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tMatchIndexPredict properties for Apache Spark Batch

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

The Spark Batch tMatchIndexPredict 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.

Select the Schema type:

  • 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.

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.

You need to manually edit the output schema to add the necessary columns that hold the fields from the lookup data.

The output schema of this component contains read-only columns:

LABEL: used only with the Possible matches output link.

CONFIDENCE_SCORE: indicates the confidence score of a prediction for a pair.

ElasticSearch configuration

Nodes: Enter the location of the cluster hosting the ElasticSearch system to be used.

Index: Enter the name of the ElasticSearch index where the lookup data is stored.

Note that the Talend components for Spark Jobs support the Elasticsearch versions up to 6.4.2.

Models

Pairing model folder: Set the path to the folder which has the model files generated by the tMatchPairing component.

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: In the Matching model folder, set the path to the folder where the model file is generated by the tMatchModel component.
  • from current Job: From the Model name list, select 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 tMatchIndexPredict component.

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

No-match label: Enter the label used for the unique records in the output.

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 the Studio supports with your distribution, ensure that you have correctly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration. Use the configuration component depending on the filesystem to be used.

Advanced settings

Maximum ElasticSearch bulk size

Maximum number of records for bulk processing.

tMatchIndexPredict uses bulk mode to process data so that big batches of data can be quickly compared with lookup data indexed in ElasticSearch.

It is recommended to leave the default value. If the Job execution ends with an error, reduce the value for this parameter.

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 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.

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