July 30, 2023

tKinesisOutput – Docs for ESB 7.x

tKinesisOutput

Acts as data producer to put data to an Amazon Kinesis stream for real-time
ingestion.

Using the Kinesis Client Library (KCL) provided by Amazon, tKinesisOutput receives serialized messages
from its preceding component and publishes these messages to an existing
Amazon Kinesis stream.

tKinesisOutput properties for Apache Spark Streaming

These properties are used to configure tKinesisOutput running in the Spark Streaming Job framework.

The Spark Streaming
tKinesisOutput component belongs to the Messaging family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.

Basic settings

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.

The schema of this component is read-only. You can click
Edit schema to view the schema.

The read-only serializedValue column is used to carry
the body of the message to be added to Kinesis. Note that you must use a Write component
such as tWriteJSONField to define a same serializedValue column in the input schema in order to send
serialized data to this read-only column.

The other columns are automatically retrieved from the schema of its preceding component.
They are added as header to the message to be outputted.

Access key

Enter the access key ID that uniquely identifies an AWS
Account. For further information about how to get your Access Key and Secret Key,
see Getting Your AWS Access
Keys
.

Secret key

Enter the secret access key, constituting the security
credentials in combination with the access Key.

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.

Stream name

Enter the name of the Kinesis stream you want to add data to.

Endpoint URL

Enter the endpoint of the Kinesis service to be used. For example, https://kinesis.us-east-1.amazonaws.com. More valid Kinesis endpoint URLs
can be found at http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region.

Number of shard

Enter the number of partitions (shards in terms of Kinesis) to be created in the target
Kinesis stream.

Advanced settings

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 an end component and requires an input link.

This component needs a Write component such as tWriteJSONField to define a serializedValue column in the input schema to send serialized data.

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 Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.
    • When using on-premise
      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 or
    tS3Configuration.

    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.

Limitation

Due to license incompatibility, one or more JARs required to use
this component are not provided. You can install the missing JARs for this particular
component by clicking the Install button
on the Component tab view. You can also
find out and add all missing JARs easily on the Modules tab in the
Integration
perspective of your studio. You can find more details about how to install external modules in
Talend Help Center (https://help.talend.com)
.

Related scenario


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