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 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.

Usage Examples

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

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

In R

In a 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 a 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

In Scala

In a Spylon Kernel

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

%%init_spark
# Configure Spark to use a local master
launcher.master = "local"
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
In an Apache Toree Kernel

Apache Toree instantiates a local SparkContext for you in variable sc when the kernel starts.

// 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 requires the following set of steps:

  1. Verify that the docker image (check the Dockerfile) and the Spark Cluster which is being deployed, run the same version of Spark.
  2. Deploy Spark in Standalone Mode.
  3. 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 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.

In Python

The same Python version need 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 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

In R

In a 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 a 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

In Scala

In a Spylon Kernel

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

%%init_spark
# Configure Spark to use a local master
launcher.master = "spark://master:7077"
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
In an Apache Toree Scala Notebook

The Apache Toree kernel automatically creates a SparkContext when it starts based on configuration information from its command line arguments and environment variables. You can pass information about your cluster via the SPARK_OPTS environment variable when you spawn a container.

For instance, to pass information about a standalone Spark master, you could start the container like so:

docker run -d -p 8888:8888 -e SPARK_OPTS='--master=spark://master:7077' \
       jupyter/all-spark-notebook

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 an Apache Toree notebook like so:

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

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

Tensorflow

The jupyter/tensorflow-notebook image supports the use of Tensorflow in 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)