# tExtractPositionalFields properties for Apache Spark Streaming

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

The Spark Streaming tExtractPositionalFields component belongs to the Processing family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

## Basic settings

| Prev.Comp.Column list | Select an incoming field from the Field list to extract. |
| --- | --- |
| Customize | Select this check box to customize the data format of the positional<br>file and define the table columns:<br><br><br>Column: Select the column you want to<br>customize.<br><br><br>Size: Enter the column size.<br><br><br>Padding char: Type in between inverted<br>commas the padding character used, in order for it to be removed from<br>the field. A space by default.<br><br><br>Alignment: Select the appropriate<br>alignment parameter. |
| Pattern | Enter the pattern to use as basis for the extraction.<br><br>A pattern is length values separated by commas, interpreted as a<br>string between quotes. Make sure the values entered in this fields are<br>consistent with the schema defined. |
| Die on error | Clear the check box to skip any rows on error and complete the process for<br>error-free rows. When errors are skipped, you can collect the rows on error using a Row &gt; Reject link. |
| 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>Click Edit<br>schema to make changes to the schema. If the current schema is of the Repository type, three options are available:<br><br><ul> <li> <p> <span>View schema</span>: choose this                option to view the schema only.</p> </li> <li> <p> <span>Change to built-in property</span>:                choose this option to change the schema to <span>Built-in</span> for local changes.</p> </li> <li> <p> <span>Update repository connection</span>:                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 <span>No</span> upon completion and choose this schema metadata                again in the <span>Repository Content</span>                window.</p> </li> </ul><br><br>Click Sync<br>columns to retrieve the schema from the previous component connected in the<br>Job. |
|  | Built-In: You create and store the schema locally for this component<br>only. |
|  | Repository: You have already created the schema and stored it in the<br>Repository. You can reuse it in various projects and Job designs. |

## Advanced settings

| 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 Column | Select this check box to remove leading and trailing whitespace from<br>all columns. |
| Check each row structure against<br>schema | Select this check box to check whether the total number of columns<br>in each row is consistent with the schema. If not consistent, an<br>error message will be displayed on the console. |

## Usage

| **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>When using Qubole, add a                                        <span>tS3Configuration</span> to your Job to write                                    your actual business data in the S3 system with Qubole. Without                                        <span>tS3Configuration</span>, this business data is                                    written in the Qubole HDFS system and destroyed once you shut                                    down your cluster.</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/7.3/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/7.3/components/hdfs/thdfsconfiguration-properties-for-apache-spark-batch">tHDFSConfiguration Apache Spark Batch</a> or <a href="https://help.qlik.com/talend/r/en-US/7.3/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. |