tWriteXMLFields
Converts records into byte arrays.
tWriteXMLFields generates strings or
byte arrays to be used by the output components, such as tKafkaOutput requiring serialized data while tJMSOutput requiring strings. tWriteXMLFields embeds the incoming data into a single XML column.
tWriteXMLFields properties for Apache Spark Streaming
These properties are used to configure tWriteXMLFields running in the Spark Streaming Job framework.
The Spark Streaming
tWriteXMLFields component belongs to the Processing family.
The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.
Basic settings
Output type |
Select the type of the data to be outputted into the target file. The data is |
Schema and Edit |
A schema is a row description. It defines the number of fields The schema of this component is read-only. You can click When the output type is String, the read-only single column is messageContent. This column is used to provide strings to the output components When the output type is byte, the read-only single column is serializedValue. This column is used to provide byte arrays to the output The output schema and its read-only column can be seen by |
Row tag |
Specify the tag that will wrap data and structure per row. |
Custom encoding |
You may encounter encoding issues when you process the stored data. In that Select the encoding from the list or select Custom |
Advanced settings
Root tags |
Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.). |
Output format |
Define the output format.
Note:
If the same column is selected in both the Output format table as an attribute
Use schema column name: By |
Use dynamic grouping |
Select this check box if you want to dynamically group the output
Column: Select a column you want
Attribute label: Enter an |
Usage
Usage rule |
This component is used as an intermediate step. This component, along with the Spark Streaming component Palette it belongs to, appears Note that in this documentation, unless otherwise explicitly stated, a scenario presents |
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:
This connection is effective on a per-Job basis. |
Related scenarios
No scenario is available for the Spark Streaming version of this component
yet.