Data Formats

The Kafka Consumer origin processes data differently based on the data format. Kafka Consumer can process the following types of data:

Avro
Generates a record for every message. Includes a precision and scale field attribute for each Decimal field.
The origin includes the Avro schema in an avroSchema record header attribute. You can use one of the following methods to specify the location of the Avro schema definition:
  • Message/Data Includes Schema - Use the schema in the message.
  • In Pipeline Configuration - Use the schema that you provide in the stage configuration.
  • Confluent Schema Registry - Retrieve the schema from Confluent Schema Registry. Confluent Schema Registry is a distributed storage layer for Avro schemas. You can configure the origin to look up the schema in Confluent Schema Registry by the schema ID embedded in the message or by the schema ID or subject specified in the stage configuration.

    You must specify the method that the origin uses to deserialize the message. If the Avro schema ID is embedded in each message, set the key and value deserializers to Confluent on the Kafka tab.

Using a schema in the stage configuration or retrieving a schema from Confluent Schema Registry overrides any schema that might be included in the message and can improve performance.
Binary
Generates a record with a single byte array field at the root of the record.
When the data exceeds the user-defined maximum data size, the origin cannot process the data. Because the record is not created, the origin cannot pass the record to the pipeline to be written as an error record. Instead, the origin generates a stage error.
Datagram
Generates a record for every message. The origin can process collectd messages, NetFlow 5 and NetFlow 9 messages, and the following types of syslog messages:
  • RFC 5424
  • RFC 3164
  • Non-standard common messages, such as RFC 3339 dates with no version digit
When processing NetFlow messages, the stage generates different records based on the NetFlow version. When processing NetFlow 9, the records are generated based on the NetFlow 9 configuration properties. For more information, see NetFlow Data Processing.
Delimited
Generates a record for each delimited line.
The CSV parser that you choose determines the delimiter properties that you configure and how the stage handles parsing errors. You can specify if the data includes a header line and whether to use it. You can define the number of lines to skip before reading, the character set of the data, and the root field type to use for the generated record.
You can also configure the stage to replace a string constant with null values and to ignore control characters.
For more information about reading delimited data, see Reading Delimited Data.
JSON
Generates a record for each JSON object. You can process JSON files that include multiple JSON objects or a single JSON array.
When an object exceeds the maximum object length defined for the origin, the origin processes the object based on the error handling configured for the stage.
Log
Generates a record for every log line.
When a line exceeds the user-defined maximum line length, the origin truncates longer lines.
You can include the processed log line as a field in the record. If the log line is truncated, and you request the log line in the record, the origin includes the truncated line.
You can define the log format or type to be read.
Protobuf
Generates a record for every protobuf message. By default, the origin assumes messages contain multiple protobuf messages.
Protobuf messages must match the specified message type and be described in the descriptor file.
When the data for a record exceeds 1 MB, the origin cannot continue processing data in the message. The origin handles the message based on the stage error handling property and continues reading the next message.
For information about generating the descriptor file, see Protobuf Data Format Prerequisites.
SDC Record
Generates a record for every record. Use to process records generated by a Data Collector pipeline using the SDC Record data format.
For error records, the origin provides the original record as read from the origin in the original pipeline, as well as error information that you can use to correct the record.
When processing error records, the origin expects the error file names and contents as generated by the original pipeline.
Text
Generates a record for each line of text or for each section of text based on a custom delimiter.
When a line or section exceeds the maximum line length defined for the origin, the origin truncates it. The origin adds a boolean field named Truncated to indicate if the line was truncated.
For more information about processing text with a custom delimiter, see Text Data Format with Custom Delimiters.
XML
Generates records based on a user-defined delimiter element. Use an XML element directly under the root element or define a simplified XPath expression. If you do not define a delimiter element, the origin treats the XML file as a single record.
Generated records include XML attributes and namespace declarations as fields in the record by default. You can configure the stage to include them in the record as field attributes.
You can include XPath information for each parsed XML element and XML attribute in field attributes. This also places each namespace in an xmlns record header attribute.
Note: Field attributes and record header attributes are written to destination systems automatically only when you use the SDC RPC data format in destinations. For more information about working with field attributes and record header attributes, and how to include them in records, see Field Attributes and Record Header Attributes.
When a record exceeds the user-defined maximum record length, the origin skips the record and continues processing with the next record. It sends the skipped record to the pipeline for error handling.
Use the XML data format to process valid XML documents. For more information about XML processing, see Reading and Processing XML Data.
Tip: If you want to process invalid XML documents, you can try using the text data format with custom delimiters. For more information, see Processing XML Data with Custom Delimiters.