tKinesisInputAvro properties for Apache Spark Streaming

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.

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, enter the password in double quotes in the pop-up dialog box, 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.

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

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 parameter(s):

• 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 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

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 Talend Studio. For details, see Installing external modules.