tDeltaLakeInput properties for Apache Spark Batch
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 subscription-based Talend products with Big Data and Talend Data Fabric.
Basic settings
| Properties | Description |
|---|---|
| Define how to save the dataset |
Select the dataset storage:
|
| 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. 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 tS3Configuration component in the Job, you can select it to write the result in a given S3 storage 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 |
|
| Schema and Edit Schema |
|
| 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.
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 Talend Studio supports with your distribution, ensure that you have properly configured the connection in a configuration component in the same Job. 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. This property is only available if you select Files from the Define how to save the dataset drop-down list. |
| Specify Time Travel version | Select this check box to read a versioned snapshot of the datasets to be
used. This property is only available if you select Files from the Define how to save the dataset drop-down list. |
When you retrieve dataset from Unity Catalog, you need to specify the Unity
Catalog related information in the following parameters:
|
Usage
| Usage guidance | Description |
|---|---|
| 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, tAzureFSConfiguration. |
| 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:
This connection is effective on a per-Job basis. |