Oracle OCI: Select this connection type to use Oracle Call Interface with a set of C-language software APIs that provide an interface to the Oracle database.
Oracle Custom: Select this connection type to access a clustered database. With this type of connection, the Username and the Password fields are deactivated and you need to enter the connection URL in the URL field that is displayed.
For further information about the valid form of this URL, see JDBC Connection strings from the Oracle documentation.
Oracle Service Name: Select this connection type to use the TNS alias that you give when you connect to the remote database.
WALLET: Select this connection type to store credentials in an Oracle wallet.
Oracle SID: Select this connection type to uniquely identify a particular database on a system.
A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.
Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
View schema: choose this option to view the schema only.
Change to built-in property: choose this option to change the schema to Built-in for local changes.
Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.
If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.
Built-In: You create and store the schema locally for this component only.
Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.
Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously.
The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time.
Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite.
Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool.
Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.
Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones.
Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed.
Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.
When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.
If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).