![]() ![]() Securing JDBC: Unless any SSL-related settings are present in the JDBC URL, the data source by default enables SSL encryption and also verifies that the Redshift server is trustworthy (that is, sslmode=verify-full). The JDBC query embeds these credentials so therefore Databricks strongly recommends that you enable SSL encryption of the JDBC connection. Set the forward_spark_s3_credentials option to true to automatically forward the AWS key credentials that Spark is using to connect to S3 over JDBC to Redshift. Sc._jsc.hadoopConfiguration().set("fs.", "") The following command relies on some Spark internals, but should work with all PySpark versions and is unlikely to change in the future: sc._jsc.hadoopConfiguration().set("fs.", "") Sc.t("fs.", "")įor the legacy s3n filesystem, add: sc.t("fs.s3n.awsAccessKeyId", "") Scalaįor example, if you are using the s3a filesystem, add: sc.t("fs.", "") If you use an s3n:// filesystem, you can provide the legacy configuration keys as shown in the following example. If your tempdir configuration points to an s3a:// filesystem, you can set the fs. and fs. properties in a Hadoop XML configuration file or call sc.t() to configure Spark’s global Hadoop configuration. Set keys in Hadoop conf: You can specify AWS keys using Hadoop configuration properties. You cannot use DBFS mounts to configure access to S3 for Redshift. Spark connects to S3 using both the Hadoop FileSystem interfaces and directly using the Amazon Java SDK’s S3 client. S3 acts as an intermediary to store bulk data when reading from or writing to Redshift. By default, this connection uses SSL encryption for more details, see Encryption. Redshift does not support the use of IAM roles to authenticate this connection. ![]() The Spark driver connects to Redshift via JDBC using a username and password. The following sections describe each connection’s authentication configuration options: You cannot use an External location defined in Unity Catalog as a tempdir location. See the Encryption section of this document for a discussion of how to encrypt these files. As a result, we recommend that you use a dedicated temporary S3 bucket with an object lifecycle configuration to ensure that temporary files are automatically deleted after a specified expiration period. The data source does not clean up the temporary files that it creates in S3. As a result, it requires AWS credentials with read and write access to an S3 bucket (specified using the tempdir configuration parameter). The data source reads and writes data to S3 when transferring data to/from Redshift. The data source involves several network connections, illustrated in the following diagram: ┌───────┐ Configuration Authenticating to S3 and Redshift ![]() If you plan to perform several queries against the same data in Redshift, Databricks recommends saving the extracted data using Delta Lake. Query execution may extract large amounts of data to S3. Recommendations for working with Redshift Write back to a table using IAM Role based authentication the data source API to write the data back to another table After you have applied transformations to the data, you can use option("forward_spark_s3_credentials", true) option("dbtable", "schema-name.table-name") /* if schema-name is not specified, default to "public". option("port", "port") /* Optional - will use default port 5439 if not specified. Read data from a table using Databricks Runtime 11.3 LTS and above Scala // Read data from a table using Databricks Runtime 10.4 LTS and below Read data using R on Databricks Runtime 11.3 LTS and above: df /", Read data using R on Databricks Runtime 10.4 LTS and below: df /", The SQL API supports only the creation of new tables and not overwriting or appending. Write data using SQL: DROP TABLE IF EXISTS redshift_table *./ĭbtable '.', /* if schema-name not provided, default to "public". Port '', /* Optional - will use default port 5439 if not specified. Read data using SQL on Databricks Runtime 11.3 LTS and above: Read data using SQL on Databricks Runtime 10.4 LTS and below: DROP TABLE IF EXISTS redshift_table # Write back to a table using IAM Role based authentication ![]() # the data source API to write the data back to another table # After you have applied transformations to the data, you can use option("query", "select x, count(*) group by x") option("dbtable", "schema-name.table-name") # if schema-name is not specified, default to "public". option("port", "port") # Optional - will use default port 5439 if not specified. # Read data from a table using Databricks Runtime 11.3 LTS and above Python # Read data from a table using Databricks Runtime 10.4 LTS and below External locations defined in Unity Catalog are not supported as tempdir locations. ![]()
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