# Jupyter Notebook Python, Scala, R, Spark, Mesos Stack

## What it Gives You

* Jupyter Notebook server v3.2.x
* Conda Python 3.4.x and Python 2.7.x environments
* Conda R 3.1.x environment
* Scala 2.10.x
* pyspark, pandas, matplotlib, scipy, seaborn, scikit-learn pre-installed for Python
* ggplot2, rcurl preinstalled for R
* 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.

### In a Python Notebook

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:

```python
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.

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

# include pyspark cells from above here ...
```

### In a R Notebook

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))
```

### In a Scala Notebook

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)
```

## Connecting to a Spark Cluster on Mesos

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

0. [Deploy Spark on Mesos](http://spark.apache.org/docs/latest/running-on-mesos.html).
1. 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](http://spark.apache.org/docs/latest/cluster-overview.html#components).)
2. Follow the language specific instructions below.

### In a Python Notebook

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:

```python
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://10.10.10.10/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](http://ipython.org/ipython-doc/stable/development/config.html?highlight=startup#startup-files), but "explicit is better than implicit." :)

### In a R Notebook

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-1.4.1-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-1.4.1-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))
```

### In a Scala Notebook

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-1.4.1-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()
```

## 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).
* `-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.
* `-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.