# 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 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 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 | 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:

Yarn mode (Yarn client or Yarn cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
- When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
- When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis. |