The SSL connection fails if the server certificate cannot be verified. To ensure that the connection to the data source is successful, click Test Connection. If you configured SSL settings for one data source, you can copy them for another data source. Click the Copy from link and select the configuration that you want to copy.
Secure Shell or SSH is a network protocol that is used to encrypt a connection between a client and a server. All created SSH connections are shared between all the data sources that you have in a project. If you do not want to share a connection between projects, select the Visible only for this project checkbox in the SSH connection settings.
Click the Add SSH configuration button. In the SSH dialog, click the Add button. If you do not want to share the configuration between projects, select the Visible only for this project checkbox.
In Host , User name , and Port fields, specify your connection details. From the Authentication type list, you can select an authentication method:.
Password : to access the host with a password. To apply this authentication method, you must have a private key on the client machine and a public key on the remote server. Specify the path to the file where your private key is stored and type the passphrase if any in the corresponding fields.
In the Private key file for authentication field, specify the path to your private key file and click Open. In the command line window, specify the username that you use for the SSH tunnel and press Enter. Do not close the command line window. In Proxy host , Proxy user , and Port fields, specify connection details. You can find the Pageant icon in the Windows taskbar. In the Windows taskbar, right-click the Pageant icon and select Add Key.
Optional Enter the private key passphrase and press Enter. Optional On macOS, you can add -K option to the ssh-add command to store passphrases in your keychain. If you have other private keys in the. Search Chat. CData Connect Universal, consolidated data connectivity on-premisis or in the cloud.
CData Sync Replicate any data source to any database or warehouse. Relational Databases. ODBC Driver. Fully-Managed ADO. NET Providers. Windows PowerShell Cmdlets offering straightforward command-line access live data. Straightforward Apps for data replication with on-premise and cloud databases. Other Instagram Drivers:. This section describes the transactional guarantees of the Redshift data source for Spark.
For general information on Redshift's transactional guarantees, see the Managing Concurrent Write Operations chapter in the Redshift documentation. In a nutshell, Redshift provides serializable isolation according to the documentation for Redshift's BEGIN command, "[although] you can use any of the four transaction isolation levels, Amazon Redshift processes all isolation levels as serializable".
According to its documentation , "Amazon Redshift supports a default automatic commit behavior in which each separately-executed SQL command commits individually. Both Spark and Redshift produce partitioned output which is stored in multiple files in S3. Appending to an existing table : In the COPY command, this library uses manifests to guard against certain eventually-consistent S3 operations.
As a result, it appends to existing tables have the same atomic and transactional properties as regular Redshift COPY commands.
Appending to an existing table : When inserting rows into Redshift, this library uses the COPY command and specifies manifests to guard against certain eventually-consistent S3 operations. As a result, spark-redshift appends to existing tables have the same atomic and transactional properties as regular Redshift COPY commands. Creating a new table SaveMode. Both of these operations are performed in a single transaction. Overwriting an existing table : By default, this library uses transactions to perform overwrites, which are implemented by deleting the destination table, creating a new empty table, and appending rows to it.
If the deprecated usestagingtable setting is set to false then this library will commit the DELETE TABLE command before appending rows to the new table, sacrificing the atomicity of the overwrite operation but reducing the amount of staging space that Redshift needs during the overwrite. As a result, queries from Redshift data source for Spark should have the same consistency properties as regular Redshift queries.
If you attempt to perform a read of a Redshift table and the regions are mismatched then you may see a confusing error, such as. Similarly, attempting to write to Redshift using a S3 bucket in a different region may cause the following error:. For writes: Redshift's COPY command allows the S3 bucket's region to be explicitly specified, so you can make writes to Redshift work properly in these cases by adding.
The Amazon S3 bucket where Amazon Redshift will write the output files must reside in the same region as your cluster. As a result, this use-case is not supported by this library. The only workaround is to use a new bucket in the same region as your Redshift cluster.
Skip to content. Star Redshift data source for Apache Spark Apache Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats commits. Failed to load latest commit information. View code. Redshift Data Source for Apache Spark Note To ensure the best experience for our customers, we have decided to inline this connector directly in Databricks Runtime.
You may use this library in your applications with the following dependency information: Scala 2. Long ], classOf [ Array [ String ]].
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