Skip to main content Skip to complementary content

tTeradataInput properties for Apache Spark Batch

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 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 drop-down list, select the desired 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.

This field is only available when you select Table from the Read from drop-down list.

Read from

Select the type of the source of the data to be read.

  • Table: tTeradataInput reads the data of the table you specify in the Table name field.

  • Query: tTeradataInput reads the result of the query you write in the Query field.

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.

You can use pushdown predicate in the query to filter the data from the database. Spark supports the Filteroperator.

These fields are only available when you select Query from the Read from drop-down list.

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, in 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 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-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.
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. You can find more details about how to install external modules in Talend Help Center (https://help.talend.com).

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – please let us know!

Leave your feedback here