# tSqlRow 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 tSqlRow 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 tSqlRow component belongs to the Processing family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Schema and Edit schema | A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Built-In: You create and store the schema locally for this component only.
Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Click Edit schema to make changes to
the schema. If you make changes, the schema automatically becomes built-in.

View schema: choose this option to view the schema only.
Change to built-in property: choose this option to change the schema to Built-in for local changes.
Update repository connection: 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 No upon completion and choose this schema metadata again in the Repository Content window.

| | SQL context | Select the query languages you want tSqlRow to use.

SQL Spark Context: the Spark native query language.
SQL Hive Context: the Hive query language supported by Spark.

In SQL Hive Context, tSqlRow does not allow you to use Hive metastore. If you need to read or write data to Hive metastore, use tHiveInput or tHiveOutput instead and in this situation, you need to design your Job differently.

For further information about the Spark supported Hive query statements, see Supported Hive features.

| | Query | Enter your query paying particularly attention to properly sequence
the fields in order to match the schema definition.

The tSqlRow
component uses the label of its input link to name the registered table that stores the
datasets from the same input link. For example, if a input link is labeled to *row1*, this *row1* is automatically
the name of the table in which you can perform queries. | ## Advanced settings | Properties | Description | | --- | --- | | Register UDF jars | Add the Spark SQL or Hive SQL UDF (user-defined function) jars you want tSqlRow to use. If you do not want to call your UDF using its
FQCN (Fully-Qualified Class Name), you must define a function alias for this UDF in the
Temporary UDF
functions table and use this alias. It is recommended to use the alias
approach, as an alias is often more practical to use to call a UDF from the query.

Once you add one row to this table, click it to display the [...] button and then click this
button to display the jar import wizard. Through this wizard, import the UDF jar files you
want to use. | | Temporary UDF functions | Complete this table to give each imported UDF class a temporary function name to be used
in the query in tSqlRow.

If you have selected SQL Spark Context from the SQL context list, the UDF output
type column is displayed. In this column, you need to select the data type of the
output of the Spark SQL UDF to be used. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as an intermediate step.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents
only Standard Jobs, that is to say traditional
Talend
data
integration Jobs. | | Spark Connection | In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

Yarn mode (Yarn client or Yarn cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
- 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 tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis. |