Metastore configuration for EMR Serverless - Amazon EMR
Using the Amazon Glue Data Catalog as a metastore
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Metastore configuration for EMR Serverless

A Hive metastore is a centralized location that stores structural information about your tables, including schemas, partition names, and data types. With EMR Serverless, persist this table metadata in a metastore that has access to your jobs.

You have two options for a Hive metastore:

  • The Amazon Glue Data Catalog

  • An external Apache Hive metastore

Using the Amazon Glue Data Catalog as a metastore

You can configure your Spark and Hive jobs to use the Amazon Glue Data Catalog as its metastore. We recommend this configuration when you require a persistent metastore or a metastore shared by different applications, services, or Amazon Web Services accounts. For more information about the Data Catalog, refer to Populating the Amazon Glue Data Catalog. For information about Amazon Glue pricing, refer to Amazon Glue pricing.

You can configure your EMR Serverless job to use the Amazon Glue Data Catalog either in the same Amazon Web Services account as your application, or in a different Amazon Web Services account.

Configure the Amazon Glue Data Catalog

To configure the Data Catalog, choose which type of EMR Serverless application that you want to use.

Spark

When you use EMR Studio to run your jobs with EMR Serverless Spark applications, the Amazon Glue Data Catalog is the default metastore.

When you use SDKs or Amazon CLI, set the spark.hadoop.hive.metastore.client.factory.class configuration to com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory in the sparkSubmit parameters of your job run. The following example shows how to configure the Data Catalog with the Amazon CLI.

aws emr-serverless start-job-run \
    --application-id application-id \
    --execution-role-arn job-role-arn \
    --job-driver '{
        "sparkSubmit": {
            "entryPoint": "s3://amzn-s3-demo-bucket/code/pyspark/extreme_weather.py",
            "sparkSubmitParameters": "--conf spark.hadoop.hive.metastore.client.factory.class=com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory --conf spark.driver.cores=1 --conf spark.driver.memory=3g --conf spark.executor.cores=4 --conf spark.executor.memory=3g"
        }
    }'

Alternatively, you can set this configuration when you create a new SparkSession in your Spark code.

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.appName("SparkSQL")
    .config(
        "spark.hadoop.hive.metastore.client.factory.class",
        "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory",
    )
    .enableHiveSupport()
    .getOrCreate()
)

# we can query tables with SparkSQL
spark.sql("SHOW TABLES").show()

# we can also them with native Spark
print(spark.catalog.listTables())
Hive

For EMR Serverless Hive applications, the Data Catalog is the default metastore. That is, when you run jobs on a EMR Serverless Hive application, Hive records metastore information in the Data Catalog in the same Amazon Web Services account as your application. You don't need a virtual private cloud (VPC) to use the Data Catalog as your metastore.

To access the Hive metastore tables, add the required Amazon Glue policies outlined in Setting up IAM Permissions for Amazon Glue.

Configure cross-account access for EMR Serverless and Amazon Glue Data Catalog

To set up cross-account access for EMR Serverless, first sign in to the following Amazon Web Services accounts:

  • AccountA – An Amazon Web Services account where you have created an EMR Serverless application.

  • AccountB – An Amazon Web Services account that contains a Amazon Glue Data Catalog that you want your EMR Serverless job runs to access.

  1. Make sure an administrator or other authorized identity in AccountB attaches a resource policy to the Data Catalog in AccountB. This policy grants AccountA specific cross-account permissions to perform operations on resources in the AccountB catalog.

    JSON
    {
  "Version":"2012-10-17",		 	 	 
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "glue:GetDatabase",
        "glue:CreateDatabase",
        "glue:GetDataBases",
        "glue:CreateTable",
        "glue:GetTable",
        "glue:UpdateTable",
        "glue:DeleteTable",
        "glue:GetTables",
        "glue:GetPartition",
        "glue:GetPartitions",
        "glue:CreatePartition",
        "glue:BatchCreatePartition",
        "glue:GetUserDefinedFunctions"
      ],
      "Resource": [
        "arn:aws:glue:*:123456789012:catalog"
      ],
      "Sid": "AllowGLUEGetdatabase"
    }
  ]
}

  2. Add an IAM policy to the EMR Serverless job runtime role in AccountA so that role can access Data Catalog resources in AccountB.

    JSON
    {
  "Version":"2012-10-17",		 	 	 
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "glue:GetDatabase",
        "glue:CreateDatabase",
        "glue:GetDataBases",
        "glue:CreateTable",
        "glue:GetTable",
        "glue:UpdateTable",
        "glue:DeleteTable",
        "glue:GetTables",
        "glue:GetPartition",
        "glue:GetPartitions",
        "glue:CreatePartition",
        "glue:BatchCreatePartition",
        "glue:GetUserDefinedFunctions"
      ],
      "Resource": [
        "arn:aws:glue:*:123456789012:catalog"
      ],
      "Sid": "AllowGLUEGetdatabase"
    }
  ]
}

  3. Start your job run. This step is slightly different depending on AccountA's EMR Serverless application type.

    Spark

    Pass the spark.hadoop.hive.metastore.glue.catalogid property in the sparkSubmitParameters as shown in the following example. Replace AccountB-catalog-id with the ID of the Data Catalog in AccountB.

    aws emr-serverless start-job-run \
--application-id "application-id" \
--execution-role-arn "job-role-arn" \
--job-driver '{
    "sparkSubmit": {
        "entryPoint": "s3://amzn-s3-demo-bucket/scripts/test.py",
         "sparkSubmitParameters": "--conf spark.hadoop.hive.metastore.client.factory.class=com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory --conf spark.hadoop.hive.metastore.glue.catalogid=AccountB-catalog-id --conf spark.executor.cores=1 --conf spark.executor.memory=1g --conf spark.driver.cores=1 --conf spark.driver.memory=1g --conf spark.executor.instances=1"
    }
  }' \
--configuration-overrides '{
    "monitoringConfiguration": {
    "s3MonitoringConfiguration": {
    "logUri": "s3://amzn-s3-demo-bucket/logs/"
    }
  }
}'
    Hive

    Set the hive.metastore.glue.catalogid property in the hive-site classification as shown in the following example. Replace AccountB-catalog-id with the ID of the Data Catalog in AccountB.

    aws emr-serverless start-job-run \
--application-id "application-id" \
--execution-role-arn "job-role-arn" \
--job-driver '{
    "hive": {
    "query": "s3://amzn-s3-demo-bucket/hive/scripts/create_table.sql",
    "parameters": "--hiveconf hive.exec.scratchdir=s3://amzn-s3-demo-bucket/hive/scratch --hiveconf hive.metastore.warehouse.dir=s3://amzn-s3-demo-bucket/hive/warehouse"
    }
}' \
--configuration-overrides '{
    "applicationConfiguration": [{
        "classification": "hive-site",
        "properties": {
            "hive.metastore.glue.catalogid": "AccountB-catalog-id"
        }
    }]
}'

Considerations when using the Amazon Glue Data Catalog

You can add auxiliary JARs with ADD JAR in your Hive scripts. For additional considerations, refer to Considerations when using Amazon Glue Data Catalog.