tHBaseOutput properties for Apache Spark Streaming - 7.3

HBase

Version
7.3
Language
English
Product
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > NoSQL components > HBase components
Data Quality and Preparation > Third-party systems > NoSQL components > HBase components
Design and Development > Third-party systems > NoSQL components > HBase components
Last publication date
2024-02-21

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

The Spark Streaming tHBaseOutput component belongs to the Databases family.

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

Basic settings

Storage configuration

Select the tHBaseConfiguration component from which the Spark system to be used reads the configuration information to connect to HBase.

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

Click this icon to open a database connection wizard and store the database connection parameters you set in the component Basic settings view.

For more information about setting up and storing database connection parameters, see Talend Studio User Guide.

Schema et 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.

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.

 

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.

Table name

Type in the name of the HBase table in which you need to write data. This table must already exist.

Row key column

Select the column used as the row key column of the HBase table.

Then if needs be, select the Store row key column to HBase column check box to make the row key column an HBase column belonging to a specific column family.

Custom Row Key

Select this check box to use the customized row keys. Once selected, the corresponding field appears. Then type in the user-defined row key to index the rows of the HBase table being created.

For example, you can type in "France"+Numeric.sequence("s1",1,1) to produce the row key series: France1, France2, France3 and so on.

Families

Complete this table to map the columns of the table to be used with the schema columns you have defined for the data flow to be processed.

The Column column of this table is automatically filled once you have defined the schema; in the Family name column, enter the column families you want to create or use to group the columns in the Column column. For further information about a column family, see Apache documentation at Column families.

Advanced settings

Use batch mode

Select this check box to activate the batch mode for data processing.

Batch size

Specify the number of records to be processed in each batch.

This field appears only when the Use batch mode check box is selected.

Use local timezone for date Select this check box to use the local date of the machine in which your Job is executed. If leaving this check box clear, UTC is automatically used to format the Date-type data.

Usage

Usage rule

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

This component uses a tHBaseConfiguration component present in the same Job to connect to HBase.

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.