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Introduction
Let’s have a look under the hood of PySpark
Requirements
A brief note about Scala
Step 1: Installing Eclipse
Step 2: Installing Spark
Step 3: Installing PyDev
Step 4: Configuring PyDev with a Python interpreter
Step 5: Configuring PyDev with Py4J
Step 6: Configuring PyDev with Spark’s variables
Step 7: Creating your Python-Spark project “CountWords”
Step 8: Executing your Python-Spark application with Eclipse
Step 9: Reading a CSV file directly as a Spark DataFrame for processing SQL
Step 10: Executing your Python-Spark application on a cluster with Hadoop YARN
Step 11: Deploying your Python-Spark application in a Production environment

Install Office 2019 or 2016 on a PC. Remember a one-time purchase version of Office is licensed for one install only. Depending on your browser, select Run (in Edge or Internet Explorer), Setup (in Chrome), or Save File (in Firefox). Select the TOOLS menu and then WORD COUNT. A dialogue box will appear containing the character count. Letter counter in LibreOffice: LibreOffice 4 displays the character count in the status bar at the bottom of the program, along with the word count. For a detailed character count, select the TOOLS menu and then WORD COUNT.

Introduction

Python is one of the most famous programming language used by Data Scientists who develop programs in order to process Feature Engineering and Machine Learning algorithms by using rich APIs like Scikit-Learn and Pandas on a single multi-cores server.

However, Spark SQL with the DataFrames and Spark Machine Learning enable Data Scientists who want to develop in Python of increasing their program’s performances using a cluster. 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 ?

In addition of using a web-based notebook development environment, there are many benefits for them for also developing with an IDE like Eclipse. Here’s some of these benefits: Improving industrialization of development processes, enabling bigger projects, better alignment with the methodologies and tools recommended by the company’s IT, easier integration with the version control systems, test-driven approach more natural, and so on… Let’s also note that for developing on a Spark cluster with Hadoop YARN, a notebook client-server approach (e.g: like with Jupyter and Zeppelin notebook servers) forces developers to depend on the same YARN configuration which is centralized on the notebook server side. In contrast, an IDE approach by using Eclipse allows developers to create their own YARN configuration.

This roadmap describes how to configure Eclipse V4.3 IDE with the PyDev 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 PyDev plugin enables Python developers to use Eclipse as a Python IDE.

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

Then you will execute in Eclipse the basic example code “Word Counts” which perfoms both Map and Reduce tasks in Spark.

Finally you will end this article by the following topics:

  • How to read a CSV file directly as a Spark DataFrame for processing SQL.
  • How to execute your Python-Spark application on a cluster with Hadoop YARN.
  • How to deploy your Python-Spark application in a production environment.

Let’s have a look under the hood of PySpark

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The Spark Python API (PySpark) exposes the Spark programming model to Python.

By default, PySpark requires python (V2.6 or higher) to be available on the system PATH and uses it to run programs. In a Spark cluster architecture this PATH must be the same for all nodes.

Let’s note that PySpark applications are executed by using a standard CPython interpreter (in order to support Python modules that use C extensions). But an alternate Python executable may be specified by setting the PYSPARK_PYTHON environment variable.
=> e.g: If you prefer to use the Python embedded in Anaconda, then the PYSPARK_PYTHON value should be something like “/home/foo/anaconda/bin/python2.7”.

All of PySpark’s library dependencies (including Py4J) are bundled with PySpark and automatically imported. In the Python driver program, SparkContext uses Py4J in order to launch a JVM with a JavaSparkContext.

Py4J is a bridge between Python and Java. It is only used on the driver for local communication between the Python and the Java SparkContext objects.

The RDD transformations in Python are mapped to transformations on PythonRDD objects in Java.

For more details about installing (and configuring) of PySpark:
https://spark.apache.org/docs/0.9.2/python-programming-guide.html

For more details about PySpark Internals:
https://cwiki.apache.org/confluence/display/SPARK/PySpark+Internals

Requirements

Let’s note that Spark V1.5 or V1.6 runs on Java V7+ and Python V2.6+, so you will need on your computer:

  • A JVM V7 or higher
  • A python V2.6 or higher

The following installation roadmap has been carried out with Spark V1.5.2, a JVM V7 and a Python interpreter V2.7.

A brief note about Scala

Keep in mind that a great idea could consist to use later the same Eclipse configuration with Spark in order to develop both in Python and Scala.

To allow a such configuration, it’s important to note that Spark V1.5 or V1.6 needs a Scala API that is compatible with Scala version V2.10.x.

That’s why this article refers for convenience to Eclipse V4.3 (Kepler) because of its compatibility with Scala V2.10.

Step 1: Installing Eclipse

Go to the Eclipse website then download and uncompress Eclipse V4.3 (Kepler) on your computer:
http://www.eclipse.org/downloads/packages/release/Kepler/SR2

Finally launch Eclipse and create your own workspace.

Step 2: Installing Spark

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”):
https://spark.apache.org/downloads.html

Step 3: Installing PyDev

From the Eclipse IDE:
Go to the menu Help > Install New Software…

From the “Install“ window:
Click on the button [Add…].

From the “Add Repository” dialog box:
Fill the field Name: PyDev
Fill the field Location: http://pydev.sf.net/updates
Validates with the button [OK].

From the “Install“ window:
Check the name PyDev and click on the button [Next >], then [Next>] again after downloadings.
Accept the terms of the license agreement and click on the button [Finish], then the PyDev installation will start.

If a “Security Warning” window appears:
If the following warning message appears: “Warning: you are installing software that contains unsigned content…”, then click on the button [OK]. Or if a message box appears like “Do you trust theses certificates?”, then select the certificate and click on the button [OK].

From the “Sofware Updates” window:
Click the button [Yes]to restart Eclipse and for the changes to take effect.

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Now PyDev V4.x+ is installed in Eclipse.
But for the moment you can’t develop in Python because PyDev is not configured yet.

Step 4: Configuring PyDev with a Python interpreter

Like PySpark, PyDev requires a Python interpreter installed on your computer.

The following installation has been carried out with a Python interpreter V2.7.

From Eclipse IDE:
Open the PyDev perspective (use the drop-down list on the top right of Eclipse to select the PyDev perspective).
Go to the menu Eclipse > Preferences… (on Mac), or Window > Preferences… (on Linux and Windows).

From the “Preferences” window:
Go to PyDev > Interpreters > Python Interpreter

Click on the button [Advanced Auto-Config].
Eclipse will introspect all the existing Python installations on your computer.

Choose a Python interpreter version V2.6+ or higher:

Validates with the button [OK].

From the “Selection needed” window:
Click on the button [OK] to accept folders having to be added to the system PYTHONPATH.

From the “Preferences” window:
Validates with the button [OK].

Now PyDev is configured in Eclipse.
You can develop in Python but not with Spark yet.

Step 5: Configuring PyDev with Py4J

You are now going to configure PyDev with Py4J (the bridge between Python and Java), this package is already included in PySpark.

Remember that Py4J is not a Python interpreter. Py4J is only used on the driver for local communication between the Python and Java SparkContext objects.

From Eclipse IDE:
Check out that you are on the PyDev perspective.
Go to the menu Eclipse > Preferences… (on Mac), or Window > Preferences… (on Linux and Windows).

From the “Preferences” window:
Go to PyDev > Interpreters > Python Interpreter.

Click on the button [New Folder].
Choose the python folder just under your Spark home directory and validate:

Click on the button [New Egg/Zip(s)].
From the File Explorer at the bottom right, select [*.zip] rather [*.egg].
Choose the file py4j-0.8.2.1-src.zip just under your Spark folder python/lib and validate:

Validates with the button [OK].

Now PyDev is configured with Py4J.
But you can’t execute Spark while its variables are not configured.

Step 6: Configuring PyDev with Spark’s variables

It’s important to configure PyDev with Spark’s variables in order to execute some code with Spark.

Note: A little further, it’s strongly recommended to specify absolute values for Spark variables in Eclipse instead of to reuse system or user environment variables. In fact, Eclipse IDE seems to be blind with the environment variables already configured in your system (e.g: with Linux Ubuntu the exported variables contained in your file “.profile” risk to be unknown for Eclipse).

From Eclipse IDE:
Check that you are on the PyDev perspective.
Go to the menu Eclipse > Preferences… (on Mac), or Window > Preferences… (on Linux and Windows).

From the “Preferences” window:
Go to PyDev > Interpreters > Python Interpreter.

Click on the central tab [Environment].
Click on the button [New…] (close to the button [Select…]) to add new variable.

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Add the variable SPARK_HOME as shown in the examples below then validate:

Advice: Use an absolute path, don’t use any environment variables already configured in your system such as another SPARK_HOME or others environment variables.

Add also the variable PYSPARK_SUBMIT_ARGS and its value as shown below then validate:

The “*” of “local[*]” indicates Spark that it must use all the cores of your machine.

The –queue option specifies the queue name (“PyDevSpark1.5.2”) that is used from Spark supervising tools.

If you want to specify your own Spark configuration directory (default=SPARK_HOME/conf), add the variable SPARK_CONF_DIR containing your new configuration directory as showed in the examples below then validate:

As shown above in the example 2, you can refer dynamically to each PyDev project by using the Eclipse variable ${project_loc}.

If later you are going to experience some issues with the variable ${project_loc}, a workaround is to overload the SPARK_CONF_DIR variable by right-clicking on the PyDev source you want to configure and go to the menu: Run As > Run Configurations…, and create into the “Environment” tab the SPARK_CONF_DIR variable as described above in the example 2.

Add the variable SPARK_LOCAL_IP to specify your local IP address instead of ‘127.0.0.1’:

Occasionally you can add other variables like TERM and so on:

Advice: If you already have an existing environment variable named SPARK_MEM in your OS session, please get rid of it. This variable is deprecated and risks to create some conflicts with other parameters later by using Hadoop YARN.

Validate with the button [OK].

Example of Spark variables in “Preferences” window

Now PyDev is full ready to develop in Python with Spark.

Step 7: Creating your Python-Spark project “CountWords”

Now you are ready to develop with Eclipse all types of Spark project you want. So you will now create the code example named “CountWords”.

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 MapReduce paradigm will be operated in memory by using the two Spark transformations named “flatMap” and “reduceByKey”.

Create the new project:
Check that you are on the PyDev perspective.
Go to the Eclipse menu File > New > PyDev project
Name your new project “MyPythonSparkProject”, then click on the button [Finish].

Create a configuration folder:
This step is particularly usefull if you plan to use your own file “log4j.properties”.

To add a config folder in order to put your own log4j file, right-click on the project icone and do: New > Folder.
Name the new folder “conf”, then click on the button [Finish].

It’s recommended to handle your own “log4j.properties” files for your PyDev projects. 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.

If you experience some issues with the variable SPARK_CONF_DIR and its value ${project_loc}, a workaround is to overload the SPARK_CONF_DIR variable by right-clicking on the PyDev source you want to configure and go to the menu: Run As > Run Configurations…, and create into the “Environment” tab the SPARK_CONF_DIR variable as described below then validate:

Create a source folder:
To add a source folder in order to create your Python source, right-click on the project icone and do: New > Folder
Name the new folder “src”, then click on the button [Finish].

Create your source code:
To add your new Python source, right-click on the source folder icon and do: New > PyDev Module.
Name the new Python source “MyWordCounts”, then click on the button [Finish], then click on the button [OK].

Copy-paste the following Python code below into your PyDev module “MyWordCounts.py”:

In PyDev please be cautious with unused imports and also the unused variables. Please comment them all, otherwise you will get some errors at the execution. Note that even the following directives like @PydevCodeAnalysisIgnore and @UnusedImport aren’t be able to solve that kind of issue.

Step 8: Executing your Python-Spark application with Eclipse

To execute your code, right-click on the Python module “MyWordCounts.py”, then choose Run As > 1 Python Run.

Have fun 🙂

Step 9: Reading a CSV file directly as a Spark DataFrame for processing SQL

To read a CSV file as a Spark DataFrame in order to process SQL, you will need to import the Databricks spark-csv library with its dependencies. For information about spark-csv, please visit the GitHub project available at: https://github.com/databricks/spark-csv

There’s an online method to import automatically the spark-csv library with its dependencies from Internet, it’s via the option –package during the launching of your Spark application. This option forces to download from Internet the new libraries and stores them into your local directory “~./ivy2/jars”:

You can also download explicitly the new libraries from a repository via Internet.

The paths for downloading spark-csv and its dependencies from a Maven repository:
commons-csv-1.1.jar: http://mvnrepository.com/artifact/org.apache.commons/commons-csv/1.1
spark-csv_2.10-1.2.0.jar: http://mvnrepository.com/artifact/com.databricks/spark-csv_2.10/1.2.0
univocity-parsers-1.5.1.jar: http://mvnrepository.com/artifact/com.univocity/univocity-parsers/1.5.1

When you launch your Spark application in offline mode, either you use the option –jars for specifying exactly the file system paths where your libraries are, or you can use the option –package but only if you already launched your application before at least once in online mode.

The steps below are going to use the option –jars because of deployment facilities especially in offline mode. For instance you will embed the new jar libraries in the same directory as your Spark installation.

Let’s create under your Spark home a sub-directory named “lib-external”, then let’s copy the three libraries inside it:

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From the “Preferences” window of your Eclipse:
Go to PyDev > Interpreters > Python Interpreter.

Click on the central button [Environment].
Update the variablePYSPARK_SUBMIT_ARGS as the example below (by removing the characters at the end of the lines) then validate:

Advice: Concerning the path values above, it’s strongly recommended to use absolute paths instead of including system environment variables.

Validate with the button [OK].

Now you are ready to load CSV files directly as Spark DataFrames, let’s try an example.

Download an open-source data sample related to the bank domain:
http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip
Extract the “bank.csv’ file from this zip file.

Note that if you want to adapt your code to visualize your data with a diagram like a Bar-Chart, then you will have to install MathPlotLib:

Create a new Python source code:
To add your new Python source, right-click on the source folder icon and do: New > PyDev Module.
Name the new Python source “MyBankDataFrame”, then click on the button [Finish], then click on the button [OK].

Copy-paste the following Python code below into your PyDev module “MyBankDataFrame.py” and modify the bankCsvFile variable (line number 7) with the right path corresponding to the “bank.csv” file you have uncompressed. If you see at the line number 52 the HTML code ‘&lt;‘ or ‘&amp;lt;‘, please replace it by the character ‘<‘:

To execute the code above, right-click on the Python module “MyBankDataFrame.py”, then choose Run As > 1 Python Run.

Version

Have fun 🙂

Output 1:

Output 2 (the 10 first rows of the DataFrame):

Output 3:

Output 4 – The diagram representing the SQL result:

Step 10: Executing your Python-Spark application on a cluster with Hadoop YARN

If you want to execute remotely your Python-Spark application on a Hadoop cluster, then you will need to specify the Spark mode “yarn-client” (see a little further). This option indicates to Hadoop YARN that your Spark driver will run on your computer and the rest of tasks on the Hadoop YARN cluster.

For more informations about the differences between the two modes “yarn-client” and “yarn-cluster” please visit the following web page: http://spark.apache.org/docs/latest/running-on-yarn.html

You will have also to specify the memory used by the driver (option –driver-memory), the number of executors needed on the YARN cluster (option –num-executors), the memory used for each executor (option –executor-memory) and the number of cores for each executor (option –executor-cores).

The YARN client side configuration consists to a set of files that you have to copy from one of the nodes of your YARN cluster to your computer where you installed Eclipse. The YARN client side configuration files are often installed on each node in the directory “/etc/hadoop/“, so you will have to copy that directory to your computer. Try to keep the same target path on your computer (“/etc/hadoop/”), however if you can’t do that then you will have to parse all the files of this directory to replace the strings “/etc/hadoop/” by the path value corresponding to your new directory on your computer. Also maybe you will need to replace some DNS addresses by IP addresses (e.g: host1 by 192.168.1.11), or more simply to modify your local file ‘/etc/hosts’. Once you have performed this step, you will have to configure the YARN_CONF_DIR variable like explained further.

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Note that concerning your Spark version and YARN, the deployment of your Spark on every node of the YARN cluster will be automatic. Indeed when you launch your Spark application, YARN will download from the driver side (your computer) your Spark version to deploy it onto every node of its cluster.

Now let’s say you want to execute on the YARN cluster your program named “MyBankDataFrame.py”. For that, instead of going to the main menu “PyDev > Interpreters > Python Interpreter” for updating the common YARN_CONF_DIR variable, it’s possible to overload this variable for each Python source that you want to run on YARN.

From Eclipse IDE, right-click on your Python file “MyBankDataFrame.py” then:
Choose Properties, then Run/Debug Settings, then click on the configuration corresponding to your Python file.

Click on the button [Edit] then select the tab [Environment].

Add the new variable YARN_CONF_DIR as shown in the example below then validate:

To run your application on cluster, you will need to overload the variable PYSPARK_SUBMIT_ARGS as showed in the example below (don’t forget to remove the character at the end of each line) then validate:

In the example above, the Spark parameters for running on YARN correspond to a Hadoop cluster with 5 nodes, assuming we could execute roughly 5 concurrent drivers, each node has maximum 96 GBytes of memory with a 4 cores processor 2.3 GHz.

Advice:Concerning the path values above, it’s strongly recommended to use absolute paths instead of including system environment variables.

Validate with the button [OK].

Before to execute your Python code on YARN you will need to upload your CSV file on HDFS.

Example of shell commands for uploading the ‘bank.csv’ file to HDFS:

Finally from your Python code you will need to change the path of your CSV file. Because of using YARN, this path isn’t a local File System path, but a HDFS path. So let’s comment the line with the local File System access (line number 7) and let’s enable the line with the HDFS access (line number 13) as below. If you use the URL notation don’t forget to configure the Port and the IP address of your HDFS NameNode, and the HDFS account (e.g: hadoop). You can also use directly the HDFS path (e.g: /user/hadoop/bank.csv):

To execute your code on Hadoop YARN, right-click on the Python module “MyBankDataFrame.py”, then choose Run As > 1 Python Run.

Have fun 🙂

Step 11: Deploying your Python-Spark application in a Production environment

For a deployment in a production environment, let’s say you have decided to embed all your Python application in one directory including your Spark and the libraries you downloaded previously. Moreover you also had the good idea to create your own shell variables like in the example below to facilitate that deployment:

Execution in a production environment with Hadoop YARN

The shell script for executing your application on Hadoop YARN could be like:

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Quick reminder concerning the execution in local mode

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In certain case you’d like to execute in local mode the application you deployed in production environment (e.g: for performance comparisons). So, your shell script could be like:

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Have fun 🙂