Overview How it works Enable Lake Formation Enable runtime permissions Set up runtime permissions Submitting a job run

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

Overview

With Amazon EMR releases 7.2.0 and higher, 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, refer to 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, refer to What is AWS Lake Formation and Considerations and limitations.

When you use pre-initialized capacity with Lake Formation, we suggest 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, 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, 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 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, 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.

How Amazon EMR accesses data protected by Lake Formation security policies.

A user submits Spark job to an AWS Lake Formation-enabled EMR Serverless application.
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.
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.
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.
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, 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-7.12.0 \
    --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 do not need 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.

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, refer to What is AWS Lake Formation? in the AWS Lake Formation Developer Guide.

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

Submitting a job run

After you finish setting up the Lake Formation grants, you can submit Spark jobs on EMR Serverless. The section that follows shows examples of how to configure and submit job run properties.

Permission requirements

Tables not registered in AWS Lake Formation

For tables not registered with AWS Lake Formation, the job runtime role accesses both the AWS Glue Data Catalog and the underlying table data in Amazon S3. This requires the job runtime role to have appropriate IAM permissions for both AWS Glue and Amazon S3 operations.

Tables registered in AWS Lake Formation

For tables registered with AWS Lake Formation, the job runtime role accesses the AWS Glue Data Catalog metadata, while temporary credentials vended by Lake Formation access the underlying table data in Amazon S3. The Lake Formation permissions required to execute an operation depend on the AWS Glue Data Catalog and Amazon S3 API calls that the Spark job initiates and can be summarized as follows:

DESCRIBE permission allows the runtime role to read table or database metadata in the Data Catalog
ALTER permission allows the runtime role to modify table or database metadata in the Data Catalog

DROP permission allows the runtime role to delete table or database metadata from the Data Catalog
SELECT permission allows the runtime role to read table data from Amazon S3
INSERT permission allows the runtime role to write table data to Amazon S3
DELETE permission allows the runtime role to delete table data from Amazon S3

Note
Lake Formation evaluates permissions lazily when a Spark job calls AWS Glue to retrieve table metadata and Amazon S3 to retrieve table data. Jobs that use a runtime role with insufficient permissions will not fail until Spark makes an AWS Glue or Amazon S3 call that requires the missing permission.

Note

In the following supported table matrix:

Operations marked as Supported exclusively use Lake Formation credentials to access table data for tables registered with Lake Formation. If Lake Formation permissions are insufficient, the operation will not fall back to runtime role credentials. For tables not registered with Lake Formation, the job runtime role credentials access the table data.

Operations marked as Supported with IAM permissions on Amazon S3 location do not use Lake Formation credentials to access underlying table data in Amazon S3. To run these operations, the job runtime role must have the necessary Amazon S3 IAM permissions to access the table data, regardless of whether the table is registered with Lake Formation.

Hive

Operation	AWS Lake Formation permissions	Support status
SELECT	SELECT	Supported
CREATE TABLE	CREATE_TABLE	Supported
CREATE TABLE LIKE	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
CREATE TABLE AS SELECT	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
DESCRIBE TABLE	DESCRIBE	Supported
SHOW TBLPROPERTIES	DESCRIBE	Supported
SHOW COLUMNS	DESCRIBE	Supported
SHOW PARTITIONS	DESCRIBE	Supported
SHOW CREATE TABLE	DESCRIBE	Supported
ALTER TABLE `tablename`	SELECT and ALTER	Supported
ALTER TABLE `tablename` SET LOCATION	-	Not supported
ALTER TABLE `tablename`ADD PARTITION	SELECT, INSERT and ALTER	Supported
REPAIR TABLE	SELECT and ALTER	Supported
LOAD DATA		Not supported
INSERT	INSERT and ALTER	Supported
INSERT OVERWRITE	SELECT, INSERT, DELETE and ALTER	Supported
DROP TABLE	SELECT, DROP, DELETE and ALTER	Supported
TRUNCATE TABLE	SELECT, INSERT, DELETE and ALTER	Supported
Dataframe Writer V1	Same as corresponding SQL operation	Supported when appending data to an existing table. Refer to considerations and limitations for more information
Dataframe Writer V2	Same as corresponding SQL operation	Supported when appending data to an existing table. Refer to considerations and limitations for more information

Iceberg

Operation	AWS Lake Formation permissions	Support status
SELECT	SELECT	Supported
CREATE TABLE	CREATE_TABLE	Supported
CREATE TABLE LIKE	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
CREATE TABLE AS SELECT	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
REPLACE TABLE AS SELECT	SELECT, INSERT and ALTER	Supported
DESCRIBE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW TBLPROPERTIES	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW CREATE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
ALTER TABLE	SELECT, INSERT and ALTER	Supported
ALTER TABLE SET LOCATION	SELECT, INSERT and ALTER	Supported with IAM permissions on Amazon S3 location
ALTER TABLE WRITE ORDERED BY	SELECT, INSERT and ALTER	Supported with IAM permissions on Amazon S3 location
ALTER TABLE WRITE DISTRIBUTED BY	SELECT, INSERT, and ALTER	Supported with IAM permissions on Amazon S3 location
ALTER TABLE RENAME TABLE	CREATE_TABLE, and DROP	Supported
INSERT INTO	SELECT, INSERT and ALTER	Supported
INSERT OVERWRITE	SELECT, INSERT and ALTER	Supported
DELETE	SELECT, INSERT and ALTER	Supported
UPDATE	SELECT, INSERT and ALTER	Supported
MERGE INTO	SELECT, INSERT and ALTER	Supported
DROP TABLE	SELECT, DELETE and DROP	Supported
DataFrame Writer V1	-	Not supported
DataFrame Writer V2	Same as corresponding SQL operation	Supported when appending data to an existing table. Refer to considerations and limitations for more information.
Metadata tables	SELECT	Supported. Certain tables are hidden. Refer to considerations and limitations for more information.
Stored procedures	-	Supported for tables that meet the following conditions: Tables not registered in AWS Lake Formation Tables that do not use `register_table` and `migrate` Refer to considerations and limitations for more information.

Spark configuration for Iceberg: The following sample shows how to configure Spark with Iceberg. To run Iceberg jobs, 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

Hudi

Operation	AWS Lake Formation permissions	Support status
SELECT	SELECT	Supported
CREATE TABLE	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
CREATE TABLE LIKE	CREATE_TABLE	Supported with IAM permissions on Amazon S3 location
CREATE TABLE AS SELECT	-	Not supported
DESCRIBE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW TBLPROPERTIES	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW COLUMNS	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW CREATE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
ALTER TABLE	SELECT	Supported with IAM permissions on Amazon S3 location
INSERT INTO	SELECT and ALTER	Supported with IAM permissions on Amazon S3 location
INSERT OVERWRITE	SELECT and ALTER	Supported with IAM permissions on Amazon S3 location
DELETE	-	Not supported
UPDATE	-	Not supported
MERGE INTO	-	Not supported
DROP TABLE	SELECT and DROP	Supported with IAM permissions on Amazon S3 location
DataFrame Writer V1	-	Not supported
DataFrame Writer V2	Same as corresponding SQL operation	Supported with IAM permissions on Amazon S3 location
Metadata tables	-	Not supported
Table maintenance and utility features	-	Not supported

The following samples configure Spark with Hudi, specifying file locations and other properties necessary for use.

Spark config for Hudi: This snippet when used in a notebook specifies the path to the Hudi Spark bundle JAR file, which enables Hudi functionality in Spark. It also configures Spark to use the AWS Glue Data Catalog as the metastore.


%%configure -f
{
    "conf": {
        "spark.jars": "/usr/lib/hudi/hudi-spark-bundle.jar",
        "spark.hadoop.hive.metastore.client.factory.class": "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory",
        "spark.serializer": "org.apache.spark.serializer.JavaSerializer",
        "spark.sql.catalog.spark_catalog": "org.apache.spark.sql.hudi.catalog.HoodieCatalog",
        "spark.sql.extensions": "org.apache.spark.sql.hudi.HoodieSparkSessionExtension"
    }
}

Spark config for Hudi with AWS Glue: This snippet when used in a notebook enables Hudi as a supported data-lake format and ensures that Hudi libraries and dependencies are available.


%%configure
{
    "--conf": "spark.serializer=org.apache.spark.serializer.JavaSerializer --conf 
spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog --conf 
spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension",
    "--datalake-formats": "hudi",
    "--enable-glue-datacatalog": True,
    "--enable-lakeformation-fine-grained-access": "true"
}

Delta Lake

Operation	AWS Lake Formation permissions	Support status
SELECT	SELECT	Supported
CREATE TABLE	CREATE_TABLE	Supported
CREATE TABLE LIKE	-	Not supported
CREATE TABLE AS SELECT	CREATE_TABLE	Supported
REPLACE TABLE AS SELECT	SELECT, INSERT and ALTER	Supported
DESCRIBE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW TBLPROPERTIES	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW COLUMNS	DESCRIBE	Supported with IAM permissions on Amazon S3 location
SHOW CREATE TABLE	DESCRIBE	Supported with IAM permissions on Amazon S3 location
ALTER TABLE	SELECT and INSERT	Supported
ALTER TABLE SET LOCATION	SELECT and INSERT	Supported with IAM permissions on Amazon S3 location
ALTER TABLE `tablename` CLUSTER BY	SELECT and INSERT	Supported with IAM permissions on Amazon S3 location
ALTER TABLE `tablename` ADD CONSTRAINT	SELECT and INSERT	Supported with IAM permissions on Amazon S3 location
ALTER TABLE `tablename` DROP CONSTRAINT	SELECT and INSERT	Supported with IAM permissions on Amazon S3 location
INSERT INTO	SELECT and INSERT	Supported
INSERT OVERWRITE	SELECT and INSERT	Supported
DELETE	SELECT and INSERT	Supported
UPDATE	SELECT and INSERT	Supported
MERGE INTO	SELECT and INSERT	Supported
DROP TABLE	SELECT, DELETE and DROP	Supported
DataFrame Writer V1	-	Not supported
DataFrame Writer V2	Same as corresponding SQL operation	Supported
Table maintenance and utility features	-	Not supported

EMR Serverless with Delta Lake: To use Delta Lake with Lake Formation on EMR Serverless, run the following command:


spark-sql \
  --conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension,com.amazonaws.emr.recordserver.connector.spark.sql.RecordServerSQLExtension \
  --conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog \

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Lake Formation full table access for EMR Serverless

Debugging jobs