tExtractPositionalFields MapReduce properties (deprecated) - 7.3

Prev.Comp.Column list

Select an incoming field from the Field list to extract.

Customize

Select this check box to customize the data format of the positional file and define the table columns:

Column: Select the column you want to customize.

Size: Enter the column size.

Padding char: Type in between inverted commas the padding character used, in order for it to be removed from the field. A space by default.

Alignment: Select the appropriate alignment parameter.

Pattern

Enter the pattern to use as basis for the extraction.

A pattern is length values separated by commas, interpreted as a string between quotes. Make sure the values entered in this fields are consistent with the schema defined.

Die on error

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.

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.

Click Sync columns to retrieve the schema from the previous component connected in the Job.

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.

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.

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 Column

Select this check box to remove leading and trailing whitespace from all columns.

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.

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

This component handles flow of data therefore it requires input and output components. It allows you to extract data from a delimited field, using a Row > Main link, and enables you to create a reject flow filtering data which type does not match the defined type.

In a Talend Map/Reduce Job, this component is used as an intermediate step and 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.

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.

For further information about a Talend Map/Reduce Job, see the sections describing how to create, convert and configure a Talend Map/Reduce Job of the Talend Big Data Getting Started Guide .

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.