Month: September 2021

I write a fair amount of Spark applications that run on Hadoop platforms and do lots of data parsing, transformation, and loading to HDFS, Hive, or other data repositories. When I code my applications in Scala, I usually use Eclipse and the ScalaTest framework to test my work.

However, I like to write PySpark solutions, too, but haven’t found a great way to test my solutions in an editor like VS Code. Recently, though, it occurred to me that maybe I could just test my code in a ready-made Hadoop environment, like a Docker image for Hadoop. So, here’s a sample application I put together to unit test a PySpark application with Docker.

Setting up my virtual environment

Python has a neat way of creating virtual environments for your projects so you can keep your projects’ dependencies safely isolated from each other. Here are the general steps I followed to create a virtual environment for my PySpark project:

1. In my WSL2 command shell, navigate to my development folder (change your path as needed): cd /mnt/c/Users/brad/dev
2. Create a directory for my project: mkdir ./pyspark-unit-testing
3. Enter the new project folder: cd pyspark-unit-testing
4. Create a subdir for my tests: mkdir ./tests
5. Create my virtual environment (note: I initially had some issues doing this in WSL2 and found this blog post helpful in overcoming them): python3 -m venv app-env
6. Start the new virtual environment: source app-env/bin/activate
7. Install the pytest package: pip3 install pytest –trusted-host pypi.org –trusted-host files.pythonhosted.org
8. (Optional) Create a requirements doc of your development environment: pip3 freeze -> requirements-dev.txt
9. Leave the virtual environment: deactivate
10. Now, fire up VS Code: code .

Download the Databricks Spark-XML binary

I’ve coded this sample application to ingest an XML file and transform it into a Spark dataframe. Much of the time, you can easily parse such XML files using the Databricks Spark-XML library. This library, though, is not native to Spark, so you must load it as you would a third party library. So, my project will demonstrate that, too! A two-for-one! You can download the JAR file I used from its Maven repository and save it to your project directory.

Write some code

I kept my code sample extremely simple and the full project is available on Github. Here’s a small snippet of my function that takes an XML file, transforms the data into a dataframe, and then returns the dataframe:

def parse_xml(xml_file):
    """Simple function using the Databricks Spark-XML API to parse XML documents into dataframes"""
    df = spark.read.format('com.databricks.spark.xml').\
        option('rootTag', 'catalog').\
        option('rowTag', 'book').load(xml_file)
    return df

Yeah, basically two lines of code…thanks to the Databricks API. If your XML is simple enough, the API can tackle it, but I’ve had some rather horrible XML to parse in the past that went well beyond the capabilities of the Spark-XML library.

Write some tests to test your code

I expect my client application to take an XML file and return a valid dataframe, but I need to test those expectations with unit tests. Here’s two I wrote for this sample application:

def test_parse_xml():
    test_file = './tests/test.xml'
    test_df = mpc.parse_xml(test_file)
    
    assert test_df is not None, 'Expected a dataframe of data to be returned from function'
    assert test_df.count() == 12, 'Received unexpected count from test data'
    assert len(test_df.columns) == 7, 'Expected 7 columns in the test data'

def test_something_else():
    # some other test
    assert True

Set up your Docker container

For most of my Spark needs, I like to use the Jupyter “all-spark-notebook” image. I tend to use the spark-2 version since I usually use Spark v2 at work, so I pulled this image like so:

docker pull /jupyter/all-spark-notebook:spark-2

My Dockerfile is quite simple. It’s two big jobs are copying the Databricks JAR to the container and installing my Python dependencies, which, for this project, is just the pytest package:

# from https://hub.docker.com/r/jupyter/all-spark-notebook
FROM jupyter/all-spark-notebook:spark-2

WORKDIR /home/jovyan/work/

# from https://mvnrepository.com/artifact/com.databricks/spark-xml_2.11/0.11.0
COPY ./spark-xml_2.11-0.11.0.jar /usr/local/spark/jars/.
COPY ./requirements-dev.txt /home/jovyan/work/.
RUN pip install -r requirements-dev.txt --trusted-host pypi.org --trusted-host files.pythonhosted.org

Run the container

When I start my Docker container, I like to include several commands that are not necessary for unit testing but helpful with other uses of the container like its Jupyter Notebook capabilities. Here’s the command I normally use:

docker run -d -p 9000:8888 -e JUPYTER_ENABLE_LAB=yes -e GRANT_SUDO=yes -v /mnt/c/Users/brad/dev/pyspark-unit-testing:/home/jovyan/work my_spark_image:v1

One of the more important arguments here is mounting my project directory to the container so that it has access to my code and tests.

Finally, do some testing

With the container now running, from your terminal, open up a bash shell:

docker exec -it <container_pid> bash

Once you have shell access to your container, you can use the pytest command line utility to run your tests:

pytest tests/ -s --disable-pytest-warnings

If all goes well, you should see a message indicating that your two tests have passed:

Hope that helps with your unit testing! Grab my entire project from my Github page.