tFileOutputPositional properties for Apache Spark Batch - 7.0

Positional

author
Talend Documentation Team
EnrichVersion
7.0
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Talend Big Data
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task
Data Governance > Third-party systems > File components (Integration) > Positional components
Data Quality and Preparation > Third-party systems > File components (Integration) > Positional components
Design and Development > Third-party systems > File components (Integration) > Positional components
EnrichPlatform
Talend Studio

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

The Spark Batch tFileOutputPositional component belongs to the File family.

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

Basic settings

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.

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.

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 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.

Folder

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

This path must point to a folder rather than a file.

Warning: Use absolute path (instead of relative path) for this field to avoid possible errors.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

Action

Select an operation for writing data:

Create: Creates a file and write data in it.

Overwrite: Overwrites the file existing in the directory specified in the Folder field.

Compress the data

Select the Compress the data check box to compress the output data.

Row separator

The separator used to identify the end of a row.

Include header

Select this check box to include the column header to the file.

Custom encoding

You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list.

Select the encoding from the list or select Custom and define it manually.

Merge result to single file

Select this check box to merge the final part files into a single file and put that file in a specified directory.

Once selecting it, you need to enter the path to, or browse to the folder you want to store the merged file in. This directory is automatically created if it does not exist.

The following check boxes are used to manage the source and the target files:
  • Remove source dir: select this check box to remove the source files after the merge.

  • Override target file: select this check box to override the file already existing in the target location. This option does not override the folder.

This option is not available for a Sequence file.

Formats

Customize the positional file data format and fill in the columns in the Formats table.

Column: Select the column you want to customize.

Size: Enter the column size.

Padding char: Type in between quotes the padding characters used. A space by default.

Alignment: Select the appropriate alignment parameter.

Keep: If the data in the column or in the field are too long, select the part you want to keep.

Advanced settings

Advanced separator (for numbers)

Select this check box to modify the separators used for numbers:

Thousands separator: define separators for thousands.

Decimal separator: define separators for decimals.

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, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch 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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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 or tS3Configuration.

This connection is effective on a per-Job basis.