tBigQueryInput properties for Apache Spark Streaming - 7.3

Google BigQuery
Version
7.3
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Content
Data Governance > Third-party systems > Cloud storages > Google components > Google BigQuery components
Data Quality and Preparation > Third-party systems > Cloud storages > Google components > Google BigQuery components
Design and Development > Third-party systems > Cloud storages > Google components > Google BigQuery components
Last publication date
2024-02-21

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

The Spark Streaming tBigQueryInput component belongs to the Databases family.

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

Basic settings

Source type

Select the way you want tBigQueryInput to read data from Google BigQuery:

  • Table: copy the whole table.

  • Query: write a query to select data.

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. 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.

When the Source type is Table:

Project ID

If your Google BigQuery service uses the Google Cloud Platform project ID, keep this check box clear to allow tBigQueryInput to read this ID from the Spark configuration tab or the tBigQueryConfiguration component.

If the Google BigQuery service uses a custom ID, select this check box and enter the ID.

The ID of your project can be found in the URL of the Google API Console, or by hovering your mouse pointer over the name of the project in the BigQuery Browser Tool.

Dataset

Enter the name of the dataset to which the table to be copied belongs.

When you use Google BigQuery with Dataproc, in Google Cloud Platform, select the same region for your BigQuery dataset as for the Dataproc cluster to be run.

Table

Enter the name of the table to be copied.

When the Source type is Query:

Query

Enter the query to be used.

When you use Google BigQuery with Dataproc, in Google Cloud Platform, select the same region for your BigQuery dataset as for the Dataproc cluster to be run.

If the query to be used is the legacy SQL of BigQuery, select this Use legacy SQL check box. For further information about this legacy SQL, see Legacy SQL query reference from the Google BigQuery documentation.

Usage

Usage rule

This is an input component. It sends data extracted from BigQuery to the component that follows it.

Place a tBigQueryConfiguration component in the same Job because it needs to use the BigQuery configuration information provided by tBigQueryConfiguration.

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.