Use AWS Glue Data Catalog catalog with Spark on Amazon EMR - Amazon EMR
Specifying AWS Glue Data Catalog as the Apache Hive metastoreSpecifying AWS Glue Data Catalog as the Apache Iceberg catalogIAM permissionsConsiderations

Use AWS Glue Data Catalog catalog with Spark on Amazon EMR

Using Amazon EMR release 5.8.0 or later, you can configure Spark to use the AWS Glue Data Catalog as its Apache Hive metastore. We recommend this configuration when you require a persistent Hive metastore or a Hive metastore shared by different clusters, services, applications, or AWS accounts.

Using Amazon EMR release 6.5.0 or later, you can configure Spark to use the AWS Glue Data Catalog with Apache Iceberg.

Using Amazon EMR release 7.5.0 or later, you can configure Spark to use the AWS Glue Data Catalog as its Iceberg REST catalog.

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. The AWS Glue Data Catalog provides a unified metadata repository across a variety of data sources and data formats, integrating with Amazon EMR as well as Amazon RDS, Amazon Redshift, Redshift Spectrum, Athena, and any application compatible with the Apache Hive metastore. AWS Glue crawlers can automatically infer schema from source data in Amazon S3 and store the associated metadata in the Data Catalog. For more information about the Data Catalog, see Populating the AWS Glue Data Catalog in the AWS Glue Developer Guide.

Separate charges apply for AWS Glue. There is a monthly rate for storing and accessing the metadata in the Data Catalog, an hourly rate billed per minute for AWS Glue ETL jobs and crawler runtime, and an hourly rate billed per minute for each provisioned development endpoint. The Data Catalog allows you to store up to a million objects at no charge. If you store more than a million objects, you are charged USD$1 for each 100,000 objects over a million. An object in the Data Catalog is a table, partition, or database. For more information, see Glue Pricing.

Important

If you created tables using Amazon Athena or Amazon Redshift Spectrum before August 14, 2017, databases and tables are stored in an Athena-managed catalog, which is separate from the AWS Glue Data Catalog. To integrate Amazon EMR with these tables, you must upgrade to the AWS Glue Data Catalog. For more information, see Upgrading to the AWS Glue Data Catalog in the Amazon Athena User Guide.

Specifying AWS Glue Data Catalog as the Apache Hive metastore

You can specify the AWS Glue Data Catalog as the metastore using the AWS Management Console, AWS CLI, or Amazon EMR API. When you use the CLI or API, you use the configuration classification for Spark to specify the Data Catalog. In addition, with Amazon EMR 5.16.0 and later, you can use the configuration classification to specify a Data Catalog in a different AWS account. When you use the console, you can specify the Data Catalog using Advanced Options or Quick Options.

Note

The option to use AWS Glue Data Catalog is also available with Zeppelin because Zeppelin is installed with Spark components.

Console
To specify AWS Glue Data Catalog as the Apache Hive metastore with the new console

  1. Sign in to the AWS Management Console, and open the Amazon EMR console at https://console.aws.amazon.com/emr.

  2. Under Amazon EMR on EC2 in the left navigation pane, choose Clusters, and then choose Create cluster.

  3. Under Application bundle, choose Spark or Custom. If you customize your cluster, make sure that you select Zeppelin or Spark as one of your applications.

  4. Under AWS Glue Data Catalog settings, select the Use for Spark table metadata check box.

  5. Choose any other options that apply to your cluster.

  6. To launch your cluster, choose Create cluster.

AWS CLI
To specify the AWS Glue Data Catalog as the Apache Hive metastore with the AWS CLI

For more information about specifying a configuration classification using the AWS CLI and Amazon EMR API, see Configure applications.

  • Specify the value for hive.metastore.client.factory.class using the spark-hive-site classification as shown in the following example:

    [
  {
    "Classification": "spark-hive-site",
    "Properties": {
      "hive.metastore.client.factory.class": "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory"
    }
  }
]

    To specify a Data Catalog in a different AWS account, add the hive.metastore.glue.catalogid property as shown in the following example. Replace acct-id with the AWS account of the Data Catalog.

    [
  {
    "Classification": "spark-hive-site",
    "Properties": {
      "hive.metastore.client.factory.class": "com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory",
      "hive.metastore.glue.catalogid": "acct-id"
    }
  }
]

Specifying AWS Glue Data Catalog as the Apache Iceberg catalog

You can specify the AWS Glue Data Catalog as the Apache Iceberg catalog implementation, or the Apache Iceberg REST catalog endpoint, using the AWS Management Console, the AWS CLI, or the Amazon EMR API, or at Spark session runtime configuration. When you use the CLI or API, you use the configuration classification for Spark to specify the Data Catalog. For more details, see Specifying AWS Glue Data Catalog as the Apache Iceberg catalog.

IAM permissions

The EC2 instance profile for a cluster must have IAM permissions for AWS Glue actions. In addition, if you enable encryption for AWS Glue Data Catalog objects, the role must also be allowed to encrypt, decrypt and generate the AWS KMS key used for encryption.

Permissions for AWS Glue actions

If you use the default EC2 instance profile for Amazon EMR, no action is required. The AmazonElasticMapReduceforEC2Role managed policy that is attached to the EMR_EC2_DefaultRole allows all necessary AWS Glue actions. However, if you specify a custom EC2 instance profile and permissions, you must configure the appropriate AWS Glue actions. Use the AmazonElasticMapReduceforEC2Role managed policy as a starting point. For more information, see Service role for cluster EC2 instances (EC2 instance profile) in the Amazon EMR Management Guide.

Permissions for encrypting and decrypting AWS Glue Data Catalog

Your instance profile needs permission to encrypt and decrypt data using your key. You do not need to configure these permissions if both of the following statements apply:

  • You enable encryption for AWS Glue Data Catalog objects using managed keys for AWS Glue.

  • You use a cluster that's in the same AWS account as the AWS Glue Data Catalog.

Otherwise, you must add the following statement to the permissions policy attached to your EC2 instance profile. 

[
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "kms:Decrypt",
                    "kms:Encrypt",
                    "kms:GenerateDataKey"
                ],
                "Resource": "arn:aws:kms:region:acct-id:key/12345678-1234-1234-1234-123456789012"
            }
        ]
    }
]

For more information about AWS Glue Data Catalog encryption, see Encrypting your data catalog in the AWS Glue Developer Guide.

Resource-based permissions

If you use AWS Glue in conjunction with Hive, Spark, or Presto in Amazon EMR, AWS Glue supports resource-based policies to control access to Data Catalog resources. These resources include databases, tables, connections, and user-defined functions. For more information, see AWS Glue Resource Policies in the AWS Glue Developer Guide.

When using resource-based policies to limit access to AWS Glue from within Amazon EMR, the principal that you specify in the permissions policy must be the role ARN associated with the EC2 instance profile that is specified when a cluster is created. For example, for a resource-based policy attached to a catalog, you can specify the role ARN for the default service role for cluster EC2 instances, EMR_EC2_DefaultRole as the Principal, using the format shown in the following example:

arn:aws:iam::acct-id:role/EMR_EC2_DefaultRole

The acct-id can be different from the AWS Glue account ID. This enables access from EMR clusters in different accounts. You can specify multiple principals, each from a different account.

Considerations when using AWS Glue Data Catalog

Consider the following items when using AWS Glue Data Catalog as an Apache Hive metastore with Spark:

  • Having a default database without a location URI causes failures when you create a table. As a workaround, use the LOCATION clause to specify a bucket location, such as s3://amzn-s3-demo-bucket1, when you use CREATE TABLE. Alternatively create tables within a database other than the default database.

  • Renaming tables from within AWS Glue is not supported.

  • When you create a Hive table without specifying a LOCATION, the table data is stored in the location specified by the hive.metastore.warehouse.dir property. By default, this is a location in HDFS. If another cluster needs to access the table, it fails unless it has adequate permissions to the cluster that created the table. Furthermore, because HDFS storage is transient, if the cluster terminates, the table data is lost, and the table must be recreated. We recommend that you specify a LOCATION in Amazon S3 when you create a Hive table using AWS Glue. Alternatively, you can use the hive-site configuration classification to specify a location in Amazon S3 for hive.metastore.warehouse.dir, which applies to all Hive tables. If a table is created in an HDFS location and the cluster that created it is still running, you can update the table location to Amazon S3 from within AWS Glue. For more information, see Working with Tables on the AWS Glue Console in the AWS Glue Developer Guide.

  • Partition values containing quotes and apostrophes are not supported, for example, PARTITION (owner="Doe's").

  • Column statistics are supported for emr-5.31.0 and later.

  • Using Hive authorization is not supported. As an alternative, consider using AWS Glue Resource-Based Policies. For more information, see Use Resource-Based Policies for Amazon EMR Access to AWS Glue Data Catalog.

Consider the following when using AWS Glue Data Catalog as Apache Iceberg REST Catalog with Spark: