tTeradataInput properties for Apache Spark Batch - 7.0

Teradata

author
Talend Documentation Team
EnrichVersion
7.0
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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 tTeradataInput running in the Spark Batch Job framework.

The Spark Batch tTeradataInput component belongs to the Databases family.

The component in this framework is available in all subscription-based Talend products with Big Data 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 in which the properties are stored. The fields that follow are completed automatically using the data retrieved.

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.

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.

 

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.

Table Name

Type in the name of the table from which you need to read data.

Query type and Query

Specify the database query statement paying particularly attention to the properly sequence of the fields which must correspond to the schema definition.

If you are using Spark V2.0 onwards, the Spark SQL does not recognize the prefix of a database table anymore. This means that you must enter only the table name without adding any prefix that indicates for example the schema this table belongs to.

For example, if you need to perform a query in a table system.mytable, in which the system prefix indicates the schema that the mytable table belongs to, in the query, you must enter mytable only.

Advanced settings

Additional JDBC parameters

Specify additional connection properties in the existing DB connection, to allow specific character set support. E.G.: CHARSET=KANJISJIS_OS to get support of Japanese characters.

Spark SQL JDBC parameters

Add the JDBC properties supported by Spark SQL to this table. For a list of the user configurable properties, see JDBC to other database.

This component automatically set the url, dbtable and driver properties by using the configuration from the Basic settings tab.

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.

Enable partitioning

Select this check box to read data in partitions.

Define, within double quotation marks, the following parameters to configure the partitioning:
  • Partition column: the numeric column used as partition key.

  • Lower bound of the partition stride and Upper bound of the partition stride: enter the upper bounds and the lower bound to determine the partition stride. These bounds do not filter the table rows. All rows in the table are partitioned and returned.

  • Number of partitions: the number of partitions into which the table rows are split. Each Spark worker handles only one of the partitions at a time.

The average size of the partitions is the result of the difference between the upper bound and the lower bound divided by the number of partitions, that is to say, (upperBound - lowerBound)/partitionNumber, while the first and the last partitions also include all the other rows that are not contained in the other partitions.

For example, to partition 1000 rows into 4 partitions, if you enter 0 for the lower bound and 1000 for the upper bound, each partition will contain 250 rows and so the partitioning is even. If you enter 250 for the lower bound and 750 for the upper bound, the second and the third partition will each contain 125 rows and the first and the last partitions each 375 rows. With this configuration, the partitioning is skewed.

Usage

Usage rule

This component is used as a start component and requires an output 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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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.

This connection is effective on a per-Job basis.

Limitation

Due to license incompatibility, one or more JARs required to use this component are not provided. You can install the missing JARs for this particular component by clicking the Install button on the Component tab view. You can also find out and add all missing JARs easily on the Modules tab in the Integration perspective of your studio. For details, see Installing external modules. .