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.

Sampling fraction

Enter the sample size ratio to the data being processed. For
example, enter 0.1, then the
ratio of the sampled data to the total data being processed is
10%.

Use a seed for random number generator

Enter a positive seed number (the starting number for a random
sequence of generated numbers) so that the same sample is
reproducible.

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 is an intermediate component that passes sampled datasets to the
component that follows. This sampling proceeds without
replacement.

Note that the knowledge of statistics and sampling is
required.

This component, along with the MapReduce family it belongs to, appears only when you are
creating a Map/Reduce 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, 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.

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.

Sampling with replacement

Select this check box to proceed the sampling with replacement to
make each sampling result independent from each other. If you keep
this check box clear, the sampling goes without replacement.

Sampling fraction

Enter the sample size ratio to the data being processed. For
example, enter 0.1, then the
ratio of the sampled data to the total data being processed is
10%.

Use a seed for random number generator

Enter a positive seed number (the starting number for a random
sequence of generated numbers) so that the same sample is
reproducible.

Usage rule

This component is an intermediate component that passes sampled datasets to the
component that follows.

Note that the knowledge of statistics and sampling is
required.

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.