old_contents: # Run me with:
  #
  # PYSPARK_DRIVER_PYTHON=ipython pyspark --jars ../lib/mongo-hadoop-spark-1.5.1.jar,../lib/mongo-java-driver-3.2.2.jar,../lib/mongo-hadoop-1.5.1.jar \
  # --driver-class-path ../lib/mongo-hadoop-spark-1.5.1.jar:../lib/mongo-java-driver-3.2.2.jar:../lib/mongo-hadoop-1.5.1.jar
  
  import pymongo
  import pymongo_spark
  # Important: activate pymongo_spark.
  pymongo_spark.activate()
  
  csv_lines = sc.textFile("data/example.csv")
  data = csv_lines.map(lambda line: line.split(","))
  schema_data = data.map(lambda x: {'name': x[0], 'company': x[1], 'title': x[2]})
  schema_data.saveToMongoDB('mongodb://localhost:27017/agile_data_science.executives')
  
  
new_contents: # This code sample is meant to be executed line-by-line in a 
  # pyspark session.
  #
  # Prior to launching pyspark, run the following line in the 
  # shell where pyspark will be launched.
  #
  # export PYSPARK_DRIVER_PYTHON=ipython
  #
  # The pyspark launch command needs to have additional command line
  # arguments passed to ensure that Java classes used to connect to
  # MongoDB are found.
  #
  # The Java classes reside in JAR files that were
  # preinstalled via the boostrap.sh script and placed in the 
  # lib directory. You will need to note the version of the
  # libraries by inspecting the JAR filenames.  For example,
  # if running the following shell command:
  #
  # $ ls Agile_Data_Code_2/lib/mongo*.jar
  #
  # yields the following listing:
  #
  # Agile_Data_Code_2/lib/mongo-hadoop-2.0.2.jar	    
  # Agile_Data_Code_2/lib/mongo-hadoop-spark-2.0.2.jar
  # Agile_Data_Code_2/lib/mongo-java-driver-3.4.2.jar
  #
  # then the mongo-hadoop version would be 2.0.2, and the 
  # Mongo-Java version would be 3.4.2.  
  #
  # Choosing to set these versions as environment variables
  # will make the invocation of the command much less error
  # prone.
  #
  # MONGOHADOOP_VERSION=2.0.2
  # MONGOJAVA_VERSION=3.4.2
  #
  # The names of the JAR files can then be pieced together
  # from the version strings.
  #
  # MONGOHADOOPSPARK_JAR=./lib/mongo-hadoop-spark-$MONGOHADOOP_VERSION.jar
  # MONGOJAVADRIVER_JAR=./lib/mongo-java-driver-$MONGOJAVA_VERSION.jar
  # MONGOHADOOP_JAR=./lib/mongo-hadoop-$MONGOHADOOP_VERSION.jar 
  #
  # You can then launch the pyspark session using the following
  # shell command from the Agile_Data_Code_2 directory:
  #
  # pyspark \
  #   --jars $MONGOHADOOPSPARK_JAR,$MONGOJAVADRIVER_JAR,$MONGOHADOOP_JAR \
  #   --driver-class-path $MONGOHADOOPSPARK_JAR:$MONGOJAVADRIVER_JAR:$MONGOHADOOP_JAR
  
  import pymongo
  import pymongo_spark
  # Important: activate pymongo_spark.
  pymongo_spark.activate()
  
  csv_lines = sc.textFile("data/example.csv")
  data = csv_lines.map(lambda line: line.split(","))
  schema_data = data.map(lambda x: {'name': x[0], 'company': x[1], 'title': x[2]})
  schema_data.saveToMongoDB('mongodb://localhost:27017/agile_data_science.executives')
  
  
subject: rewrite the instruction comment to provide better guidance