John Ramey Statistics and Machine Learning

Configuring IPython Notebook Support for PySpark

Apache Spark is a great way for performing large-scale data processing. Lately, I have begun working with PySpark, a way of interfacing with Spark through Python. After a discussion with a coworker, we were curious whether PySpark could run from within an IPython Notebook. It turns out that this is fairly straightforward by setting up an IPython profile.

Here’s the tl;dr summary:

  1. Install Spark
  2. Create PySpark profile for IPython
  3. Some config
  4. Simple word count example

The steps below were successfully executed using Mac OS X 10.10.2 and Homebrew. The majority of the steps should be similar for non-Windows environments. For demonstration purposes, Spark will run in local mode, but the configuration can be updated to submit code to a cluster.

Many thanks to my coworker Steve Wampler who did much of the work.

Installing Spark

  1. Download the source for the latest Spark release
  2. Unzip source to ~/spark-1.2.0/ (or wherever you wish to install Spark)
  3. From the CLI, type: cd ~/spark-1.2.0/
  4. Install the Scala build tool: brew install sbt
  5. Build Spark: sbt assembly (Takes a while)

Create PySpark Profile for IPython

After Spark is installed, let’s start by creating a new IPython profile for PySpark.

ipython profile create pyspark

To avoid port conflicts with other IPython profiles, I updated the default port to 42424 within ~/.ipython/profile_pyspark/

c = get_config()

# Simply find this line and change the port value
c.NotebookApp.port = 42424

Set the following environment variables in .bashrc or .bash_profile:

# set this to whereever you installed spark
export SPARK_HOME="$HOME/spark-1.2.0"

# Where you specify options you would normally add after bin/pyspark
export PYSPARK_SUBMIT_ARGS="--master local[2]"

Create a file named ~/.ipython/profile_pyspark/startup/ containing the following:

# Configure the necessary Spark environment
import os
import sys

spark_home = os.environ.get('SPARK_HOME', None)
sys.path.insert(0, spark_home + "/python")

# Add the py4j to the path.
# You may need to change the version number to match your install
sys.path.insert(0, os.path.join(spark_home, 'python/lib/'))

# Initialize PySpark to predefine the SparkContext variable 'sc'
execfile(os.path.join(spark_home, 'python/pyspark/'))

Now we are ready to launch a notebook using the PySpark profile

ipython notebook --profile=pyspark

Word Count Example

Make sure the ipython pyspark profile created a SparkContext by typing sc within the notebook. You should see output similar to <pyspark.context.SparkContext at 0x1097e8e90>.

Next, load a text file into a Spark RDD. For example, load the Spark README file:

import os

spark_home = os.environ.get('SPARK_HOME', None)
text_file = sc.textFile(spark_home + "/")

The word count script below is quite simple. It takes the following steps:

  1. Split each line from the file into words
  2. Map each word to a tuple containing the word and an initial count of 1
  3. Sum up the count for each word
word_counts = text_file \
    .flatMap(lambda line: line.split()) \
    .map(lambda word: (word, 1)) \
    .reduceByKey(lambda a, b: a + b)

At this point, the word count has not been executed (lazy evaluation). To actually count the words, execute the pipeline:


Here’s a portion of the output:

[(u'all', 1),
 (u'when', 1),
 (u'"local"', 1),
 (u'including', 3),
 (u'computation', 1),
 (u'Spark](#building-spark).', 1),
 (u'using:', 1),
 (u'guidance', 3),
 (u'spark://', 1),
 (u'programs', 2),
 (u'documentation', 3),
 (u'It', 2),
 (u'graphs', 1),
 (u'./dev/run-tests', 1),
 (u'first', 1),
 (u'latest', 1)]