tElasticSearchConfiguration properties for Apache Spark Streaming

These properties are used to configure tElasticSearchConfiguration running in the Spark Streaming Job framework.

The Spark Streaming tElasticSearchConfiguration component belongs to the ElasticSearch family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

Nodes	Enter the location of the cluster hosting the Elasticsearch system to be used. The Nodes parameter is mandatory and eventually taken into account only when the ElasticSearch component to be connected to ElasticSearch uses the ElasticSearch Node Client, that is to say, the tElasticSearchInput component and the tElasticSearchOutput component. For further information about the ElasticSearch Node Client and the ElasticSearch Transport Client, see https://www.elastic.co/guide/en/elasticsearch/guide/current/_transport_client_versus_node_client.html.
Transport addresses	Enter the addresses of the ElasticSearch nodes you need the component to connect to. This parameter is required when your ElasticSearch Job, exactly speaking, tElasticSearchLookupInput in your Job, to use the ElasticSearch Transport Client to connect to the ElasticSearch cluster to be used. If you do not use ElasticSearch Transport Client, leave empty double quoation marks ("") in this field. For further information about the ElasticSearch Node Client and the ElasticSearch Transport Client, see https://www.elastic.co/guide/en/elasticsearch/guide/current/_transport_client_versus_node_client.html.
Cluster name	Enter the name the ElasticSearch cluster to be used. This parameter is required when your ElasticSearch Job, exactly speaking, tElasticSearchLookupInput in your Job, to use the ElasticSearch Transport Client to connect to the ElasticSearch cluster to be used. If you do not use ElasticSearch Transport Client, leave empty double quoation marks ("") in this field.
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.
User authentication	If the ElasticSearch system to be used requires authentication information, select this check box and enter the credentials. To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.
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.

Advanced settings

Connection pool	In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases. Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously. The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time. Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite. Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool. Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.
Evict connections	Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it. Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones. Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed. Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.

Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.

Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously.

The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time.
Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite.
Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool.
Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.

Evict connections

Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.

Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones.
Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed.
Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.

Usage

Usage rule	This component is used with no need to be connected to other components. Drop tElasticSearchConfiguration along with the ElasticSearch-related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime. Note that the Talend components for Spark Jobs support the Elasticsearch versions up to 6.4.2. This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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-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.

Usage rule

This component is used with no need to be connected to other components.

Drop tElasticSearchConfiguration along with the ElasticSearch-related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime.

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

This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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-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.