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tElasticSearchOutput properties for Apache Spark Batch

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

The Spark Batch tElasticSearchOutput component belongs to the ElasticSearch family.

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

Basic settings

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.


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.

Use an existing configuration

Select this check box and in the Component List drop-down list, select the desired connection component to reuse the connection details you already defined.


Enter the location of the cluster hosting the Elasticsearch system to be used.


Enter the name of the index in which you want to write documents.

An index is the largest unit of storage in the Elasticsearch system.


Enter the name of the type the documents to be written belong to.

For example, blogpost_en and blogpost_fr can be two types that represent given English blog posts and French blog posts, respectively.

You can dynamically uses the values of a given column to be document types. If you need to do so, enter the name of that column into a pair of braces ({}), for example, {blog_author}.

Output document

Select how the document is written into Elasticsearch.

  • JAVABEAN: if you select this option, tElasticsearchOutput directly uses the input schema to construct the JSON strings to be written.

    For example, if a record with its schema reads as follows
    id name age
    1  user 18
    the document outputted by this JAVABEAN option is {"id":1,"name":"user","age":18}.
  • JSON: with this option, a read-only json_document column is automatically added to the output schema to receive JSON strings (documents in terms of Elasticsearch) from its preceding components. This means you need to use tWriteJSONField in the same Job to construct JSON strings before outputting them to tElasticsearchOutput. The other columns of the schema can be used as metadata of these JSON documents.

    Since tWriteJSONField allows you to construct JSON trees of different complexities, you can thus manage how the JSON strings to be written should look like.

Advanced settings

Document metadata

Complete this table to select the input columns to be used to provide metadata for each document. This table is typically used along with the json_document option from the Output document drop-down list in the Basic settings view.

The Column column is automatically fed with the columns of the input schema. Then in the As metadata column, you need to select the check box(es) that correspond to the column(s) to be used.

In the Metadata type column, select which type of document metadata each column is used to provide.

For further information about the metadata types of an Elasticsearch document, see


Select this check box to enable the SSL or TLS encrypted connection.

Then you need to use the tSetKeystore component in the same Job to specify the encryption information.


Add the parameters accepted by Elasticsearch to perform more customized actions.

For example, enter in the Key column and true in the Value column to make the document field/property name contain the document ID. Note that you must put double quotation marks around the entered information.

For a list of the parameters you can use, see


Usage rule

This component is used as an end component and requires an input link.

Place a tElasticSearchConfiguration component in the same Job to connect to Elasticsearch. Then you need to select the Use an existing configuration check box and then select the tElasticSearchConfiguration component to be used.
  • Note that the Talend components support the Elasticsearch 6.4.x version for Spark Streaming Jobs, and Elasticsearch 7.x and 8.x versions for Spark Batch Jobs.

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 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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