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apache / spark / refs/tags/v2.4.8-rc3 / . / python / pyspark / rdd.py
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#
# 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 copy
import sys
import os
import re
import operator
import shlex
import warnings
import heapq
import bisect
import random
import socket
from subprocess import Popen, PIPE
from tempfile import NamedTemporaryFile
from threading import Thread
from collections import defaultdict
from itertools import chain
from functools import reduce
from math import sqrt, log, isinf, isnan, pow, ceil
if sys.version > '3':
basestring = unicode = str
else:
from itertools import imap as map, ifilter as filter
from pyspark.java_gateway import local_connect_and_auth
from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
BatchedSerializer, CloudPickleSerializer, PairDeserializer, \
PickleSerializer, pack_long, AutoBatchedSerializer, write_with_length, \
UTF8Deserializer
from pyspark.join import python_join, python_left_outer_join, \
python_right_outer_join, python_full_outer_join, python_cogroup
from pyspark.statcounter import StatCounter
from pyspark.rddsampler import RDDSampler, RDDRangeSampler, RDDStratifiedSampler
from pyspark.storagelevel import StorageLevel
from pyspark.resultiterable import ResultIterable
from pyspark.shuffle import Aggregator, ExternalMerger, \
get_used_memory, ExternalSorter, ExternalGroupBy
from pyspark.traceback_utils import SCCallSiteSync
from pyspark.util import fail_on_stopiteration, _exception_message
__all__ = ["RDD"]
class PythonEvalType(object):
"""
Evaluation type of python rdd.
These values are internal to PySpark.
These values should match values in org.apache.spark.api.python.PythonEvalType.
"""
NON_UDF = 0
SQL_BATCHED_UDF = 100
SQL_SCALAR_PANDAS_UDF = 200
SQL_GROUPED_MAP_PANDAS_UDF = 201
SQL_GROUPED_AGG_PANDAS_UDF = 202
SQL_WINDOW_AGG_PANDAS_UDF = 203
def portable_hash(x):
"""
This function returns consistent hash code for builtin types, especially
for None and tuple with None.
The algorithm is similar to that one used by CPython 2.7
>>> portable_hash(None)
0
>>> portable_hash((None, 1)) & 0xffffffff
219750521
"""
if sys.version_info >= (3, 2, 3) and 'PYTHONHASHSEED' not in os.environ:
raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED")
if x is None:
return 0
if isinstance(x, tuple):
h = 0x345678
for i in x:
h ^= portable_hash(i)
h *= 1000003
h &= sys.maxsize
h ^= len(x)
if h == -1:
h = -2
return int(h)
return hash(x)
class BoundedFloat(float):
"""
Bounded value is generated by approximate job, with confidence and low
bound and high bound.
>>> BoundedFloat(100.0, 0.95, 95.0, 105.0)
100.0
"""
def __new__(cls, mean, confidence, low, high):
obj = float.__new__(cls, mean)
obj.confidence = confidence
obj.low = low
obj.high = high
return obj
def _parse_memory(s):
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MB
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {'g': 1024, 'm': 1, 't': 1 << 20, 'k': 1.0 / 1024}
if s[-1].lower() not in units:
raise ValueError("invalid format: " + s)
return int(float(s[:-1]) * units[s[-1].lower()])
def _load_from_socket(sock_info, serializer):
(sockfile, sock) = local_connect_and_auth(*sock_info)
# The RDD materialization time is unpredicable, if we set a timeout for socket reading
# operation, it will very possibly fail. See SPARK-18281.
sock.settimeout(None)
# The socket will be automatically closed when garbage-collected.
return serializer.load_stream(sockfile)
def ignore_unicode_prefix(f):
"""
Ignore the 'u' prefix of string in doc tests, to make it works
in both python 2 and 3
"""
if sys.version >= '3':
# the representation of unicode string in Python 3 does not have prefix 'u',
# so remove the prefix 'u' for doc tests
literal_re = re.compile(r"(\W|^)[uU](['])", re.UNICODE)
f.__doc__ = literal_re.sub(r'\1\2', f.__doc__)
return f
class Partitioner(object):
def __init__(self, numPartitions, partitionFunc):
self.numPartitions = numPartitions
self.partitionFunc = partitionFunc
def __eq__(self, other):
return (isinstance(other, Partitioner) and self.numPartitions == other.numPartitions
and self.partitionFunc == other.partitionFunc)
def __call__(self, k):
return self.partitionFunc(k) % self.numPartitions
class RDD(object):
"""
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
Represents an immutable, partitioned collection of elements that can be
operated on in parallel.
"""
def __init__(self, jrdd, ctx, jrdd_deserializer=AutoBatchedSerializer(PickleSerializer())):
self._jrdd = jrdd
self.is_cached = False
self.is_checkpointed = False
self.ctx = ctx
self._jrdd_deserializer = jrdd_deserializer
self._id = jrdd.id()
self.partitioner = None
def _pickled(self):
return self._reserialize(AutoBatchedSerializer(PickleSerializer()))
def id(self):
"""
A unique ID for this RDD (within its SparkContext).
"""
return self._id
def __repr__(self):
return self._jrdd.toString()
def __getnewargs__(self):
# This method is called when attempting to pickle an RDD, which is always an error:
raise Exception(
"It appears that you are attempting to broadcast an RDD or reference an RDD from an "
"action or transformation. RDD transformations and actions can only be invoked by the "
"driver, not inside of other transformations; for example, "
"rdd1.map(lambda x: rdd2.values.count() * x) is invalid because the values "
"transformation and count action cannot be performed inside of the rdd1.map "
"transformation. For more information, see SPARK-5063."
)
@property
def context(self):
"""
The L{SparkContext} that this RDD was created on.
"""
return self.ctx
def cache(self):
"""
Persist this RDD with the default storage level (C{MEMORY_ONLY}).
"""
self.is_cached = True
self.persist(StorageLevel.MEMORY_ONLY)
return self
def persist(self, storageLevel=StorageLevel.MEMORY_ONLY):
"""
Set this RDD's storage level to persist its values across operations
after the first time it is computed. This can only be used to assign
a new storage level if the RDD does not have a storage level set yet.
If no storage level is specified defaults to (C{MEMORY_ONLY}).
>>> rdd = sc.parallelize(["b", "a", "c"])
>>> rdd.persist().is_cached
True
"""
self.is_cached = True
javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
self._jrdd.persist(javaStorageLevel)
return self
def unpersist(self):
"""
Mark the RDD as non-persistent, and remove all blocks for it from
memory and disk.
"""
self.is_cached = False
self._jrdd.unpersist()
return self
def checkpoint(self):
"""
Mark this RDD for checkpointing. It will be saved to a file inside the
checkpoint directory set with L{SparkContext.setCheckpointDir()} and
all references to its parent RDDs will be removed. This function must
be called before any job has been executed on this RDD. It is strongly
recommended that this RDD is persisted in memory, otherwise saving it
on a file will require recomputation.
"""
self.is_checkpointed = True
self._jrdd.rdd().checkpoint()
def isCheckpointed(self):
"""
Return whether this RDD is checkpointed and materialized, either reliably or locally.
"""
return self._jrdd.rdd().isCheckpointed()
def localCheckpoint(self):
"""
Mark this RDD for local checkpointing using Spark's existing caching layer.
This method is for users who wish to truncate RDD lineages while skipping the expensive
step of replicating the materialized data in a reliable distributed file system. This is
useful for RDDs with long lineages that need to be truncated periodically (e.g. GraphX).
Local checkpointing sacrifices fault-tolerance for performance. In particular, checkpointed
data is written to ephemeral local storage in the executors instead of to a reliable,
fault-tolerant storage. The effect is that if an executor fails during the computation,
the checkpointed data may no longer be accessible, causing an irrecoverable job failure.
This is NOT safe to use with dynamic allocation, which removes executors along
with their cached blocks. If you must use both features, you are advised to set
L{spark.dynamicAllocation.cachedExecutorIdleTimeout} to a high value.
The checkpoint directory set through L{SparkContext.setCheckpointDir()} is not used.
"""
self._jrdd.rdd().localCheckpoint()
def isLocallyCheckpointed(self):
"""
Return whether this RDD is marked for local checkpointing.
Exposed for testing.
"""
return self._jrdd.rdd().isLocallyCheckpointed()
def getCheckpointFile(self):
"""
Gets the name of the file to which this RDD was checkpointed
Not defined if RDD is checkpointed locally.
"""
checkpointFile = self._jrdd.rdd().getCheckpointFile()
if checkpointFile.isDefined():
return checkpointFile.get()
def map(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each element of this RDD.
>>> rdd = sc.parallelize(["b", "a", "c"])
>>> sorted(rdd.map(lambda x: (x, 1)).collect())
[('a', 1), ('b', 1), ('c', 1)]
"""
def func(_, iterator):
return map(fail_on_stopiteration(f), iterator)
return self.mapPartitionsWithIndex(func, preservesPartitioning)
def flatMap(self, f, preservesPartitioning=False):
"""
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
>>> rdd = sc.parallelize([2, 3, 4])
>>> sorted(rdd.flatMap(lambda x: range(1, x)).collect())
[1, 1, 1, 2, 2, 3]
>>> sorted(rdd.flatMap(lambda x: [(x, x), (x, x)]).collect())
[(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)]
"""
def func(s, iterator):
return chain.from_iterable(map(fail_on_stopiteration(f), iterator))
return self.mapPartitionsWithIndex(func, preservesPartitioning)
def mapPartitions(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each partition of this RDD.
>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
>>> def f(iterator): yield sum(iterator)
>>> rdd.mapPartitions(f).collect()
[3, 7]
"""
def func(s, iterator):
return f(iterator)
return self.mapPartitionsWithIndex(func, preservesPartitioning)
def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each partition of this RDD,
while tracking the index of the original partition.
>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
>>> def f(splitIndex, iterator): yield splitIndex
>>> rdd.mapPartitionsWithIndex(f).sum()
6
"""
return PipelinedRDD(self, f, preservesPartitioning)
def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
"""
Deprecated: use mapPartitionsWithIndex instead.
Return a new RDD by applying a function to each partition of this RDD,
while tracking the index of the original partition.
>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
>>> def f(splitIndex, iterator): yield splitIndex
>>> rdd.mapPartitionsWithSplit(f).sum()
6
"""
warnings.warn("mapPartitionsWithSplit is deprecated; "
"use mapPartitionsWithIndex instead", DeprecationWarning, stacklevel=2)
return self.mapPartitionsWithIndex(f, preservesPartitioning)
def getNumPartitions(self):
"""
Returns the number of partitions in RDD
>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
>>> rdd.getNumPartitions()
2
"""
return self._jrdd.partitions().size()
def filter(self, f):
"""
Return a new RDD containing only the elements that satisfy a predicate.
>>> rdd = sc.parallelize([1, 2, 3, 4, 5])
>>> rdd.filter(lambda x: x % 2 == 0).collect()
[2, 4]
"""
def func(iterator):
return filter(fail_on_stopiteration(f), iterator)
return self.mapPartitions(func, True)
def distinct(self, numPartitions=None):
"""
Return a new RDD containing the distinct elements in this RDD.
>>> sorted(sc.parallelize([1, 1, 2, 3]).distinct().collect())
[1, 2, 3]
"""
return self.map(lambda x: (x, None)) \
.reduceByKey(lambda x, _: x, numPartitions) \
.map(lambda x: x[0])
def sample(self, withReplacement, fraction, seed=None):
"""
Return a sampled subset of this RDD.
:param withReplacement: can elements be sampled multiple times (replaced when sampled out)
:param fraction: expected size of the sample as a fraction of this RDD's size
without replacement: probability that each element is chosen; fraction must be [0, 1]
with replacement: expected number of times each element is chosen; fraction must be >= 0
:param seed: seed for the random number generator
.. note:: This is not guaranteed to provide exactly the fraction specified of the total
count of the given :class:`DataFrame`.
>>> rdd = sc.parallelize(range(100), 4)
>>> 6 <= rdd.sample(False, 0.1, 81).count() <= 14
True
"""
assert fraction >= 0.0, "Negative fraction value: %s" % fraction
return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True)
def randomSplit(self, weights, seed=None):
"""
Randomly splits this RDD with the provided weights.
:param weights: weights for splits, will be normalized if they don't sum to 1
:param seed: random seed
:return: split RDDs in a list
>>> rdd = sc.parallelize(range(500), 1)
>>> rdd1, rdd2 = rdd.randomSplit([2, 3], 17)
>>> len(rdd1.collect() + rdd2.collect())
500
>>> 150 < rdd1.count() < 250
True
>>> 250 < rdd2.count() < 350
True
"""
s = float(sum(weights))
cweights = [0.0]
for w in weights:
cweights.append(cweights[-1] + w / s)
if seed is None:
seed = random.randint(0, 2 ** 32 - 1)
return [self.mapPartitionsWithIndex(RDDRangeSampler(lb, ub, seed).func, True)
for lb, ub in zip(cweights, cweights[1:])]
# this is ported from scala/spark/RDD.scala
def takeSample(self, withReplacement, num, seed=None):
"""
Return a fixed-size sampled subset of this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> rdd = sc.parallelize(range(0, 10))
>>> len(rdd.takeSample(True, 20, 1))
20
>>> len(rdd.takeSample(False, 5, 2))
5
>>> len(rdd.takeSample(False, 15, 3))
10
"""
numStDev = 10.0
if num < 0:
raise ValueError("Sample size cannot be negative.")
elif num == 0:
return []
initialCount = self.count()
if initialCount == 0:
return []
rand = random.Random(seed)
if (not withReplacement) and num >= initialCount:
# shuffle current RDD and return
samples = self.collect()
rand.shuffle(samples)
return samples
maxSampleSize = sys.maxsize - int(numStDev * sqrt(sys.maxsize))
if num > maxSampleSize:
raise ValueError(
"Sample size cannot be greater than %d." % maxSampleSize)
fraction = RDD._computeFractionForSampleSize(
num, initialCount, withReplacement)
samples = self.sample(withReplacement, fraction, seed).collect()
# If the first sample didn't turn out large enough, keep trying to take samples;
# this shouldn't happen often because we use a big multiplier for their initial size.
# See: scala/spark/RDD.scala
while len(samples) < num:
# TODO: add log warning for when more than one iteration was run
seed = rand.randint(0, sys.maxsize)
samples = self.sample(withReplacement, fraction, seed).collect()
rand.shuffle(samples)
return samples[0:num]
@staticmethod
def _computeFractionForSampleSize(sampleSizeLowerBound, total, withReplacement):
"""
Returns a sampling rate that guarantees a sample of
size >= sampleSizeLowerBound 99.99% of the time.
How the sampling rate is determined:
Let p = num / total, where num is the sample size and total is the
total number of data points in the RDD. We're trying to compute
q > p such that
- when sampling with replacement, we're drawing each data point
with prob_i ~ Pois(q), where we want to guarantee
Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to
total), i.e. the failure rate of not having a sufficiently large
sample < 0.0001. Setting q = p + 5 * sqrt(p/total) is sufficient
to guarantee 0.9999 success rate for num > 12, but we need a
slightly larger q (9 empirically determined).
- when sampling without replacement, we're drawing each data point
with prob_i ~ Binomial(total, fraction) and our choice of q
guarantees 1-delta, or 0.9999 success rate, where success rate is
defined the same as in sampling with replacement.
"""
fraction = float(sampleSizeLowerBound) / total
if withReplacement:
numStDev = 5
if (sampleSizeLowerBound < 12):
numStDev = 9
return fraction + numStDev * sqrt(fraction / total)
else:
delta = 0.00005
gamma = - log(delta) / total
return min(1, fraction + gamma + sqrt(gamma * gamma + 2 * gamma * fraction))
def union(self, other):
"""
Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> rdd.union(rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3]
"""
if self._jrdd_deserializer == other._jrdd_deserializer:
rdd = RDD(self._jrdd.union(other._jrdd), self.ctx,
self._jrdd_deserializer)
else:
# These RDDs contain data in different serialized formats, so we
# must normalize them to the default serializer.
self_copy = self._reserialize()
other_copy = other._reserialize()
rdd = RDD(self_copy._jrdd.union(other_copy._jrdd), self.ctx,
self.ctx.serializer)
if (self.partitioner == other.partitioner and
self.getNumPartitions() == rdd.getNumPartitions()):
rdd.partitioner = self.partitioner
return rdd
def intersection(self, other):
"""
Return the intersection of this RDD and another one. The output will
not contain any duplicate elements, even if the input RDDs did.
.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
>>> rdd2 = sc.parallelize([1, 6, 2, 3, 7, 8])
>>> rdd1.intersection(rdd2).collect()
[1, 2, 3]
"""
return self.map(lambda v: (v, None)) \
.cogroup(other.map(lambda v: (v, None))) \
.filter(lambda k_vs: all(k_vs[1])) \
.keys()
def _reserialize(self, serializer=None):
serializer = serializer or self.ctx.serializer
if self._jrdd_deserializer != serializer:
self = self.map(lambda x: x, preservesPartitioning=True)
self._jrdd_deserializer = serializer
return self
def __add__(self, other):
"""
Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> (rdd + rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3]
"""
if not isinstance(other, RDD):
raise TypeError
return self.union(other)
def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
ascending=True, keyfunc=lambda x: x):
"""
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their keys.
>>> rdd = sc.parallelize([(0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)])
>>> rdd2 = rdd.repartitionAndSortWithinPartitions(2, lambda x: x % 2, True)
>>> rdd2.glom().collect()
[[(0, 5), (0, 8), (2, 6)], [(1, 3), (3, 8), (3, 8)]]
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m"))
serializer = self._jrdd_deserializer
def sortPartition(iterator):
sort = ExternalSorter(memory * 0.9, serializer).sorted
return iter(sort(iterator, key=lambda k_v: keyfunc(k_v[0]), reverse=(not ascending)))
return self.partitionBy(numPartitions, partitionFunc).mapPartitions(sortPartition, True)
def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
"""
Sorts this RDD, which is assumed to consist of (key, value) pairs.
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortByKey().first()
('1', 3)
>>> sc.parallelize(tmp).sortByKey(True, 1).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> sc.parallelize(tmp).sortByKey(True, 2).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> tmp2 = [('Mary', 1), ('had', 2), ('a', 3), ('little', 4), ('lamb', 5)]
>>> tmp2.extend([('whose', 6), ('fleece', 7), ('was', 8), ('white', 9)])
>>> sc.parallelize(tmp2).sortByKey(True, 3, keyfunc=lambda k: k.lower()).collect()
[('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5),...('white', 9), ('whose', 6)]
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
memory = self._memory_limit()
serializer = self._jrdd_deserializer
def sortPartition(iterator):
sort = ExternalSorter(memory * 0.9, serializer).sorted
return iter(sort(iterator, key=lambda kv: keyfunc(kv[0]), reverse=(not ascending)))
if numPartitions == 1:
if self.getNumPartitions() > 1:
self = self.coalesce(1)
return self.mapPartitions(sortPartition, True)
# first compute the boundary of each part via sampling: we want to partition
# the key-space into bins such that the bins have roughly the same
# number of (key, value) pairs falling into them
rddSize = self.count()
if not rddSize:
return self # empty RDD
maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner
fraction = min(maxSampleSize / max(rddSize, 1), 1.0)
samples = self.sample(False, fraction, 1).map(lambda kv: kv[0]).collect()
samples = sorted(samples, key=keyfunc)
# we have numPartitions many parts but one of the them has
# an implicit boundary
bounds = [samples[int(len(samples) * (i + 1) / numPartitions)]
for i in range(0, numPartitions - 1)]
def rangePartitioner(k):
p = bisect.bisect_left(bounds, keyfunc(k))
if ascending:
return p
else:
return numPartitions - 1 - p
return self.partitionBy(numPartitions, rangePartitioner).mapPartitions(sortPartition, True)
def sortBy(self, keyfunc, ascending=True, numPartitions=None):
"""
Sorts this RDD by the given keyfunc
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[1]).collect()
[('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
"""
return self.keyBy(keyfunc).sortByKey(ascending, numPartitions).values()
def glom(self):
"""
Return an RDD created by coalescing all elements within each partition
into a list.
>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
>>> sorted(rdd.glom().collect())
[[1, 2], [3, 4]]
"""
def func(iterator):
yield list(iterator)
return self.mapPartitions(func)
def cartesian(self, other):
"""
Return the Cartesian product of this RDD and another one, that is, the
RDD of all pairs of elements C{(a, b)} where C{a} is in C{self} and
C{b} is in C{other}.
>>> rdd = sc.parallelize([1, 2])
>>> sorted(rdd.cartesian(rdd).collect())
[(1, 1), (1, 2), (2, 1), (2, 2)]
"""
# Due to batching, we can't use the Java cartesian method.
deserializer = CartesianDeserializer(self._jrdd_deserializer,
other._jrdd_deserializer)
return RDD(self._jrdd.cartesian(other._jrdd), self.ctx, deserializer)
def groupBy(self, f, numPartitions=None, partitionFunc=portable_hash):
"""
Return an RDD of grouped items.
>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
[(0, [2, 8]), (1, [1, 1, 3, 5])]
"""
return self.map(lambda x: (f(x), x)).groupByKey(numPartitions, partitionFunc)
@ignore_unicode_prefix
def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the shell command.
"""
if env is None:
env = dict()
def func(iterator):
pipe = Popen(
shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
def pipe_objs(out):
for obj in iterator:
s = unicode(obj).rstrip('\n') + '\n'
out.write(s.encode('utf-8'))
out.close()
Thread(target=pipe_objs, args=[pipe.stdin]).start()
def check_return_code():
pipe.wait()
if checkCode and pipe.returncode:
raise Exception("Pipe function `%s' exited "
"with error code %d" % (command, pipe.returncode))
else:
for i in range(0):
yield i
return (x.rstrip(b'\n').decode('utf-8') for x in
chain(iter(pipe.stdout.readline, b''), check_return_code()))
return self.mapPartitions(func)
def foreach(self, f):
"""
Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
"""
f = fail_on_stopiteration(f)
def processPartition(iterator):
for x in iterator:
f(x)
return iter([])
self.mapPartitions(processPartition).count() # Force evaluation
def foreachPartition(self, f):
"""
Applies a function to each partition of this RDD.
>>> def f(iterator):
... for x in iterator:
... print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
"""
def func(it):
r = f(it)
try:
return iter(r)
except TypeError:
return iter([])
self.mapPartitions(func).count() # Force evaluation
def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as css:
sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
return list(_load_from_socket(sock_info, self._jrdd_deserializer))
def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 for _ in range(10))).map(lambda x: 1).cache().reduce(add)
10
>>> sc.parallelize([]).reduce(add)
Traceback (most recent call last):
...
ValueError: Can not reduce() empty RDD
"""
f = fail_on_stopiteration(f)
def func(iterator):
iterator = iter(iterator)
try:
initial = next(iterator)
except StopIteration:
return
yield reduce(f, iterator, initial)
vals = self.mapPartitions(func).collect()
if vals:
return reduce(f, vals)
raise ValueError("Can not reduce() empty RDD")
def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(add)
-5
>>> rdd.treeReduce(add, 1)
-5
>>> rdd.treeReduce(add, 2)
-5
>>> rdd.treeReduce(add, 5)
-5
>>> rdd.treeReduce(add, 10)
-5
"""
if depth < 1:
raise ValueError("Depth cannot be smaller than 1 but got %d." % depth)
zeroValue = None, True # Use the second entry to indicate whether this is a dummy value.
def op(x, y):
if x[1]:
return y
elif y[1]:
return x
else:
return f(x[0], y[0]), False
reduced = self.map(lambda x: (x, False)).treeAggregate(zeroValue, op, op, depth)
if reduced[1]:
raise ValueError("Cannot reduce empty RDD.")
return reduced[0]
def fold(self, zeroValue, op):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given associative function and a neutral "zero value."
The function C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avoid object allocation; however, it should not
modify C{t2}.
This behaves somewhat differently from fold operations implemented
for non-distributed collections in functional languages like Scala.
This fold operation may be applied to partitions individually, and then
fold those results into the final result, rather than apply the fold
to each element sequentially in some defined ordering. For functions
that are not commutative, the result may differ from that of a fold
applied to a non-distributed collection.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add)
15
"""
op = fail_on_stopiteration(op)
def func(iterator):
acc = zeroValue
for obj in iterator:
acc = op(acc, obj)
yield acc
# collecting result of mapPartitions here ensures that the copy of
# zeroValue provided to each partition is unique from the one provided
# to the final reduce call
vals = self.mapPartitions(func).collect()
return reduce(op, vals, zeroValue)
def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avoid object allocation; however, it should not
modify C{t2}.
The first function (seqOp) can return a different result type, U, than
the type of this RDD. Thus, we need one operation for merging a T into
an U and one operation for merging two U
>>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
>>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
>>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
(10, 4)
>>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp)
(0, 0)
"""
seqOp = fail_on_stopiteration(seqOp)
combOp = fail_on_stopiteration(combOp)
def func(iterator):
acc = zeroValue
for obj in iterator:
acc = seqOp(acc, obj)
yield acc
# collecting result of mapPartitions here ensures that the copy of
# zeroValue provided to each partition is unique from the one provided
# to the final reduce call
vals = self.mapPartitions(func).collect()
return reduce(combOp, vals, zeroValue)
def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
"""
Aggregates the elements of this RDD in a multi-level tree
pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeAggregate(0, add, add)
-5
>>> rdd.treeAggregate(0, add, add, 1)
-5
>>> rdd.treeAggregate(0, add, add, 2)
-5
>>> rdd.treeAggregate(0, add, add, 5)
-5
>>> rdd.treeAggregate(0, add, add, 10)
-5
"""
if depth < 1:
raise ValueError("Depth cannot be smaller than 1 but got %d." % depth)
if self.getNumPartitions() == 0:
return zeroValue
def aggregatePartition(iterator):
acc = zeroValue
for obj in iterator:
acc = seqOp(acc, obj)
yield acc
partiallyAggregated = self.mapPartitions(aggregatePartition)
numPartitions = partiallyAggregated.getNumPartitions()
scale = max(int(ceil(pow(numPartitions, 1.0 / depth))), 2)
# If creating an extra level doesn't help reduce the wall-clock time, we stop the tree
# aggregation.
while numPartitions > scale + numPartitions / scale:
numPartitions /= scale
curNumPartitions = int(numPartitions)
def mapPartition(i, iterator):
for obj in iterator:
yield (i % curNumPartitions, obj)
partiallyAggregated = partiallyAggregated \
.mapPartitionsWithIndex(mapPartition) \
.reduceByKey(combOp, curNumPartitions) \
.values()
return partiallyAggregated.reduce(combOp)
def max(self, key=None):
"""
Find the maximum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0
"""
if key is None:
return self.reduce(max)
return self.reduce(lambda a, b: max(a, b, key=key))
def min(self, key=None):
"""
Find the minimum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0
"""
if key is None:
return self.reduce(min)
return self.reduce(lambda a, b: min(a, b, key=key))
def sum(self):
"""
Add up the elements in this RDD.
>>> sc.parallelize([1.0, 2.0, 3.0]).sum()
6.0
"""
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
def count(self):
"""
Return the number of elements in this RDD.
>>> sc.parallelize([2, 3, 4]).count()
3
"""
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
def stats(self):
"""
Return a L{StatCounter} object that captures the mean, variance
and count of the RDD's elements in one operation.
"""
def redFunc(left_counter, right_counter):
return left_counter.mergeStats(right_counter)
return self.mapPartitions(lambda i: [StatCounter(i)]).reduce(redFunc)
def histogram(self, buckets):
"""
Compute a histogram using the provided buckets. The buckets
are all open to the right except for the last which is closed.
e.g. [1,10,20,50] means the buckets are [1,10) [10,20) [20,50],
which means 1<=x<10, 10<=x<20, 20<=x<=50. And on the input of 1
and 50 we would have a histogram of 1,0,1.
If your histogram is evenly spaced (e.g. [0, 10, 20, 30]),
this can be switched from an O(log n) inseration to O(1) per
element (where n is the number of buckets).
Buckets must be sorted, not contain any duplicates, and have
at least two elements.
If `buckets` is a number, it will generate buckets which are
evenly spaced between the minimum and maximum of the RDD. For
example, if the min value is 0 and the max is 100, given `buckets`
as 2, the resulting buckets will be [0,50) [50,100]. `buckets` must
be at least 1. An exception is raised if the RDD contains infinity.
If the elements in the RDD do not vary (max == min), a single bucket
will be used.
The return value is a tuple of buckets and histogram.
>>> rdd = sc.parallelize(range(51))
>>> rdd.histogram(2)
([0, 25, 50], [25, 26])
>>> rdd.histogram([0, 5, 25, 50])
([0, 5, 25, 50], [5, 20, 26])
>>> rdd.histogram([0, 15, 30, 45, 60]) # evenly spaced buckets
([0, 15, 30, 45, 60], [15, 15, 15, 6])
>>> rdd = sc.parallelize(["ab", "ac", "b", "bd", "ef"])
>>> rdd.histogram(("a", "b", "c"))
(('a', 'b', 'c'), [2, 2])
"""
if isinstance(buckets, int):
if buckets < 1:
raise ValueError("number of buckets must be >= 1")
# filter out non-comparable elements
def comparable(x):
if x is None:
return False
if type(x) is float and isnan(x):
return False
return True
filtered = self.filter(comparable)
# faster than stats()
def minmax(a, b):
return min(a[0], b[0]), max(a[1], b[1])
try:
minv, maxv = filtered.map(lambda x: (x, x)).reduce(minmax)
except TypeError as e:
if " empty " in str(e):
raise ValueError("can not generate buckets from empty RDD")
raise
if minv == maxv or buckets == 1:
return [minv, maxv], [filtered.count()]
try:
inc = (maxv - minv) / buckets
except TypeError:
raise TypeError("Can not generate buckets with non-number in RDD")
if isinf(inc):
raise ValueError("Can not generate buckets with infinite value")
# keep them as integer if possible
inc = int(inc)
if inc * buckets != maxv - minv:
inc = (maxv - minv) * 1.0 / buckets
buckets = [i * inc + minv for i in range(buckets)]
buckets.append(maxv) # fix accumulated error
even = True
elif isinstance(buckets, (list, tuple)):
if len(buckets) < 2:
raise ValueError("buckets should have more than one value")
if any(i is None or isinstance(i, float) and isnan(i) for i in buckets):
raise ValueError("can not have None or NaN in buckets")
if sorted(buckets) != list(buckets):
raise ValueError("buckets should be sorted")
if len(set(buckets)) != len(buckets):
raise ValueError("buckets should not contain duplicated values")
minv = buckets[0]
maxv = buckets[-1]
even = False
inc = None
try:
steps = [buckets[i + 1] - buckets[i] for i in range(len(buckets) - 1)]
except TypeError:
pass # objects in buckets do not support '-'
else:
if max(steps) - min(steps) < 1e-10: # handle precision errors
even = True
inc = (maxv - minv) / (len(buckets) - 1)
else:
raise TypeError("buckets should be a list or tuple or number(int or long)")
def histogram(iterator):
counters = [0] * len(buckets)
for i in iterator:
if i is None or (type(i) is float and isnan(i)) or i > maxv or i < minv:
continue
t = (int((i - minv) / inc) if even
else bisect.bisect_right(buckets, i) - 1)
counters[t] += 1
# add last two together
last = counters.pop()
counters[-1] += last
return [counters]
def mergeCounters(a, b):
return [i + j for i, j in zip(a, b)]
return buckets, self.mapPartitions(histogram).reduce(mergeCounters)
def mean(self):
"""
Compute the mean of this RDD's elements.
>>> sc.parallelize([1, 2, 3]).mean()
2.0
"""
return self.stats().mean()
def variance(self):
"""
Compute the variance of this RDD's elements.
>>> sc.parallelize([1, 2, 3]).variance()
0.666...
"""
return self.stats().variance()
def stdev(self):
"""
Compute the standard deviation of this RDD's elements.
>>> sc.parallelize([1, 2, 3]).stdev()
0.816...
"""
return self.stats().stdev()
def sampleStdev(self):
"""
Compute the sample standard deviation of this RDD's elements (which
corrects for bias in estimating the standard deviation by dividing by
N-1 instead of N).
>>> sc.parallelize([1, 2, 3]).sampleStdev()
1.0
"""
return self.stats().sampleStdev()
def sampleVariance(self):
"""
Compute the sample variance of this RDD's elements (which corrects
for bias in estimating the variance by dividing by N-1 instead of N).
>>> sc.parallelize([1, 2, 3]).sampleVariance()
1.0
"""
return self.stats().sampleVariance()
def countByValue(self):
"""
Return the count of each unique value in this RDD as a dictionary of
(value, count) pairs.
>>> sorted(sc.parallelize([1, 2, 1, 2, 2], 2).countByValue().items())
[(1, 2), (2, 3)]
"""
def countPartition(iterator):
counts = defaultdict(int)
for obj in iterator:
counts[obj] += 1
yield counts
def mergeMaps(m1, m2):
for k, v in m2.items():
m1[k] += v
return m1
return self.mapPartitions(countPartition).reduce(mergeMaps)
def top(self, num, key=None):
"""
Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
.. note:: It returns the list sorted in descending order.
>>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
[12]
>>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)
[6, 5]
>>> sc.parallelize([10, 4, 2, 12, 3]).top(3, key=str)
[4, 3, 2]
"""
def topIterator(iterator):
yield heapq.nlargest(num, iterator, key=key)
def merge(a, b):
return heapq.nlargest(num, a + b, key=key)
return self.mapPartitions(topIterator).reduce(merge)
def takeOrdered(self, num, key=None):
"""
Get the N elements from an RDD ordered in ascending order or as
specified by the optional key function.
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6)
[1, 2, 3, 4, 5, 6]
>>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7], 2).takeOrdered(6, key=lambda x: -x)
[10, 9, 7, 6, 5, 4]
"""
def merge(a, b):
return heapq.nsmallest(num, a + b, key)
return self.mapPartitions(lambda it: [heapq.nsmallest(num, it, key)]).reduce(merge)
def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RDD#take().
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> sc.parallelize([2, 3, 4, 5, 6]).cache().take(2)
[2, 3]
>>> sc.parallelize([2, 3, 4, 5, 6]).take(10)
[2, 3, 4, 5, 6]
>>> sc.parallelize(range(100), 100).filter(lambda x: x > 90).take(3)
[91, 92, 93]
"""
items = []
totalParts = self.getNumPartitions()
partsScanned = 0
while len(items) < num and partsScanned < totalParts:
# The number of partitions to try in this iteration.
# It is ok for this number to be greater than totalParts because
# we actually cap it at totalParts in runJob.
numPartsToTry = 1
if partsScanned > 0:
# If we didn't find any rows after the previous iteration,
# quadruple and retry. Otherwise, interpolate the number of
# partitions we need to try, but overestimate it by 50%.
# We also cap the estimation in the end.
if len(items) == 0:
numPartsToTry = partsScanned * 4
else:
# the first parameter of max is >=1 whenever partsScanned >= 2
numPartsToTry = int(1.5 * num * partsScanned / len(items)) - partsScanned
numPartsToTry = min(max(numPartsToTry, 1), partsScanned * 4)
left = num - len(items)
def takeUpToNumLeft(iterator):
iterator = iter(iterator)
taken = 0
while taken < left:
try:
yield next(iterator)
except StopIteration:
return
taken += 1
p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
res = self.context.runJob(self, takeUpToNumLeft, p)
items += res
partsScanned += numPartsToTry
return items[:num]
def first(self):
"""
Return the first element in this RDD.
>>> sc.parallelize([2, 3, 4]).first()
2
>>> sc.parallelize([]).first()
Traceback (most recent call last):
...
ValueError: RDD is empty
"""
rs = self.take(1)
if rs:
return rs[0]
raise ValueError("RDD is empty")
def isEmpty(self):
"""
Returns true if and only if the RDD contains no elements at all.
.. note:: an RDD may be empty even when it has at least 1 partition.
>>> sc.parallelize([]).isEmpty()
True
>>> sc.parallelize([1]).isEmpty()
False
"""
return self.getNumPartitions() == 0 or len(self.take(1)) == 0
def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the new Hadoop OutputFormat API (mapreduce package). Keys/values are
converted for output using either user specified converters or, by default,
L{org.apache.spark.api.python.JavaToWritableConverter}.
:param conf: Hadoop job configuration, passed in as a dict
:param keyConverter: (None by default)
:param valueConverter: (None by default)
"""
jconf = self.ctx._dictToJavaMap(conf)
pickledRDD = self._pickled()
self.ctx._jvm.PythonRDD.saveAsHadoopDataset(pickledRDD._jrdd, True, jconf,
keyConverter, valueConverter, True)
def saveAsNewAPIHadoopFile(self, path, outputFormatClass, keyClass=None, valueClass=None,
keyConverter=None, valueConverter=None, conf=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the new Hadoop OutputFormat API (mapreduce package). Key and value types
will be inferred if not specified. Keys and values are converted for output using either
user specified converters or L{org.apache.spark.api.python.JavaToWritableConverter}. The
C{conf} is applied on top of the base Hadoop conf associated with the SparkContext
of this RDD to create a merged Hadoop MapReduce job configuration for saving the data.
:param path: path to Hadoop file
:param outputFormatClass: fully qualified classname of Hadoop OutputFormat
(e.g. "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat")
:param keyClass: fully qualified classname of key Writable class
(e.g. "org.apache.hadoop.io.IntWritable", None by default)
:param valueClass: fully qualified classname of value Writable class
(e.g. "org.apache.hadoop.io.Text", None by default)
:param keyConverter: (None by default)
:param valueConverter: (None by default)
:param conf: Hadoop job configuration, passed in as a dict (None by default)
"""
jconf = self.ctx._dictToJavaMap(conf)
pickledRDD = self._pickled()
self.ctx._jvm.PythonRDD.saveAsNewAPIHadoopFile(pickledRDD._jrdd, True, path,
outputFormatClass,
keyClass, valueClass,
keyConverter, valueConverter, jconf)
def saveAsHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the old Hadoop OutputFormat API (mapred package). Keys/values are
converted for output using either user specified converters or, by default,
L{org.apache.spark.api.python.JavaToWritableConverter}.
:param conf: Hadoop job configuration, passed in as a dict
:param keyConverter: (None by default)
:param valueConverter: (None by default)
"""
jconf = self.ctx._dictToJavaMap(conf)
pickledRDD = self._pickled()
self.ctx._jvm.PythonRDD.saveAsHadoopDataset(pickledRDD._jrdd, True, jconf,
keyConverter, valueConverter, False)
def saveAsHadoopFile(self, path, outputFormatClass, keyClass=None, valueClass=None,
keyConverter=None, valueConverter=None, conf=None,
compressionCodecClass=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the old Hadoop OutputFormat API (mapred package). Key and value types
will be inferred if not specified. Keys and values are converted for output using either
user specified converters or L{org.apache.spark.api.python.JavaToWritableConverter}. The
C{conf} is applied on top of the base Hadoop conf associated with the SparkContext
of this RDD to create a merged Hadoop MapReduce job configuration for saving the data.
:param path: path to Hadoop file
:param outputFormatClass: fully qualified classname of Hadoop OutputFormat
(e.g. "org.apache.hadoop.mapred.SequenceFileOutputFormat")
:param keyClass: fully qualified classname of key Writable class
(e.g. "org.apache.hadoop.io.IntWritable", None by default)
:param valueClass: fully qualified classname of value Writable class
(e.g. "org.apache.hadoop.io.Text", None by default)
:param keyConverter: (None by default)
:param valueConverter: (None by default)
:param conf: (None by default)
:param compressionCodecClass: (None by default)
"""
jconf = self.ctx._dictToJavaMap(conf)
pickledRDD = self._pickled()
self.ctx._jvm.PythonRDD.saveAsHadoopFile(pickledRDD._jrdd, True, path,
outputFormatClass,
keyClass, valueClass,
keyConverter, valueConverter,
jconf, compressionCodecClass)
def saveAsSequenceFile(self, path, compressionCodecClass=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the L{org.apache.hadoop.io.Writable} types that we convert from the
RDD's key and value types. The mechanism is as follows:
1. Pyrolite is used to convert pickled Python RDD into RDD of Java objects.
2. Keys and values of this Java RDD are converted to Writables and written out.
:param path: path to sequence file
:param compressionCodecClass: (None by default)
"""
pickledRDD = self._pickled()
self.ctx._jvm.PythonRDD.saveAsSequenceFile(pickledRDD._jrdd, True,
path, compressionCodecClass)
def saveAsPickleFile(self, path, batchSize=10):
"""
Save this RDD as a SequenceFile of serialized objects. The serializer
used is L{pyspark.serializers.PickleSerializer}, default batch size
is 10.
>>> tmpFile = NamedTemporaryFile(delete=True)
>>> tmpFile.close()
>>> sc.parallelize([1, 2, 'spark', 'rdd']).saveAsPickleFile(tmpFile.name, 3)
>>> sorted(sc.pickleFile(tmpFile.name, 5).map(str).collect())
['1', '2', 'rdd', 'spark']
"""
if batchSize == 0:
ser = AutoBatchedSerializer(PickleSerializer())
else:
ser = BatchedSerializer(PickleSerializer(), batchSize)
self._reserialize(ser)._jrdd.saveAsObjectFile(path)
@ignore_unicode_prefix
def saveAsTextFile(self, path, compressionCodecClass=None):
"""
Save this RDD as a text file, using string representations of elements.
@param path: path to text file
@param compressionCodecClass: (None by default) string i.e.
"org.apache.hadoop.io.compress.GzipCodec"
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> sc.parallelize(range(10)).saveAsTextFile(tempFile.name)
>>> from fileinput import input
>>> from glob import glob
>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
'0\\n1\\n2\\n3\\n4\\n5\\n6\\n7\\n8\\n9\\n'
Empty lines are tolerated when saving to text files.
>>> tempFile2 = NamedTemporaryFile(delete=True)
>>> tempFile2.close()
>>> sc.parallelize(['', 'foo', '', 'bar', '']).saveAsTextFile(tempFile2.name)
>>> ''.join(sorted(input(glob(tempFile2.name + "/part-0000*"))))
'\\n\\n\\nbar\\nfoo\\n'
Using compressionCodecClass
>>> tempFile3 = NamedTemporaryFile(delete=True)
>>> tempFile3.close()
>>> codec = "org.apache.hadoop.io.compress.GzipCodec"
>>> sc.parallelize(['foo', 'bar']).saveAsTextFile(tempFile3.name, codec)
>>> from fileinput import input, hook_compressed
>>> result = sorted(input(glob(tempFile3.name + "/part*.gz"), openhook=hook_compressed))
>>> b''.join(result).decode('utf-8')
u'bar\\nfoo\\n'
"""
def func(split, iterator):
for x in iterator:
if not isinstance(x, (unicode, bytes)):
x = unicode(x)
if isinstance(x, unicode):
x = x.encode("utf-8")
yield x
keyed = self.mapPartitionsWithIndex(func)
keyed._bypass_serializer = True
if compressionCodecClass:
compressionCodec = self.ctx._jvm.java.lang.Class.forName(compressionCodecClass)
keyed._jrdd.map(self.ctx._jvm.BytesToString()).saveAsTextFile(path, compressionCodec)
else:
keyed._jrdd.map(self.ctx._jvm.BytesToString()).saveAsTextFile(path)
# Pair functions
def collectAsMap(self):
"""
Return the key-value pairs in this RDD to the master as a dictionary.
.. note:: this method should only be used if the resulting data is expected
to be small, as all the data is loaded into the driver's memory.
>>> m = sc.parallelize([(1, 2), (3, 4)]).collectAsMap()
>>> m[1]
2
>>> m[3]
4
"""
return dict(self.collect())
def keys(self):
"""
Return an RDD with the keys of each tuple.
>>> m = sc.parallelize([(1, 2), (3, 4)]).keys()
>>> m.collect()
[1, 3]
"""
return self.map(lambda x: x[0])
def values(self):
"""
Return an RDD with the values of each tuple.
>>> m = sc.parallelize([(1, 2), (3, 4)]).values()
>>> m.collect()
[2, 4]
"""
return self.map(lambda x: x[1])
def reduceByKey(self, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" in MapReduce.
Output will be partitioned with C{numPartitions} partitions, or
the default parallelism level if C{numPartitions} is not specified.
Default partitioner is hash-partition.
>>> from operator import add
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> sorted(rdd.reduceByKey(add).collect())
[('a', 2), ('b', 1)]
"""
return self.combineByKey(lambda x: x, func, func, numPartitions, partitionFunc)
def reduceByKeyLocally(self, func):
"""
Merge the values for each key using an associative and commutative reduce function, but
return the results immediately to the master as a dictionary.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" in MapReduce.
>>> from operator import add
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> sorted(rdd.reduceByKeyLocally(add).items())
[('a', 2), ('b', 1)]
"""
func = fail_on_stopiteration(func)
def reducePartition(iterator):
m = {}
for k, v in iterator:
m[k] = func(m[k], v) if k in m else v
yield m
def mergeMaps(m1, m2):
for k, v in m2.items():
m1[k] = func(m1[k], v) if k in m1 else v
return m1
return self.mapPartitions(reducePartition).reduce(mergeMaps)
def countByKey(self):
"""
Count the number of elements for each key, and return the result to the
master as a dictionary.
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> sorted(rdd.countByKey().items())
[('a', 2), ('b', 1)]
"""
return self.map(lambda x: x[0]).countByValue()
def join(self, other, numPartitions=None):
"""
Return an RDD containing all pairs of elements with matching keys in
C{self} and C{other}.
Each pair of elements will be returned as a (k, (v1, v2)) tuple, where
(k, v1) is in C{self} and (k, v2) is in C{other}.
Performs a hash join across the cluster.
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2), ("a", 3)])
>>> sorted(x.join(y).collect())
[('a', (1, 2)), ('a', (1, 3))]
"""
return python_join(self, other, numPartitions)
def leftOuterJoin(self, other, numPartitions=None):
"""
Perform a left outer join of C{self} and C{other}.
For each element (k, v) in C{self}, the resulting RDD will either
contain all pairs (k, (v, w)) for w in C{other}, or the pair
(k, (v, None)) if no elements in C{other} have key k.
Hash-partitions the resulting RDD into the given number of partitions.
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2)])
>>> sorted(x.leftOuterJoin(y).collect())
[('a', (1, 2)), ('b', (4, None))]
"""
return python_left_outer_join(self, other, numPartitions)
def rightOuterJoin(self, other, numPartitions=None):
"""
Perform a right outer join of C{self} and C{other}.
For each element (k, w) in C{other}, the resulting RDD will either
contain all pairs (k, (v, w)) for v in this, or the pair (k, (None, w))
if no elements in C{self} have key k.
Hash-partitions the resulting RDD into the given number of partitions.
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2)])
>>> sorted(y.rightOuterJoin(x).collect())
[('a', (2, 1)), ('b', (None, 4))]
"""
return python_right_outer_join(self, other, numPartitions)
def fullOuterJoin(self, other, numPartitions=None):
"""
Perform a right outer join of C{self} and C{other}.
For each element (k, v) in C{self}, the resulting RDD will either
contain all pairs (k, (v, w)) for w in C{other}, or the pair
(k, (v, None)) if no elements in C{other} have key k.
Similarly, for each element (k, w) in C{other}, the resulting RDD will
either contain all pairs (k, (v, w)) for v in C{self}, or the pair
(k, (None, w)) if no elements in C{self} have key k.
Hash-partitions the resulting RDD into the given number of partitions.
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2), ("c", 8)])
>>> sorted(x.fullOuterJoin(y).collect())
[('a', (1, 2)), ('b', (4, None)), ('c', (None, 8))]
"""
return python_full_outer_join(self, other, numPartitions)
# TODO: add option to control map-side combining
# portable_hash is used as default, because builtin hash of None is different
# cross machines.
def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[0]).intersection(set(sets[1])))
0
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
partitioner = Partitioner(numPartitions, partitionFunc)
if self.partitioner == partitioner:
return self
# Transferring O(n) objects to Java is too expensive.
# Instead, we'll form the hash buckets in Python,
# transferring O(numPartitions) objects to Java.
# Each object is a (splitNumber, [objects]) pair.
# In order to avoid too huge objects, the objects are
# grouped into chunks.
outputSerializer = self.ctx._unbatched_serializer
limit = (_parse_memory(self.ctx._conf.get(
"spark.python.worker.memory", "512m")) / 2)
def add_shuffle_key(split, iterator):
buckets = defaultdict(list)
c, batch = 0, min(10 * numPartitions, 1000)
for k, v in iterator:
buckets[partitionFunc(k) % numPartitions].append((k, v))
c += 1
# check used memory and avg size of chunk of objects
if (c % 1000 == 0 and get_used_memory() > limit
or c > batch):
n, size = len(buckets), 0
for split in list(buckets.keys()):
yield pack_long(split)
d = outputSerializer.dumps(buckets[split])
del buckets[split]
yield d
size += len(d)
avg = int(size / n) >> 20
# let 1M < avg < 10M
if avg < 1:
batch *= 1.5
elif avg > 10:
batch = max(int(batch / 1.5), 1)
c = 0
for split, items in buckets.items():
yield pack_long(split)
yield outputSerializer.dumps(items)
keyed = self.mapPartitionsWithIndex(add_shuffle_key, preservesPartitioning=True)
keyed._bypass_serializer = True
with SCCallSiteSync(self.context) as css:
pairRDD = self.ctx._jvm.PairwiseRDD(
keyed._jrdd.rdd()).asJavaPairRDD()
jpartitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
id(partitionFunc))
jrdd = self.ctx._jvm.PythonRDD.valueOfPair(pairRDD.partitionBy(jpartitioner))
rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer))
rdd.partitioner = partitioner
return rdd
# TODO: add control over map-side aggregation
def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None, partitionFunc=portable_hash):
"""
Generic function to combine the elements for each key using a custom
set of aggregation functions.
Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined
type" C.
Users provide three functions:
- C{createCombiner}, which turns a V into a C (e.g., creates
a one-element list)
- C{mergeValue}, to merge a V into a C (e.g., adds it to the end of
a list)
- C{mergeCombiners}, to combine two C's into a single one (e.g., merges
the lists)
To avoid memory allocation, both mergeValue and mergeCombiners are allowed to
modify and return their first argument instead of creating a new C.
In addition, users can control the partitioning of the output RDD.
.. note:: V and C can be different -- for example, one might group an RDD of type
(Int, Int) into an RDD of type (Int, List[Int]).
>>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 2)])
>>> def to_list(a):
... return [a]
...
>>> def append(a, b):
... a.append(b)
... return a
...
>>> def extend(a, b):
... a.extend(b)
... return a
...
>>> sorted(x.combineByKey(to_list, append, extend).collect())
[('a', [1, 2]), ('b', [1])]
"""
if numPartitions is None:
numPartitions = self._defaultReducePartitions()
serializer = self.ctx.serializer
memory = self._memory_limit()
agg = Aggregator(createCombiner, mergeValue, mergeCombiners)
def combineLocally(iterator):
merger = ExternalMerger(agg, memory * 0.9, serializer)
merger.mergeValues(iterator)
return merger.items()
locally_combined = self.mapPartitions(combineLocally, preservesPartitioning=True)
shuffled = locally_combined.partitionBy(numPartitions, partitionFunc)
def _mergeCombiners(iterator):
merger = ExternalMerger(agg, memory, serializer)
merger.mergeCombiners(iterator)
return merger.items()
return shuffled.mapPartitions(_mergeCombiners, preservesPartitioning=True)
def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None,
partitionFunc=portable_hash):
"""
Aggregate the values of each key, using given combine functions and a neutral
"zero value". This function can return a different result type, U, than the type
of the values in this RDD, V. Thus, we need one operation for merging a V into
a U and one operation for merging two U's, The former operation is used for merging
values within a partition, and the latter is used for merging values between
partitions. To avoid memory allocation, both of these functions are
allowed to modify and return their first argument instead of creating a new U.
"""
def createZero():
return copy.deepcopy(zeroValue)
return self.combineByKey(
lambda v: seqFunc(createZero(), v), seqFunc, combFunc, numPartitions, partitionFunc)
def foldByKey(self, zeroValue, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative function "func"
and a neutral "zeroValue" which may be added to the result an
arbitrary number of times, and must not change the result
(e.g., 0 for addition, or 1 for multiplication.).
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> from operator import add
>>> sorted(rdd.foldByKey(0, add).collect())
[('a', 2), ('b', 1)]
"""
def createZero():
return copy.deepcopy(zeroValue)
return self.combineByKey(lambda v: func(createZero(), v), func, func, numPartitions,
partitionFunc)
def _memory_limit(self):
return _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m"))
# TODO: support variant with custom partitioner
def groupByKey(self, numPartitions=None, partitionFunc=portable_hash):
"""
Group the values for each key in the RDD into a single sequence.
Hash-partitions the resulting RDD with numPartitions partitions.
.. note:: If you are grouping in order to perform an aggregation (such as a
sum or average) over each key, using reduceByKey or aggregateByKey will
provide much better performance.
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
>>> sorted(rdd.groupByKey().mapValues(len).collect())
[('a', 2), ('b', 1)]
>>> sorted(rdd.groupByKey().mapValues(list).collect())
[('a', [1, 1]), ('b', [1])]
"""
def createCombiner(x):
return [x]
def mergeValue(xs, x):
xs.append(x)
return xs
def mergeCombiners(a, b):
a.extend(b)
return a
memory = self._memory_limit()
serializer = self._jrdd_deserializer
agg = Aggregator(createCombiner, mergeValue, mergeCombiners)
def combine(iterator):
merger = ExternalMerger(agg, memory * 0.9, serializer)
merger.mergeValues(iterator)
return merger.items()
locally_combined = self.mapPartitions(combine, preservesPartitioning=True)
shuffled = locally_combined.partitionBy(numPartitions, partitionFunc)
def groupByKey(it):
merger = ExternalGroupBy(agg, memory, serializer)
merger.mergeCombiners(it)
return merger.items()
return shuffled.mapPartitions(groupByKey, True).mapValues(ResultIterable)
def flatMapValues(self, f):
"""
Pass each value in the key-value pair RDD through a flatMap function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
>>> def f(x): return x
>>> x.flatMapValues(f).collect()
[('a', 'x'), ('a', 'y'), ('a', 'z'), ('b', 'p'), ('b', 'r')]
"""
flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1]))
return self.flatMap(flat_map_fn, preservesPartitioning=True)
def mapValues(self, f):
"""
Pass each value in the key-value pair RDD through a map function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
>>> def f(x): return len(x)
>>> x.mapValues(f).collect()
[('a', 3), ('b', 1)]
"""
map_values_fn = lambda kv: (kv[0], f(kv[1]))
return self.map(map_values_fn, preservesPartitioning=True)
def groupWith(self, other, *others):
"""
Alias for cogroup but with support for multiple RDDs.
>>> w = sc.parallelize([("a", 5), ("b", 6)])
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2)])
>>> z = sc.parallelize([("b", 42)])
>>> [(x, tuple(map(list, y))) for x, y in sorted(list(w.groupWith(x, y, z).collect()))]
[('a', ([5], [1], [2], [])), ('b', ([6], [4], [], [42]))]
"""
return python_cogroup((self, other) + others, numPartitions=None)
# TODO: add variant with custom parittioner
def cogroup(self, other, numPartitions=None):
"""
For each key k in C{self} or C{other}, return a resulting RDD that
contains a tuple with the list of values for that key in C{self} as
well as C{other}.
>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2)])
>>> [(x, tuple(map(list, y))) for x, y in sorted(list(x.cogroup(y).collect()))]
[('a', ([1], [2])), ('b', ([4], []))]
"""
return python_cogroup((self, other), numPartitions)
def sampleByKey(self, withReplacement, fractions, seed=None):
"""
Return a subset of this RDD sampled by key (via stratified sampling).
Create a sample of this RDD using variable sampling rates for
different keys as specified by fractions, a key to sampling rate map.
>>> fractions = {"a": 0.2, "b": 0.1}
>>> rdd = sc.parallelize(fractions.keys()).cartesian(sc.parallelize(range(0, 1000)))
>>> sample = dict(rdd.sampleByKey(False, fractions, 2).groupByKey().collect())
>>> 100 < len(sample["a"]) < 300 and 50 < len(sample["b"]) < 150
True
>>> max(sample["a"]) <= 999 and min(sample["a"]) >= 0
True
>>> max(sample["b"]) <= 999 and min(sample["b"]) >= 0
True
"""
for fraction in fractions.values():
assert fraction >= 0.0, "Negative fraction value: %s" % fraction
return self.mapPartitionsWithIndex(
RDDStratifiedSampler(withReplacement, fractions, seed).func, True)
def subtractByKey(self, other, numPartitions=None):
"""
Return each (key, value) pair in C{self} that has no pair with matching
key in C{other}.
>>> x = sc.parallelize([("a", 1), ("b", 4), ("b", 5), ("a", 2)])
>>> y = sc.parallelize([("a", 3), ("c", None)])
>>> sorted(x.subtractByKey(y).collect())
[('b', 4), ('b', 5)]
"""
def filter_func(pair):
key, (val1, val2) = pair
return val1 and not val2
return self.cogroup(other, numPartitions).filter(filter_func).flatMapValues(lambda x: x[0])
def subtract(self, other, numPartitions=None):
"""
Return each value in C{self} that is not contained in C{other}.
>>> x = sc.parallelize([("a", 1), ("b", 4), ("b", 5), ("a", 3)])
>>> y = sc.parallelize([("a", 3), ("c", None)])
>>> sorted(x.subtract(y).collect())
[('a', 1), ('b', 4), ('b', 5)]
"""
# note: here 'True' is just a placeholder
rdd = other.map(lambda x: (x, True))
return self.map(lambda x: (x, True)).subtractByKey(rdd, numPartitions).keys()
def keyBy(self, f):
"""
Creates tuples of the elements in this RDD by applying C{f}.
>>> x = sc.parallelize(range(0,3)).keyBy(lambda x: x*x)
>>> y = sc.parallelize(zip(range(0,5), range(0,5)))
>>> [(x, list(map(list, y))) for x, y in sorted(x.cogroup(y).collect())]
[(0, [[0], [0]]), (1, [[1], [1]]), (2, [[], [2]]), (3, [[], [3]]), (4, [[2], [4]])]
"""
return self.map(lambda x: (f(x), x))
def repartition(self, numPartitions):
"""
Return a new RDD that has exactly numPartitions partitions.
Can increase or decrease the level of parallelism in this RDD.
Internally, this uses a shuffle to redistribute data.
If you are decreasing the number of partitions in this RDD, consider
using `coalesce`, which can avoid performing a shuffle.
>>> rdd = sc.parallelize([1,2,3,4,5,6,7], 4)
>>> sorted(rdd.glom().collect())
[[1], [2, 3], [4, 5], [6, 7]]
>>> len(rdd.repartition(2).glom().collect())
2
>>> len(rdd.repartition(10).glom().collect())
10
"""
return self.coalesce(numPartitions, shuffle=True)
def coalesce(self, numPartitions, shuffle=False):
"""
Return a new RDD that is reduced into `numPartitions` partitions.
>>> sc.parallelize([1, 2, 3, 4, 5], 3).glom().collect()
[[1], [2, 3], [4, 5]]
>>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
[[1, 2, 3, 4, 5]]
"""
if shuffle:
# Decrease the batch size in order to distribute evenly the elements across output
# partitions. Otherwise, repartition will possibly produce highly skewed partitions.
batchSize = min(10, self.ctx._batchSize or 1024)
ser = BatchedSerializer(PickleSerializer(), batchSize)
selfCopy = self._reserialize(ser)
jrdd_deserializer = selfCopy._jrdd_deserializer
jrdd = selfCopy._jrdd.coalesce(numPartitions, shuffle)
else:
jrdd_deserializer = self._jrdd_deserializer
jrdd = self._jrdd.coalesce(numPartitions, shuffle)
return RDD(jrdd, self.ctx, jrdd_deserializer)
def zip(self, other):
"""
Zips this RDD with another one, returning key-value pairs with the
first element in each RDD second element in each RDD, etc. Assumes
that the two RDDs have the same number of partitions and the same
number of elements in each partition (e.g. one was made through
a map on the other).
>>> x = sc.parallelize(range(0,5))
>>> y = sc.parallelize(range(1000, 1005))
>>> x.zip(y).collect()
[(0, 1000), (1, 1001), (2, 1002), (3, 1003), (4, 1004)]
"""
def get_batch_size(ser):
if isinstance(ser, BatchedSerializer):
return ser.batchSize
return 1 # not batched
def batch_as(rdd, batchSize):
return rdd._reserialize(BatchedSerializer(PickleSerializer(), batchSize))
my_batch = get_batch_size(self._jrdd_deserializer)
other_batch = get_batch_size(other._jrdd_deserializer)
if my_batch != other_batch or not my_batch:
# use the smallest batchSize for both of them
batchSize = min(my_batch, other_batch)
if batchSize <= 0:
# auto batched or unlimited
batchSize = 100
other = batch_as(other, batchSize)
self = batch_as(self, batchSize)
if self.getNumPartitions() != other.getNumPartitions():
raise ValueError("Can only zip with RDD which has the same number of partitions")
# There will be an Exception in JVM if there are different number
# of items in each partitions.
pairRDD = self._jrdd.zip(other._jrdd)
deserializer = PairDeserializer(self._jrdd_deserializer,
other._jrdd_deserializer)
return RDD(pairRDD, self.ctx, deserializer)
def zipWithIndex(self):
"""
Zips this RDD with its element indices.
The ordering is first based on the partition index and then the
ordering of items within each partition. So the first item in
the first partition gets index 0, and the last item in the last
partition receives the largest index.
This method needs to trigger a spark job when this RDD contains
more than one partitions.
>>> sc.parallelize(["a", "b", "c", "d"], 3).zipWithIndex().collect()
[('a', 0), ('b', 1), ('c', 2), ('d', 3)]
"""
starts = [0]
if self.getNumPartitions() > 1:
nums = self.mapPartitions(lambda it: [sum(1 for i in it)]).collect()
for i in range(len(nums) - 1):
starts.append(starts[-1] + nums[i])
def func(k, it):
for i, v in enumerate(it, starts[k]):
yield v, i
return self.mapPartitionsWithIndex(func)
def zipWithUniqueId(self):
"""
Zips this RDD with generated unique Long ids.
Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
n is the number of partitions. So there may exist gaps, but this
method won't trigger a spark job, which is different from
L{zipWithIndex}
>>> sc.parallelize(["a", "b", "c", "d", "e"], 3).zipWithUniqueId().collect()
[('a', 0), ('b', 1), ('c', 4), ('d', 2), ('e', 5)]
"""
n = self.getNumPartitions()
def func(k, it):
for i, v in enumerate(it):
yield v, i * n + k
return self.mapPartitionsWithIndex(func)
def name(self):
"""
Return the name of this RDD.
"""
n = self._jrdd.name()
if n:
return n
@ignore_unicode_prefix
def setName(self, name):
"""
Assign a name to this RDD.
>>> rdd1 = sc.parallelize([1, 2])
>>> rdd1.setName('RDD1').name()
u'RDD1'
"""
self._jrdd.setName(name)
return self
def toDebugString(self):
"""
A description of this RDD and its recursive dependencies for debugging.
"""
debug_string = self._jrdd.toDebugString()
if debug_string:
return debug_string.encode('utf-8')
def getStorageLevel(self):
"""
Get the RDD's current storage level.
>>> rdd1 = sc.parallelize([1,2])
>>> rdd1.getStorageLevel()
StorageLevel(False, False, False, False, 1)
>>> print(rdd1.getStorageLevel())
Serialized 1x Replicated
"""
java_storage_level = self._jrdd.getStorageLevel()
storage_level = StorageLevel(java_storage_level.useDisk(),
java_storage_level.useMemory(),
java_storage_level.useOffHeap(),
java_storage_level.deserialized(),
java_storage_level.replication())
return storage_level
def _defaultReducePartitions(self):
"""
Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
If spark.default.parallelism is set, then we'll use the value from SparkContext
defaultParallelism, otherwise we'll use the number of partitions in this RDD.
This mirrors the behavior of the Scala Partitioner#defaultPartitioner, intended to reduce
the likelihood of OOMs. Once PySpark adopts Partitioner-based APIs, this behavior will
be inherent.
"""
if self.ctx._conf.contains("spark.default.parallelism"):
return self.ctx.defaultParallelism
else:
return self.getNumPartitions()
def lookup(self, key):
"""
Return the list of values in the RDD for key `key`. This operation
is done efficiently if the RDD has a known partitioner by only
searching the partition that the key maps to.
>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
>>> rdd.lookup(42) # slow
[42]
>>> sorted = rdd.sortByKey()
>>> sorted.lookup(42) # fast
[42]
>>> sorted.lookup(1024)
[]
>>> rdd2 = sc.parallelize([(('a', 'b'), 'c')]).groupByKey()
>>> list(rdd2.lookup(('a', 'b'))[0])
['c']
"""
values = self.filter(lambda kv: kv[0] == key).values()
if self.partitioner is not None:
return self.ctx.runJob(values, lambda x: x, [self.partitioner(key)])
return values.collect()
def _to_java_object_rdd(self):
""" Return a JavaRDD of Object by unpickling
It will convert each Python object into Java object by Pyrolite, whenever the
RDD is serialized in batch or not.
"""
rdd = self._pickled()
return self.ctx._jvm.SerDeUtil.pythonToJava(rdd._jrdd, True)
def countApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate version of count() that returns a potentially incomplete
result within a timeout, even if not all tasks have finished.
>>> rdd = sc.parallelize(range(1000), 10)
>>> rdd.countApprox(1000, 1.0)
1000
"""
drdd = self.mapPartitions(lambda it: [float(sum(1 for i in it))])
return int(drdd.sumApprox(timeout, confidence))
def sumApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate operation to return the sum within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000))
>>> abs(rdd.sumApprox(1000) - r) / r < 0.05
True
"""
jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.sumApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
def meanApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate operation to return the mean within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000)) / 1000.0
>>> abs(rdd.meanApprox(1000) - r) / r < 0.05
True
"""
jrdd = self.map(float)._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
def countApproxDistinct(self, relativeSD=0.05):
"""
.. note:: Experimental
Return approximate number of distinct elements in the RDD.
The algorithm used is based on streamlib's implementation of
`"HyperLogLog in Practice: Algorithmic Engineering of a State
of The Art Cardinality Estimation Algorithm", available here
<http://dx.doi.org/10.1145/2452376.2452456>`_.
:param relativeSD: Relative accuracy. Smaller values create
counters that require more space.
It must be greater than 0.000017.
>>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
>>> 900 < n < 1100
True
>>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct()
>>> 16 < n < 24
True
"""
if relativeSD < 0.000017:
raise ValueError("relativeSD should be greater than 0.000017")
# the hash space in Java is 2^32
hashRDD = self.map(lambda x: portable_hash(x) & 0xFFFFFFFF)
return hashRDD._to_java_object_rdd().countApproxDistinct(relativeSD)
def toLocalIterator(self):
"""
Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
>>> rdd = sc.parallelize(range(10))
>>> [x for x in rdd.toLocalIterator()]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
with SCCallSiteSync(self.context) as css:
sock_info = self.ctx._jvm.PythonRDD.toLocalIteratorAndServe(self._jrdd.rdd())
return _load_from_socket(sock_info, self._jrdd_deserializer)
def barrier(self):
"""
.. note:: Experimental
Marks the current stage as a barrier stage, where Spark must launch all tasks together.
In case of a task failure, instead of only restarting the failed task, Spark will abort the
entire stage and relaunch all tasks for this stage.
The barrier execution mode feature is experimental and it only handles limited scenarios.
Please read the linked SPIP and design docs to understand the limitations and future plans.
:return: an :class:`RDDBarrier` instance that provides actions within a barrier stage.
.. seealso:: :class:`BarrierTaskContext`
.. seealso:: `SPIP: Barrier Execution Mode
<http://jira.apache.org/jira/browse/SPARK-24374>`_
.. seealso:: `Design Doc <https://jira.apache.org/jira/browse/SPARK-24582>`_
.. versionadded:: 2.4.0
"""
return RDDBarrier(self)
def _is_barrier(self):
"""
Whether this RDD is in a barrier stage.
"""
return self._jrdd.rdd().isBarrier()
def _prepare_for_python_RDD(sc, command):
# the serialized command will be compressed by broadcast
ser = CloudPickleSerializer()
pickled_command = ser.dumps(command)
if len(pickled_command) > (1 << 20): # 1M
# The broadcast will have same life cycle as created PythonRDD
broadcast = sc.broadcast(pickled_command)
pickled_command = ser.dumps(broadcast)
broadcast_vars = [x._jbroadcast for x in sc._pickled_broadcast_vars]
sc._pickled_broadcast_vars.clear()
return pickled_command, broadcast_vars, sc.environment, sc._python_includes
def _wrap_function(sc, func, deserializer, serializer, profiler=None):
assert deserializer, "deserializer should not be empty"
assert serializer, "serializer should not be empty"
command = (func, profiler, deserializer, serializer)
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
return sc._jvm.PythonFunction(bytearray(pickled_command), env, includes, sc.pythonExec,
sc.pythonVer, broadcast_vars, sc._javaAccumulator)
class RDDBarrier(object):
"""
.. note:: Experimental
Wraps an RDD in a barrier stage, which forces Spark to launch tasks of this stage together.
:class:`RDDBarrier` instances are created by :func:`RDD.barrier`.
.. versionadded:: 2.4.0
"""
def __init__(self, rdd):
self.rdd = rdd
def mapPartitions(self, f, preservesPartitioning=False):
"""
.. note:: Experimental
Returns a new RDD by applying a function to each partition of the wrapped RDD,
where tasks are launched together in a barrier stage.
The interface is the same as :func:`RDD.mapPartitions`.
Please see the API doc there.
.. versionadded:: 2.4.0
"""
def func(s, iterator):
return f(iterator)
return PipelinedRDD(self.rdd, func, preservesPartitioning, isFromBarrier=True)
class PipelinedRDD(RDD):
"""
Pipelined maps:
>>> rdd = sc.parallelize([1, 2, 3, 4])
>>> rdd.map(lambda x: 2 * x).cache().map(lambda x: 2 * x).collect()
[4, 8, 12, 16]
>>> rdd.map(lambda x: 2 * x).map(lambda x: 2 * x).collect()
[4, 8, 12, 16]
Pipelined reduces:
>>> from operator import add
>>> rdd.map(lambda x: 2 * x).reduce(add)
20
>>> rdd.flatMap(lambda x: [x, x]).reduce(add)
20
"""
def __init__(self, prev, func, preservesPartitioning=False, isFromBarrier=False):
if not isinstance(prev, PipelinedRDD) or not prev._is_pipelinable():
# This transformation is the first in its stage:
self.func = func
self.preservesPartitioning = preservesPartitioning
self._prev_jrdd = prev._jrdd
self._prev_jrdd_deserializer = prev._jrdd_deserializer
else:
prev_func = prev.func
def pipeline_func(split, iterator):
return func(split, prev_func(split, iterator))
self.func = pipeline_func
self.preservesPartitioning = \
prev.preservesPartitioning and preservesPartitioning
self._prev_jrdd = prev._prev_jrdd # maintain the pipeline
self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
self.is_cached = False
self.is_checkpointed = False
self.ctx = prev.ctx
self.prev = prev
self._jrdd_val = None
self._id = None
self._jrdd_deserializer = self.ctx.serializer
self._bypass_serializer = False
self.partitioner = prev.partitioner if self.preservesPartitioning else None
self.is_barrier = prev._is_barrier() or isFromBarrier
def getNumPartitions(self):
return self._prev_jrdd.partitions().size()
@property
def _jrdd(self):
if self._jrdd_val:
return self._jrdd_val
if self._bypass_serializer:
self._jrdd_deserializer = NoOpSerializer()
if self.ctx.profiler_collector:
profiler = self.ctx.profiler_collector.new_profiler(self.ctx)
else:
profiler = None
wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer,
self._jrdd_deserializer, profiler)
python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(), wrapped_func,
self.preservesPartitioning, self.is_barrier)
self._jrdd_val = python_rdd.asJavaRDD()
if profiler:
self._id = self._jrdd_val.id()
self.ctx.profiler_collector.add_profiler(self._id, profiler)
return self._jrdd_val
def id(self):
if self._id is None:
self._id = self._jrdd.id()
return self._id
def _is_pipelinable(self):
return not (self.is_cached or self.is_checkpointed)
def _is_barrier(self):
return self.is_barrier
def _test():
import doctest
from pyspark.context import SparkContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
globs['sc'] = SparkContext('local[4]', 'PythonTest')
(failure_count, test_count) = doctest.testmod(
globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()