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This roadmap describes how to configure Eclipse V4.3 IDE with the Py Dev V4.x plugin in order to develop with Python V2.6 or higher, Spark V1.5 or Spark V1.6, in local running mode and also in cluster mode with Hadoop YARN.

The Py Dev plugin enables Python developers to use Eclipse as a Python IDE.

First you will install Eclipse, Spark and Py Dev, then you will configure Py Dev for Spark.

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If you want to specify your own Spark configuration directory (default=SPARK_HOME/conf), add the variable If later you are going to experience some issues with the variable $, a workaround is to overload the SPARK_CONF_DIR variable by right-clicking on the Py Dev source you want to configure and go to the menu: Run As Now you are ready to develop with Eclipse all types of Spark project you want.

So you will now create the code example named “Count Words”.

Go to the Eclipse website then download and uncompress Eclipse V4.3 (Kepler) on your computer: Finally launch Eclipse and create your own workspace.

Go to the Spark website then download and uncompress on your computer Spark V1.5 or V1.6 pre-built with the Hadoop version of your choice (e.g: “Pre-built for Hadoop 2.6 and later”): Click the button [Yes] to restart Eclipse and for the changes to take effect.

Thus in a same web-based Python Notebook project (e.g: Jupyter), those Data Scientists may execute some cells of code vertically on the Notebook server, and also other cells of code horizontally on a Spark cluster.

But in a general way, what about if Data Scientists want their new projects in Python to be more industrial ?

The example below will count the frequency of each word present in the “README.md” file belonging to the Spark installation.

To allow that, the well-known Map Reduce paradigm will be operated in memory by using the two Spark transformations named “flat Map” and “reduce By Key”. To allow this, go to your Spark home directory and copy the template file “conf/log4j.properties.template” into your Eclipse project to “conf/log4j.properties“, then modify your log4j file to specify the log levels you want.

Finally you will end this article by the following topics: The Spark Python API (Py Spark) exposes the Spark programming model to Python.

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