# tLogRow properties for Apache Spark Batch

These properties are used to configure tLogRow running in the Spark Batch Job framework.

The Spark Batch tLogRow component belongs to the Misc family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

## Basic settings

| Define a storage configuration<br>component | Select the configuration component to be used to provide the configuration<br>information for the connection to the target file system such as HDFS.<br><br>If you leave this check box clear, the target file system is the local<br>system.<br><br>The configuration component to be used must be present in the same Job.<br>For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write<br>the result in a given HDFS system. |
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| Schema and Edit 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> |
|  | 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. |
| Sync columns | Click to synchronize the output file schema with the input file schema.<br>The Sync function is available only when the component is linked with the<br>preceding component using a Row<br>connection. |
| Basic | Displays the output flow in basic mode. |
| Table | Displays the output flow in table cells. |
| Vertical | Displays each row of the output flow as a key-value list.<br><br>With this mode selected, you can choose to show either<br>the unique name or the label of component, or both of them, for each<br>output row. |
| Separator<br><br>(For Basic mode only) | Enter the separator which will delimit data on the Log<br>display. |
| Print header<br><br>(For Basic mode only) | Select this check box to include the header of the input flow in the<br>output display. |
| Print component unique name in front of each<br>output row<br><br>(For Basic mode only) | Select this check box to show the unique name the<br>component in front of each output row to differentiate outputs in case<br>several tLogRow components are<br>used. |
| Print schema column name in front of each<br>value<br><br>(For Basic mode only) | Select this check box to retrieve column labels from<br>output schema. |
| Use fixed length for values<br><br>(For Basic mode only) | Select this check box to set a fixed width for the value<br>display. |

## Advanced settings

| Use local timezone for date | Select this check box to use the local date of the machine in which your Job is executed. If leaving this check box clear, UTC is automatically used to format the Date-type data. |
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## Usage

| **Usage rule** | This component is used as an intermediate or an end step.<br><br>This component, along with the Spark Batch component Palette it belongs to,<br>appears only when you are creating a Spark Batch Job.<br><br>Note that in this documentation, unless otherwise explicitly stated, a<br>scenario presents only Standard Jobs, that is to<br>say traditional<br>Talend<br>data integration Jobs. |
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| 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-premise distributions, use the configuration component                                        corresponding to the file system your cluster is using.                                        Typically, this system is HDFS and so use <span></span>.</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 <span>tHDFSConfiguration</span> or <span>tS3Configuration</span>.</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. |