tSample properties for Apache Spark Batch

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.