tElasticSearchInput properties in Spark Batch Jobs - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Component family

ElasticSearch

 

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. The schema is either Built-In or stored remotely in the Repository.

The schema of the data outputted by this component is read-only, id_document and json_document. The json_document column contains the body of the documents read from ElasticSearch. If you need to explore data from this json_document column, you have to use tExtractJSONFields to extract the data to be used.

 

Use an existing configuration

Select this check box and in the Component List click the relevant connection component to reuse the connection details you already defined.

 

Nodes

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

 

Index

Enter the name of the index you want to read documents from.

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

 

Type

Enter the name of the type the documents to be read 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}.

 

Query

Enter the ElasticSearch query to be performed by this component.

In editing queries, you need to use the syntax required by ElasticSearch along with escape characters required by Java, and put the query within double quotation marks.

For example, in the ElasticSearch documentation, an example query reads as follows:

es.query = { "query" : { "term" : { "user" : "costinl" } } }

In this Query field, you should write the same query in the following way:

"{ \"query\" : { \"term\" : {\"user\" : \"costinl\" } } }"

Advanced settings

Use SSL/TLS

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 authentication information.

For further information about tSetKeystore, see tSetKeystore.

 

Configuration

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

For example, enter es.mapping.id 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 https://www.elastic.co/guide/en/elasticsearch/hadoop/master/configuration.html.

Usage in Spark Batch Jobs

In a Talend Spark Batch Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Batch components, too. They generate native Spark code that can be executed directly in a Spark cluster.

This component should use a tElasticSearchConfiguration component present in the same Job to connect to ElasticSearch. You need to select the Use an existing configuration check box and then select the tElasticSearchConfiguration component to be used.

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.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.