August 15, 2023

tElasticSearchInput – Docs for ESB 6.x

tElasticSearchInput

Reads documents from a given Elasticsearch system based on a user-defined
query.

tElasticSearchInput reads ElasticSearch documents from the ElasticSearch system based on user-defined queries, translates the documents into RDDs
(Resilient Distributed Datasets) and sends the RDDs to the Job.

Only one query is allowed per tElasticSearchInput.

tElasticSearchInput properties for Apache Spark Batch

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

The Spark Batch
tElasticSearchInput component belongs to the ElasticSearch family.

This component is available in the Palette of the Studio only if you have subscribed to one of the

Talend
solutions with Big Data.

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:

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

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 encryption 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

Usage rule

This component is used as a start component and requires an output link..

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.

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, 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: when using Google
    Dataproc, specify a bucket in the Google Storage staging
    bucket
    field in the Spark
    configuration
    tab; when using other distributions, use a
    tHDFSConfiguration
    component to specify the directory.

  • Standalone mode: you need to choose
    the configuration component depending on the file system you are using, such
    as tHDFSConfiguration
    or tS3Configuration.

This connection is effective on a per-Job basis.

Related scenarios

No scenario is available for the Spark Batch version of this component
yet.


Document get from Talend https://help.talend.com
Thank you for watching.
Subscribe
Notify of
guest
0 Comments
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x