Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Authentication with the Spark connector

The following diagram describes the authentication between Amazon S3, Amazon Redshift, the Spark driver, and Spark executors.

Authentication between Redshift and Spark

You can use the Amazon Redshift provided JDBC driver version 2 driver to connect to Amazon Redshift with the Spark connector by specifying sign-in credentials. To use IAM, configure your JDBC url to use IAM authentication. To connect to a Redshift cluster from Amazon EMR or Amazon Glue, make sure that your IAM role has the necessary permissions to retrieve temporary IAM credentials. The following list describes all of the permissions that your IAM role needs to retrieve credentials and run Amazon S3 operations.

For more information about GetClusterCredentials, see Resource policies for GetClusterCredentials.

You also must make sure that Amazon Redshift can assume the IAM role during COPY and UNLOAD operations.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "redshift.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

If you’re using the latest JDBC driver, the driver will automatically manage the transition from an Amazon Redshift self-signed certificate to an ACM certificate. However, you must specify the SSL options to the JDBC url.

The following is an example of how to specify the JDBC driver URL and aws_iam_role to connect to Amazon Redshift.

df.write \
  .format("io.github.spark_redshift_community.spark.redshift ") \
  .option("url", "jdbc:redshift:iam://<the-rest-of-the-connection-string>") \
  .option("dbtable", "<your-table-name>") \
  .option("tempdir", "s3a://<your-bucket>/<your-directory-path>") \
  .option("aws_iam_role", "<your-aws-role-arn>") \
  .mode("error") \
  .save()

Authentication between Amazon S3 and Spark

If you’re using an IAM role to authenticate between Spark and Amazon S3, use one of the following methods:

For example, if you are using the s3n filesystem, add:

sc.hadoopConfiguration.set("fs.s3n.awsAccessKeyId", "YOUR_KEY_ID")
sc.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey", "YOUR_SECRET_ACCESS_KEY")

For the s3a filesystem, add:

sc.hadoopConfiguration.set("fs.s3a.access.key", "YOUR_KEY_ID")
sc.hadoopConfiguration.set("fs.s3a.secret.key", "YOUR_SECRET_ACCESS_KEY")

If you’re using Python, use the following operations:

sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "YOUR_KEY_ID")
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", "YOUR_SECRET_ACCESS_KEY")

Authentication between Redshift and Amazon S3

If you’re using the COPY and UNLOAD commands in your query, you also must grant Amazon S3 access to Amazon Redshift to run queries on your behalf. To do so, first authorize Amazon Redshift to access other Amazon services, then authorize the COPY and UNLOAD operations using IAM roles.

As a best practice, we recommend attaching permissions policies to an IAM role and then assigning it to users and groups as needed. For more information, see Identity and access management in Amazon Redshift.

Integration with Amazon Secrets Manager

You can retrieve your Redshift username and password credentials from a stored secret in Amazon Secrets Manager. To automatically supply Redshift credentials, use the secret.id parameter. For more information about how to create a Redshift credentials secret, see Create an Amazon Secrets Manager database secret.