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This code sample shows how to use Spark (http://spark.apache.org/) for distributed processing on the PROBA-V Mission Exploitation Platform. (https://proba-v-mep.esa.int/) The sample intentionally implements a very simple computation: for each PROBA-V tile in a given bounding box and time range, a histogram is computed. The results are then summed and printed. Computation of the histograms runs in parallel.

Development environment setup

PyCharm integration: http://stackoverflow.com/a/34714207/1158374

Note: at least PyCharm Community Edition 2016.2.3 requires that you expand SPARK_HOME yourself, so PYTHONPATH becomes:

/path/to/the/spark/directory/python:/path/to/the/spark/directory/python/lib/py4j-some-version.src.zip

Running the code

Locally

Run the spark.py script directly in your development environment. This allows for debugging your script. Run the run-local bash script on the command line for a local test run.

Both methods will use the local cpu's of your system.

On the Hadoop cluster

Log into Hadoop

Run kinit and provide your MEP password. (Same as VM/portal.)

Submit your job on Hadoop

Run the run-cluster script. It will package your project and submit it to the Hadoop cluster.

Inside this script, you can adjust options such as --num-executors to specify the number of parallel executors to use.

Additional Python dependencies as .zip, .egg or .py files can be appended to the --py-files argument.

View job logs Hadoop

As your job now runs on remote servers, the output generated there is not directly visible. You can retrieve the full logs like this:

Find the application ID: it is printed in the console output of the Spark job, for example application_1484394506558_0055. Then run: yarn logs -log_files_pattern std.* -applicationId application_1484394506558_0055

Using the Spark UI to inspect the running job

Use X2Go Client to start a remote desktop session with your VM; run Firefox and navigate to the job's tracking URL printed in Spark's console output, e.g: http://epod-master1.vgt.vito.be:8088/proxy/application_1484394506558_0055.

An overview of the jobs submitted to the cluster is available at http://epod-master1.vgt.vito.be:8088/cluster.

Spark 3 support

Python3.8

Python3.8 no longer supports spark 2.4. This means if you have python3.8 code, Spark 3 will have to be used. Spark 3.2 is available under /opt/spark3_2_0/ on your userVM.

Getting an environment

Since the Hadoop cluster does not have Python 3.8 installed by default, your own environment will have to be created. This environment will need to have the Python 3.8 binaries as well as the required libraries installed (as those are also not available on the epods). The preferred way to achieve this is by using conda and conda-pack to package your environment as venv and venv-pack will not package Python 3.8 binaries and only install the Python 3.8 packages you install inside the virtual environment. More information on how to get a conda environment packages for Spark can be found here

For our example, a simple environment where only numpy is installed as an extra package is used. The package is located on HDFS at hdfs://hacluster/spark3_sample/spark3_example.tar.gz. This package was created by performing: conda create -y -n spark3_example python=3.8 numpy conda activate spark3_example conda pack -o spark3_example.tar.gz

The spark-submit command is located at run-cluster-spark3. The configuration of note is:

  • Setting SPARK_HOME
  • Setting PYSPARK_PYTHON
  • Adding the environment using --archives