# tMatchPairing Enables you to compute pairs of suspect duplicates from any source data including large volumes in the context of machine learning on Spark. This component reads a data set row by row, excludes unique rows and exact duplicates in separate files, computes pairs of suspect records based on a blocking key definition and creates a sample of suspect records representative of the data set. You can label suspect pairs manually or load them into a Grouping campaign which is already defined in Talend Data Stewardship. In local mode, Apache Spark 2.4.0 and later versions are supported. This component is not shipped with your Talend Studio by default. You need to install it using the Feature Manager. For more information, see [Installing features using the Feature Manager](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/install-features-to-talend-studio). ## tMatchPairing properties for Apache Spark Batch These properties are used to configure tMatchPairing running in the Spark Batch Job framework. The Spark Batch tMatchPairing component belongs to the Data Quality family. The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Define a storage configuration
component | Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job.
For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system. | | 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.
The output schema of this component has read-only columns in its output links:

PAIR_ID and SCORE: used only with the Pairs and Pairs sample output links. The first column holds the identifiers of the suspect pairs, and the second holds the similarities between the records in each pair.

LABEL: used only with the Pairs sample output link. You must fill in this column manually in the Job using the tMatchModel component.

COUNT: used only with the Exact duplicates output link. This column gives the occurrences of the records which exactly match.
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.

| | Blocking key | Select the columns with which you want to construct the
blocking key.

This blocking key is used to generate suffixes which are
used to group records. | | Suffix array blocking parameters | Min suffix length: Set the
minimum suffix length you want to reach or stop at in each group.

Max block size: Set the maximum
number of the records you want to have in each block. This helps in
filtering in large blocks where the suffix is too common, as with *tion* and *ing* for
example. | | Pairing model location | Folder: Set the path to the local
folder where you want to generate the model files.

If you want to store the model in a specific file system,
for example S3 or HDFS, you must use the corresponding component in the
Job and select the Define a storage
configuration component check box in the component basic
settings.

The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that Talend Studio
supports with your distribution, ensure that you have properly configured the connection
in a configuration component in the same Job. Use the configuration component depending
on the filesystem to be used. | | Integration with Data Stewardship | Select this check box to set the connection parameters to the Talend Data Stewardship
server.

If you select this check box, tMatchPairing loads the
suspect pairs into a Grouping campaign, which means this component is
used as an end component. | | Data Stewardship Configuration |

URL:

Enter the address to access the Talend Data Stewardship server suffixed with /data-stewardship/, for example http://<server_address>:19999/data-stewardship/.
If you are working with Talend Cloud Data Stewardship, use the URL for the corresponding data center suffixed with /data-stewardship/ to access the application, for example, https://tds.us.cloud.talend.com/data-stewardship for the AWS US data center.

For the URLs of available data centers, see Accessing Talend Cloud applications.
Username and Password:

Enter the authentication information to log in to Talend Data Stewardship.
If you are working with Talend Cloud Data Stewardship and if:
- SSO is enabled, enter an access token in the field.
- SSO is not enabled, enter either an access token or your password in the field.
Campaign:

Displays the technical name of the campaign once it is selected in the basic settings. However, you can modify the field value to replace it with a context parameter for example and pass context variables to the Job at runtime. This technical name is always used to identify a campaign when the Job communicates with Talend Data Stewardship whatever is the value in the Campaign field.

Click Find a Campaign to open a dialog box which lists the grouping campaigns on the server for which you are the Campaign owner or you have the access rights.

Click the refresh button to retrieve the campaign details from the Talend Data Stewardship server.
Assignee:

Specify the campaign participant whose tasks you want to create.

| ## Advanced settings | Properties | Description | | --- | --- | | Filtering threshold | Enter a value between 0.2 and 0.85 to filter the pairs of
suspect records based on the calculated scores. This value helps to
exclude the pairs which are not very similar.

0.3 is the default value. The higher the value is, the
more similar the records will be. | | Pairs sample | Number of pairs: Enter a size for
the sample of the suspect pairs you want to generate. The default value
is set to 10000.

Set a random seed: Select this
check box and in the Seed field
that is displayed, enter a random number if you want to have the same
pairs sample in different executions of the Job. Repeating the execution
with a different value for the seed will result in different pairs
samples and the scores of the pairs could be different as well depending
whether the total number of the suspect pairs is greater than 10 000 or
not. | | Data Stewardship Configuration | Campaign ID:

Displays the technical name of the campaign once it is selected
in the basic settings. However, you can modify the field value to replace it with a
context parameter for example and pass context variables to the Job at runtime. This
technical name is always used to identify a campaign when the Job communicates with
Talend Data Stewardship whatever is the value in the Campaign
field.

Max tasks per commit: Set the number of lines you
want to have in each commit.

Do not change the default value unless you are facing performance issues.
Increasing the commit size can improve the performance but setting a too
high value could cause Job failures. | ## Usage | Usage guidance | Description | | --- | --- | | **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 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. |