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FusionInsight HD V100R002C60SPC200 Product Description 06

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Task Priority Scheduling

In the native YARN resource scheduling mechanism, if the whole Hadoop cluster resources are occupied by those MapReduce jobs submitted earlier, jobs submitted later will be kept in pending state until all running jobs are executed and resources are released.

Huawei provides a mechanism for scheduling tasks by priority. With this feature, you can define jobs of different priorities. Jobs of high priority can preempt resources of jobs of low priority though they are submitted later. Jobs of low priority will be suspended and cannot start unless those jobs of high priority are completed and resources are released.

This feature enables services to flexibly control their own computing tasks, achieving optimal utilization of cluster resources.


The Container Reuse feature is in conflict with the Task Priority Scheduling feature. If Container Reuse is enabled, resources will not be released. In other words, the Task Priority Scheduling feature does not work.

Figure 4-12 Task Priority Scheduling

The Timeout Parameter can be Set When a User Submits a MapReduce Job

Open-source function: If a MapReduce job is executed for a long time, it will be suspended. Users have to wait, but cannot determine the cause.

Therefore, a timeout parameter -Dapplication.timeout.interval = is added for setting the MapReduce job execution timeout. The unit of the timeout parameter is second. If the job execution time expires, then the job will be stopped.

yarn jar <App_Jar_Name> [Main_Class] -Dapplication.timeout.interval = <timeout>


The value should be an integer. If the value is or not configured (null), then the execution timeout function will not be executed. If the configured value is invalid (other than integer value), then by default the value is set to 5 minutes.

Updated: 2019-04-10

Document ID: EDOC1000104139

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