# tSample Returns a sample subset of the data being processed. tSample generates a sample dataset from the incoming flow. ## tSample properties for Apache Spark Batch These properties are used to configure tSample running in the Spark Batch Job framework. The Spark Batch tSample component belongs to the Processing family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | 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 | Usage guidance | Description | | --- | --- | | **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 | In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

Yarn mode (Yarn client or Yarn cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
- When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis. |