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train | _check_dataframe_localize_timestamps | Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone
:param pdf: pandas.DataFrame
:param timezone: the timezone to convert. if None then use local timezone
:return pandas.DataFrame where any timezone aware columns have been converted to tz-naive | python/pyspark/sql/types.py | def _check_dataframe_localize_timestamps(pdf, timezone):
"""
Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone
:param pdf: pandas.DataFrame
:param timezone: the timezone to convert. if None then use local timezone
:return pandas.DataFrame where any time... | def _check_dataframe_localize_timestamps(pdf, timezone):
"""
Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone
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train | _check_series_convert_timestamps_internal | Convert a tz-naive timestamp in the specified timezone or local timezone to UTC normalized for
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train | _check_series_convert_timestamps_localize | Convert timestamp to timezone-naive in the specified timezone or local timezone
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train | StructType.add | Construct a StructType by adding new elements to it to define the schema. The method accepts
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train | UserDefinedType._cachedSqlType | Cache the sqlType() into class, because it's heavy used in `toInternal`. | python/pyspark/sql/types.py | def _cachedSqlType(cls):
"""
Cache the sqlType() into class, because it's heavy used in `toInternal`.
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train | Row.asDict | Return as an dict
:param recursive: turns the nested Row as dict (default: False).
>>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
True
>>> row = Row(key=1, value=Row(name='a', age=2))
>>> row.asDict() == {'key': 1, 'value': Row(age=2, name='a')}
... | python/pyspark/sql/types.py | def asDict(self, recursive=False):
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Return as an dict
:param recursive: turns the nested Row as dict (default: False).
>>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
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>>> row = Row(key=1, value=Row(name='a', age=2))
>>> row.asDict(... | def asDict(self, recursive=False):
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>>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
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>>> row.asDict(... | [
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train | LinearRegressionModel.summary | Gets summary (e.g. residuals, mse, r-squared ) of model on
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train | LinearRegressionModel.evaluate | Evaluates the model on a test dataset.
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Test dataset to evaluate model on, where dataset is an
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train | GeneralizedLinearRegressionModel.summary | Gets summary (e.g. residuals, deviance, pValues) of model on
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train | _get_local_dirs | Get all the directories | python/pyspark/shuffle.py | def _get_local_dirs(sub):
""" Get all the directories """
path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
dirs = path.split(",")
if len(dirs) > 1:
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rnd = random.Random(os.getpid() + id(dirs))
random.shuffle(dirs, rnd.random)... | def _get_local_dirs(sub):
""" Get all the directories """
path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
dirs = path.split(",")
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train | ExternalMerger._get_spill_dir | Choose one directory for spill by number n | python/pyspark/shuffle.py | 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 _get_spill_dir(self, n):
""" Choose one directory for spill by number n """
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train | ExternalMerger.mergeValues | Combine the items by creator and combiner | python/pyspark/shuffle.py | 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... | def mergeValues(self, iterator):
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train | ExternalMerger.mergeCombiners | Merge (K,V) pair by mergeCombiner | python/pyspark/shuffle.py | 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
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train | ExternalMerger._spill | dump already partitioned data into disks.
It will dump the data in batch for better performance. | python/pyspark/shuffle.py | 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):
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"""
dump already partitioned data into disks.
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train | ExternalMerger.items | Return all merged items as iterator | python/pyspark/shuffle.py | def items(self):
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train | ExternalMerger._external_items | Return all partitioned items as iterator | python/pyspark/shuffle.py | def _external_items(self):
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# disable partitioning and spilling when merge combiners from disk
self.pdata = []
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train | ExternalMerger._recursive_merged_items | merge the partitioned items and return the as iterator
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train | ExternalSorter._get_path | Choose one directory for spill by number n | python/pyspark/shuffle.py | def _get_path(self, n):
""" Choose one directory for spill by number n """
d = self.local_dirs[n % len(self.local_dirs)]
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train | ExternalSorter.sorted | Sort the elements in iterator, do external sort when the memory
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"""
Sort the elements in iterator, do external sort when the memory
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global MemoryBytesSpilled, DiskBytesSpilled
batch, limit = 100, self._next_limit()
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... | def sorted(self, iterator, key=None, reverse=False):
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train | ExternalList._spill | dump the values into disk | python/pyspark/shuffle.py | def _spill(self):
""" dump the values into disk """
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self._open_file()
used_memory = get_used_memory()
pos = self._file.tell()
self._ser.dump_stream(self.values, self._file)
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train | ExternalGroupBy._spill | dump already partitioned data into disks. | python/pyspark/shuffle.py | def _spill(self):
"""
dump already partitioned data into disks.
"""
global MemoryBytesSpilled, DiskBytesSpilled
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train | ExternalGroupBy._merge_sorted_items | load a partition from disk, then sort and group by key | python/pyspark/shuffle.py | def _merge_sorted_items(self, index):
""" load a partition from disk, then sort and group by key """
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path = self._get_spill_dir(j)
p = os.path.join(path, str(index))
with open(p, 'rb', 65536) as f:
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""" load a partition from disk, then sort and group by key """
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train | worker | Called by a worker process after the fork(). | python/pyspark/daemon.py | def worker(sock, authenticated):
"""
Called by a worker process after the fork().
"""
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signal.signal(SIGCHLD, SIG_DFL)
signal.signal(SIGTERM, SIG_DFL)
# restore the handler for SIGINT,
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signal.... | def worker(sock, authenticated):
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train | portable_hash | This function returns consistent hash code for builtin types, especially
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The algorithm is similar to that one used by CPython 2.7
>>> portable_hash(None)
0
>>> portable_hash((None, 1)) & 0xffffffff
219750521 | python/pyspark/rdd.py | def portable_hash(x):
"""
This function returns consistent hash code for builtin types, especially
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>>> portable_hash(None)
0
>>> portable_hash((None, 1)) & 0xffffffff
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if sys.ve... | def portable_hash(x):
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train | _parse_memory | Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
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>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048 | python/pyspark/rdd.py | def _parse_memory(s):
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
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>>> _parse_memory("256m")
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>>> _parse_memory("2g")
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if s[-1].lower() not in units:
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train | ignore_unicode_prefix | Ignore the 'u' prefix of string in doc tests, to make it works
in both python 2 and 3 | python/pyspark/rdd.py | def ignore_unicode_prefix(f):
"""
Ignore the 'u' prefix of string in doc tests, to make it works
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"""
if sys.version >= '3':
# the representation of unicode string in Python 3 does not have prefix 'u',
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literal_re ... | def ignore_unicode_prefix(f):
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Ignore the 'u' prefix of string in doc tests, to make it works
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train | RDD.cache | Persist this RDD with the default storage level (C{MEMORY_ONLY}). | python/pyspark/rdd.py | def cache(self):
"""
Persist this RDD with the default storage level (C{MEMORY_ONLY}).
"""
self.is_cached = True
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"""
Persist this RDD with the default storage level (C{MEMORY_ONLY}).
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"""
Set this RDD's storage level to persist its values across operations
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train | RDD.unpersist | Mark the RDD as non-persistent, and remove all blocks for it from
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.. versionchanged:: 3.0.0
Added optional argument `blocking` to specify whether to block until all
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"""
Mark the RDD as non-persistent, and remove all blocks for it from
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.. versionchanged:: 3.0.0
Added optional argument `blocking` to specify whether to block until all
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se... | def unpersist(self, blocking=False):
"""
Mark the RDD as non-persistent, and remove all blocks for it from
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Added optional argument `blocking` to specify whether to block until all
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train | RDD.getCheckpointFile | Gets the name of the file to which this RDD was checkpointed
Not defined if RDD is checkpointed locally. | python/pyspark/rdd.py | 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()
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return checkpointFile.get() | def getCheckpointFile(self):
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train | RDD.map | 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)] | python/pyspark/rdd.py | def map(self, f, preservesPartitioning=False):
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train | RDD.flatMap | Return a new RDD by first applying a function to all elements of this
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>>> rdd = sc.parallelize([2, 3, 4])
>>> sorted(rdd.flatMap(lambda x: range(1, x)).collect())
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"""
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>>> rdd = sc.parallelize([2, 3, 4])
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train | RDD.mapPartitions | 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)
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>>> def f(iterator): yield sum(iterator)
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train | RDD.mapPartitionsWithSplit | Deprecated: use mapPartitionsWithIndex instead.
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>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
>>> def f(splitIndex, iterator): yield splitIndex
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"""
Deprecated: use mapPartitionsWithIndex instead.
Return a new RDD by applying a function to each partition of this RDD,
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train | RDD.distinct | Return a new RDD containing the distinct elements in this RDD.
>>> sorted(sc.parallelize([1, 1, 2, 3]).distinct().collect())
[1, 2, 3] | python/pyspark/rdd.py | 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())
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"""
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train | RDD.sample | 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]... | python/pyspark/rdd.py | 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
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Return a sampled subset of this RDD.
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train | RDD.randomSplit | Randomly splits this RDD with the provided weights.
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:param seed: random seed
:return: split RDDs in a list
>>> rdd = sc.parallelize(range(500), 1)
>>> rdd1, rdd2 = rdd.randomSplit([2, 3], 17)
... | python/pyspark/rdd.py | def randomSplit(self, weights, seed=None):
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Randomly splits this RDD with the provided weights.
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:param seed: random seed
:return: split RDDs in a list
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train | RDD.takeSample | Return a fixed-size sampled subset of this RDD.
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>>> rdd = sc.parallelize(range(0, 10))
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train | RDD._computeFractionForSampleSize | Returns a sampling rate that guarantees a sample of
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train | RDD.union | 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] | python/pyspark/rdd.py | 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]
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Return the union of this RDD and another one.
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train | RDD.intersection | 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])
... | python/pyspark/rdd.py | def intersection(self, other):
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.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
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train | RDD.repartitionAndSortWithinPartitions | Repartition the RDD according to the given partitioner and, within each resulting partition,
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>>> 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()... | python/pyspark/rdd.py | def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
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"""
Repartition the RDD according to the given partitioner and, within each resulting partition,
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train | RDD.sortByKey | 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'... | python/pyspark/rdd.py | 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.p... | 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)]
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train | RDD.sortBy | Sorts this RDD by the given keyfunc
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>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
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[('a', 1), ('b', ... | python/pyspark/rdd.py | def sortBy(self, keyfunc, ascending=True, numPartitions=None):
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train | RDD.cartesian | Return the Cartesian product of this RDD and another one, that is, the
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>>> rdd = sc.parallelize([1, 2])
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[(1, 1), (1, 2), (2, 1), (2, 2)] | python/pyspark/rdd.py | def cartesian(self, other):
"""
Return the Cartesian product of this RDD and another one, that is, the
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>>> sorted(rdd.cartesian(rdd).collect())
... | def cartesian(self, other):
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train | RDD.groupBy | Return an RDD of grouped items.
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>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
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Return an RDD of grouped items.
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train | RDD.pipe | 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. | python/pyspark/rdd.py | def pipe(self, command, env=None, checkCode=False):
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Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
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Return an RDD created by piping elements to a forked external process.
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train | RDD.foreach | Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) | python/pyspark/rdd.py | 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):
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... | def foreach(self, f):
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Applies a function to all elements of this RDD.
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>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
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train | RDD.foreachPartition | Applies a function to each partition of this RDD.
>>> def f(iterator):
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>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f) | python/pyspark/rdd.py | def foreachPartition(self, f):
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train | RDD.collect | 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
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train | RDD.reduce | Reduces the elements of this RDD using the specified commutative and
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>>> 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).cac... | python/pyspark/rdd.py | def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
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>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
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Reduces the elements of this RDD using the specified commutative and
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train | RDD.treeReduce | Reduces the elements of this RDD in a multi-level tree pattern.
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>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
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... | python/pyspark/rdd.py | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
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>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(ad... | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
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train | RDD.fold | Aggregate the elements of each partition, and then the results for all
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The function C{op(t1, t2)} is allowed to modify C{t1} and return it
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... | python/pyspark/rdd.py | def fold(self, zeroValue, op):
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Aggregate the elements of each partition, and then the results for all
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train | RDD.aggregate | Aggregate the elements of each partition, and then the results for all
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The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
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Aggregate the elements of each partition, and then the results for all
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train | RDD.treeAggregate | Aggregates the elements of this RDD in a multi-level tree
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: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)
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-5
>>> rdd.tr... | python/pyspark/rdd.py | def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
"""
Aggregates the elements of this RDD in a multi-level tree
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:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
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Aggregates the elements of this RDD in a multi-level tree
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train | RDD.max | Find the maximum item in this RDD.
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>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0 | python/pyspark/rdd.py | 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()
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>>> rdd.max(key=str)
5.0
"""
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"""
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train | RDD.min | Find the minimum item in this RDD.
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train | RDD.sum | Add up the elements in this RDD.
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"""
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train | RDD.stats | Return a L{StatCounter} object that captures the mean, variance
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"""
Return a L{StatCounter} object that captures the mean, variance
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"""
def redFunc(left_counter, right_counter):
return left_counter.mergeStats(right_counter)
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train | RDD.countByValue | Return the count of each unique value in this RDD as a dictionary of
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>>> sorted(sc.parallelize([1, 2, 1, 2, 2], 2).countByValue().items())
[(1, 2), (2, 3)] | python/pyspark/rdd.py | def countByValue(self):
"""
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>>> sorted(sc.parallelize([1, 2, 1, 2, 2], 2).countByValue().items())
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train | RDD.top | Get the top N elements from an RDD.
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.. note:: It returns the list sorted in descending order.
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... | python/pyspark/rdd.py | 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
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.. note:: It returns the list sorted in descending order.
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Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
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train | RDD.takeOrdered | Get the N elements from an RDD ordered in ascending order or as
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.. note:: this method should only be used if the resulting array is expected
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>>> sc.parallelize([10, 1, 2, 9, 3, ... | python/pyspark/rdd.py | def takeOrdered(self, num, key=None):
"""
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.. note:: this method ... | python/pyspark/rdd.py | def take(self, num):
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Take the first num elements of the RDD.
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train | RDD.saveAsNewAPIHadoopDataset | Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
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Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
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train | RDD.saveAsNewAPIHadoopFile | Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
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train | RDD.saveAsSequenceFile | Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
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train | RDD.saveAsPickleFile | Save this RDD as a SequenceFile of serialized objects. The serializer
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>>> tmpFile = NamedTemporaryFile(delete=True)
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train | RDD.saveAsTextFile | Save this RDD as a text file, using string representations of elements.
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@param path: path to text file
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train | RDD.reduceByKey | Merge the values for each key using an associative and commutative reduce function.
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sending results to a reducer, similarly to a "combiner" in MapReduce.
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train | RDD.reduceByKeyLocally | Merge the values for each key using an associative and commutative reduce function, but
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This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" in MapReduce.
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Merge the values for each key using an associative and commutative reduce function, but
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Merge the values for each key using an associative and commutative reduce function, but
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train | RDD.partitionBy | 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 | python/pyspark/rdd.py | 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[... | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
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train | RDD.combineByKey | 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
... | python/pyspark/rdd.py | def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
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"""
Generic function to combine the elements for each key using a custom
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train | RDD.aggregateByKey | 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
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Aggregate the values of each key, using given combine functions and a neutral
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train | RDD.foldByKey | Merge the values for each key using an associative function "func"
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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"... | python/pyspark/rdd.py | def foldByKey(self, zeroValue, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative function "func"
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train | RDD.groupByKey | 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
p... | python/pyspark/rdd.py | def groupByKey(self, numPartitions=None, partitionFunc=portable_hash):
"""
Group the values for each key in the RDD into a single sequence.
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.. note:: If you are grouping in order to perform an aggregation (such as a
... | def groupByKey(self, numPartitions=None, partitionFunc=portable_hash):
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Group the values for each key in the RDD into a single sequence.
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train | RDD.flatMapValues | 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()
... | python/pyspark/rdd.py | def flatMapValues(self, f):
"""
Pass each value in the key-value pair RDD through a flatMap function
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partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
>>> def f(x): return x
... | def flatMapValues(self, f):
"""
Pass each value in the key-value pair RDD through a flatMap function
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partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
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train | RDD.mapValues | Pass each value in the key-value pair RDD through a map function
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>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
>>> def f(x): return len(x)
>>> x.mapValues(f).collect(... | python/pyspark/rdd.py | def mapValues(self, f):
"""
Pass each value in the key-value pair RDD through a map function
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>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
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train | RDD.sampleByKey | 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()).carte... | python/pyspark/rdd.py | 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.
>>> fractio... | 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
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train | RDD.subtractByKey | 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)] | python/pyspark/rdd.py | 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.... | def subtractByKey(self, other, numPartitions=None):
"""
Return each (key, value) pair in C{self} that has no pair with matching
key in C{other}.
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>>> y = sc.parallelize([("a", 3), ("c", None)])
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train | RDD.subtract | Return each value in C{self} that is not contained in C{other}.
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>>> y = sc.parallelize([("a", 3), ("c", None)])
>>> sorted(x.subtract(y).collect())
[('a', 1), ('b', 4), ('b', 5)] | python/pyspark/rdd.py | 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', ... | 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())
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train | RDD.coalesce | 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]] | python/pyspark/rdd.py | 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]]
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train | RDD.zipWithIndex | Zips this RDD with its element indices.
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the first partition gets index 0, and the last item in the last
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Zips this RDD with its element indices.
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train | RDD.zipWithUniqueId | 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
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>>> sc.parallelize(["a", "b... | python/pyspark/rdd.py | def zipWithUniqueId(self):
"""
Zips this RDD with generated unique Long ids.
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n is the number of partitions. So there may exist gaps, but this
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train | RDD.getStorageLevel | 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 | python/pyspark/rdd.py | 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... | 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
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train | RDD._defaultReducePartitions | 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.
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"""
Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
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... | def _defaultReducePartitions(self):
"""
Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
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>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
>>> rdd.lookup(42) # slow
[42]
... | python/pyspark/rdd.py | def lookup(self, key):
"""
Return the list of values in the RDD for key `key`. This operation
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>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
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train | RDD._to_java_object_rdd | 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. | python/pyspark/rdd.py | def _to_java_object_rdd(self):
""" Return a JavaRDD of Object by unpickling
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"""
rdd = self._pickled()
return self.ctx._jvm.SerDeUtil.pythonToJava(rdd._jrdd, T... | def _to_java_object_rdd(self):
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train | RDD.countApprox | .. 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 | python/pyspark/rdd.py | 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(1... | 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)
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train | RDD.sumApprox | .. note:: Experimental
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>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000))
>>> abs(rdd.sumApprox(1000) - r) / r < 0.05
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"""
.. note:: Experimental
Approximate operation to return the sum within a timeout
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>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000))
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Approximate operation to return the sum within a timeout
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train | RDD.meanApprox | .. note:: Experimental
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>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000)) / 1000.0
>>> abs(rdd.meanApprox(1000) - r) / r < 0.05
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"""
.. 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) ... | def meanApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
Approximate operation to return the mean within a timeout
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>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(range(1000)) / 1000.0
>>> abs(rdd.meanApprox(1000) ... | [
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train | RDD.countApproxDistinct | .. 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
<https://doi... | python/pyspark/rdd.py | def countApproxDistinct(self, relativeSD=0.05):
"""
.. note:: Experimental
Return approximate number of distinct elements in the RDD.
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Return approximate number of distinct elements in the RDD.
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train | RDD.toLocalIterator | 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] | python/pyspark/rdd.py | 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... | 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.
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train | RDDBarrier.mapPartitions | .. 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 | python/pyspark/rdd.py | 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`.
... | 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`.
... | [
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train | _to_seq | Convert a list of Column (or names) into a JVM Seq of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects. | python/pyspark/sql/column.py | def _to_seq(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM Seq of Column.
An optional `converter` could be used to convert items in `cols`
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"""
if converter:
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return sc._jvm.PythonUtils.toSeq(c... | def _to_seq(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM Seq of Column.
An optional `converter` could be used to convert items in `cols`
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"""
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train | _to_list | Convert a list of Column (or names) into a JVM (Scala) List of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects. | python/pyspark/sql/column.py | def _to_list(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM (Scala) List of Column.
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into JVM Column objects.
"""
if converter:
cols = [converter(c) for c in cols]
return sc._jvm.PythonUti... | def _to_list(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM (Scala) List of Column.
An optional `converter` could be used to convert items in `cols`
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train | _unary_op | Create a method for given unary operator | python/pyspark/sql/column.py | def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _ | def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _ | [
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train | _bin_op | Create a method for given binary operator | python/pyspark/sql/column.py | def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _ | def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _ | [
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] | apache/spark | python | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L110-L118 | [
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