tTachyonConfiguration properties for Apache Spark Streaming

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

The Spark Streaming tTachyonConfiguration component belongs to the Storage family.

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

Basic settings

Tachyon master URI	Enter the address of the master server of the Tachyon cluster to be used. This information can be found in the conf/tachyon-env.sh file of your Tachyon system. For details about the version compatibility between Tachyon and Spark, see Tachyon documentation at http://tachyon-project.org/documentation/Running-Spark-on-Tachyon.html.
UnderFS username	Enter the credential required by the file system (underlayer storage system in terms of Tachyon) used by your Tachyon cluster. The default file system is HDFS. This information can be found in the conf/tachyon-env.sh file of your Tachyon system.

Tachyon master URI

Enter the address of the master server of the Tachyon cluster to be used.

This information can be found in the conf/tachyon-env.sh file of your Tachyon system.

For details about the version compatibility between Tachyon and Spark, see Tachyon documentation at http://tachyon-project.org/documentation/Running-Spark-on-Tachyon.html.

UnderFS username

Enter the credential required by the file system (underlayer storage system in terms of Tachyon) used by your Tachyon cluster. The default file system is HDFS.

This information can be found in the conf/tachyon-env.sh file of your Tachyon system.

Usage

Usage rule	This component is used with no need to be connected to other components. You need to drop tTachyonConfiguration along with the file system related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime. 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 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.

You need to drop tTachyonConfiguration along with the file system related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime.

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 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.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – please let us know!

Leave your feedback here