July 30, 2023

tKinesisInput – Docs for ESB 7.x

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tKinesisInput

Acts as consumer of an Amazon Kinesis stream to pull messages from this Kinesis
stream.

Using the Kinesis Client Library (KCL) provided by Amazon, tKinesisInput consumes data from a given
Amazon Kinesis stream (an ordered sequence of data records), constructs
an RDD out of this data and sends the RDD to its following
components.

tKinesisInput properties for Apache Spark Streaming

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

The Spark Streaming
tKinesisInput 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.

This read-only payload column is used to carry the body
of the Kinesis message to be processed.

The input message body can use very different data formats. For example, if its format is
JSON, you need to use tExtractJSONField following tKinesisInput to extract the data to be processed from this
body.

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 tKinesisInput to pull data from.

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.

Explicitly set authentication
parameters

Select this check box to use the explicit authentication mechanism to connect to Kinesis.
Note that this mechanism is supported by Spark V1.4+ only.

Since this security mechanism requires the AWS Region parameter to be explicitly set, you
need to enter the region value to be used in the Region
field that is displayed. For example, us-west-2.

It is recommended to use the explicit authentication to gain better security when the
Spark version you are using supports this mechanism. With this check box selected, the
access credentials are provided directly to Kinesis.

While if you leave this check box clear, an older authentication mechanism is used. This
way, the access credentials are used by Spark as context variables for Kinesis
connection.

Advanced settings

Checkpoint interval

Enter the time interval (in millisecond) at the end of which tKinesisInput saves the position of its read in the Kinesis stream.

Data records in a Kinesis stream are grouped into partitions (shards in terms of Kinesis)
and indexed with sequence numbers. A sequence number uniquely identifies the position of a
record. For further information about the terms used by Amazon in Kinesis, see http://docs.aws.amazon.com/kinesis/latest/dev/key-concepts.html.

Initial position stream

Select the starting position to read data from the stream in the absence of the Kinesis
checkpoint information.

  • Start with the oldest data: starts from the
    beginning of the stream within the limit of 24 hours.

  • Start after the most recent data: starts at
    the position after the latest data of the stream.

Storage level

Select how you want the received data to be cached. For further information about the
different levels, see https://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.

Usage

Usage rule

This component is used as a start component and requires an output link.

At runtime, this component keeps listening to the stream and reads new messages once they
are buffered in this stream.

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).

Working with Amazon Kinesis and Big Data Streaming Jobs

This scenario shows how to work with Amazon Kinesis and Big Data Streaming Jobs using
the Spark Streaming framework.

This scenario applies only to Talend Real Time Big Data Platform and Talend Data Fabric.

This example uses Talend Real-Time Big Data Platform v6.1. In
addition, it uses these licensed products provided by Amazon: Amazon EC2, Amazon
Kinesis, and Amazon EMR.

In this example, you will build the following Job, to read and and write data to an
Amazon Kinesis stream and display results in the Console.

tKinesisInput_1.png

tHDFSConfiguration is used in this scenario by Spark to connect
to the HDFS system where the jar files dependent on the Job are transferred.

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).

Launching an Amazon Kinesis Stream

  1. From the Amazon Web Services home page, navigate to
    Kinesis.

    tKinesisInput_2.png

  2. Click Go to Kinesis Streams and click Create
    Stream    .
  3. In the Stream Name field, enter the Kinesis stream name
    and provide the number of shards.

    For the current example, 1 shard is enough.

    tKinesisInput_3.png

  4. Click Create. Then, you will reach the Kinesis stream
    list. Your new stream will be available when its status changes from CREATING to
    ACTIVE. Your stream is now ready.

Writing data to an Amazon Kinesis Stream

In this section, it is assumed that you have an Amazon EMR cluster up and running and
that you have created the corresponding cluster connection metadata in the
repository. It is also assumed that you have created an Amazon Kinesis stream.

  1. Create a Big Data Streaming Job using the Spark framework.

    tKinesisInput_4.png

  2. In this example the data, which will be written to Amazon Kinesis, are
    generated with a tRowGenerator component.
  3. The data must be serialized as byte arrays before being written to the Amazon
    Kinesis stream. Add a tWriteDelimitedFields component and
    connect it to the tRowGenerator component.
  4. Configure the Output type to
    byte[].
  5. To write the data to your Kinesis stream, add a
    tKinesisOutput component and connect the
    tWriteDelimitedFields component to it.
  6. Provide your Amazon credentials.
  7. To access your Kinesis stream, provide the Stream name and the corresponding
    endpoint url.

    To get the right endpoint url, refer to AWS Regions and Endpoints.

  8. Provide the number of shards, as specified when you created the Kinesis
    stream.

    tKinesisInput_5.png

Reading Data from an Amazon Kinesis Stream

  1. To read data from your Kinesis stream, add a
    tKinesisInput component and connect the
    tRowGenerator component to it with an
    InParallel trigger.
  2. In the Basic settings view of the
    tKinesisInput component, provide your Amazon
    credentials.
  3. Provide your Kinesis Stream name and the corresponding Endpoint url.
  4. Select the Explicitly set authentication parameters
    option and enter your Region, as mentioned in AWS Regions and Endpoints.
  5. Add a tReplicate component and connect it with
    tKinesisInput.

    The purpose of the tReplicate component is to have a
    processing component in the Job; otherwise, the execution of the Job will
    fail. The tReplicate component allows the Job to
    compile without modifying the data.

  6. Add a tExtracDelimitedFields component and connect it to
    the tReplicate component.

    The tExtractDelimitedFields will extract the data from
    the serialized message generated by the tKinesisInput
    component.

  7. Add a tLogRow component to display the output on the
    console and on its Basic settings view select
    Table (print values in cells of a table) to display
    the data in a table.

Configuring a Big Data Streaming Job using the Spark Streaming Framework

Before running your Job, you need to configure it to use your Amazon EMR
cluster.

  1. Because your Job will run on Spark, it is necessary to add a
    tHDFSConfiguration component and then configure it to
    use the HDFS connection metadata from the repository.

    tKinesisInput_6.png

  2. In the Run view, click the Spark
    Configuration     tab.
  3. In the Cluster Version panel, configure your Job to user
    your cluster connection metadata.

    tKinesisInput_7.png

  4. Set the Batch size to 2000 ms.
  5. Because you will set some advanced properties, change the Property type to
    Built-In.
  6. In the Tuning panel, select the Set tuning
    properties     option and configure the fields as follows.

    tKinesisInput_8.png

  7. Run your Job.

    It takes a couple of minutes to have data displayed in the Console.

    tKinesisInput_9.png

Document get from Talend https://help.talend.com
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Docs 7.x
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