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When dealing with a repetitive workflow such as spreadsheet row/column manipulation, there is no support for conditional interaction during a sequential workflow run.

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Originally, Kepler was designed to download data sets into a cache on the machine where it is running, so Kepler actors run as local Java threads. However, in order to provide access to web-based resources, actors have been implemented that spawn distributed execution threads to access distributed resources.

Using Jython in Kepler

Python scripts run in a Jython interpreter within the same JVM as Kepler. This interpreter is only instantiated once so it does not need to be reloaded by each Python actor. On the downside, a global variable set in one actor can cause unexpected side effects if it is reset in another actor. We discovered that if a function is defined as a global (i.e. outside of a class), the method name cannot be reused. The Jython interpreter will always persist the code from the first instantiation of the method.

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Kepler 1.0

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Add-on packages such as FCRepoKepler must be put in the user's home directory under the Lib directory. For example, on a Windows box, this would be C:\Documents and Settings\username\Lib.

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Kepler 2.0 (dev)

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Add-on packages such as FCRepoKepler must be put in the Kepler installation directory under the core/lib/jar/Lib directory.

Using R and MATLAB in Kepler

R . R and Matlab actors require that those applications be installed outside of Kepler. When an associated script is run, the code and data are passed to an external process running R or Matlab.  The actor waits for that process to finish before proceeding. Thus workflows with multiple R/Matlab steps will incur the corresponding application startup overhead for each instance of the actor. This can significantly affect workflow performance. Kepler is a very good tool for building scientific workflows for those who are familiar with software development. However, it is not particularly friendly to those who are used to .  (Google Code project hydrant-kepler discussion of this issue)Originally, Kepler was designed to download data sets into a cache on the machine where it is running, so Kepler actors run as local Java threads. However, in order to provide access to web-based resources, actors have been implemented that spawn distributed execution threads to access distributed resources.

Issues with Kepler 1.0

Python actors use the Jython 2.2.1 interpreter which implements a subset of the Python 2.2 language. See http://www.jython.org/archive/22/index.html for details. The PythonActor is not in the list of availbale actors. In order to use it you must open one of the Python demo worflows and copy one of those actors to your new workflow.

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Kepler 2.0 is very promising with a lot of features that will help our project but it has proven to be very buggy at times. Python actors use the Jython 2.5.1 interpreter which implements a most of the Python 2.5 language. See http://wiki.python.org/jython/JythonFaq/GeneralInfo#IsJythonthesamelanguageasPython.3F for details. This is a significant improvement of the version of Jython supplied with Kepler 1.0.

Downloads

Kepler 1.0

Download Kepler 1.0 at https://kepler-project.org/users/downloads

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