July 30, 2023

tJDBCInput properties for Apache Spark Batch – Docs for ESB 7.x

tJDBCInput properties for Apache Spark Batch

These properties are used to configure tJDBCInput running in the Spark Batch Job

The Spark Batch
tJDBCInput component belongs to the Databases family.

This component also allows you to connect and read data from a
RDS MariaDB, a RDS PostgreSQL or a RDS SQLServer database.

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 where the
properties are stored.

Use an existing

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


The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

If you are using Spark V1.3, this URL should contain the
authentication information, such

Driver JAR

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41- for the Redshift database.

For more information, see Importing a database driver.

Class Name

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41- driver, the name to be entered is

Username and Password

Enter the authentication information to the database you need
to connect to.

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.

Available only for Spark V1.4. and onwards.

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


Built-In: You create and store the schema locally for this component


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
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

Table Name

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

Query type and

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.

Guess Query

Click the Guess Query button to generate the query which
corresponds to your table schema in the Query field.

Guess schema

Click the Guess schema button to retrieve the table

Advanced settings

Additional JDBC

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
check box is selected.


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

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

Trim column

This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed.

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
    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

  • 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 rule

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

This component should use a tJDBCConfiguration component present in the same Job to
connect to a database. You need to drop a tJDBCConfiguration component alongside this component and configure the
Basic settings of this component
to use tJDBCConfiguration.

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
data integration Jobs.

Spark Connection

In the Spark
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

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      area in the Spark

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
    • 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

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks

This connection is effective on a per-Job basis.

Document get from Talend https://help.talend.com
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