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tOracleLookupInput properties for Apache Spark Streaming

These properties are used to configure tOracleLookupInput running in the Spark Streaming Job framework.

The Spark Streaming tOracleLookupInput component belongs to the Databases family.

The streaming version of this component is available in Talend Real-Time Big Data Platform and in Talend Data Fabric.

Basic settings

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

Click this icon to open a database connection wizard and store the database connection parameters you set in the component Basic settings view.

For more information about setting up and storing database connection parameters, see Centralizing database metadata.

Use an existing connection

Select this check box and in the Component List drop-down list, select the desired connection component to reuse the connection details you already defined.

Connection type

The available drivers are:

  • 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.

DB Version

Select the Oracle version in use.

Host

Database server IP address.

Port

Listening port number of DB server.

Database

Name of the database.

Oracle schema

Oracle schema name.

Username and Password

DB user authentication data.

To enter the password, click the [...] button next to the password field, enter the password in double quotes in the pop-up dialog box, and click OK to save the settings.

Schema and Edit Schema

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.

Table name

Database table name.

Query type and Query

Enter your DB query paying particularly attention to properly sequence the fields in order to match the schema definition.

The result of the query must contain only records that match join key you need to use in tMap. In other words, you must use the schema of the main flow to tMap to construct the SQL statement here in order to load only the matched records into the lookup flow.

This approach ensures that no redundant records are loaded into memory and outputted to the component that follows.

Advanced settings

Additional JDBC parameters

Specify additional connection properties for the database connection you are creating. The properties are separated by semicolon and each property is a key-value pair, for example, encryption=1;clientname=Talend.

This field is not available if the Use an existing connection check box is selected.

Trim all the String/Char columns

Select this check box to remove leading and trailing whitespace from all the String/Char columns.

Trim column

Remove leading and trailing whitespace from defined columns.

Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.

  • 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.

Evict connections

Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.

  • 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.

Usage

Usage rule

This component is used as a start component and requires an output link.

This component should use a tOracleConfiguration component present in the same Job to connect to Oracle. You need to select the Use an existing connection check box and then select the tOracleConfiguration component to be used.

This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Spark Connection

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode (Yarn client or Yarn cluster):
    • 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 Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment 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).

This connection is effective on a per-Job basis.

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