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 the Palette of the Studio only if you have subscribed to Talend Real-time Big Data Platform or 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 (columns) to The schema of this component is read-only. You can click Edit schema to view the schema. When the output type is String, the read-only When the output type is byte[], the read-only The output schema and its read-only column can be seen by clicking the Row > Output link to the component that follows in the same Job. |
|
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 and define it manually. This field is compulsory for database |
Advanced settings
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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
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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 |
You need to use the Spark Configuration tab in
the Run view to 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.