tExtractJSONFields properties for Apache Spark Batch - 7.1

Processing (Integration)

Version
7.1
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Content
Data Governance > Third-party systems > Processing components (Integration)
Data Quality and Preparation > Third-party systems > Processing components (Integration)
Design and Development > Third-party systems > Processing components (Integration)

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

The Spark Batch tExtractJSONFields component belongs to the Processing family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

Basic settings

Property type

Either Built-In or Repository.

 

Built-In: No property data stored centrally.

 

Repository: Select the repository file where the properties are stored.

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 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.

 

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.

Read by

Select a way of extracting the JSON data in the file.

  • Xpath: Extracts the JSON data based on the XPath query.

  • JsonPath: Extracts the JSON data based on the JSONPath query. Note that it is recommended to read the data by JSONPath in order to gain better performance.

JSON field

List of the JSON fields to be extracted.

Loop Jasonpath query

Enter the path pointing to the node within the JSON field, on which the loop is based.

Note if you have selected Xpath from the Read by drop-down list, the Loop Xpath query field is displayed instead.

Mapping

Complete this table to map the columns defined in the schema to the corresponding JSON nodes.

  • Column: The Column cells are automatically filled with the defined schema column names.

  • Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/.

    This column is available only when JsonPath is selected from the Read By list.

  • XPath query: Specify the XPath node that holds the desired data.

    This column is available only when Xpath is selected from the Read By list.

  • Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node.

    This column is available only when Xpath is selected from the Read By list.

  • Is Array: select this check box when the JSON field to be extracted is an array instead of an object.

    This column is available only when Xpath is selected from the Read By list.

Die on error

Select the check box to stop the execution of the Job when an error occurs.

Advanced settings

Encoding

Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling. The supported encodings depend on the JVM that you are using. For more information, see https://docs.oracle.com.

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 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-premise 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.

    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.