tKinesisInput properties for Apache Spark Structured Streaming

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.

• Built-In: You create and store the schema locally for this component only.

• Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Click Edit schema to make changes to the schema. If you make changes, the schema automatically becomes built-in.

• View schema: choose this option to view the schema only.

• Change to built-in property: choose this option to change the schema to Built-in for local changes.

• Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.

  If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

• String: reads the payload as plain text.
• Avro: reads and deserializes the payload as Avro data.

Enter the name of the Kinesis stream you want tKinesisInput to pull data from.

• Latest: starts after the most recent records and reads only new incoming data.
• Earliest: starts from the oldest available records in the stream.
• At Timestamp: starts from records at or after the specified timestamp. Enter the timestamp value in the field that appears.

• Event time: Select to use a timestamp column representing when each event occurred, then select the column in the Watermark column (timestamp) drop-down list.
• Processing time: Select to use the time when Spark reads each record.

In the Watermark delay field, specify how long Spark waits for late data before finalizing each window.

For example, set kinesis.maxFetchRecordsPerShard to control how many records are fetched from each shard per trigger.

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 Structured Streaming component Palette it belongs to, appears only when you are creating a Spark Structured Streaming Job.

You need to use the Structured Streaming Configuration tab in the Run view to define the connection to a Spark cluster for the whole Job.

This connection is effective on a per-Job basis.