tFileInputDelimited properties for Apache Spark Batch

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.

For further information about the Hadoop Cluster node, see Managing Hadoop connection metadata.

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 you make changes, the schema automatically becomes built-in.

The dynamic column in the schema is available when you have installed the 8.0.1-R2022-09 Talend Studio Monthly update or a later one delivered by Talend. For more information, check with your administrator. Note that the dynamic column is only available when you set a header. Make sure to put 1 as a value for the Header property. When you use the dynamic columns, the columns are loaded as strings by default.

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/File

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

If the file to be read is a compressed one, enter the file name with its extension; then this component automatically decompresses it at runtime. The supported compression formats and their corresponding extensions are:

• DEFLATE: *.deflate

• gzip: *.gz

• bzip2: *.bz2

• LZO: *.lzo

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.

Die on error

Select the check box to stop the execution of the Job when an error occurs.

Use S3 Select

This check box is only available when you use a tS3Configuration component as a storage configuration component and when you run your Job with Spark Universal in either YARN cluster (with an Amazon EMR cluster) or Databricks modes.

• Use GZIP compression: select this check box to use GZIP compression on your files.
• Comment character: enter the character to be used to comment.
• Null value: enter the value to be considered as null.
• Values can contain quoted record delimiters: select this check box to allow quoted record delimiters in the values. This option applies only for Databricks.

This option is available only when you have installed the 8.0.1-R2022-12 Talend Studio Monthly update or a later one delivered by Talend. For more information, check with your administrator.

Row separator

The separator used to identify the end of a row.

Field separator

Enter a character, a string, or a regular expression to separate fields for the transferred data.

Header

Enter the number of rows to be skipped in the beginning of file.

CSV options

This check box is only available when you select the CSV options check box.

Select this check box to enable two lines to result in one in the input file.

This check box is only available when you select the CSV options check box.

Skip empty rows

Select this check box to skip the empty rows.

Set minimum partitions

Select this check box to control the number of partitions to be created from the input data over the default partitioning behavior of Spark.

In the displayed field, enter, without quotation marks, the minimum number of partitions you want to obtain.

When you want to control the partition number, you can generally set at least as many partitions as the number of executors for parallelism, while bearing in mind the available memory and the data transfer pressure on your network.

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.

Then select the encoding to be used from the list or select Custom and define it manually.

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Trim all columns

Select this check box to remove the leading and trailing whitespaces from all columns. When this check box is cleared, the Check column to trim table is displayed, which lets you select particular columns to trim.

Check column to trim

This table is filled automatically with the schema being used. Select the check box(es) corresponding to the column(s) to be trimmed.

Check each row structure against schema

Select this check box to check whether the total number of columns in each row is consistent with the schema. If not consistent, an error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Decode String for long, int, short, byte Types

Select this check box if any of your numeric types (long, integer, short, or byte type), will be parsed from a hexadecimal or octal string.

Usage rule

This component is used as a start component and requires an output 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

This connection is effective on a per-Job basis.