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tDeltaLakeInput properties for Apache Spark Batch

Availability-notepreview

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

The Spark Batch tDeltaLakeInput component belongs to the Technical family.

The component in this framework is available in all Talend products with Big Data and Talend Data Fabric.

Basic settings

Define the source of the dataset

Select the source of the dataset you want to use between the following options:

 

Metastore: Retrieves data in table format from a metastore.

 

Files: Retrieves data in delta format from files.

  Query: Retrieves data from SQL queries.

Define a storage configuration component

Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local system.

The configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.

This field is available only when you select Files from the Define the source of the dataset drop-down list in the Basic settings view.

Property type

Either Built-In or Repository.

 

Built-In: No property data stored centrally.

 

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

The properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

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.

Spark automatically infers data types for the columns in a PARQUET schema. In a Talend Job for Apache Spark, the Date type is inferred and stored as int96.

 

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.

Database

Enter, in double quotation marks, the name of the Delta Lake database to be used.

This field is available only when you select Metastore from the Define the source of the dataset drop-down list in the Basic settings view.

Table

Enter, in double quotation marks, the name of the table to be used.

This field is available only when you select Metastore from the Define the source of the dataset drop-down list in the Basic settings view.

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be true in the Advanced properties table in the Spark configuration tab.
  • Depending on the filesystem to be used, properly configure the corresponding configuration component placed in your Job, for example, a tHDFSConfiguration component for HDFS, a tS3Configuration component for S3 and a tAzureFSConfiguration for Azure Storage and Azure Data Lake Storage.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that the Studio supports with your distribution, ensure that you have correctly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration. Use the configuration component depending on the filesystem to be used.

This field is available only when you select Files from the Define the source of the dataset drop-down list in the Basic settings view.

SQL Query Enter the SQL query you want to use to retrieve data.

This field is available only when you select SQL Query from the Define the source of the dataset drop-down list in the Basic settings view.

Specify Time Travel timestamp

Select this check box to read a given timestamp-defined snapshot of the datasets to be used.

The format used by Deltalake is yyyy-MM-dd HH:mm:ss.

Delta Lake systematically creates slight differences between the upload time of a file and the metadata timestamp of this file. Bear in mind these differences when you need to filter data.

Specify Time Travel version Select this check box to read a versioned snapshot of the datasets to be used.

Usage

Usage rule

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

This Delta Lake layer is built on top of your Data Lake system, thus to be connected as part of your Data Lake system using the configuration component corresponding to your Data Lake system, for example, tAzureFSCofiguration.

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.

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