Github user mateiz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1460#discussion_r15073412
  
    --- Diff: python/pyspark/rdd.py ---
    @@ -168,6 +170,123 @@ def _replaceRoot(self, value):
                 self._sink(1)
     
     
    +class Merger(object):
    +    """
    +    External merger will dump the aggregated data into disks when memory 
usage is above
    +    the limit, then merge them together.
    +
    +    >>> combiner = lambda x, y:x+y
    +    >>> merger = Merger(combiner, 10)
    +    >>> N = 10000
    +    >>> merger.merge(zip(xrange(N), xrange(N)) * 10)
    +    >>> merger.spills
    +    100
    +    >>> sum(1 for k,v in merger.iteritems())
    +    10000
    +    """
    +
    +    PARTITIONS = 64
    +    BATCH = 1000
    +
    +    def __init__(self, combiner, memory_limit=256, path="/tmp/pyspark", 
serializer=None):
    +        self.combiner = combiner
    +        self.path = os.path.join(path, str(os.getpid()))
    +        self.memory_limit = memory_limit
    +        self.serializer = serializer or 
BatchedSerializer(AutoSerializer(), 1024)
    +        self.item_limit = None
    +        self.data = {}
    +        self.pdata = []
    +        self.spills = 0
    +
    +    def used_memory(self):
    +        rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
    +        if platform.system() == 'Linux':
    +            rss >>= 10
    +        elif platform.system() == 'Darwin':
    +            rss >>= 20
    +        return rss
    +
    +    def merge(self, iterator):
    +        iterator = iter(iterator)
    +        d = self.data
    +        comb = self.combiner
    +        c = 0
    +        for k, v in iterator:
    +            if k in d:
    +                d[k] = comb(d[k], v)
    +            else:
    +                d[k] = v
    +
    +            if self.item_limit is not None:
    +                continue
    +
    +            c += 1
    +            if c % self.BATCH == 0 and self.used_memory() > 
self.memory_limit:
    +                self.item_limit = c
    +                self._first_spill()
    +                self._partitioned_merge(iterator)
    +                return
    +
    +    def _partitioned_merge(self, iterator):
    +        comb = self.combiner
    +        c = 0
    +        for k, v in iterator:
    +            d = self.pdata[hash(k) % self.PARTITIONS]
    +            if k in d:
    +                d[k] = comb(d[k], v)
    +            else:
    +                d[k] = v
    +            c += 1
    +            if c >= self.item_limit:
    +                self._spill()
    +                c = 0
    +
    +    def _first_spill(self):
    +        path = os.path.join(self.path, str(self.spills))
    +        if not os.path.exists(path):
    +            os.makedirs(path)
    +        streams = [open(os.path.join(path, str(i)), 'w')
    +                   for i in range(self.PARTITIONS)]
    --- End diff --
    
    In the future we might want to compress these with GzipFile, but for now 
just add a TODO


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