# tExtractDelimitedFields properties for Apache Spark Batch These properties are used to configure tExtractDelimitedFields running in the Spark Batch Job framework. The Spark Batch tExtractDelimitedFields component belongs to the Processing family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. [Preview](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Introduction/feature-preview-program-overview.htm) Information noteNote: This component is not supported with the Databricks Serverless runtime. ## Basic settings | Properties | Description | | --- | --- | | 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 you make changes, the schema automatically becomes built-in.
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.

| | Prev.Comp.Column list | Select the column you need to extract data from. | | Die on error | Select the checkbox to stop the execution of the Job when an error
occurs. | | Field separator | Enter a character, a string, or a regular expression to separate
fields for the transferred data. | | CSV options | Select this check box to include CSV specific parameters such as
Escape char and Text enclosure.

Information noteImportant: With Spark version 2.0
and onward, special characters must be escaped, that is "\\" and
"\"" instead of "\" and
""". | ## Advanced settings | Properties | Description | | --- | --- | | Custom Encoding | You may encounter encoding issues when you process the stored data. In that
situation, select this check box to display the Encoding list.

Then select the encoding to be used from the list or select Custom and define it manually. | | Advanced separator (for number) | Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (*,*) and the decimal separator is a period (*.*). | | Trim all columns | Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim. | | Check column to trim | This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed. | | Check each row structure against
schema | Select this check box to check whether the total number of columns
in each row is consistent with the schema. If not consistent, an
error message will be displayed on the console. | | Check date | Select this check box to check the date format strictly against the input schema. | | Decode String for long, int, short, byte
Types | Select this check box if any of your numeric types (long, integer, short, or byte type), will
be parsed from a hexadecimal or octal string. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as an intermediate step.

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 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. |