# tRedshiftOutput properties for Apache Spark Batch These properties are used to configure tRedshiftOutput running in the Spark Batch Job framework. The Spark Batch tRedshiftOutput component belongs to the Databases family. The component in this framework is available in all Talend products with Big Data and Talend Data Fabric. ## Basic settings | Property type | Either Built-In or Repository.


Built-In: No property data stored centrally.


Repository: Select the repository file where the
properties are stored. | | --- | --- | | ![](https://help.qlik.com/talend/en-US/components/7.3/Resources/images/Save_Icon.png) | Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide. | | Use an existing connection | Select this check box and in the Component
List drop-down list, select the desired connection component to
reuse the connection details you already defined. | | Host | Enter the endpoint of the database you need to connect to in
Redshift. | | Port | Enter the port number of the database you need to connect to in
Redshift.

The related information can be found in the Cluster Database
Properties area in the Web console of your Redshift.

For further information, see [Managing clusters console](http://docs.aws.amazon.com/redshift/latest/mgmt/managing-clusters-console.html). | | Username and Password | Enter the authentication information to the Redshift database you
need to connect to.

To enter the password, click the [...] button next to the
password field, and then in the pop-up dialog box enter the password between double quotes
and click OK to save the settings. | | Database | Enter the name of the database you need to connect to in
Redshift.

The related information can be found in the Cluster Database
Properties area in the Web console of your Redshift.

For further information, see [Managing clusters console](http://docs.aws.amazon.com/redshift/latest/mgmt/managing-clusters-console.html).

The bucket and the Redshift database to be used
must be in the same region on Amazon. This could avoid the S3ServiceException errors known to
Amazon. For further information about these errors, see [S3ServiceException Errors](https://docs.aws.amazon.com/redshift/latest/dg/s3serviceexception-error.html). | | Schema | Enter the name of the database schema to be used in Redshift. The
default schema is called *PUBLIC*.

A schema in terms of Redshift is similar to a operating system
directory. For further information about a Redshift schema, see [Schemas](http://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html). | | Additional JDBC Parameters | Specify additional JDBC properties for the connection you are creating. The
properties are separated by ampersand & and each property is a key-value pair. For
example, *ssl=true &
sslfactory=com.amazon.redshift.ssl.NonValidatingFactory*, which means the
connection will be created using SSL. | | S3 configuration | Select the tS3Configuration component from which you want Spark to use the
configuration details to connect to S3.

You need drop
the tS3Configuration component
to be used alongside tRedshiftConfiguration in the same Job so that this tS3Configuration is displayed on the
S3 configuration
list. | | S3 temp path | Enter the
location in S3 in which the data to be transferred from or to Redshift is
temporarily stored.

This path is independent of
the temporary path you need to set in the Basic settings tab of tS3Configuration. | | Table | Enter the name of the table to which the data will be written.
Note that only one table can be written at a time.

If this table does not exist, you need to select Create from the Save mode list to allow tRedshiftOutput to create it. | | 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 the current schema is of the Repository type, three options are available:

| | Save mode | Select the actions you want tRedshiftOutput to perform on the specified table.

| ## Advanced settings | Distribution style | Select the distribution style to be applied by tRedshiftOutput on the data to be written.

For further information about each of the distribution style, see
[Distribution styles](http://docs.aws.amazon.com/redshift/latest/dg/c_choosing_dist_sort.html). | | --- | --- | | Define sort key | Select this check box to sort the data to be written based on
given columns of the data.

Once selecting it, you need to select the column(s) to be used as
the key(s) of the sort. For further information about the different type of sort keys, see
[Choosing sort keys](http://docs.aws.amazon.com/redshift/latest/dg/t_Sorting_data.html). | | Use staging table | Select the Use staging
table check box to make tRedshiftOutput create and write data in a staging table and upon the success
of this write, replace the target table with the staging one.

This feature is available only when you have selected Overwrite from the Save mode list and is recommended when you need to keep the
target table of the overwrite highly available. | | Define pre-actions | Select this check box and in the field that is displayed, add a
semicolon-separated(;) list of SQL statements that are executed before tRedshiftOutput starts to write data.

For example, using the following statement, you remove all of the
rows from the *Movie* table that meet the condition over the
*Movie* and the *Director*
tables.

```
delete from movie using director where movie.movieid=director.directorid;
``` | | Define post-actions | Select this check box and in the field that is displayed, add a
semicolon-separated(;) list of SQL statements that are executed after tRedshiftOutput has successfully written data.

For example, using the following statement, you grant the Select
privilege on the *Movie* table to the user *ychen*.

```
grant select on table movie to ychen;
``` | | Define extra copy options | Select this check box and in the field that is displayed, add a
semicolon-separated(;) list of SQL statements that are executed along with the write of the
data.


tRedshiftOutput uses the Copy statement of
Redshift SQL to write data. The list of SQL statements you add here is actually appended to
the Copy statement and so only the statements that make sense at the end of this Copy command
should be used. For example, COMPUPDATE that is used to control whether compression encodings
are automatically applied during the execution of a Copy.

For further information about the extra options you can choose,
see [Optional parameters](http://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html#r_COPY-syntax-overview-optional-parameters). | ## Usage | **Usage rule** | This component is used as an end component and requires an input link.

This component should use a tRedshiftConfiguration component present in the same Job to
connect to Redshift. You need to drop a tRedshiftConfiguration component alongside this component and configure the
Basic settings of this component to use
tRedshiftConfiguration.

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 | 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:



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