tSparkFilterRow – Docs for ESB 5.x

Component family

Big Data / Spark

Function

tSparkFilterRow reads datasets
incoming from its preceding Spark component and select input rows by
setting one or more conditions on the specified columns.

Purpose

tSparkFilterRow allows you to set
sophisticated conditions to select the data to be used.

Basic settings

Schema and Edit
Schema

A schema is a row description. It defines the number of fields to be processed and passed on
to the next component. The schema is either Built-In or
stored remotely in the Repository.

Click Edit schema to make changes to the schema. If the
current schema is of the Repository type, three options are
available:

Filter configuration

Click the [+] button beneath the
Filter configuration table to
set one or more filter conditions.

Logical: select a logical operator
to combine the filter conditions. The first row of this column is
automatically ignored.

Column: select the column of the
schema the filter is to be applied on

Operator: select the operator to
bind the input column with the value

Value: type in the filtered value,
between quotes if needed, for example, if the value is a
string.

Advanced settings

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

This component is an intermediate component of a Spark
process.

Limitations

It is strongly recommended to use this component in a Spark-only Job, that is to say, to
design and run a Spark Job separately from the non Spark components or Jobs. For example, it
is not recommended to use the tRunJob component to
coordinate a Spark Job and a non Spark Job, or to use the tHDFSPut component along with the Spark components in the same Job.