August 16, 2023

tRedshiftLookupInput – Docs for ESB 6.x

tRedshiftLookupInput

Reads a Redshift database and extracts fields based on a query.

tRedshiftLookupInput
executes a database query with a strictly defined order which must correspond to the schema
definition.

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 only if you have
subscribed to Talend Real-Time Big Data Platform or 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.

Use an existing connection

Select this check box and in the Component
List
click the relevant 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.

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.

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.

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. 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.
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
properly 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

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 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

You need to use the Spark Configuration tab in
the Run view to 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: when using Google
    Dataproc, specify a bucket in the Google Storage staging
    bucket
    field in the Spark
    configuration
    tab; when using other distributions, use a
    tHDFSConfiguration
    component to specify the directory.

  • Standalone mode: you need to choose
    the configuration component depending on the file system you are using, such
    as tHDFSConfiguration
    or tS3Configuration.

This connection is effective on a per-Job basis.

Related scenarios

For a scenario about how to use the same type of component in a Spark Streaming Job, see
Reading and writing data in MongoDB using a Spark Streaming Job.


Document get from Talend https://help.talend.com
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