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.

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-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.
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. For details, see Installing external modules. You can find more details about how to install external modules in Talend Help Center (https://help.talend.com).

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-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.

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. For details, see Installing external modules. You can find more details about how to install external modules in Talend Help Center (https://help.talend.com).

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