scaling: YARN can dynamically adjust the size of the cluster according to actual needs. When a new job is submitted, YARN can dynamically allocate the available resources and release the corresponding resources after the job is completed. This elastic scaling mechanism enables clusters to better adapt to changes in workload.

In a word, the YARN architecture plays a key role in resource management and job scheduling in the Hadoop ecosystem. By effectively managing and allocating cluster resources, it improves the utilization and performance of the cluster. At the same time, it supports multi tenant and elastic scaling and other functions, providing users with a stable and efficient big data processing platform.