tMatchPairing properties for Apache Spark Batch - 7.0

Matching with machine learning

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Data Management Platform
Talend Data Services Platform
Talend MDM Platform
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
Design and Development > Third-party systems > Data Quality components > Matching components > Matching with machine learning components
EnrichPlatform
Talend Data Stewardship
Talend Studio

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

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. Related topic: see Talend Studio User Guide.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

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 Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

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 one of the following addresses to access the application:

    • https://tds.us.cloud.talend.com/data-stewardship for the US data center.
    • https://tds.eu.cloud.talend.com/data-stewardship for the EU data center.
  • Username and Password:

    Enter the authentication information to the Talend Data Stewardship server.

  • 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

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.

Use Timestamp format for Date type Select the check box to output dates, hours, minutes and seconds contained in your Date-type data. If you clear this check box, only years, months and days are outputted.

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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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 or tS3Configuration.

This connection is effective on a per-Job basis.