Help Center > > User Guide> Configuring Clusters> Overview


Updated at: Mar 17, 2020 GMT+08:00

You can view the overall cluster status on the Active Clusters page, and obtain relevant MRS documents by clicking the document name under Helpful Links on the Help page.

MRS helps manage and analyze massive amounts of data, and allows you to create clusters easily. You can add MapReduce, Spark, and Hive jobs to the clusters to process and analyze user data. Processed data can be encrypted using Secure Sockets Layer (SSL) and transmitted to OBS, ensuring data security and integrity.

Cluster Status

Table 1 describes the possible status of each cluster on the MRS management console.
Table 1 Cluster status




A cluster is being created.


A cluster has been created and all components in the cluster are running properly.

Scaling out

Core/Task nodes are being added to a cluster.


If a cluster scale-out failure occurs, you can scale out the cluster again.

Scaling in

Nodes are being deleted in the following operations: shutting down or deleting the nodes, changing or reinstalling the OSs of the nodes, and modifying node specifications.


A cluster becomes abnormal because some components in the cluster are abnormal.


When a cluster charged in Pay-per-use mode is being deleted, its status is Terminating.


Clusters charged in Yearly/Monthly mode cannot be terminated.


The balance is insufficient for purchasing a cluster.


A cluster in the Frozen state is unavailable and all ECSs in the cluster are shut down. After being unfrozen, the cluster returns to the Running state. If no renewal fee is paid, the cluster will be deleted after a specified period (called the freeze period) and the cluster status will be changed to Terminated.


Cluster creation, termination, or scale-out fails.


A cluster has been terminated.

Job Status

Table 2 describes the status of jobs that you execute after logging in to the MRS management console.

Table 2 Job status




A job is being executed.


A job has been executed and completed successfully.


A job is stopped during execution.


An error occurs during job execution or job execution fails.

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