tReplace properties for Apache Spark Batch
These properties are used to configure tReplace running in the Spark Batch Job framework.
The Spark Batch tReplace component belongs to the Processing family.
The component in this framework is available in all Talend products with Big Data and Talend Data Fabric.
Basic settings
Schema and Edit Schema |
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. Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
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Built-In: You create and store the schema locally for this component only. |
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Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. |
Simple Mode Search / Replace |
Click the button to add as many conditions as needed. The conditions are performed one after the other for each row. Input column: Select the column of the schema the search & replace is to be operated on Search: Type in the value to search in the input column Replace with: Type in the substitution value. Whole word: Select this check box if the searched value is to be considered as whole. Case sensitive: Select this check box to care about the case. Note that you cannot use regular expression in these columns. |
Advanced mode |
Select this check box when the operation you want to perform cannot be carried out through the simple mode. In the text field, type in the regular expression as required. |
Usage
Usage rule |
This component is used as an intermediate step. This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job. Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs. |
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. |