# tWriteDelimitedFields properties for Apache Spark Streaming (deprecated)

Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated)

This content is deprecated.

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

Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead.

The Spark Streaming tWriteDelimitedFields component belongs to the Processing family.

The streaming version of this component is available in Talend Real-Time Big Data Platform and in Talend Data Fabric.

## Basic settings

| Properties | Description |
| --- | --- |
| Output type | Select the type of the data to be outputted into the target file. The data is<br>byte arrays if you select byte. |
| Schema and Edit<br>Schema | A schema is a row description. It defines the number of fields<br>(columns) to be processed and passed on to the next component. When you create a Spark<br>Job, avoid the reserved word line when naming the<br>fields.<br><br>The schema of this component is read-only. You can click<br>Edit schema to view the schema.<br><br>When the output type is String, the read-only single column is messageContent. This column is used to provide strings to the output components<br>such as tJMSOutput.<br><br>When the output type is byte, the read-only single column is serializedValue. This column is used to provide byte arrays to the output<br>components such as tKafkaOutput. |
| Include header | Select this check box to include the column header to the file. |
| Field separator | Enter a character, a string, or a regular expression to separate<br>fields for the transferred data. |
| Custom encoding | You may encounter encoding issues when you process the stored data. In that<br>situation, select this check box to display the Encoding list.<br><br>Select the encoding from the list or select Custom<br>and define it manually. This field is compulsory for database data handling. The<br>supported encodings depend on the JVM that you are using. For more information, see<br>[https://docs.oracle.com](https://docs.oracle.com/). |

## Advanced settings

| Properties | Description |
| --- | --- |
| 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 (*.*). |
| CSV options | Select this check box to include CSV specific parameters such as<br>Escape char and Text enclosure.<br><br>Information noteImportant: With Spark version 2.0<br>and onward, special characters must be escaped, that is "\\" and<br>"\"" instead of "\" and<br>""". |

## Usage

| Usage guidance | Description |
| --- | --- |
| **Usage rule** | This component is used as an intermediate step.<br><br>This component, along with the Spark Streaming component Palette it belongs to, appears<br>only when you are creating a Spark Streaming Job.<br><br>Note that in this documentation, unless otherwise explicitly stated, a scenario presents<br>only Standard Jobs, that is to say traditional<br>Talend<br>data<br>integration Jobs. |
| Spark Connection | In the Spark<br>Configuration tab in the Run<br>view, define the connection to a given Spark cluster for the whole Job. In<br>addition, since the Job expects its dependent jar files for execution, you must<br>specify the directory in the file system to which these jar files are<br>transferred so that Spark can access these files:<br><br><ul> <li> <div> <span>Yarn mode</span> (Yarn client or Yarn cluster):<ul> <li> <p>When using Google Dataproc, specify a bucket in the                                            <span>Google Storage staging bucket</span>                                        field in the <span>Spark configuration</span>                                        tab.</p> </li> <li> <p>When using HDInsight, specify the blob to be used for Job                                        deployment in the <span>Windows Azure Storage                                            configuration</span> area in the <span>Spark                                            configuration</span> tab.</p> </li> <li>When using Altus, specify the S3 bucket or the Azure                                    Data Lake Storage for Job deployment in the <span>Spark                                        configuration</span> tab.</li> <li> <p>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 <a href="https://help.qlik.com/talend/r/en-US/8.0/components/hdfs/thdfsconfiguration">tHDFSConfiguration</a>.</p> </li> </ul> </div> </li> <li> <p> <span>Standalone mode</span>: use the                            configuration component corresponding to the file system your cluster is                            using, such as <a href="https://help.qlik.com/talend/r/en-US/8.0/components/hdfs/thdfsconfiguration-properties-for-apache-spark-batch">tHDFSConfiguration Apache Spark Batch</a> or <a href="https://help.qlik.com/talend/r/en-US/8.0/components/amazon-s3/ts3configuration-properties-for-apache-spark-batch">tS3Configuration Apache Spark Batch</a>.</p> <p>If you are using Databricks without any configuration component present                            in your Job, your business data is written directly in DBFS (Databricks                            Filesystem).</p> </li> </ul><br><br>This connection is effective on a per-Job basis. |