tUniqRow properties for Apache Spark Streaming
These properties are used to configure tUniqRow running in the Spark Streaming Job framework.
The Spark Streaming tUniqRow component belongs to the Processing family.
This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric.
Basic settings
Schema et 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:
|
|
Built-In: You create and store the schema locally for this component only. |
|
Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. |
Unique key |
In this area, select one or more columns to carry out deduplication on the particular column(s) - Select the Key attribute check box to carry out deduplication on all the columns - Select the Case sensitive check box to differentiate upper case and lower case |
Advanced settings
Only once each duplicated key |
Select this check box if you want to have only the first duplicated entry in the column(s) defined as key(s) sent to the output flow for duplicates. |
Usage
Usage rule |
This component is used as an intermediate step. This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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. |