tMatchPredict 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 tMatchPredict running in the Spark Batch Job framework.

The Spark Batch tMatchPredict component belongs to the Data Quality family.

This component is available in 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:

LABEL: used only with the Suspect duplicates output link. It holds the prediction labels.

COUNT: used only with the Exact duplicates output link. It holds the number of exact duplicates.

GROUPID: used only with the Suspect duplicates output link. It holds the group identifiers.

 

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.

Pairing

From the Input type list, select:

paired: to use as input the suspect duplicates generated by the tMatchPairing component.

unpaired: to use as input new data set which has not been paired by tMatchPairing.

Pairing model folder: (available only with the unpaired input type) Set the path to the folder which has the model files generated by the tMatchPairing component.

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.

For further information, see tMatchPairing.

Matching

Matching model location: Select from the list where to get the model file generated by the classification Job with the tMatchModel component:

- from file system: Set the path to the folder where the model file is generated by the classification component. For further information, see tMatchModel.

- from current Job: Set the name of the model file generated by the classification component. You can use this option only if the classification Job with the tMatchModel component is integrated in the Job with the tMatchPredict component.

Matching model folder: Set the path to the folder which has the model files generated by the tMatchModel component.

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.

For further information, see tMatchModel.

Clustering classes

Add in the table one or more of the label(s) you used on the sample suspects generated by tMatchPairing. Make sure to use the same text.

The component then groups suspect records which match the label(s) set in the table.

If you labeled a sample of suspect records using Talend Data Stewardship, add the answer(s) defined in the Grouping campaign to the table.

The field is case-sensitive.

Advanced settings

Set Checkpoint Interval

Set the frequency of checkpoints. It is recommended to leave the default value (2).

Before setting a value for this parameter, activate checkpointing and set the checkpoint directory in the Spark Configuration tab of the Run view.

For further information about checkpointing, see Logging and checkpointing the activities of your Apache Spark Job.

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.

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 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.