# tFilterRow properties for Apache Spark Batch

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

The Spark Batch tFilterRow 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.

## Basic settings

| Properties | Description |
| --- | --- |
| 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. |
| Logical operator used to combine conditions | Select a logical operator to combine simple conditions and to<br>combine the filter results of both modes if any advanced conditions<br>are defined.<br><br><br>And: returns the boolean value of<br>*true* if all conditions are<br>true; otherwise *false*. For each<br>two conditions combined using a logical AND, the second condition is<br>evaluated only if the first condition is evaluated to be<br>true.<br><br><br>Or: returns the boolean value of<br>*true* if any condition is<br>true; otherwise *false*. For each<br>two conditions combined using a logical OR, the second condition is<br>evaluated only if the first condition is evaluated to be<br>false. |
| Conditions | Click the plus button to add as many simple conditions as needed.<br>Based on the logical operator selected, the conditions are evaluated<br>one after the other in sequential order for each row. When<br>evaluated, each condition returns the boolean value of *true* or *false*.<br><br><br>Input column: Select the column of<br>the schema the function is to be operated on<br><br><br>Function: Select the function on<br>the list<br><br><br>Operator: Select the operator to<br>bind the input column with the value<br><br><br>Value: Type in the filtered value,<br>between quotes if needed. |
| Use advanced mode | Select this check box when the operations you want to perform<br>cannot be carried out through the standard functions offered, for<br>example, different logical operations in the same component. In the<br>text field, type in the regular expression as required.<br><br>If multiple advanced conditions are defined, use a logical<br>operator between two conditions:<br><br><br>&& (logical AND): returns the boolean value<br>of *true* if both conditions are<br>true; otherwise *false*. The second<br>condition is evaluated only if the first condition is evaluated to<br>be true.<br><br><br>\|\| (logical OR): returns the boolean value of<br>*true* if either condition is<br>true; otherwise *false*. The second<br>condition is evaluated only if the first condition is evaluated to<br>be false. |

## Usage

| Usage guidance | Description |
| --- | --- |
| **Usage rule** | This component is used as an intermediate 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. |
| 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. |