• Jupyter Notebook 4.0.x
• Conda Python 3.x and Python 2.7.x environments
• Conda R 3.2.x environment
• Scala 2.10.x
• pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed for Python
• ggplot2, rcurl preinstalled for R
• Spark 1.5.1 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
• Unprivileged user jovyan (uid=1000, configurable, see options) in group users (gid=100) with ownership over /home/jovyan and /opt/conda
• tini as the container entrypoint and start-notebook.sh as the default command
• Options for HTTPS, password auth, and passwordless sudo

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/all-spark-notebook

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 ...

0. Run the container as shown above.
1. Open a R notebook.
2. Initialize sparkR for local mode.
3. Initialize sparkRSQL.

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

library(SparkR)

sc <- sparkR.init("local[*]")
sqlContext <- sparkRSQL.init(sc)

# do something to prove it works
data(iris)
df <- createDataFrame(sqlContext, iris)
head(filter(df, df$Petal_Width > 0.2))

0. Run the container as shown above.
1. Open a Scala notebook.
2. Use the pre-configured SparkContext in variable sc.

For example:

val rdd = sc.parallelize(0 to 999)
rdd.takeSample(false, 5)

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

0. Deploy Spark on Mesos.
1. Configure each slave with the --no-switch_user flag or create the jovyan user on every slave node.
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.)
   • NOTE: When using --net=host, you must also use the flags --pid=host -e TINI_SUBREAPER=true. See jupyter#64 for details.
3. Follow the language specific instructions below.

0. Open a Python 2 or 3 notebook.
1. Create a SparkConf instance in a new notebook pointing to your Mesos master node (or Zookeeper instance) and Spark binary package location.
2. 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-1.5.1-bin-hadoop2.6.tgz) 
conf.set("spark.executor.uri", "hdfs://10.10.10.10/spark/spark-1.5.1-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." :)

0. Run the container as shown above.
1. Open a R notebook.
2. Initialize sparkR Mesos master node (or Zookeeper instance) and Spark binary package location.
3. Initialize sparkRSQL.

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

library(SparkR)

# point to mesos master or zookeeper entry (e.g., zk://10.10.10.10:2181/mesos)\
# as the first argument
# point to spark binary package in HDFS or on local filesystem on all slave
# nodes (e.g., file:///opt/spark/spark-1.5.1-bin-hadoop2.6.tgz) in sparkEnvir
# set other options in sparkEnvir
sc <- sparkR.init("mesos://10.10.10.10:5050", sparkEnvir=list(
    spark.executor.uri="hdfs://10.10.10.10/spark/spark-1.5.1-bin-hadoop2.6.tgz",
    spark.executor.memory="8g"
    )
)
sqlContext <- sparkRSQL.init(sc)

# do something to prove it works
data(iris)
df <- createDataFrame(sqlContext, iris)
head(filter(df, df$Petal_Width > 0.2))

0. Open a terminal via New -> Terminal in the notebook interface.
1. Add information about your cluster to the Scala kernel spec file in ~/.ipython/kernels/scala/kernel.json. (See below.)
2. Open a Scala notebook.
3. Use the pre-configured SparkContext in variable sc.

The Scala kernel automatically creates a SparkContext when it starts based on configuration information from its command line arguments and environments. Therefore, you must add it to the Scala kernel spec file. You cannot, at present, configure it yourself within a notebook.

For instance, a kernel spec file with information about a Mesos master, Spark binary location in HDFS, and an executor option appears here:

{
    "display_name": "Scala 2.10.4",
    "language": "scala",
    "argv": [
        "/opt/sparkkernel/bin/sparkkernel",
        "--profile",
        "{connection_file}",
        "--master=mesos://10.10.10.10:5050"
    ],
    "env": {
        "SPARK_CONFIGURATION": "spark.executor.memory=8g,spark.executor.uri=hdfs://10.10.10.10/spark/spark-1.5.1-bin-hadoop2.6.tgz"
    }
}

Note that this is the same information expressed in a notebook in the Python case above. Once the kernel spec has your cluster information, you can test your cluster in a Scala notebook like so:

// should print the value of --master in the kernel spec
println(sc.master)

// do something to prove it works
val rdd = sc.parallelize(0 to 99999999)
rdd.sum()

You can pass Jupyter command line options through the start-notebook.sh command when launching the container. For example, to set the base URL of the notebook server you might do the following:

docker run -d -p 8888:8888 jupyter/all-spark-notebook start-notebook.sh --NotebookApp.base_url=/some/path

You can sidestep the start-notebook.sh script entirely by specifying a command other than start-notebook.sh. If you do, the NB_USER and GRANT_SUDO features documented below will not work. See the Docker Options section for details.

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 NB_UID=1000 - Specify the uid of the jovyan user. Useful to mount host volumes with specific file ownership. For this option to take effect, you must run the container with --user root. (The start-notebook.sh script will su jovyan after adjusting the user id.)
• -e GRANT_SUDO=yes - Gives the jovyan user passwordless sudo capability. Useful for installing OS packages. For this option to take effect, you must run the container with --user root. (The start-notebook.sh script will su jovyan after adding jovyan to sudoers.) 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).
• -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.
• -p 4040:4040 - Opens the port for the Spark Monitoring and Instrumentation UI. Note every new spark context that is created is put onto an incrementing port (ie. 4040, 4041, 4042, etc.), and it might be necessary to open multiple ports. docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/all-spark-notebook

The default Python 3.x Conda environment resides in /opt/conda. A second Python 2.x Conda environment exists in /opt/conda/envs/python2. You can switch to the python2 environment in a shell by entering the following:

source activate python2

You can return to the default environment with this command:

source deactivate

The commands ipython, python, pip, easy_install, and conda (among others) are available in both environments.