August 16, 2023

tWindow – Docs for ESB 6.x

tWindow

Applies a given Spark window on the incoming RDDs and sends the window-based RDDs
to its following component.

tWindow enables the Spark Job you are designing to
perform window operations. For further information about a Spark window, see the related
documentation at Window operations.

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 the Palette of the Studio only if you have subscribed to Talend Real-time Big Data Platform or 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

You need to use the Spark Configuration tab in
the Run view to 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: when using Google
    Dataproc, specify a bucket in the Google Storage staging
    bucket
    field in the Spark
    configuration
    tab; when using other distributions, use a
    tHDFSConfiguration
    component to specify the directory.

  • Standalone mode: you need to choose
    the configuration component depending on the file system you are using, such
    as tHDFSConfiguration
    or tS3Configuration.

This connection is effective on a per-Job basis.

Related scenarios

For a related scenario, see Analyzing a Twitter flow in near real-time.


Document get from Talend https://help.talend.com
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