tTeradataOutput properties for Apache Spark Streaming - 7.1

Teradata

author
Talend Documentation Team
EnrichVersion
7.1
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Talend Big Data
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task
Data Governance > Third-party systems > Database components > Teradata components
Data Quality and Preparation > Third-party systems > Database components > Teradata components
Design and Development > Third-party systems > Database components > Teradata components
EnrichPlatform
Talend Studio

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

The Spark Streaming tTeradataOutput component belongs to the Databases family.

This component is available in Talend Real Time Big Data Platform and 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.

Use an existing configuration

Select this check box and in the Component List click the relevant connection component to reuse the connection details you already defined.

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 Talend Studio User Guide.

Host

Database server IP address

Database

Name of the database

Username and Password

DB user authentication data.

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

Table

Name of the table to be written. Note that only one table can be written at a time.

Action on table

On the table defined, you can perform one of the following operations:

None: No operation is carried out.

Drop and create a table: The table is removed and created again.

Create a table: The table does not exist and gets created.

Create a table if not exists: The table is created if it does not exist.

Drop a table if exists and create: The table is removed if it already exists and created again.

Clear a table: The table content is deleted.

Action on data

On the data of the table defined, you can perform:

Insert: Add new entries to the table. If duplicates are found, job stops.

Update: Make changes to existing entries.

Insert or update: Insert a new record. If the record with the given reference already exists, an update would be made.

Update or insert: Update the record with the given reference. If the record does not exist, a new record would be inserted.

Delete: Remove entries corresponding to the input flow.

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.

 

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.

When the schema to be reused has default values that are integers or functions, ensure that these default values are not enclosed within quotation marks. If they are, you must remove the quotation marks manually.

For more details, see Verifying default values in a retrieved schema.

 

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.

Die on error

This check box is selected by default. Clear the check box to skip the row on error and complete the process for error-free rows. If needed, you can retrieve the rows on error via a Row > Rejects link.

Advanced settings

Additional JDBC parameters

Specify additional connection properties for the DB connection you are creating. This option is not available if you have selected the Use an existing connection check box in the Basic settings.

This is intended to allow specific character set support. E.G.: CHARSET=KANJISJIS_OS to get support of Japanese characters.

Note:

You can press Ctrl+Space to access a list of predefined global variables.

Use batch per partition

Select this check box to activate the batch mode for data processing.

Batch size

Specify the number of records to be processed in each batch..

This field appears only when the Use batch mode check box is selected.

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 an end component and requires an input link.

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

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch 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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premise 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 or tS3Configuration.

    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.