PySpark sorted reduce question: is reduce/sorted() the best way to prepare tuples that hold a sorted list?

grouping.py

# Load the parquet file
on_time_dataframe = sqlContext.read.parquet('../data/on_time_performance.parquet')

# Filter down to the fields we need to identify and link to a flight
flights = on_time_dataframe.rdd.map(lambda x: 
  (x.Carrier, x.FlightDate, x.FlightNum, x.Origin, x.Dest, x.TailNum)
  )

# Group flights by tail number, sorted by date, then flight number, then origin/dest
flights_per_airplane = flights\
  .map(lambda nameTuple: (nameTuple[5], [nameTuple]))\
  .reduceByKey(lambda a, b: sorted(a + b, key=lambda x: (x[1],x[2],x[3],x[4])))

# Do same in a map step, more efficient or does pySpark know how to optimize the above?
flights_per_airplane = flights\
  .map(lambda nameTuple: (nameTuple[5], [nameTuple]))\
  .reduceByKey(lambda a, b: a + b)\
  .map(lambda tuple: 
    (
      tuple[0], sorted(tuple[1], key=lambda x: (x[1],x[2],x[3],x[4])))
    )

flights_per_airplane.first()

output.txt

(u'N163US',
 [(u'AA', u'2015-07-01', 425, u'SEA', u'PHX', u'N163US'),
  (u'AA', u'2015-07-01', 466, u'BOS', u'CLT', u'N163US'),
  (u'AA', u'2015-07-01', 466, u'CLT', u'DEN', u'N163US'),
  (u'AA', u'2015-07-01', 521, u'DEN', u'PHX', u'N163US'),
  (u'AA', u'2015-07-01', 521, u'PHX', u'SEA', u'N163US'),
  (u'AA', u'2015-07-02', 462, u'PHX', u'DFW', u'N163US'),
  (u'AA', u'2015-07-02', 550, u'LAX', u'PHX', u'N163US'),
  (u'AA', u'2015-07-02', 550, u'PHX', u'BOS', u'N163US'),
  (u'AA', u'2015-07-02', 721, u'CLT', u'LAX', u'N163US'),
  (u'AA', u'2015-07-02', 1807, u'LGA', u'CLT', u'N163US'),
  (u'AA', u'2015-07-02', 2064, u'CLT', u'LGA', u'N163US'),
  (u'AA', u'2015-07-02', 2064, u'DFW', u'CLT', u'N163US'),
  (u'AA', u'2015-07-03', 450, u'BOS', u'PHX', u'N163US'),
  (u'AA', u'2015-07-03', 450, u'PHX', u'SLC', u'N163US'),
  (u'AA', u'2015-07-03', 494, u'PHX', u'DEN', u'N163US'),
  (u'AA', u'2015-07-03', 494, u'SLC', u'PHX', u'N163US'),
  (u'AA', u'2015-07-04', 1783, u'CLT', u'BOS', u'N163US'),
  (u'AA', u'2015-07-04', 1861, u'LGA', u'CLT', u'N163US'),
  (u'AA', u'2015-07-04', 2068, u'CLT', u'LGA', u'N163US'),
  (u'AA', u'2015-07-04', 2068, u'DEN', u'CLT', u'N163US'),
  (u'AA', u'2015-07-05', 752, u'BOS', u'CLT', u'N163US'),
  (u'US', u'2015-04-18', 622, u'PHX', u'JFK', u'N163US'),
  (u'US', u'2015-04-19', 1820, u'CLT', u'RSW', u'N163US'),
  (u'US', u'2015-04-19', 2057, u'JFK', u'CLT', u'N163US'),
  ...])

This question was answered at http://stackoverflow.com/questions/36376369/what-is-the-most-efficient-way-to-do-a-sorted-reduce-in-pyspark