# tRedshiftLookupInput (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. Reads a Redshift database and extracts fields based on a query. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. It passes on the extracted data to tMap in order to provide the lookup data to the main flow. It must be directly connected to a tMap component and requires this tMap to use Reload at each row or Reload at each row (cache) for the lookup flow. ## tRedshiftLookupInput properties for Apache Spark Streaming These properties are used to configure tRedshiftLookupInput running in the Spark Streaming Job framework. The Spark Streaming tRedshiftLookupInput component belongs to the Databases family. The component in this framework is available in Talend Real-Time Big Data Platform and in 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. | | 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, 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 | 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. | | 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:
- 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.

| | Table name | Enter the name of the table from which the data will be read. | | Query Type and Query | Specify the database query statement paying particularly attention to the sequence of the fields which must correspond to the schema definition.

The result of the query must contain only records that match join key you need to use in
tMap. In other words, you must use the schema of the
main flow to tMap to construct the SQL statement here in
order to load only the matched records into the lookup flow.

This approach ensures that no redundant records are loaded into memory and outputted to
the component that follows. | | Guess Query | Click the Guess Query button to
generate the query which corresponds to your table schema in the Query
field. | | Guess schema | Click the Guess schema button to
retrieve the table schema. | ## Advanced settings | Properties | Description | | --- | --- | | Trim all the String/Char columns | Select this check box to remove leading whitespace and trailing
whitespace from all String/Char columns. | | Trim column | This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed. | | 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 as a start component and requires an output 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 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. |