AWS emr documentation change
Summary
Updated terminology from 'view' to 'access' in multiple sections related to CloudWatch metrics, application UIs, and logs. Adjusted phrasing for clarity and consistency in monitoring instructions.
Security assessment
The changes are editorial in nature, focusing on verb consistency (e.g., 'access' instead of 'view') and grammatical improvements. There is no evidence of security vulnerability fixes, access control changes, or new security features. The updates clarify how to interact with monitoring tools but do not alter security controls or disclose security risks.
Diff
diff --git a/emr/latest/EMR-Serverless-UserGuide/app-job-metrics.md b/emr/latest/EMR-Serverless-UserGuide/app-job-metrics.md index 3f75082c1..fe3e36826 100644 --- a//emr/latest/EMR-Serverless-UserGuide/app-job-metrics.md +++ b//emr/latest/EMR-Serverless-UserGuide/app-job-metrics.md @@ -9 +9 @@ Monitoring metrics release updatesApplication-level monitoringJob-level monitori -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. +With Amazon CloudWatch metrics for EMR Serverless, you can receive 1-minute CloudWatch metrics and access CloudWatch dashboards to access near-real-time operations and performance of your EMR Serverless applications. @@ -17 +17 @@ To get started, use the EMR Serverless CloudWatch dashboard template provided in -[EMR Serverless interactive workloads](./interactive-workloads.html) 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](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-studio-debug.html#emr-studio-debug-serverless). +[EMR Serverless interactive workloads](./interactive-workloads.html) have only application-level monitoring enabled, and have a new worker type dimension, `Spark_Kernel`. To monitor and debug your interactive workloads, access the logs and Apache Spark UI from [within your EMR Studio Workspace](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-studio-debug.html#emr-studio-debug-serverless). @@ -40 +40 @@ Dimensions for EMR Serverless metrics Dimension | Description -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. +You can monitor capacity usage at the EMR Serverless application level with Amazon CloudWatch metrics. You can also set up a single display to monitor application capacity usage in a CloudWatch dashboard. @@ -57 +57 @@ EMR Serverless application metrics Metric | Description | Unit | Dimension -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_. +Amazon EMR Serverless sends the following job-level metrics to Amazon CloudWatch every one minute. You can access the metric values for aggregate job runs by job run state. The unit for each of the metrics is _count_. @@ -70 +70 @@ EMR Serverless job-level metrics Metric | Description | Dimension -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. +You can monitor engine-specific metrics for running and completed EMR Serverless jobs with engine-specific application UIs. When you access the UI for a running job, the live application UI displays with real-time updates. When you access the UI for a completed job, the persistent app UI displays. @@ -74 +74 @@ You can monitor engine-specific metrics for both running and completed EMR Serve -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 AWS Command Line Interface. +For your running EMR Serverless jobs, access 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 AWS Command Line Interface. @@ -78 +78 @@ For your running EMR Serverless jobs, you can view a real-time interface that pr -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 AWS Command Line Interface. +For your completed EMR Serverless jobs, use the Spark History Server or the Persistent Hive Tez UI to access 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 AWS Command Line Interface. @@ -95 +95 @@ EMR Serverless job worker-level metrics Metric | Description | Unit | Dimension -The steps below describe how to view the various types of metrics. +The steps below describe how to access the various types of metrics. @@ -104 +104 @@ Console - 2. To view engine-specific application UIs and logs for a running job: + 2. To access engine-specific application UIs and logs for a running job: @@ -112 +112 @@ Console - 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. + 4. To access Spark engine logs, navigate to the **Executors** tab in the Spark UI, and choose the **Logs** link for the driver. To access Hive engine logs, choose the **Logs** link for the appropriate DAG in the Hive Tez UI. @@ -114 +114 @@ Console - 3. To view engine-specific application UIs and logs for a completed job: + 3. To access engine-specific application UIs and logs for a completed job: @@ -122 +122 @@ Console - 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. + 4. To access Spark engine logs, navigate to the **Executors** tab in the Spark UI, and choose the **Logs** link for the driver. To access Hive engine logs, choose the **Logs** link for the appropriate DAG in the Hive Tez UI. @@ -132 +132 @@ AWS CLI - * To generate a URL that you can use to access your application UI for both running and completed jobs, call the `GetDashboardForJobRun` API. + * To generate a URL that use to access your application UI for running and completed jobs, call the `GetDashboardForJobRun` API.