pyspark: grouby and then get max value of each group

Question

I would like to group by a value and then find the max value in each group using PySpark. I have the following code but now I am bit stuck on how to extract the max value.

# some file contains tuples ('user', 'item', 'occurrences')
data_file = sc.textData('file:///some_file.txt')
# Create the triplet so I index stuff
data_file = data_file.map(lambda l: l.split()).map(lambda l: (l[0], l[1], float(l[2])))
# Group by the user i.e. r[0]
grouped = data_file.groupBy(lambda r: r[0])
# Here is where I am stuck 
group_list = grouped.map(lambda x: (list(x[1]))) #?

Returns something like:

[[(u'u1', u's1', 20), (u'u1', u's2', 5)], [(u'u2', u's3', 5), (u'u2', u's2', 10)]]

I want to find max 'occurrence' for each user now. The final result after doing the max would result in a RDD that looked like this:

[[(u'u1', u's1', 20)], [(u'u2', u's2', 10)]]

Where only the max dataset would remain for each of the users in the file. In other words, I want to change the value of the RDD to contain only a single triplet the each users max occurrences.

Answer 1

There is no need for groupBy here. Simple reduceByKey would do just fine and most of the time will be more efficient:

data_file = sc.parallelize([
   (u'u1', u's1', 20), (u'u1', u's2', 5),
   (u'u2', u's3', 5), (u'u2', u's2', 10)])

max_by_group = (data_file
  .map(lambda x: (x[0], x))  # Convert to PairwiseRD
  # Take maximum of the passed arguments by the last element (key)
  # equivalent to:
  # lambda x, y: x if x[-1] > y[-1] else y
  .reduceByKey(lambda x1, x2: max(x1, x2, key=lambda x: x[-1])) 
  .values()) # Drop keys

max_by_group.collect()
## [('u2', 's2', 10), ('u1', 's1', 20)]

Answer 2

I think I found the solution:

from pyspark import SparkContext, SparkConf

def reduce_by_max(rdd):
    """
    Helper function to find the max value in a list of values i.e. triplets. 
    """
    max_val = rdd[0][2]
    the_index = 0

    for idx, val in enumerate(rdd):
        if val[2] > max_val:
            max_val = val[2]
            the_index = idx

    return rdd[the_index]

conf = SparkConf() \
    .setAppName("Collaborative Filter") \
    .set("spark.executor.memory", "5g")
sc = SparkContext(conf=conf)

# some file contains tuples ('user', 'item', 'occurrences')
data_file = sc.textData('file:///some_file.txt')

# Create the triplet so I can index stuff
data_file = data_file.map(lambda l: l.split()).map(lambda l: (l[0], l[1], float(l[2])))

# Group by the user i.e. r[0]
grouped = data_file.groupBy(lambda r: r[0])

# Get the values as a list
group_list = grouped.map(lambda x: (list(x[1]))) 

# Get the max value for each user. 
max_list = group_list.map(reduce_by_max).collect()