tSchemaComplianceCheck Standard properties

Base 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.

It describes the structure and nature of your data to be processed as it is.

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.

Check all columns from schema

Select this option to carry out all checks on all columns against the base schema.

Custom defined

Select this option to carry out particular checks on particular columns. When this option is selected, the Checked Columns table shows.

Checked Columns

In this table, define what checks are to be carried out on which columns. This table appears when you select the Custom defined option.

Column: Displays the columns names.

Type: Select the type of data each column is supposed to contain. This validation is mandatory for all columns.

Date pattern: Define the expected date format for each column with the data type of Date.

Nullable: Select the check box in an individual column to define the column to be nullable, that is, to allow empty rows in this column to go to the output flow regardless of the base schema definition. To define all columns to be nullable, select the check box in the table header.

Max length: Select the check box in an individual column to verify the data length of the column against the length definition of the base schema. To carry out this verification on all the columns, select the check box in the table header.

Use another schema for compliance check

Define a reference schema as you expect the data to be, in order to reject the non-compliant data.

It can be restrictive on data type, null values, and/or length.

Trim the excess content of column when length checking chosen and the length is greater than defined length

With any of the three modes of tSchemaComplianceCheck, select this check box to truncate the data that exceeds the length specified rather than reject it.

Use Fastest Date Check

Select this check box to perform a fast date format check using the TalendDate.isDate() method of the Talend Date system routine if Date pattern is not defined. For more information about routines, see What are routines.

Use Strict Data Check

Once selected, the Use Fastest Data Check check box will be hidden.

Ignore TimeZone when Check Date

Select this check box to ignore the time zone setup upon date check.

Not available when the Check all columns from schema mode is selected.

Treat all empty string as NULL

Select this check box to treat any empty fields in any columns as null values, instead of empty strings.

By default, this check box is selected. When it is cleared, the Choose Column(s) table shows to let you select individual columns.

Check string by byte length

Select this check box to check the string column with byte length according to the charset set in the Charset text box.

By default, this check box is not selected and only character length is checked.

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 more information about variables, see Using contexts and variables.

Usage rule

This component is an intermediary step in the flow allowing to exclude from the main flow the non-compliant data. This component cannot be a start component as it requires an input flow. It also requires at least one output component to gather the validated flow, and possibly a second output component for rejected data using Rejects link. For more information, see Rejects.