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. The schema is either Built-In or stored remotely in the Repository.

• Built-In: You create and store the
  schema locally for this component only. Related topic: see
  Talend Studio
  
  User Guide.

• Repository: You have already created
  the schema and stored it in the Repository. You can reuse it in various projects and
  Job designs. Related topic: see
  Talend Studio
  
  User Guide.

Click Edit schema to make changes to the
schema. Note that 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.

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 rule

This component is used as an intermediate step.

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

This connection is effective on a per-Job basis.

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. The schema is either Built-In or stored remotely in the Repository.

• Built-In: You create and store the
  schema locally for this component only. Related topic: see
  Talend Studio
  
  User Guide.

• Repository: You have already created
  the schema and stored it in the Repository. You can reuse it in various projects and
  Job designs. Related topic: see
  Talend Studio
  
  User Guide.

Click Edit schema to make changes to the
schema. Note that 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.

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 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

This connection is effective on a per-Job basis.