# tRedshiftConfiguration 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 tRedshiftConfiguration 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 tRedshiftConfiguration component belongs to the Storage and the Databases families. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Property type | Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the
properties are stored. | | Driver version | Select the Redshift driver version to be used between Driver
v1 or Driver v2.

Information noteNote: This option is available only when you have installed the 8.0.1-R2022-06
Talend Studio Monthly update or a later one delivered by Talend. For more information, check with your administrator. | | 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, enter the password in double quotes in the pop-up dialog box,
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). | | 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 (field) | 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.

This option is only available when you select Driver v1
from the Driver version drop-down list or when you
select Driver v2 from the Driver
version drop-down list with Use String JDBC
parameters selected. | | Additional JDBC Parameters (table) | Specify JDBC properties in table rows.

To specify a JDBC property, add a row in the table by clicking the plus button
on the bottom of this table, enter the property name in the
Key column, and then enter the property value in the
Value column.

This table is available when you select Driver v2 from
the Driver version drop-down list and clear
Use String JDBC parameters option.

Information noteNote: This option is available only when you have installed the 8.0.1-R2022-03
Talend Studio Monthly update or a later one delivered by Talend. For more information, check with your administrator. | | 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. | ## Advanced settings | Properties | Description | | --- | --- | | Connection pool | In this area, you configure, for each Spark executor, the connection pool used to control
the number of connections that stay open simultaneously. The default values given to the
following connection pool parameters are good enough for most use cases.

Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously.

The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time.
Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite.
Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool.
Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.

| | Evict connections | Select this check box to define criteria to destroy connections in the connection pool. The
following fields are displayed once you have selected it.

Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones.
Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed.
Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.

| ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used with no need to be connected to other components.

You need to drop tRedshiftConfiguration
alongside the other Redshift related subJobs to be run in the same Job so that the
configuration is used by the whole Job at runtime.

Since Redshift uses S3 to store temporary
data, you need to drop a tS3Configuration component alongside tRedshiftConfiguration in the same Job so that the
S3 configuration is used by the whole Job at runtime.

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. |