tCompareColumns properties for Apache Spark Batch

These properties are used to configure tCompareColumns running in the Spark Batch Job framework.

The Spark Batch tCompareColumns component belongs to the Natural Language Processing family.

The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric.

Basic settings

Schema and Edit Schema	A schema is a row description. It defines the number of fields (columns) to Repository. When you create a Spark Job, avoid the reserved word `line` when naming the fields. Click Sync columns to retrieve the schema from the previous component connected in the Job. Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available: View schema: choose this option to view the schema only. Change to built-in property: choose this option to change the schema to Built-in for local changes. Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the [Repository Content] window. Add as many columns as necessary to the output schema according the algorithms defined in the Comparison options table: two columns for the Most similar in list (2 outputs) algorithm, two columns for the First letter corresponds (1 output) algorithm, two columns for the Is substring (1 output) algorithm.
	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.
Comparison options	In this table, set the rules for comparing tokens in two columns. The column specified in Main column contains the tokens to be compared with the reference tokens in Reference column. In the Algorithms column, select the algorithm to be used for each comparison: Most similar in list (2 outputs): The column specified in Main column contains one token per row. Each row in the column specified in Reference column can contain one token or one string with multiple tokens separated by a tab. The first output is the biggest Jaro-Winkler distance between the token in Main column and all the tokens in Reference column. The second one is the most similar token in Reference column. First letter corresponds (1 output): The columns specified in Main column and Reference column contain one token per row. In the output, `T` is returned if the first letter of the two tokens are the same. `F` is returned if they are different. Is substring (1 output): The columns specified in Main column and Reference column contain one token per row. In the output, `T` is returned if the token in Main column is a substring of the token from Reference column. If not, `F` is returned. Output column(s): Specify the columns that contain the comparison results in the output schema.

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.
Spark Batch 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: Yarn mode: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory. Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration. This connection is effective on a per-Job basis.

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.

Spark Batch 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:

Yarn mode: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.
Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.