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) at default port 4040, this option maps the 4040 port inside the docker container to the 4040 port on the host machine.

Note

Every new spark context that is created is put onto an incrementing port (i.e. 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.

c.IPKernelApp.capture_fd_output = True

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

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 Spark, Hadoop and Scala versions and verified by the package checksum, see Download Apache Spark and the archive repo for more information.
- spark_version: The Spark version to install (3.3.0).
- hadoop_version: The Hadoop version (3.2).
- scala_version: The Scala version (2.13, optional).
- spark_checksum: The package checksum (BFE4540...).
- openjdk_version: The version of the OpenJDK (JRE headless) distribution (17).
  - This version needs to match the version supported by the Spark distribution used above.
  - See Spark Overview and Ubuntu packages.
Starting with Spark >= 3.2, the distribution file might contain Scala version.

For example, here is how to build a pyspark-notebook image with Spark 3.2.0, Hadoop 3.2 and OpenJDK 11.

# From the root of the project
# Build the image with different arguments
docker build --rm --force-rm \
    -t jupyter/pyspark-notebook:spark-3.2.0 ./pyspark-notebook \
    --build-arg spark_version=3.2.0 \
    --build-arg hadoop_version=3.2 \
    --build-arg spark_checksum=707DDE035926A50B75E53FCA72CADA519F3239B14A96546911CB4916A58DCF69A1D2BFDD2C7DD5899324DBD82B6EEAB9797A7B4ABF86736FFCA4C26D0E0BF0EE \
    --build-arg openjdk_version=11

# Check the newly built image
docker run -it --rm jupyter/pyspark-notebook:spark-3.2.0 pyspark --version

# Welcome to
#       ____              __
#      / __/__  ___ _____/ /__
#     _\ \/ _ \/ _ `/ __/  '_/
#    /___/ .__/\_,_/_/ /_/\_\   version 3.2.0
#       /_/

# Using Scala version 2.13.5, OpenJDK 64-Bit Server VM, 11.0.15

Usage Examples#

The jupyter/pyspark-notebook and jupyter/all-spark-notebook images support the use of Apache Spark in Python and R 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.

Warning

In these examples, Spark spawns all the main execution components in the same single JVM. You can read additional info about local mode here. If you want to use all the CPU, one of the simplest ways is to set up a Spark Standalone Cluster.

Local Mode in Python#

In a Python notebook.

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 an R notebook with SparkR.

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 an R notebook with sparklyr.

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

Connecting to a Spark Cluster in Standalone Mode#

Connection to Spark Cluster on Standalone Mode requires the following set of steps:

Verify that the docker image (check the Dockerfile) and the Spark Cluster, which is being deployed, run the same version of Spark.
Deploy Spark in Standalone Mode.
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/docker-stacks#64 for details._

Note: In the following examples, we are using the Spark master URL spark://master:7077 which 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 the driver and worker side can be adjusted by setting the environment variables PYSPARK_PYTHON and/or PYSPARK_DRIVER_PYTHON, see Spark Configuration for more information.

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 an R notebook with SparkR.

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 an R notebook with sparklyr.

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

Define Spark Dependencies#

Note

This example is given for Elasticsearch.

Spark dependencies can be declared thanks to the spark.jars.packages property (see Spark Configuration for more information).

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

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.

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).

Tensorflow#

The jupyter/tensorflow-notebook image supports the use of Tensorflow in a single machine or distributed mode.

Single Machine Mode#

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#

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)

Image Specifics

Contents

Image Specifics#

Apache Spark™#

Specific Docker Image Options#

IPython low-level output capture and forward#

Build an Image with a Different Version of Spark#

Usage Examples#

Using Spark Local Mode#

Local Mode in Python#

Local Mode in R#

Connecting to a Spark Cluster in Standalone Mode#

Standalone Mode in Python#

Standalone Mode in R#

Define Spark Dependencies#

Tensorflow#

Single Machine Mode#

Distributed Mode#