# tSocketTextStreamInput properties for Apache Spark Streaming

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

The Spark Streaming tSocketTextStreamInput component belongs to the Internet family.

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

## Basic settings

| Host name | Enter the name or the IP address of the server to be connected to. |
| --- | --- |
| Port | Enter the number of the listening port of the server to be connected to. |
| 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>This read-only *line* column is used to carry the strings<br>to be passed to the next component in the Job. Depending on the format of the strings, you<br>select the corresponding component to process the strings, for example, tExtractJSONFields to process JSON Strings. |

## Usage

| **Usage rule** | This component is used as a start component and requires an output link.<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<br>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. |
| 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. |