#

# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

#

import os

import platform

import shutil

import warnings

import gc

import itertools

import operator

import random

import pyspark.heapq3 as heapq

from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \

CompressedSerializer, AutoBatchedSerializer

try:

import psutil

process = None

def get_used_memory():

""" Return the used memory in MB """

global process

if process is None or process._pid != os.getpid():

process = psutil.Process(os.getpid())

if hasattr(process, "memory_info"):

info = process.memory_info()

else:

info = process.get_memory_info()

return info.rss >> 20

except ImportError:

def get_used_memory():

""" Return the used memory in MB """

if platform.system() == 'Linux':

for line in open('/proc/self/status'):

if line.startswith('VmRSS:'):

return int(line.split()[1]) >> 10

else:

warnings.warn("Please install psutil to have better "

"support with spilling")

if platform.system() == "Darwin":

import resource

rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss

return rss >> 20

# TODO: support windows

return 0

def _get_local_dirs(sub):

""" Get all the directories """

path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")

dirs = path.split(",")

if len(dirs) > 1:

# different order in different processes and instances

rnd = random.Random(os.getpid() + id(dirs))

random.shuffle(dirs, rnd.random)

return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs]

# global stats

MemoryBytesSpilled = 0

DiskBytesSpilled = 0

class Aggregator(object):

"""

Aggregator has tree functions to merge values into combiner.

createCombiner: (value) -> combiner

mergeValue: (combine, value) -> combiner

mergeCombiners: (combiner, combiner) -> combiner

"""

def __init__(self, createCombiner, mergeValue, mergeCombiners):

self.createCombiner = createCombiner

self.mergeValue = mergeValue

self.mergeCombiners = mergeCombiners

class SimpleAggregator(Aggregator):

"""

SimpleAggregator is useful for the cases that combiners have

same type with values

"""

def __init__(self, combiner):

Aggregator.__init__(self, lambda x: x, combiner, combiner)

class Merger(object):

"""

Merge shuffled data together by aggregator

"""

def __init__(self, aggregator):

self.agg = aggregator

def mergeValues(self, iterator):

""" Combine the items by creator and combiner """

raise NotImplementedError

def mergeCombiners(self, iterator):

""" Merge the combined items by mergeCombiner """

raise NotImplementedError

def items(self):

""" Return the merged items ad iterator """

raise NotImplementedError

def _compressed_serializer(self, serializer=None):

# always use PickleSerializer to simplify implementation

ser = PickleSerializer()

return AutoBatchedSerializer(CompressedSerializer(ser))

class ExternalMerger(Merger):

"""

External merger will dump the aggregated data into disks when

memory usage goes above the limit, then merge them together.

This class works as follows:

- It repeatedly combine the items and save them in one dict in

memory.

- When the used memory goes above memory limit, it will split

the combined data into partitions by hash code, dump them

into disk, one file per partition.

- Then it goes through the rest of the iterator, combine items

into different dict by hash. Until the used memory goes over

memory limit, it dump all the dicts into disks, one file per

dict. Repeat this again until combine all the items.

- Before return any items, it will load each partition and

combine them separately. Yield them before loading next

partition.

- During loading a partition, if the memory goes over limit,

it will partition the loaded data and dump them into disks

and load them partition by partition again.

`data` and `pdata` are used to hold the merged items in memory.

At first, all the data are merged into `data`. Once the used

memory goes over limit, the items in `data` are dumped into

disks, `data` will be cleared, all rest of items will be merged

into `pdata` and then dumped into disks. Before returning, all

the items in `pdata` will be dumped into disks.

Finally, if any items were spilled into disks, each partition

will be merged into `data` and be yielded, then cleared.

>>> agg = SimpleAggregator(lambda x, y: x + y)

>>> merger = ExternalMerger(agg, 10)

>>> N = 10000

>>> merger.mergeValues(zip(range(N), range(N)))

>>> assert merger.spills > 0

>>> sum(v for k,v in merger.items())

49995000

>>> merger = ExternalMerger(agg, 10)

>>> merger.mergeCombiners(zip(range(N), range(N)))

>>> assert merger.spills > 0

>>> sum(v for k,v in merger.items())

49995000

"""

# the max total partitions created recursively

MAX_TOTAL_PARTITIONS = 4096

def __init__(self, aggregator, memory_limit=512, serializer=None,

localdirs=None, scale=1, partitions=59, batch=1000):

Merger.__init__(self, aggregator)

self.memory_limit = memory_limit

self.serializer = _compressed_serializer(serializer)

self.localdirs = localdirs or _get_local_dirs(str(id(self)))

# number of partitions when spill data into disks

self.partitions = partitions

# check the memory after # of items merged

self.batch = batch

# scale is used to scale down the hash of key for recursive hash map

self.scale = scale

# un-partitioned merged data

self.data = {}

# partitioned merged data, list of dicts

self.pdata = []

# number of chunks dumped into disks

self.spills = 0

# randomize the hash of key, id(o) is the address of o (aligned by 8)

self._seed = id(self) + 7

def _get_spill_dir(self, n):

""" Choose one directory for spill by number n """

return os.path.join(self.localdirs[n % len(self.localdirs)], str(n))

def _next_limit(self):

"""

Return the next memory limit. If the memory is not released

after spilling, it will dump the data only when the used memory

starts to increase.

"""

return max(self.memory_limit, get_used_memory() * 1.05)

def mergeValues(self, iterator):

""" Combine the items by creator and combiner """

# speedup attribute lookup

creator, comb = self.agg.createCombiner, self.agg.mergeValue

c, data, pdata, hfun, batch = 0, self.data, self.pdata, self._partition, self.batch

limit = self.memory_limit

for k, v in iterator:

d = pdata[hfun(k)] if pdata else data

d[k] = comb(d[k], v) if k in d else creator(v)

c += 1

if c >= batch:

if get_used_memory() >= limit:

self._spill()

limit = self._next_limit()

batch /= 2

c = 0

else:

batch *= 1.5

if get_used_memory() >= limit:

self._spill()

def _partition(self, key):

""" Return the partition for key """

return hash((key, self._seed)) % self.partitions

def _object_size(self, obj):

""" How much of memory for this obj, assume that all the objects

consume similar bytes of memory

"""

return 1

def mergeCombiners(self, iterator, limit=None):

""" Merge (K,V) pair by mergeCombiner """

if limit is None:

limit = self.memory_limit

# speedup attribute lookup

comb, hfun, objsize = self.agg.mergeCombiners, self._partition, self._object_size

c, data, pdata, batch = 0, self.data, self.pdata, self.batch

for k, v in iterator:

d = pdata[hfun(k)] if pdata else data

d[k] = comb(d[k], v) if k in d else v

if not limit:

continue

c += objsize(v)

if c > batch:

if get_used_memory() > limit:

self._spill()

limit = self._next_limit()

batch /= 2

c = 0

else:

batch *= 1.5

if limit and get_used_memory() >= limit:

self._spill()

def _spill(self):

"""

dump already partitioned data into disks.

It will dump the data in batch for better performance.

"""

global MemoryBytesSpilled, DiskBytesSpilled

path = self._get_spill_dir(self.spills)

if not os.path.exists(path):

os.makedirs(path)

used_memory = get_used_memory()

if not self.pdata:

# The data has not been partitioned, it will iterator the

# dataset once, write them into different files, has no

# additional memory. It only called when the memory goes

# above limit at the first time.

# open all the files for writing

streams = [open(os.path.join(path, str(i)), 'wb')

for i in range(self.partitions)]

for k, v in self.data.items():

h = self._partition(k)

# put one item in batch, make it compatible with load_stream

# it will increase the memory if dump them in batch

self.serializer.dump_stream([(k, v)], streams[h])

for s in streams:

DiskBytesSpilled += s.tell()

s.close()

self.data.clear()

self.pdata.extend([{} for i in range(self.partitions)])

else:

for i in range(self.partitions):

p = os.path.join(path, str(i))

with open(p, "wb") as f:

# dump items in batch

self.serializer.dump_stream(iter(self.pdata[i].items()), f)

self.pdata[i].clear()

DiskBytesSpilled += os.path.getsize(p)

self.spills += 1

gc.collect() # release the memory as much as possible

MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20

def items(self):

""" Return all merged items as iterator """

if not self.pdata and not self.spills:

return iter(self.data.items())

return self._external_items()

def _external_items(self):

""" Return all partitioned items as iterator """

assert not self.data

if any(self.pdata):

self._spill()

# disable partitioning and spilling when merge combiners from disk

self.pdata = []

try:

for i in range(self.partitions):

for v in self._merged_items(i):

yield v

self.data.clear()

# remove the merged partition

for j in range(self.spills):

path = self._get_spill_dir(j)

os.remove(os.path.join(path, str(i)))

finally:

self._cleanup()

def _merged_items(self, index):

self.data = {}

limit = self._next_limit()

for j in range(self.spills):

path = self._get_spill_dir(j)

p = os.path.join(path, str(index))

# do not check memory during merging

with open(p, "rb") as f:

self.mergeCombiners(self.serializer.load_stream(f), 0)

# limit the total partitions

if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS

and j < self.spills - 1

and get_used_memory() > limit):

self.data.clear() # will read from disk again

gc.collect() # release the memory as much as possible

return self._recursive_merged_items(index)

return self.data.items()

def _recursive_merged_items(self, index):

"""

merge the partitioned items and return the as iterator

If one partition can not be fit in memory, then them will be

partitioned and merged recursively.

"""

subdirs = [os.path.join(d, "parts", str(index)) for d in self.localdirs]

m = ExternalMerger(self.agg, self.memory_limit, self.serializer, subdirs,

self.scale * self.partitions, self.partitions, self.batch)

m.pdata = [{} for _ in range(self.partitions)]

limit = self._next_limit()

for j in range(self.spills):

path = self._get_spill_dir(j)

p = os.path.join(path, str(index))

with open(p, 'rb') as f:

m.mergeCombiners(self.serializer.load_stream(f), 0)

if get_used_memory() > limit:

m._spill()

limit = self._next_limit()

return m._external_items()

def _cleanup(self):

""" Clean up all the files in disks """

for d in self.localdirs:

shutil.rmtree(d, True)

class ExternalSorter(object):

"""

ExtenalSorter will divide the elements into chunks, sort them in

memory and dump them into disks, finally merge them back.

The spilling will only happen when the used memory goes above

the limit.

>>> sorter = ExternalSorter(1) # 1M

>>> import random

>>> l = list(range(1024))

>>> random.shuffle(l)

>>> sorted(l) == list(sorter.sorted(l))

True

>>> sorted(l) == list(sorter.sorted(l, key=lambda x: -x, reverse=True))

True

"""

def __init__(self, memory_limit, serializer=None):

self.memory_limit = memory_limit

self.local_dirs = _get_local_dirs("sort")

self.serializer = _compressed_serializer(serializer)

def _get_path(self, n):

""" Choose one directory for spill by number n """

d = self.local_dirs[n % len(self.local_dirs)]

if not os.path.exists(d):

os.makedirs(d)

return os.path.join(d, str(n))

def _next_limit(self):

"""

Return the next memory limit. If the memory is not released

after spilling, it will dump the data only when the used memory

starts to increase.

"""

return max(self.memory_limit, get_used_memory() * 1.05)

def sorted(self, iterator, key=None, reverse=False):

"""

Sort the elements in iterator, do external sort when the memory

goes above the limit.

"""

global MemoryBytesSpilled, DiskBytesSpilled

batch, limit = 100, self._next_limit()

chunks, current_chunk = [], []

iterator = iter(iterator)

while True:

# pick elements in batch

chunk = list(itertools.islice(iterator, batch))

current_chunk.extend(chunk)

if len(chunk) < batch:

break

used_memory = get_used_memory()

if used_memory > limit:

# sort them inplace will save memory

current_chunk.sort(key=key, reverse=reverse)

path = self._get_path(len(chunks))

with open(path, 'wb') as f:

self.serializer.dump_stream(current_chunk, f)

def load(f):

for v in self.serializer.load_stream(f):

yield v

# close the file explicit once we consume all the items

# to avoid ResourceWarning in Python3

f.close()

chunks.append(load(open(path, 'rb')))

current_chunk = []

MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20

DiskBytesSpilled += os.path.getsize(path)

os.unlink(path) # data will be deleted after close

elif not chunks:

batch = min(int(batch * 1.5), 10000)

current_chunk.sort(key=key, reverse=reverse)

if not chunks:

return current_chunk

if current_chunk:

chunks.append(iter(current_chunk))

return heapq.merge(chunks, key=key, reverse=reverse)

class ExternalList(object):

"""

ExternalList can have many items which cannot be hold in memory in

the same time.

>>> l = ExternalList(list(range(100)))

>>> len(l)

100

>>> l.append(10)

>>> len(l)

101

>>> for i in range(20240):

... l.append(i)

>>> len(l)

20341

>>> import pickle

>>> l2 = pickle.loads(pickle.dumps(l))

>>> len(l2)

20341

>>> list(l2)[100]

10

"""

LIMIT = 10240

def __init__(self, values):

self.values = values

self.count = len(values)

self._file = None

self._ser = None

def __getstate__(self):

if self._file is not None:

self._file.flush()

with os.fdopen(os.dup(self._file.fileno()), "rb") as f:

f.seek(0)

serialized = f.read()

else:

serialized = b''

return self.values, self.count, serialized

def __setstate__(self, item):

self.values, self.count, serialized = item

if serialized:

self._open_file()

self._file.write(serialized)

else:

self._file = None

self._ser = None

def __iter__(self):

if self._file is not None:

self._file.flush()

# read all items from disks first

with os.fdopen(os.dup(self._file.fileno()), 'rb') as f:

f.seek(0)

for v in self._ser.load_stream(f):

yield v

for v in self.values:

yield v

def __len__(self):

return self.count

def append(self, value):

self.values.append(value)

self.count += 1

# dump them into disk if the key is huge

if len(self.values) >= self.LIMIT:

self._spill()

def _open_file(self):

dirs = _get_local_dirs("objects")

d = dirs[id(self) % len(dirs)]

if not os.path.exists(d):

os.makedirs(d)

p = os.path.join(d, str(id(self)))

self._file = open(p, "w+b", 65536)

self._ser = BatchedSerializer(CompressedSerializer(PickleSerializer()), 1024)

os.unlink(p)

def __del__(self):

if self._file:

self._file.close()

self._file = None

def _spill(self):

""" dump the values into disk """

global MemoryBytesSpilled, DiskBytesSpilled

if self._file is None:

self._open_file()

used_memory = get_used_memory()

pos = self._file.tell()

self._ser.dump_stream(self.values, self._file)

self.values = []

gc.collect()

DiskBytesSpilled += self._file.tell() - pos

MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20

class ExternalListOfList(ExternalList):

"""

An external list for list.

>>> l = ExternalListOfList([[i, i] for i in range(100)])

>>> len(l)

200

>>> l.append(range(10))

>>> len(l)

210

>>> len(list(l))

210

"""

def __init__(self, values):

ExternalList.__init__(self, values)

self.count = sum(len(i) for i in values)

def append(self, value):

ExternalList.append(self, value)

# already counted 1 in ExternalList.append

self.count += len(value) - 1

def __iter__(self):

for values in ExternalList.__iter__(self):

for v in values:

yield v

class GroupByKey(object):

"""

Group a sorted iterator as [(k1, it1), (k2, it2), ...]

>>> k = [i // 3 for i in range(6)]

>>> v = [[i] for i in range(6)]

>>> g = GroupByKey(zip(k, v))

>>> [(k, list(it)) for k, it in g]

[(0, [0, 1, 2]), (1, [3, 4, 5])]

"""

def __init__(self, iterator):

self.iterator = iterator

def __iter__(self):

key, values = None, None

for k, v in self.iterator:

if values is not None and k == key:

values.append(v)

else:

if values is not None:

yield (key, values)

key = k

values = ExternalListOfList([v])

if values is not None:

yield (key, values)

class ExternalGroupBy(ExternalMerger):

"""

Group by the items by key. If any partition of them can not been

hold in memory, it will do sort based group by.

This class works as follows:

- It repeatedly group the items by key and save them in one dict in

memory.

- When the used memory goes above memory limit, it will split

the combined data into partitions by hash code, dump them

into disk, one file per partition. If the number of keys

in one partitions is smaller than 1000, it will sort them

by key before dumping into disk.

- Then it goes through the rest of the iterator, group items

by key into different dict by hash. Until the used memory goes over

memory limit, it dump all the dicts into disks, one file per

dict. Repeat this again until combine all the items. It

also will try to sort the items by key in each partition

before dumping into disks.

- It will yield the grouped items partitions by partitions.

If the data in one partitions can be hold in memory, then it

will load and combine them in memory and yield.

- If the dataset in one partition cannot be hold in memory,

it will sort them first. If all the files are already sorted,

it merge them by heap.merge(), so it will do external sort

for all the files.

- After sorting, `GroupByKey` class will put all the continuous

items with the same key as a group, yield the values as

an iterator.

"""

SORT_KEY_LIMIT = 1000

def flattened_serializer(self):

assert isinstance(self.serializer, BatchedSerializer)

ser = self.serializer

return FlattenedValuesSerializer(ser, 20)

def _object_size(self, obj):

return len(obj)

def _spill(self):

"""

dump already partitioned data into disks.

"""

global MemoryBytesSpilled, DiskBytesSpilled

path = self._get_spill_dir(self.spills)

if not os.path.exists(path):

os.makedirs(path)

used_memory = get_used_memory()

if not self.pdata:

# The data has not been partitioned, it will iterator the

# data once, write them into different files, has no

# additional memory. It only called when the memory goes

# above limit at the first time.

# open all the files for writing

streams = [open(os.path.join(path, str(i)), 'wb')

for i in range(self.partitions)]

# If the number of keys is small, then the overhead of sort is small

# sort them before dumping into disks

self._sorted = len(self.data) < self.SORT_KEY_LIMIT

if self._sorted:

self.serializer = self.flattened_serializer()

for k in sorted(self.data.keys()):

h = self._partition(k)

self.serializer.dump_stream([(k, self.data[k])], streams[h])

else:

for k, v in self.data.items():

h = self._partition(k)

self.serializer.dump_stream([(k, v)], streams[h])

for s in streams:

DiskBytesSpilled += s.tell()

s.close()

self.data.clear()

# self.pdata is cached in `mergeValues` and `mergeCombiners`

self.pdata.extend([{} for i in range(self.partitions)])

else:

for i in range(self.partitions):

p = os.path.join(path, str(i))

with open(p, "wb") as f:

# dump items in batch

if self._sorted:

# sort by key only (stable)

sorted_items = sorted(self.pdata[i].items(), key=operator.itemgetter(0))

self.serializer.dump_stream(sorted_items, f)

else:

self.serializer.dump_stream(self.pdata[i].items(), f)

self.pdata[i].clear()

DiskBytesSpilled += os.path.getsize(p)

self.spills += 1

gc.collect() # release the memory as much as possible

MemoryBytesSpilled += max(used_memory - get_used_memory(), 0) << 20

def _merged_items(self, index):

size = sum(os.path.getsize(os.path.join(self._get_spill_dir(j), str(index)))

for j in range(self.spills))

# if the memory can not hold all the partition,

# then use sort based merge. Because of compression,

# the data on disks will be much smaller than needed memory

if size >= self.memory_limit << 17: # * 1M / 8

return self._merge_sorted_items(index)

self.data = {}

for j in range(self.spills):

path = self._get_spill_dir(j)

p = os.path.join(path, str(index))

# do not check memory during merging

with open(p, "rb") as f:

self.mergeCombiners(self.serializer.load_stream(f), 0)

return self.data.items()

def _merge_sorted_items(self, index):

""" load a partition from disk, then sort and group by key """

def load_partition(j):

path = self._get_spill_dir(j)

p = os.path.join(path, str(index))

with open(p, 'rb', 65536) as f:

for v in self.serializer.load_stream(f):

yield v

disk_items = [load_partition(j) for j in range(self.spills)]

if self._sorted:

# all the partitions are already sorted

sorted_items = heapq.merge(disk_items, key=operator.itemgetter(0))

else:

# Flatten the combined values, so it will not consume huge

# memory during merging sort.

ser = self.flattened_serializer()

sorter = ExternalSorter(self.memory_limit, ser)

sorted_items = sorter.sorted(itertools.chain(*disk_items),

key=operator.itemgetter(0))

return ((k, vs) for k, vs in GroupByKey(sorted_items))

if __name__ == "__main__":

import doctest

(failure_count, test_count) = doctest.testmod()

if failure_count:

exit(-1)