# tWindow properties for Apache Spark Streaming These properties are used to configure tWindow running in the Spark Streaming Job framework. The Spark Streaming tWindow component belongs to the Processing family. The streaming version of this component is available in Talend Real Time Big Data Platform and in Talend Data Fabric. ## Basic settings | Window duration | Enter, without quotation marks, the duration (in milliseconds) that defines the length of
the window to be applied.

For example, if the batch size defined in the Spark configuration tab is 2 seconds, a
window duration of 6 seconds means that 3 batches are handled each time this window is
applied. | | --- | --- | | Define the slide duration | Select the Define the slide duration check box and in the
field that is displayed, enter, without quotation marks, the time in milliseconds at the end
of which the window is to be applied.

For example, if the batch size defined in the Spark
configuration tab is 2 seconds, a slide duration of 4 seconds means the window is
applied every 4 seconds; and if the window duration is 6 seconds, after two window
applications there will be the overlap of one batch.

If you leave this check box clear, the slide duration is assumed to be the batch size
defined in the Spark configuration tab.

Both the window duration and the slide duration must be multiples of the batch size defined in the Spark
configuration tab. | ## Usage | **Usage rule** | This component is used as an intermediate step.

This component does not change the data schema but controls the pace of the processing of
the micro-batches via the specific window.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming 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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
- 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. |