Explanatory data analysis requires interactive code execution. In case of spark and emr it is very convenient to run the code from jupyter notebooks on a remote cluster. EMR allows installing jupyter on the spark master. In order to do that configure "Applications" field for the emr cluster to contain also jupyter hub. For example:

"Applications": [
                "Name": "Ganglia",
                "Version": "3.7.2"
                "Name": "Spark",
                "Version": "2.4.0"
                "Name": "Zeppelin",
                "Version": "0.8.0"
                "Name": "JupyterHub",
                "Version": "0.9.4"

Set KeepJobFlowAliveWhenNoSteps: true and set up all the required python libraries using bootstrap scripts.

After cluster is created and bootstrapped you can get an address of the master in the aws console. Create a ssh tunnel to the port 9443 on the master. it is a port of jupyterhub:

export HOST=<public address of your master> 
ssh -i ~/.ssh/your_private_key.pem -L 9443:$HOST:9443 hadoop@$HOST

Now when you connect to your local 9443 port it is redirected to the master’s 9443 port.

Open the browser an connect to https://localhost:9443/hub/login. HTTPS and the endpoint is important!

Default user name is jovyan and the password is jupyter. Use with care.



Long running pyspark kernel is terminated after approximately 1 hour with the error:

An error was encountered:
Invalid status code '404' from http://ip-123-123-123-123.eu-west-1.compute.internal:8998/sessions/1 with error payload: "Session '1' not found."

To fix it add the following to your emr cluster Configurations:

[{'classification': 'livy-conf','Properties': {'livy.server.session.timeout':'5h'}}]


See also