Application-level monitoring Job-level monitoring Job worker-level monitoring

Monitoring EMR Serverless applications and jobs

With Amazon CloudWatch metrics for EMR Serverless, you can receive 1-minute CloudWatch metrics and access CloudWatch dashboards to view near-real-time operations and performance of your EMR Serverless applications.

EMR Serverless sends metrics to CloudWatch every minute. EMR Serverless emits these metrics at the application level as well as the job, worker-type, and capacity-allocation-type levels.

To get started, use the EMR Serverless CloudWatch dashboard template provided in the EMR Serverless GitHub repository and deploy it.

Note

EMR Serverless interactive workloads have only application-level monitoring enabled, and have a new worker type dimension, Spark_Kernel. To monitor and debug your interactive workloads, you can view the logs and Apache Spark UI from within your EMR Studio Workspace.

The table below describes the EMR Serverless dimensions available within the Amazon/EMRServerless namespace.

Dimensions for EMR Serverless metrics
Dimension	Description
`ApplicationId`	Filters for all metrics of an EMR Serverless application.
`JobId`	Filters for all metrics of an EMR Serverless job run.
`WorkerType`	Filters for all metrics of a given worker type. For example, you can filter for `SPARK_DRIVER` and `SPARK_EXECUTORS` for Spark jobs.
`CapacityAllocationType`	Filters for all metrics of a given capacity allocation type. For example, you can filter for `PreInitCapacity` for pre-initialized capacity and `OnDemandCapacity` for everything else.

Application-level monitoring

You can monitor capacity usage at the EMR Serverless application level with Amazon CloudWatch metrics. You can also set up a single view to monitor application capacity usage in a CloudWatch dashboard.

EMR Serverless application metrics
Metric	Description	Primary dimension	Secondary dimension
`CPUAllocated`	The total numbers of vCPUs allocated.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`IdleWorkerCount`	The number of total workers idle.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`MaxCPUAllowed`	The maximum CPU allowed for the application.	`ApplicationId`	N/A
`MaxMemoryAllowed`	The maximum memory in GB allowed for the application.	`ApplicationId`	N/A
`MaxStorageAllowed`	The maximum storage in GB allowed for the application.	`ApplicationId`	N/A
`MemoryAllocated`	The total memory in GB allocated.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`PendingCreationWorkerCount`	The number of total workers pending creation.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`RunningWorkerCount`	The number of total workers in use by the application.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`StorageAllocated`	The total disk storage in GB allocated.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`
`TotalWorkerCount`	The number of total workers available.	`ApplicationId`	`ApplicationId`, `WorkerType`, `CapacityAllocationType`

Job-level monitoring

Amazon EMR Serverless sends the following job-level metrics to Amazon CloudWatch every one minute. You can view the metric values for aggregate job runs by job run state. The unit for each of the metrics is count.

EMR Serverless job-level metrics
Metric	Description	Primary dimension
`SubmittedJobs`	The number of jobs in a Submitted state.	`ApplicationId`
`PendingJobs`	The number of jobs in a Pending state.	`ApplicationId`
`ScheduledJobs`	The number of jobs in a Scheduled state.	`ApplicationId`
`RunningJobs`	The number of jobs in a Running state.	`ApplicationId`
`SuccessJobs`	The number of jobs in a Success state.	`ApplicationId`
`FailedJobs`	The number of jobs in a Failed state.	`ApplicationId`
`CancellingJobs`	The number of jobs in a Cancelling state.	`ApplicationId`
`CancelledJobs`	The number of jobs in a Cancelled state.	`ApplicationId`

You can monitor engine-specific metrics for both running and completed EMR Serverless jobs with engine-specific application UIs. When you view the UI for a running job, you see the live application UI with real-time updates. When you view the UI for a completed job, you see the persistent app UI.

Running jobs

For your running EMR Serverless jobs, you can view a real-time interface that provides engine-specific metrics. You can use either the Apache Spark UI or the Hive Tez UI to monitor and debug your jobs. To access these UIs, use the EMR Studio console or request a secure URL endpoint with the Amazon Command Line Interface.

Completed jobs

For your completed EMR Serverless jobs, you can use the Spark History Server or the Persistent Hive Tez UI to view jobs details, stages, tasks, and metrics for Spark or Hive jobs runs. To access these UIs, use the EMR Studio console, or request a secure URL endpoint with the Amazon Command Line Interface.

Job worker-level monitoring

Amazon EMR Serverless sends the following job worker level metrics that are available in the AWS/EMRServerless namespace and Job Worker Metrics metric group to Amazon CloudWatch. EMR Serverless collects data points from individual workers during job runs at the job level, worker-type, and the capacity-allocation-type level. You can use ApplicationId as a dimension to monitor multiple jobs that belong to the same application.

EMR Serverless job worker-level metrics
Metric	Description	Unit	Primary dimension	Secondary dimension
`WorkerCpuAllocated`	The total numbers of vCPU cores allocated for workers in a job run.	None	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerCpuUsed`	The total numbers of vCPU cores utilized by workers in a job run.	None	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerMemoryAllocated`	The total memory in GB allocated for workers in a job run.	Gigabytes (GB)	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerMemoryUsed`	The total memory in GB utilized by workers in a job run.	Gigabytes (GB)	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerEphemeralStorageAllocated`	The number of bytes of ephemeral storage allocated for workers in a job run.	Gigabytes (GB)	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerEphemeralStorageUsed`	The number of bytes of ephemeral storage used by workers in a job run.	Gigabytes (GB)	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerStorageReadBytes`	The number of bytes read from storage by workers in a job run.	Bytes	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`
`WorkerStorageWriteBytes`	The number of bytes written to storage from workers in a job run.	Bytes	`JobId`	`ApplicationId`, `WorkerType`, and `CapacityAllocationType`

The steps below describe how to view the various types of metrics.

Console

To access your application UI with the console

Navigate to your EMR Serverless application on the EMR Studio with the instructions in Getting started from the console.
To view engine-specific application UIs and logs for a running job:
1. Choose a job with a RUNNING status.
2. Select the job on the Application details page, or navigate to the Job details page for your job.
3. Under the Display UI dropdown menu, choose either Spark UI or Hive Tez UI to navigate to the application UI for your job type.
4. To view Spark engine logs, navigate to the Executors tab in the Spark UI, and choose the Logs link for the driver. To view Hive engine logs, choose the Logs link for the appropriate DAG in the Hive Tez UI.
To view engine-specific application UIs and logs for a completed job:
1. Choose a job with a SUCCESS status.
2. Select the job on your application's Application details page or navigate to the job's Job details page.
3. Under the Display UI dropdown menu, choose either Spark History Server or Persistent Hive Tez UI to navigate to the application UI for your job type.
4. To view Spark engine logs, navigate to the Executors tab in the Spark UI, and choose the Logs link for the driver. To view Hive engine logs, choose the Logs link for the appropriate DAG in the Hive Tez UI.

Amazon CLI

To access your application UI with the Amazon CLI

To generate a URL that you can use to access your application UI for both running and completed jobs, call the GetDashboardForJobRun API.
```
aws emr-serverless get-dashboard-for-job-run /
--application-id <application-id> /
--job-run-id <job-id> 
```
The URL that you generate is valid for one hour.

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Monitoring

Spark engine metrics