docker-stacks/pyspark-notebook/README.md

Jupyter Notebook Python, Spark, Mesos Stack

What it Gives You

• Jupyter Notebook server (v4.0.x or v3.2.x, see tag)
• Conda Python 3.4.x and Python 2.7.x environments
• pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed
• Spark 1.4.1 for use in local mode or to connect to a cluster of Spark workers
• Mesos client 0.22 binary that can communicate with a Mesos master
• Options for HTTPS, password auth, and passwordless sudo

Basic Use

The following command starts a container with the Notebook server listening for HTTP connections on port 8888 without authentication configured.

docker run -d -p 8888:8888 jupyter/pyspark-notebook

Using Spark Local Mode

This configuration is nice for using Spark on small, local data.

0. Run the container as shown above.
1. Open a Python 2 or 3 notebook.
2. Create a SparkContext configured for local mode.

For example, the first few cells in a Python 3 notebook might read:

import pyspark
sc = pyspark.SparkContext('local[*]')

# do something to prove it works
rdd = sc.parallelize(range(1000))
rdd.takeSample(False, 5)

In a Python 2 notebook, prefix the above with the following code to ensure the local workers use Python 2 as well.

import os
os.environ['PYSPARK_PYTHON'] = 'python2'

# include pyspark cells from above here ...

Connecting to a Spark Cluster on Mesos

This configuration allows your compute cluster to scale with your data.

0. Deploy Spark on Mesos.
1. Ensure Python 2.x and/or 3.x and any Python libraries you wish to use in your Spark lambda functions are installed on your Spark workers.
2. Run the Docker container with --net=host in a location that is network addressable by all of your Spark workers. (This is a Spark networking requirement.)
3. Open a Python 2 or 3 notebook.
4. Create a SparkConf instance in a new notebook pointing to your Mesos master node (or Zookeeper instance) and Spark binary package location.
5. Create a SparkContext using this configuration.

For example, the first few cells in a Python 3 notebook might read:

import os
# make sure pyspark tells workers to use python3 not 2 if both are installed
os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'

import pyspark
conf = pyspark.SparkConf()

# point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)
conf.setMaster("mesos://10.10.10.10:5050")
# point to spark binary package in HDFS or on local filesystem on all slave
# nodes (e.g., file:///opt/spark/spark-1.4.1-bin-hadoop2.6.tgz) 
conf.set("spark.executor.uri", "hdfs://10.122.193.209/spark/spark-1.4.1-bin-hadoop2.6.tgz")
# set other options as desired
conf.set("spark.executor.memory", "8g")
conf.set("spark.core.connection.ack.wait.timeout", "1200")

# create the context
sc = pyspark.SparkContext(conf=conf)

# do something to prove it works
rdd = sc.parallelize(range(100000000))
rdd.sumApprox(3)

To use Python 2 in the notebook and on the workers, change the PYSPARK_PYTHON environment variable to point to the location of the Python 2.x interpreter binary. If you leave this environment variable unset, it defaults to python.

Of course, all of this can be hidden in an IPython kernel startup script, but "explicit is better than implicit." :)

Options

You may customize the execution of the Docker container and the Notebook server it contains with the following optional arguments.

• -e PASSWORD="YOURPASS" - Configures Jupyter Notebook to require the given password. Should be conbined with USE_HTTPS on untrusted networks.
• -e USE_HTTPS=yes - Configures Jupyter Notebook to accept encrypted HTTPS connections. If a pem file containing a SSL certificate and key is not found in /home/jovyan/.ipython/profile_default/security/notebook.pem, the container will generate a self-signed certificate for you.
• -e GRANT_SUDO=yes - Gives the jovyan user passwordless sudo capability. Useful for installing OS packages. You should only enable sudo if you trust the user or if the container is running on an isolated host.
• -v /some/host/folder/for/work:/home/jovyan/work - Host mounts the default working directory on the host to preserve work even when the container is destroyed and recreated (e.g., during an upgrade).
• (v3.2.x) -v /some/host/folder/for/server.pem:/home/jovyan/.ipython/profile_default/security/notebook.pem - Mounts a SSL certificate plus key for USE_HTTPS. Useful if you have a real certificate for the domain under which you are running the Notebook server.
• (v4.0.x) -v /some/host/folder/for/server.pem:/home/jovyan/.local/share/jupyter/notebook.pem - Mounts a SSL certificate plus key for USE_HTTPS. Useful if you have a real certificate for the domain under which you are running the Notebook server.
• -e INTERFACE=10.10.10.10 - Configures Jupyter Notebook to listen on the given interface. Defaults to '*', all interfaces, which is appropriate when running using default bridged Docker networking. When using Docker's --net=host, you may wish to use this option to specify a particular network interface.
• -e PORT=8888 - Configures Jupyter Notebook to listen on the given port. Defaults to 8888, which is the port exposed within the Dockerfile for the image. When using Docker's --net=host, you may wish to use this option to specify a particular port.