August 16, 2023

tKinesisInputAvro – Docs for ESB 6.x

tKinesisInputAvro

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

Using the Kinesis Client Library (KCL) provided by Amazon, tKinesisInputAvro consumes Avro-formatted
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.

tKinesisInputAvro properties for Apache Spark Streaming

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

The Spark Streaming
tKinesisInputAvro component belongs to the Messaging family.

The streaming version of this component is available in the Palette of the Studio only if you have subscribed to Talend Real-time Big Data Platform or 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. The schema is either Built-In or stored remotely in the Repository.

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.

Use hierarchical mode

Select this check box to map the binary (including hierarchical) Avro schema to the
flat schema defined in the schema editor of the current component. If the Avro
message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameters:

  • Local path to the avro
    schema
    : browse to the file which defines the
    schema of the Avro data to be processed.

  • Mapping: create the map
    between the schema columns of the current component and the data stored
    in the hierarchical Avro message to be handled. In the
    Node column, you need to
    enter the JSON path pointing to the data to be read from the
    Avro message.

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

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.

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 scenarios

No scenario is available for the Spark Streaming version of this component
yet.


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