Using EMR Serverless with AWS Lake Formation for fine-grained access control

Overview

With Amazon EMR releases 7.2.0 and higher, you can leverage AWS Lake Formation to apply fine-grained access controls on Data Catalog tables that are backed by S3. This capability lets you configure table, row, column, and cell level access controls for read queries within your Amazon EMR Serverless Spark jobs. To configure fine-grained access control for Apache Spark batch jobs and interactive sessions, use EMR Studio. See the following sections to learn more about Lake Formation and how to use it with EMR Serverless.

Using Amazon EMR Serverless with AWS Lake Formation incurs additional charges. For more information, see Amazon EMR pricing.

How EMR Serverless works with AWS Lake Formation

Using EMR Serverless with Lake Formation lets you enforce a layer of permissions on each Spark job to apply Lake Formation permissions control when EMR Serverless executes jobs. EMR Serverless uses Spark resource profiles to create two profiles to effectively execute jobs. The user profile executes user-supplied code, while the system profile enforces Lake Formation policies. For more information, see What is AWS Lake Formation and Considerations and limitations.

When you use pre-initialized capacity with Lake Formation, we recommend that you have a minimum of two Spark drivers. Each Lake Formation-enabled job utilizes two Spark drivers, one for the user profile and one for the system profile. For the best performance, you should use double the number of drivers for Lake Formation-enabled jobs compared to if you don't use Lake Formation.

When you run Spark jobs on EMR Serverless, you must also consider the impact of dynamic allocation on resource management and cluster performance. The configuration spark.dynamicAllocation.maxExecutors of the maximum number of executors per resource profile applies to both user and system executors. If you configure that number to be equal to the maximum allowed number of executors, your job run might get stuck because of one type of executor that uses all available resources, which prevents the other executor when you run jobs jobs.

So you don't run out of resources, EMR Serverless sets the default maximum number of executors per resource profile to 90% of the spark.dynamicAllocation.maxExecutors value. You can override this configuration when you specify spark.dynamicAllocation.maxExecutorsRatio with a value between 0 and 1. Additionally, you can also configure the following properties to optimize resource allocation and overall performance:

• spark.dynamicAllocation.cachedExecutorIdleTimeout

• spark.dynamicAllocation.shuffleTracking.timeout

• spark.cleaner.periodicGC.interval

The following is a high-level overview of how EMR Serverless gets access to data protected by Lake Formation security policies.

1. A user submits Spark job to an AWS Lake Formation-enabled EMR Serverless application.

2. EMR Serverless sends the job to a user driver and runs the job in the user profile. The user driver runs a lean version of Spark that has no ability to launch tasks, request executors, access S3 or the Glue Catalog. It builds a job plan.

3. EMR Serverless sets up a second driver called the system driver and runs it in the system profile (with a privileged identity). EMR Serverless sets up an encrypted TLS channel between the two drivers for communication. The user driver uses the channel to send the job plans to the system driver. The system driver does not run user-submitted code. It runs full Spark and communicates with S3, and the Data Catalog for data access. It request executors and compiles the Job Plan into a sequence of execution stages.

4. EMR Serverless then runs the stages on executors with the user driver or system driver. User code in any stage is run exclusively on user profile executors.

5. Stages that read data from Data Catalog tables protected by AWS Lake Formation or those that apply security filters are delegated to system executors.

Enabling Lake Formation in Amazon EMR

To enable Lake Formation, you must set spark.emr-serverless.lakeformation.enabled to true under spark-defaults classification for the runtime-configuration parameter when creating an EMR Serverless application.

aws emr-serverless create-application \
    --release-label emr-7.6.0 \
    --runtime-configuration '{
     "classification": "spark-defaults", 
     "properties": {
      "spark.emr-serverless.lakeformation.enabled": "true"
      }
    }' \
    --type "SPARK"

You can also enable Lake Formation when you create a new application in EMR Studio. Choose Use Lake Formation for fine-grained access control, available under Additional configurations.

Inter-worker encryption is enabled by default when you use Lake Formation with EMR Serverless, so you don't have to explicitly enable inter-worker encryption again.

Enabling Lake Formation for Spark jobs

To enable Lake Formation for individual Spark jobs, set spark.emr-serverless.lakeformation.enabled to true when using spark-submit.

--conf spark.emr-serverless.lakeformation.enabled=true

Job runtime role IAM permissions

Lake Formation permissions control access to AWS Glue Data Catalog resources, Amazon S3 locations, and the underlying data at those locations. IAM permissions control access to the Lake Formation and AWS Glue APIs and resources. Although you might have the Lake Formation permission to access a table in the Data Catalog (SELECT), your operation fails if you don’t have the IAM permission on the glue:Get* API operation.

The following is an example policy of how to provide IAM permissions to access a script in S3, uploading logs to S3, AWS Glue API permissions, and permission to access Lake Formation.

{
"Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "ScriptAccess",
            "Effect": "Allow",
            "Action": [ 
                "s3:GetObject", 
                "s3:ListBucket" 
            ], 
            "Resource": [ 
            "arn:aws:s3:::*.amzn-s3-demo-bucket/scripts", 
            "arn:aws:s3:::*.amzn-s3-demo-bucket/*" ]
        },
        {
         "Sid": "LoggingAccess",
            "Effect": "Allow",
            "Action": [
                "s3:PutObject"
            ],
            "Resource": [
               "arn:aws:s3:::amzn-s3-demo-bucket/logs/*"
            ]
        },
        {
            "Sid": "GlueCatalogAccess",
            "Effect": "Allow",
            "Action": [
                 "glue:Get*", 
                 "glue:Create*", 
                 "glue:Update*"
            ],
            "Resource": ["*"]
        },
        {
            "Sid": "LakeFormationAccess",
            "Effect": "Allow",
            "Action": [
             "lakeformation:GetDataAccess"
             ],
            "Resource": ["*"]
        }
    ]
}

Setting up Lake Formation permissions for job runtime role

First, register the location of your Hive table with Lake Formation. Then create permissions for your job runtime role on your desired table. For more details about Lake Formation, see What is AWS Lake Formation? in the AWS Lake Formation Developer Guide.

After you set up the Lake Formation permissions, you can submit Spark jobs on Amazon EMR Serverless. For more information about Spark jobs, see Spark examples.

Submitting a job run

After you finish setting up the Lake Formation grants, you can submit Spark jobs on EMR Serverless. To run Iceberg jobs, you must provide the following spark-submit properties.

--conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog
--conf spark.sql.catalog.spark_catalog.warehouse=<S3_DATA_LOCATION>
--conf spark.sql.catalog.spark_catalog.glue.account-id=<ACCOUNT_ID>
--conf spark.sql.catalog.spark_catalog.client.region=<REGION>
--conf spark.sql.catalog.spark_catalog.glue.endpoint=https://glue.<REGION>.amazonaws.com

Open-table format support

Amazon EMR release 7.2.0 includes support for fine-grained access control based on Lake Formation. EMR Serverless supports Hive and Iceberg table types. The following table describes all of the supported operations.

Operations	Hive	Iceberg
DDL commands	With IAM role permissions only	With IAM role permissions only
Incremental queries	Not applicable	Fully supported
Time travel queries	Not applicable to this table format	Fully supported
Metadata tables	Not applicable to this table format	Supported, but certain tables are hidden. See considerations and limitations for more information.
DML INSERT	With IAM permissions only	With IAM permissions only
DML UPDATE	Not applicable to this table format	With IAM permissions only
DML DELETE	Not applicable to this table format	With IAM permissions only
Read operations	Fully supported	Fully supported
Stored procedures	Not applicable	Supported with the exceptions of register_table and migrate. See considerations and limitations for more information.