I wrote this a while back because I wanted to interact with the PySpark shell from within the bpython(1) code-auto-complete enabled interpreter and/or WING IDE and/or any code-auto-complete enabled IDE for that matter. Simply invoking the supplied pyspark(1) command wasn't good enough, since there's no code intelligence to assist. While this was…

INTERACTIVE.PYSPARK.SHELL.README.txt

How to use this code.

NOTE: You can place all of the following in your .profile (or shell equivalent).

(1) linux$ export MASTER='yarn-client | local[NN] | spark://host:port'
(2) linux$ export SPARK_HOME=/usr/lib/spark  # Your's will vary.
(3) linux$ export JAVA_HOME=/usr/java/latest # Your's will vary.
(4) linux$ export NAMENODE='vps00' # Your's will vary.
(5) linux$ export PYSTART=${PYTHONSTARTUP}   # See in-line commends about the reason for the need for this alias to PYTHONSTARTUP.
(6) linux$ export HADOOP_CONF_DIR=/etc/hadoop/conf # Your's will vary. This one may not be necessary to set. Try and see.
(7) linux$ export HADOOP_HOME=/usr/lib/hadoop      # Your's will vary. This one may not be necessary to set. Try and see.

(8) bpython -i /path/to/script/below  # The moment of truth. Note that this is 'bpython' (not just plain 'python', which would not give the code completion you desire).
>>> sc
<pyspark.context.SparkContext object at 0x2798110>
>>> 

pythonstartup.for.spark.shell.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# ===========================================================================
# Author: Noel Milton Vega (PRISMALYTICS, LLC.)
# ===========================================================================
# Start-up script for 'python(1)', 'bpython(1)', and Python IDE iterpreters
# when you want a 'client-mode' SPARK Shell (i.e. interactive SPARK shell)
# environment either LOCALLY, on a SPARK Standalone Cluster, or on SPARK
# YARN cluster. The code-sense/intelligence of bpython(1) and IDEs, in
# particular will aid in learning the SPARK core API.
# 
# This script basically (1) first sets up an environment to launch a SPARK
# Shell, then (2) launches the SPARK Shell using the 'shell.py' python script
# provided in the distribution's SPARK_HOME; and finally (3) imports our
# favorite Python modules (for convenience; e.g. numpy, scipy; etc.).
#
# IMPORTANT:
#  DON'T RUN THIS SCRIPT DIRECTLY. It is meant to be read in by interpreters
#  (similar, in that respect, to a PYTHONSTARTUP script).
#
#  Thus, there are two ways to use this file:
#  # We can't refer to PYTHONSTARTUP inside this file b/c that causes a recursion loop
#  # when calling this from within IDEs. So in step (0) we alias PYTHONSTARTUP to
#  # PYSTARTUP at the O/S level, and use that alias here (since no conflict with that).
#  (0): user$ export PYSTARTUP=${PYTHONSTARTUP} # We can't use PYTHONSTARTUP in this file
#  (1): user$ export MASTER='yarn-client | local[NN] | spark://host:port'
#       user$ bpython|python -i /path/to/this/file
#
#  (2): From within your favorite IDE, specify it as your python startup
#       script. For example, from within a WINGIDE project, set the following
#       variables within a WING Project: 'Project -> Project Properties':
#          'PYTHONSTARTUP=/path/to/this/very/file'
#          'MASTER=yarn-client | local[NN] | spark://host:port'
# ===========================================================================
namenode  = 'vps00' # CHANGE-ME: To the hostname of your HDFS namenode.
# ===========================================================================
import sys, os, glob, subprocess, random
SPARK_HOME = os.getenv('SPARK_HOME')
# ===========================================================================


# =================================================================================
# This functions emulates the action of "source" or '.' that exists in bash(1),
# and can be used to set PYTHON environment variables (in Pythons globals dict).
# =================================================================================
def source(script, update=True):
    proc = subprocess.Popen(". %s; env -0" % script, stdout=subprocess.PIPE, shell=True)
    output = proc.communicate()[0]
    env = dict((line.split("=", 1) for line in output.split('\x00') if line))
    if update: os.environ.update(env)
    return env
# ================================================================================


# ================================================================================
# Here, we get the name of our current SPARK Assembly JAR file name (locally). We
# use that to create a HDFS URL that points to it's location in HDFS when using
# YARN (i.e. when 'export MASTER=yarn-client'; we ignore it otherwise).
# ================================================================================
# Remember to always upload/update your distribution's current SPARK Assembly JAR
# to HDFS like this:
#   $ hdfs dfs -mkdir -p /user/spark/share/lib" # Only necessary to do once!
#   $ hdfs dfs -rm "/user/spark/share/lib/spark-assembly-*.jar" # Remove old version.
#   $ hdfs dfs -put ${SPARK_HOME}/assembly/lib/spark-assembly-[0-9]*.jar /user/spark/share/lib/
# ================================================================================
SPARK_JAR_LOCATION = glob.glob(SPARK_HOME + '/lib/' + 'spark-assembly-[0-9]*.jar')[0].split("/")[-1]
SPARK_JAR_LOCATION = 'hdfs://' + namenode + ':8020/user/spark/share/lib/' + SPARK_JAR_LOCATION
# ================================================================================


# ================================================================================
# Update Pythons globals environment variable dict with necessary environment
# variables that the SPARK Shell will be looking for. Some we set explicitly via
# an in-line dictionary, as shown below. And the rest are set by 'source'ing the
# global SPARK environment file (although we could have included those explicitly
# here too, if we preferred not to touch that system-wide file -- and leave it as FCS).
# ================================================================================
spark_jar_opt = None
MASTER = os.getenv('MASTER') if os.getenv('MASTER') else 'local[8]'
if    MASTER.startswith('yarn-'): spark_jar_opt = ' -Dspark.yarn.jar=' + SPARK_JAR_LOCATION
elif  MASTER.startswith('spark://'): pass
else: HADOOP_HOME = ''
# ================================================================================


# ================================================================================
# Build '--driver-java-options' options for spark-shell, pyspark, or spark-submit.
# Many of these are set in '/etc/spark/conf/spark-defaults.conf' (and thus
# commented out here, but left here for reference completeness).
# ================================================================================
# Default UI port is 4040. The next statement allows us to run multiple SPARK shells.
DRIVER_JAVA_OPTIONS = '-Dspark.ui.port=' + str(random.randint(1025, 65535))
DRIVER_JAVA_OPTIONS += spark_jar_opt if spark_jar_opt else ''
# ================================================================================

# ================================================================================
# Build PYSPARK_SUBMIT_ARGS (i.e. the sames ones shown in 'pyspark --help'), and
# apply them to the O/S environment.
# ================================================================================
DRIVER_JAVA_OPTIONS = "'" + DRIVER_JAVA_OPTIONS + "'"
PYSPARK_SUBMIT_ARGS = ' --master ' + MASTER # Remember to set MASTER on UNIX CLI or in the IDE!
PYSPARK_SUBMIT_ARGS += ' --driver-java-options ' + DRIVER_JAVA_OPTIONS # Built above.
# ================================================================================
os.environ.update(source('/etc/spark/conf/spark-env.sh', update = False))
os.environ.update({ 'PYSPARK_SUBMIT_ARGS' : PYSPARK_SUBMIT_ARGS })
# ================================================================================


# ================================================================================
# Next, adjust 'sys.path' so SPARK Shell has the python modules it needs.
# ================================================================================
SPARK_PYTHON_DIR = SPARK_HOME + '/python'
PY4J = glob.glob(SPARK_PYTHON_DIR + '/lib/' + 'py4j-*-src.zip')[0].split("/")[-1]
sys.path = [SPARK_PYTHON_DIR, SPARK_PYTHON_DIR + '/lib/' + PY4J] + sys.path
# ================================================================================


# ================================================================================
# With our environment set, we start the SPARK Shell; and then to that, we add
# our favorite Python imports (e.g. numpy, scipy; etc).
# ================================================================================
print('PYSPARK_SUBMIT_ARGS:' + PYSPARK_SUBMIT_ARGS) # For visual debug.
execfile(SPARK_HOME + '/python/pyspark/shell.py', globals()) # Start the SPARK Shell.
execfile(os.getenv('PYSTARTUP')) # Next, load our favorite Python modules.
# ================================================================================


# ================================================================================
# Finally, import pyspark specific modules so that, once we're dropped into the
# interactive PySpark shell environment, these are already imported. =:). Modify
# as desired. This step couldn't be performed until 'sys.path' modification above.
# ================================================================================
import pyspark
import pyspark.context
from   pyspark.sql import * # SQLContext; ROW; etc.
import pyspark.storagelevel
import pyspark.serializers
# ================================================================================