# Image Specifics

This page provides details about features specific to one or more images.

## Apache Spark™

### Specific Docker Image Options

- `-p 4040:4040` - The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images open
  [SparkUI (Spark Monitoring and Instrumentation UI)](https://spark.apache.org/docs/latest/monitoring.html) at default port `4040`,
  this option map `4040` port inside docker container to `4040` port on host machine.
  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.
  For example: `docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/pyspark-notebook`.

#### IPython low-level output capture and forward

Spark images (`pyspark-notebook` and `all-spark-notebook`) have been configured to disable IPython low-level output capture and forward system-wide.
The rationale behind this choice is that Spark logs can be verbose, especially at startup when Ivy is used to load additional jars.
Those logs are still available but only in the container's logs.

If you want to make them appear in the notebook, you can overwrite the configuration in a user level IPython kernel profile.
To do that you have to uncomment the following line in your `~/.ipython/profile_default/ipython_kernel_config.py` and restart the kernel.

```Python
c.IPKernelApp.capture_fd_output = True
```

If you have no IPython profile you can initiate a fresh one by running the following command.

```bash
ipython profile create
# [ProfileCreate] Generating default config file: '/home/jovyan/.ipython/profile_default/ipython_config.py'
# [ProfileCreate] Generating default config file: '/home/jovyan/.ipython/profile_default/ipython_kernel_config.py'
```

### Build an Image with a Different Version of Spark

You can build a `pyspark-notebook` image (and also the downstream `all-spark-notebook` image) with a different version of Spark by overriding the default value of the following arguments at build time.

- Spark distribution is defined by the combination of the Spark and the Hadoop version and verified by the package checksum,
  see [Download Apache Spark](https://spark.apache.org/downloads.html) and the [archive repo](https://archive.apache.org/dist/spark/) for more information.
  - `spark_version`: The Spark version to install (`3.0.0`).
  - `hadoop_version`: The Hadoop version (`3.2`).
  - `spark_checksum`: The package checksum (`BFE4540...`).
- Spark can run with different OpenJDK versions.
  - `openjdk_version`: The version of (JRE headless) the OpenJDK distribution (`11`), see [Ubuntu packages](https://packages.ubuntu.com/search?keywords=openjdk).

For example here is how to build a `pyspark-notebook` image with Spark `2.4.7`, Hadoop `2.7` and OpenJDK `8`.

```bash
# From the root of the project
# Build the image with different arguments
docker build --rm --force-rm \
    -t jupyter/pyspark-notebook:spark-2.4.7 ./pyspark-notebook \
    --build-arg spark_version=2.4.7 \
    --build-arg hadoop_version=2.7 \
    --build-arg spark_checksum=0F5455672045F6110B030CE343C049855B7BA86C0ECB5E39A075FF9D093C7F648DA55DED12E72FFE65D84C32DCD5418A6D764F2D6295A3F894A4286CC80EF478 \
    --build-arg openjdk_version=8

# Check the newly built image
docker run -it --rm jupyter/pyspark-notebook:spark-2.4.7 pyspark --version

# Welcome to
#       ____              __
#      / __/__  ___ _____/ /__
#     _\ \/ _ \/ _ `/ __/  '_/
#    /___/ .__/\_,_/_/ /_/\_\   version 2.4.7
#       /_/
#
# Using Scala version 2.11.12, OpenJDK 64-Bit Server VM, 1.8.0_275
```

### Usage Examples

The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support the use of [Apache Spark](https://spark.apache.org/) in Python, R, and Scala notebooks.
The following sections provide some examples of how to get started using them.

#### Using Spark Local Mode

Spark **local mode** is useful for experimentation on small data when you do not have a Spark cluster available.

##### Local Mode in Python

In a Python notebook.

```python
from pyspark.sql import SparkSession

# Spark session & context
spark = SparkSession.builder.master('local').getOrCreate()
sc = spark.sparkContext

# Sum of the first 100 whole numbers
rdd = sc.parallelize(range(100 + 1))
rdd.sum()
# 5050
```

##### Local Mode in R

In a R notebook with [SparkR][sparkr].

```R
library(SparkR)

# Spark session & context
sc <- sparkR.session("local")

# Sum of the first 100 whole numbers
sdf <- createDataFrame(list(1:100))
dapplyCollect(sdf,
              function(x)
              { x <- sum(x)}
             )
# 5050
```

In a R notebook with [sparklyr][sparklyr].

```R
library(sparklyr)

# Spark configuration
conf <- spark_config()
# Set the catalog implementation in-memory
conf$spark.sql.catalogImplementation <- "in-memory"

# Spark session & context
sc <- spark_connect(master = "local", config = conf)

# Sum of the first 100 whole numbers
sdf_len(sc, 100, repartition = 1) %>%
    spark_apply(function(e) sum(e))
# 5050
```

##### Local Mode in Scala

Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell.

```python
%%init_spark
# Configure Spark to use a local master
launcher.master = "local"
```

```scala
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
```

#### Connecting to a Spark Cluster in Standalone Mode

Connection to Spark Cluster on **[Standalone Mode](https://spark.apache.org/docs/latest/spark-standalone.html)** requires the following set of steps:

0. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being
   deployed, run the same version of Spark.
1. [Deploy Spark in Standalone Mode](https://spark.apache.org/docs/latest/spark-standalone.html).
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](https://spark.apache.org/docs/latest/cluster-overview.html#components).)
   - NOTE: When using `--net=host`, you must also use the flags `--pid=host -e TINI_SUBREAPER=true`.
     See <https://github.com/jupyter/docker-stacks/issues/64> for details.

**Note**: In the following examples we are using the Spark master URL `spark://master:7077` that shall be replaced by the URL of the Spark master.

##### Standalone Mode in Python

The **same Python version** needs to be used on the notebook (where the driver is located) and on the Spark workers.
The python version used at driver and worker side can be adjusted by setting the environment variables `PYSPARK_PYTHON` and / or `PYSPARK_DRIVER_PYTHON`,
see [Spark Configuration][spark-conf] for more information.

```python
from pyspark.sql import SparkSession

# Spark session & context
spark = SparkSession.builder.master('spark://master:7077').getOrCreate()
sc = spark.sparkContext

# Sum of the first 100 whole numbers
rdd = sc.parallelize(range(100 + 1))
rdd.sum()
# 5050
```

##### Standalone Mode in R

In a R notebook with [SparkR][sparkr].

```R
library(SparkR)

# Spark session & context
sc <- sparkR.session("spark://master:7077")

# Sum of the first 100 whole numbers
sdf <- createDataFrame(list(1:100))
dapplyCollect(sdf,
              function(x)
              { x <- sum(x)}
             )
# 5050
```

In a R notebook with [sparklyr][sparklyr].

```R
library(sparklyr)

# Spark session & context
# Spark configuration
conf <- spark_config()
# Set the catalog implementation in-memory
conf$spark.sql.catalogImplementation <- "in-memory"
sc <- spark_connect(master = "spark://master:7077", config = conf)

# Sum of the first 100 whole numbers
sdf_len(sc, 100, repartition = 1) %>%
    spark_apply(function(e) sum(e))
# 5050
```

##### Standalone Mode in Scala

Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell.

```python
%%init_spark
# Configure Spark to use a local master
launcher.master = "spark://master:7077"
```

```scala
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
```

### Define Spark Dependencies

Spark dependencies can be declared thanks to the `spark.jars.packages` property
(see [Spark Configuration](https://spark.apache.org/docs/latest/configuration.html#runtime-environment) for more information).

They can be defined as a comma-separated list of Maven coordinates at the creation of the Spark session.

```python
from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.appName("elasticsearch")
    .config(
        "spark.jars.packages",
        "org.elasticsearch:elasticsearch-spark-30_2.12:7.13.0"
    )
    .getOrCreate()
)
```

Dependencies can also be defined in the `spark-defaults.conf`.
However, it has to be done by `root` so it should only be considered to build custom images.

```dockerfile
USER root
RUN echo "spark.jars.packages org.elasticsearch:elasticsearch-spark-30_2.12:7.13.0" >> "${SPARK_HOME}/conf/spark-defaults.conf"
USER ${NB_UID}
```

Jars will be downloaded dynamically at the creation of the Spark session and stored by default in `${HOME}/.ivy2/jars` (can be changed by setting `spark.jars.ivy`).

_Note: This example is given for [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/hadoop/current/install.html)._

## Tensorflow

The `jupyter/tensorflow-notebook` image supports the use of
[Tensorflow](https://www.tensorflow.org/) in single machine or distributed mode.

### Single Machine Mode

```python
import tensorflow as tf

hello = tf.Variable('Hello World!')

sess = tf.Session()
init = tf.global_variables_initializer()

sess.run(init)
sess.run(hello)
```

### Distributed Mode

```python
import tensorflow as tf

hello = tf.Variable('Hello Distributed World!')

server = tf.train.Server.create_local_server()
sess = tf.Session(server.target)
init = tf.global_variables_initializer()

sess.run(init)
sess.run(hello)
```

[sparkr]: https://spark.apache.org/docs/latest/sparkr.html
[sparklyr]: https://spark.rstudio.com/
[spark-conf]: https://spark.apache.org/docs/latest/configuration.html