Table of Contents


  1. Preface
  2. Introduction to Data Engineering Streaming
  3. Data Engineering Streaming Administration
  4. Sources in a Streaming Mapping
  5. Targets in a Streaming Mapping
  6. Streaming Mappings
  7. Window Transformation
  8. Appendix A: Connections
  9. Appendix B: Monitoring REST API Reference
  10. Appendix C: Sample Files

MapR Streams Data Objects

MapR Streams Data Objects

A MapR Streams data object is a physical data object that represents data in a MapR Stream. After you create a MapR Streams connection, create a MapR Streams data object to read data from MapR Streams.
Before you create and use MapR Stream data objects in streaming mappings, complete the required prerequisites.
For more information about the prerequisite tasks, see the
Data Engineering Integration Guide
When you configure the MapR Streams data object, specify the stream name that you read from in the following format:
/pathname:topic name
You can specify the stream name or use a regular expression for the stream name pattern only when you read from MapR Streams. The regular expression that you specify applies to the topic name and not the path name. To subscribe to multiple topics that match a pattern, you can specify a regular expression. When you run the application on the cluster, the pattern matching is done against topics before the application runs. If you add a topic with a similar pattern when the application is already running, the application will not read from the topic.
After you create a MapR Streams data object, create a read data object operation. You can then add the data object read operation as a source in streaming mappings.
You can associate the data object with an intelligent structure model and directly parse input from text, CSV, XML, or JSON input files.
When you configure the data operation properties, specify the format in which the MapR Streams data object reads data. You can specify XML, JSON, or Avro as format. When you specify XML format, you must provide a XSD file. When you specify Avro format, provide a sample Avro schema in a .avsc file. When you specify JSON or Flat format, you must provide a sample file.
You can pass any payload format directly from source to target in Streaming mappings. You can project columns in binary format pass a payload from source to target in its original form or to pass a payload format that is not supported.
Streaming mappings can read, process, and write hierarchical data. You can use array, struct, and map complex data types to process the hierarchical data. You assign complex data types to ports in a mapping to flow hierarchical data. Ports that flow hierarchical data are called complex ports.
For more information about processing hierarchical data, see the
Data Engineering Integration User Guide
For more information about how to use topic patterns in MapR Streams data objects, see
If you use a MapR Streams data object in a streaming mapping, you cannot use an Apache Kafka data object in the same mapping.


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