![]() ![]() ![]() 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: 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 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 Read data using SQL: 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("forward_spark_s3_credentials", True) Once you have configured your AWS credentials, you can use the data source with the Spark data source API in Python, SQL, R, or Scala: Python # Read data from a table Replace the url parameter values if you’re using the PostgreSQL JDBC driver. The following examples demonstrate connecting with the Redshift driver. In Databricks Runtime 11.1 and below, manual installation of the Redshift JDBC driver is required, and queries should use the driver ( ) for the format. User-provided drivers are still supported and take precedence over the bundled JDBC driver. See Databricks Runtime release notes versions and compatibility for driver versions included in each Databricks Runtime. In Databricks Runtime 11.2 and above, Databricks Runtime includes the Redshift JDBC driver, accessible using the redshift keyword for the format option. ![]()
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