AWS emr documentation change
Summary
Minor wording changes and link reference updates (e.g., 'see' to 'refer to'), clarification of dynamic allocation recommendations, and formatting adjustments for Spark property tables.
Security assessment
Changes are primarily grammatical improvements, terminology consistency, and formatting adjustments. No new security-related content was added, and there's no indication of addressing vulnerabilities or security weaknesses. The existing security references to IAM roles and permissions remain unchanged in substance.
Diff
diff --git a/emr/latest/EMR-Serverless-UserGuide/jobs-spark.md b/emr/latest/EMR-Serverless-UserGuide/jobs-spark.md index a240400dc..5289eb9f2 100644 --- a//emr/latest/EMR-Serverless-UserGuide/jobs-spark.md +++ b//emr/latest/EMR-Serverless-UserGuide/jobs-spark.md @@ -9 +9 @@ Spark parametersSpark propertiesResource configuration best practicesSpark examp -You can run Spark jobs on an application with the `type` parameter set to `SPARK`. Jobs must be compatible with the Spark version compatible with the Amazon EMR release version. For example, when you run jobs with Amazon EMR release 6.6.0, your job must be compatible with Apache Spark 3.2.0. For information on the application versions for each release, see [Amazon EMR Serverless release versions](./release-versions.html). +You can run Spark jobs on an application with the `type` parameter set to `SPARK`. Jobs must be compatible with the Spark version compatible with the Amazon EMR release version. For example, when you run jobs with Amazon EMR release 6.6.0, your job must be compatible with Apache Spark 3.2.0. For information on the application versions for each release, refer to [Amazon EMR Serverless release versions](./release-versions.html). @@ -13 +13 @@ You can run Spark jobs on an application with the `type` parameter set to `SPARK -When you use the [`StartJobRun` API](https://docs.aws.amazon.com/emr-serverless/latest/APIReference/API_StartJobRun.html) to run a Spark job, you can specify the following parameters. +When you use the [`StartJobRun` API](https://docs.aws.amazon.com/emr-serverless/latest/APIReference/API_StartJobRun.html) to run a Spark job, specify the following parameters. @@ -47 +47 @@ Use **`executionRoleArn`** to specify the ARN for the IAM role that your applica -If your Spark job reads or writes data to or from other data sources, specify the appropriate permissions in this IAM role. If you don't provide these permissions to the IAM role, the job might fail. For more information, see [Job runtime roles for Amazon EMR Serverless](./security-iam-runtime-role.html) and [Storing logs](./logging.html). +If your Spark job reads or writes data to or from other data sources, specify the appropriate permissions in this IAM role. If you don't provide these permissions to the IAM role, the job might fail. For more information, refer to [Job runtime roles for Amazon EMR Serverless](./security-iam-runtime-role.html) and [Storing logs](./logging.html). @@ -62 +62 @@ Use **`jobDriver`** to provide input to the job. The job driver parameter accept -For additional information, see [Launching Applications with spark-submit](https://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit). +For additional information, refer to [Launching Applications with spark-submit](https://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit). @@ -96 +96 @@ If you use the same configuration in an application override and in Spark submit -For more information on declaring configurations at the application level, and overriding configurations during job run, see [Default application configuration for EMR Serverless](./default-configs.html). +For more information on declaring configurations at the application level, and overriding configurations during job run, refer to [Default application configuration for EMR Serverless](./default-configs.html). @@ -120 +120 @@ Consider the following if you're using dynamic allocation optimization: - * To achieve the best cost efficiency, we recommend configuring an upper scaling bound on workers using either the job-level setting `spark.dynamicAllocation.maxExecutors` or the [application-level maxium capacity](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/app-behavior.html#max-capacity) setting based on your workload. + * To achieve the best cost efficiency, we suggest configuration of an upper scaling bound on workers using either the job-level setting `spark.dynamicAllocation.maxExecutors` or the [application-level maxium capacity](https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/app-behavior.html#max-capacity) setting based on your workload. @@ -122 +122 @@ Consider the following if you're using dynamic allocation optimization: - * You might not see cost improvement in simpler jobs. For example, if your job runs on a small dataset or finishes running in one stage, Spark might not need a larger number of executors or multiple scaling events. + * You might not notice cost improvement in simpler jobs. For example, if your job runs on a small dataset or finishes running in one stage, Spark might not need a larger number of executors or multiple scaling events. @@ -133 +133 @@ The following table lists optional Spark properties and their default values tha -Key | Description | Default value +Optional Spark properties and default values Key | Description | Default value @@ -166 +166 @@ The following table lists the default Spark submit parameters. -Key | Description | Default value +Default Spark submit parameters Key | Description | Default value @@ -223 +223 @@ Configuring your resources in this manner ensures that EMR Serverless can alloca -The following example shows how to use the `StartJobRun` API to run a Python script. For an end-to-end tutorial that uses this example, see [Getting started with Amazon EMR Serverless](./getting-started.html). You can find additional examples of how to run PySpark jobs and add Python dependencies in the [EMR Serverless Samples](https://github.com/aws-samples/emr-serverless-samples/tree/main/examples/pyspark) GitHub repository. +The following example shows how to use the `StartJobRun` API to run a Python script. For an end-to-end tutorial that uses this example, refer to [Getting started with Amazon EMR Serverless](./getting-started.html). You can find additional examples of how to run PySpark jobs and add Python dependencies in the [EMR Serverless Samples](https://github.com/aws-samples/emr-serverless-samples/tree/main/examples/pyspark) GitHub repository.