tHDFSInput MapReduce properties (deprecated)

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 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.

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 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 mapreduce.input.fileinputformat.input.dir.recursive to be true in the Hadoop properties table in the Hadoop configuration tab.

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

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

• DEFLATE: *.deflate

• gzip: *.gz

• bzip2: *.bz2

• LZO: *.lzo

Note that you need to ensure you have properly configured the connection to the Hadoop distribution to be used in the Hadoop configuration tab in the Run view.

Die on error

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

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Type

Row separator

The separator used to identify the end of a row.

This field is not available for a Sequence file.

Field separator

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

This field is not available for a Sequence file.

Header

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

For example, enter 0 to ignore no rows for the data without header and set 1 for the data with header at the first row.

This field is not available for a Sequence 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.

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

This option is not available for a Sequence file.

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.

tStatCatcher Statistics

Select this check box to collect log data at the component level.

Global Variables

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.

Usage rule

In a Talend Map/Reduce Job, it is used as a start component and requires a transformation component as output link. The other components used along with it must be Map/Reduce components, too. They generate native Map/Reduce code that can be executed directly in Hadoop.

Once a Map/Reduce Job is opened in the workspace, tHDFSInput as well as the MapReduce family appears in the Palette of the Studio.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs, and non Map/Reduce Jobs.

Hadoop Connection

You need to use the Hadoop Configuration tab in the Run view to define the connection to a given Hadoop distribution for the whole Job.

This connection is effective on a per-Job basis.