INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
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launch jvm gateway
:param conf: spark configuration passed to spark-submit
:param popen_kwargs: Dictionary of kwargs to pass to Popen when spawning
the py4j JVM. This is a developer feature intended for use in
customizing how pyspark interacts with the py4j JVM (e.g., capturing
stdout/st... | def launch_gateway(conf=None, popen_kwargs=None):
"""
launch jvm gateway
:param conf: spark configuration passed to spark-submit
:param popen_kwargs: Dictionary of kwargs to pass to Popen when spawning
the py4j JVM. This is a developer feature intended for use in
customizing how pyspark ... |
Performs the authentication protocol defined by the SocketAuthHelper class on the given
file-like object 'conn'. | def _do_server_auth(conn, auth_secret):
"""
Performs the authentication protocol defined by the SocketAuthHelper class on the given
file-like object 'conn'.
"""
write_with_length(auth_secret.encode("utf-8"), conn)
conn.flush()
reply = UTF8Deserializer().loads(conn)
if reply != "ok":
... |
Connect to local host, authenticate with it, and return a (sockfile,sock) for that connection.
Handles IPV4 & IPV6, does some error handling.
:param port
:param auth_secret
:return: a tuple with (sockfile, sock) | def local_connect_and_auth(port, auth_secret):
"""
Connect to local host, authenticate with it, and return a (sockfile,sock) for that connection.
Handles IPV4 & IPV6, does some error handling.
:param port
:param auth_secret
:return: a tuple with (sockfile, sock)
"""
sock = None
error... |
Start callback server if not already started. The callback server is needed if the Java
driver process needs to callback into the Python driver process to execute Python code. | def ensure_callback_server_started(gw):
"""
Start callback server if not already started. The callback server is needed if the Java
driver process needs to callback into the Python driver process to execute Python code.
"""
# getattr will fallback to JVM, so we cannot test by hasattr()
if "_cal... |
Find the SPARK_HOME. | def _find_spark_home():
"""Find the SPARK_HOME."""
# If the environment has SPARK_HOME set trust it.
if "SPARK_HOME" in os.environ:
return os.environ["SPARK_HOME"]
def is_spark_home(path):
"""Takes a path and returns true if the provided path could be a reasonable SPARK_HOME"""
... |
Calculates URL contributions to the rank of other URLs. | def computeContribs(urls, rank):
"""Calculates URL contributions to the rank of other URLs."""
num_urls = len(urls)
for url in urls:
yield (url, rank / num_urls) |
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists. | def summary(self):
"""
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists.
"""
if self.hasSummary:
return GaussianMixtureSummary(super(GaussianMixtureModel, self).summary)
... |
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists. | def summary(self):
"""
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists.
"""
if self.hasSummary:
return KMeansSummary(super(KMeansModel, self).summary)
else:
... |
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists. | def summary(self):
"""
Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the
training set. An exception is thrown if no summary exists.
"""
if self.hasSummary:
return BisectingKMeansSummary(super(BisectingKMeansModel, self).summary)
... |
Returns the image schema.
:return: a :class:`StructType` with a single column of images
named "image" (nullable) and having the same type returned by :meth:`columnSchema`.
.. versionadded:: 2.3.0 | def imageSchema(self):
"""
Returns the image schema.
:return: a :class:`StructType` with a single column of images
named "image" (nullable) and having the same type returned by :meth:`columnSchema`.
.. versionadded:: 2.3.0
"""
if self._imageSchema is Non... |
Returns the OpenCV type mapping supported.
:return: a dictionary containing the OpenCV type mapping supported.
.. versionadded:: 2.3.0 | def ocvTypes(self):
"""
Returns the OpenCV type mapping supported.
:return: a dictionary containing the OpenCV type mapping supported.
.. versionadded:: 2.3.0
"""
if self._ocvTypes is None:
ctx = SparkContext._active_spark_context
self._ocvTypes... |
Returns the schema for the image column.
:return: a :class:`StructType` for image column,
``struct<origin:string, height:int, width:int, nChannels:int, mode:int, data:binary>``.
.. versionadded:: 2.4.0 | def columnSchema(self):
"""
Returns the schema for the image column.
:return: a :class:`StructType` for image column,
``struct<origin:string, height:int, width:int, nChannels:int, mode:int, data:binary>``.
.. versionadded:: 2.4.0
"""
if self._columnSchema i... |
Returns field names of image columns.
:return: a list of field names.
.. versionadded:: 2.3.0 | def imageFields(self):
"""
Returns field names of image columns.
:return: a list of field names.
.. versionadded:: 2.3.0
"""
if self._imageFields is None:
ctx = SparkContext._active_spark_context
self._imageFields = list(ctx._jvm.org.apache.spar... |
Returns the name of undefined image type for the invalid image.
.. versionadded:: 2.3.0 | def undefinedImageType(self):
"""
Returns the name of undefined image type for the invalid image.
.. versionadded:: 2.3.0
"""
if self._undefinedImageType is None:
ctx = SparkContext._active_spark_context
self._undefinedImageType = \
ctx._... |
Converts an image to an array with metadata.
:param `Row` image: A row that contains the image to be converted. It should
have the attributes specified in `ImageSchema.imageSchema`.
:return: a `numpy.ndarray` that is an image.
.. versionadded:: 2.3.0 | def toNDArray(self, image):
"""
Converts an image to an array with metadata.
:param `Row` image: A row that contains the image to be converted. It should
have the attributes specified in `ImageSchema.imageSchema`.
:return: a `numpy.ndarray` that is an image.
.. vers... |
Converts an array with metadata to a two-dimensional image.
:param `numpy.ndarray` array: The array to convert to image.
:param str origin: Path to the image, optional.
:return: a :class:`Row` that is a two dimensional image.
.. versionadded:: 2.3.0 | def toImage(self, array, origin=""):
"""
Converts an array with metadata to a two-dimensional image.
:param `numpy.ndarray` array: The array to convert to image.
:param str origin: Path to the image, optional.
:return: a :class:`Row` that is a two dimensional image.
.. ... |
Reads the directory of images from the local or remote source.
.. note:: If multiple jobs are run in parallel with different sampleRatio or recursive flag,
there may be a race condition where one job overwrites the hadoop configs of another.
.. note:: If sample ratio is less than 1, sampli... | def readImages(self, path, recursive=False, numPartitions=-1,
dropImageFailures=False, sampleRatio=1.0, seed=0):
"""
Reads the directory of images from the local or remote source.
.. note:: If multiple jobs are run in parallel with different sampleRatio or recursive flag,
... |
Construct this object from given Java classname and arguments | def _create_from_java_class(cls, java_class, *args):
"""
Construct this object from given Java classname and arguments
"""
java_obj = JavaWrapper._new_java_obj(java_class, *args)
return cls(java_obj) |
Create a Java array of given java_class type. Useful for
calling a method with a Scala Array from Python with Py4J.
If the param pylist is a 2D array, then a 2D java array will be returned.
The returned 2D java array is a square, non-jagged 2D array that is big
enough for all elements. T... | def _new_java_array(pylist, java_class):
"""
Create a Java array of given java_class type. Useful for
calling a method with a Scala Array from Python with Py4J.
If the param pylist is a 2D array, then a 2D java array will be returned.
The returned 2D java array is a square, non-j... |
>>> _convert_epytext("L{A}")
:class:`A` | def _convert_epytext(line):
"""
>>> _convert_epytext("L{A}")
:class:`A`
"""
line = line.replace('@', ':')
for p, sub in RULES:
line = re.sub(p, sub, line)
return line |
Return string prefix-time(.suffix)
>>> rddToFileName("spark", None, 12345678910)
'spark-12345678910'
>>> rddToFileName("spark", "tmp", 12345678910)
'spark-12345678910.tmp' | def rddToFileName(prefix, suffix, timestamp):
"""
Return string prefix-time(.suffix)
>>> rddToFileName("spark", None, 12345678910)
'spark-12345678910'
>>> rddToFileName("spark", "tmp", 12345678910)
'spark-12345678910.tmp'
"""
if isinstance(timestamp, datetime):
seconds = time.mk... |
Add a profiler for RDD `id` | def add_profiler(self, id, profiler):
""" Add a profiler for RDD `id` """
if not self.profilers:
if self.profile_dump_path:
atexit.register(self.dump_profiles, self.profile_dump_path)
else:
atexit.register(self.show_profiles)
self.profiler... |
Dump the profile stats into directory `path` | def dump_profiles(self, path):
""" Dump the profile stats into directory `path` """
for id, profiler, _ in self.profilers:
profiler.dump(id, path)
self.profilers = [] |
Print the profile stats to stdout | def show_profiles(self):
""" Print the profile stats to stdout """
for i, (id, profiler, showed) in enumerate(self.profilers):
if not showed and profiler:
profiler.show(id)
# mark it as showed
self.profilers[i][2] = True |
Print the profile stats to stdout, id is the RDD id | def show(self, id):
""" Print the profile stats to stdout, id is the RDD id """
stats = self.stats()
if stats:
print("=" * 60)
print("Profile of RDD<id=%d>" % id)
print("=" * 60)
stats.sort_stats("time", "cumulative").print_stats() |
Dump the profile into path, id is the RDD id | def dump(self, id, path):
""" Dump the profile into path, id is the RDD id """
if not os.path.exists(path):
os.makedirs(path)
stats = self.stats()
if stats:
p = os.path.join(path, "rdd_%d.pstats" % id)
stats.dump_stats(p) |
Runs and profiles the method to_profile passed in. A profile object is returned. | def profile(self, func):
""" Runs and profiles the method to_profile passed in. A profile object is returned. """
pr = cProfile.Profile()
pr.runcall(func)
st = pstats.Stats(pr)
st.stream = None # make it picklable
st.strip_dirs()
# Adds a new profile to the exis... |
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
"""
if cls._instantiatedContext is None:
jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
sparkSession = SparkSession(... |
Sets the given Spark SQL configuration property. | def setConf(self, key, value):
"""Sets the given Spark SQL configuration property.
"""
self.sparkSession.conf.set(key, value) |
Returns the value of Spark SQL configuration property for the given key.
If the key is not set and defaultValue is set, return
defaultValue. If the key is not set and defaultValue is not set, return
the system default value.
>>> sqlContext.getConf("spark.sql.shuffle.partitions")
... | def getConf(self, key, defaultValue=_NoValue):
"""Returns the value of Spark SQL configuration property for the given key.
If the key is not set and defaultValue is set, return
defaultValue. If the key is not set and defaultValue is not set, return
the system default value.
>>>... |
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the start value
:param end: the end value (exclusive)
:param step: the in... | def range(self, start, end=None, step=1, numPartitions=None):
"""
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the sta... |
An alias for :func:`spark.udf.register`.
See :meth:`pyspark.sql.UDFRegistration.register`.
.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead. | def registerFunction(self, name, f, returnType=None):
"""An alias for :func:`spark.udf.register`.
See :meth:`pyspark.sql.UDFRegistration.register`.
.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead.
"""
warnings.warn(
"Deprecated in 2.3.0. Use spa... |
An alias for :func:`spark.udf.registerJavaFunction`.
See :meth:`pyspark.sql.UDFRegistration.registerJavaFunction`.
.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.registerJavaFunction` instead. | def registerJavaFunction(self, name, javaClassName, returnType=None):
"""An alias for :func:`spark.udf.registerJavaFunction`.
See :meth:`pyspark.sql.UDFRegistration.registerJavaFunction`.
.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.registerJavaFunction` instead.
"""
warn... |
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
from ``data... | def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
When ``schema`` is a list of column names, the type of each column
will be inferred from ``data``.
... |
Creates an external table based on the dataset in a data source.
It returns the DataFrame associated with the external table.
The data source is specified by the ``source`` and a set of ``options``.
If ``source`` is not specified, the default data source configured by
``spark.sql.sourc... | def createExternalTable(self, tableName, path=None, source=None, schema=None, **options):
"""Creates an external table based on the dataset in a data source.
It returns the DataFrame associated with the external table.
The data source is specified by the ``source`` and a set of ``options``.
... |
Returns a :class:`DataFrame` containing names of tables in the given database.
If ``dbName`` is not specified, the current database will be used.
The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
(a column with :class:`BooleanType` indicating if a table is a temporary o... | def tables(self, dbName=None):
"""Returns a :class:`DataFrame` containing names of tables in the given database.
If ``dbName`` is not specified, the current database will be used.
The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
(a column with :class:`BooleanTy... |
Returns a list of names of tables in the database ``dbName``.
:param dbName: string, name of the database to use. Default to the current database.
:return: list of table names, in string
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> "table1" in sqlContext.tableNames()
... | def tableNames(self, dbName=None):
"""Returns a list of names of tables in the database ``dbName``.
:param dbName: string, name of the database to use. Default to the current database.
:return: list of table names, in string
>>> sqlContext.registerDataFrameAsTable(df, "table1")
... |
Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving. | def streams(self):
"""Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving.
"""
from pyspark.sql.streaming import StreamingQueryManager
return StreamingQueryManager(sel... |
Converts a binary column of avro format into its corresponding catalyst value. The specified
schema must match the read data, otherwise the behavior is undefined: it may fail or return
arbitrary result.
Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the
applicatio... | def from_avro(data, jsonFormatSchema, options={}):
"""
Converts a binary column of avro format into its corresponding catalyst value. The specified
schema must match the read data, otherwise the behavior is undefined: it may fail or return
arbitrary result.
Note: Avro is built-in but external data ... |
Get the absolute path of a file added through C{SparkContext.addFile()}. | def get(cls, filename):
"""
Get the absolute path of a file added through C{SparkContext.addFile()}.
"""
path = os.path.join(SparkFiles.getRootDirectory(), filename)
return os.path.abspath(path) |
Get the root directory that contains files added through
C{SparkContext.addFile()}. | def getRootDirectory(cls):
"""
Get the root directory that contains files added through
C{SparkContext.addFile()}.
"""
if cls._is_running_on_worker:
return cls._root_directory
else:
# This will have to change if we support multiple SparkContexts:
... |
Gets summary (e.g. accuracy/precision/recall, objective history, total iterations) of model
trained on the training set. An exception is thrown if `trainingSummary is None`. | def summary(self):
"""
Gets summary (e.g. accuracy/precision/recall, objective history, total iterations) of model
trained on the training set. An exception is thrown if `trainingSummary is None`.
"""
if self.hasSummary:
if self.numClasses <= 2:
return... |
Evaluates the model on a test dataset.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame` | def evaluate(self, dataset):
"""
Evaluates the model on a test dataset.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame`
"""
if not isinstance(dataset, DataFrame):
raise ValueErro... |
Creates a copy of this instance with a randomly generated uid
and some extra params. This creates a deep copy of the embedded paramMap,
and copies the embedded and extra parameters over.
:param extra: Extra parameters to copy to the new instance
:return: Copy of this instance | def copy(self, extra=None):
"""
Creates a copy of this instance with a randomly generated uid
and some extra params. This creates a deep copy of the embedded paramMap,
and copies the embedded and extra parameters over.
:param extra: Extra parameters to copy to the new instance
... |
Given a Java OneVsRestModel, create and return a Python wrapper of it.
Used for ML persistence. | def _from_java(cls, java_stage):
"""
Given a Java OneVsRestModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
featuresCol = java_stage.getFeaturesCol()
labelCol = java_stage.getLabelCol()
predictionCol = java_stage.getPredictionCol()
... |
Transfer this instance to a Java OneVsRestModel. Used for ML persistence.
:return: Java object equivalent to this instance. | def _to_java(self):
"""
Transfer this instance to a Java OneVsRestModel. Used for ML persistence.
:return: Java object equivalent to this instance.
"""
sc = SparkContext._active_spark_context
java_models = [model._to_java() for model in self.models]
java_models_a... |
Return the message from an exception as either a str or unicode object. Supports both
Python 2 and Python 3.
>>> msg = "Exception message"
>>> excp = Exception(msg)
>>> msg == _exception_message(excp)
True
>>> msg = u"unicöde"
>>> excp = Exception(msg)
>>> msg == _exception_message(ex... | def _exception_message(excp):
"""Return the message from an exception as either a str or unicode object. Supports both
Python 2 and Python 3.
>>> msg = "Exception message"
>>> excp = Exception(msg)
>>> msg == _exception_message(excp)
True
>>> msg = u"unicöde"
>>> excp = Exception(msg)... |
Get argspec of a function. Supports both Python 2 and Python 3. | def _get_argspec(f):
"""
Get argspec of a function. Supports both Python 2 and Python 3.
"""
if sys.version_info[0] < 3:
argspec = inspect.getargspec(f)
else:
# `getargspec` is deprecated since python3.0 (incompatible with function annotations).
# See SPARK-23569.
arg... |
Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError'
prevents silent loss of data when 'f' is used in a for loop in Spark code | def fail_on_stopiteration(f):
"""
Wraps the input function to fail on 'StopIteration' by raising a 'RuntimeError'
prevents silent loss of data when 'f' is used in a for loop in Spark code
"""
def wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
except StopIteratio... |
Given a Spark version string, return the (major version number, minor version number).
E.g., for 2.0.1-SNAPSHOT, return (2, 0).
>>> sparkVersion = "2.4.0"
>>> VersionUtils.majorMinorVersion(sparkVersion)
(2, 4)
>>> sparkVersion = "2.3.0-SNAPSHOT"
>>> VersionUtils.majorMi... | def majorMinorVersion(sparkVersion):
"""
Given a Spark version string, return the (major version number, minor version number).
E.g., for 2.0.1-SNAPSHOT, return (2, 0).
>>> sparkVersion = "2.4.0"
>>> VersionUtils.majorMinorVersion(sparkVersion)
(2, 4)
>>> sparkVe... |
Checks whether a SparkContext is initialized or not.
Throws error if a SparkContext is already running. | def _ensure_initialized(cls, instance=None, gateway=None, conf=None):
"""
Checks whether a SparkContext is initialized or not.
Throws error if a SparkContext is already running.
"""
with SparkContext._lock:
if not SparkContext._gateway:
SparkContext._g... |
Get or instantiate a SparkContext and register it as a singleton object.
:param conf: SparkConf (optional) | def getOrCreate(cls, conf=None):
"""
Get or instantiate a SparkContext and register it as a singleton object.
:param conf: SparkConf (optional)
"""
with SparkContext._lock:
if SparkContext._active_spark_context is None:
SparkContext(conf=conf or Spark... |
Set a Java system property, such as spark.executor.memory. This must
must be invoked before instantiating SparkContext. | def setSystemProperty(cls, key, value):
"""
Set a Java system property, such as spark.executor.memory. This must
must be invoked before instantiating SparkContext.
"""
SparkContext._ensure_initialized()
SparkContext._jvm.java.lang.System.setProperty(key, value) |
Shut down the SparkContext. | def stop(self):
"""
Shut down the SparkContext.
"""
if getattr(self, "_jsc", None):
try:
self._jsc.stop()
except Py4JError:
# Case: SPARK-18523
warnings.warn(
'Unable to cleanly shutdown Spark JVM... |
Create a new RDD of int containing elements from `start` to `end`
(exclusive), increased by `step` every element. Can be called the same
way as python's built-in range() function. If called with a single argument,
the argument is interpreted as `end`, and `start` is set to 0.
:param sta... | def range(self, start, end=None, step=1, numSlices=None):
"""
Create a new RDD of int containing elements from `start` to `end`
(exclusive), increased by `step` every element. Can be called the same
way as python's built-in range() function. If called with a single argument,
the ... |
Distribute a local Python collection to form an RDD. Using xrange
is recommended if the input represents a range for performance.
>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()
[[0], [2], [3], [4], [6]]
>>> sc.parallelize(xrange(0, 6, 2), 5).glom().collect()
[[], [0], [... | def parallelize(self, c, numSlices=None):
"""
Distribute a local Python collection to form an RDD. Using xrange
is recommended if the input represents a range for performance.
>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()
[[0], [2], [3], [4], [6]]
>>> sc.parall... |
Using py4j to send a large dataset to the jvm is really slow, so we use either a file
or a socket if we have encryption enabled.
:param data:
:param serializer:
:param reader_func: A function which takes a filename and reads in the data in the jvm and
returns a JavaRDD. ... | def _serialize_to_jvm(self, data, serializer, reader_func, createRDDServer):
"""
Using py4j to send a large dataset to the jvm is really slow, so we use either a file
or a socket if we have encryption enabled.
:param data:
:param serializer:
:param reader_func: A functio... |
Load an RDD previously saved using L{RDD.saveAsPickleFile} method.
>>> tmpFile = NamedTemporaryFile(delete=True)
>>> tmpFile.close()
>>> sc.parallelize(range(10)).saveAsPickleFile(tmpFile.name, 5)
>>> sorted(sc.pickleFile(tmpFile.name, 3).collect())
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9... | def pickleFile(self, name, minPartitions=None):
"""
Load an RDD previously saved using L{RDD.saveAsPickleFile} method.
>>> tmpFile = NamedTemporaryFile(delete=True)
>>> tmpFile.close()
>>> sc.parallelize(range(10)).saveAsPickleFile(tmpFile.name, 5)
>>> sorted(sc.pickleFi... |
Read a text file from HDFS, a local file system (available on all
nodes), or any Hadoop-supported file system URI, and return it as an
RDD of Strings.
The text files must be encoded as UTF-8.
If use_unicode is False, the strings will be kept as `str` (encoding
as `utf-8`), which... | def textFile(self, name, minPartitions=None, use_unicode=True):
"""
Read a text file from HDFS, a local file system (available on all
nodes), or any Hadoop-supported file system URI, and return it as an
RDD of Strings.
The text files must be encoded as UTF-8.
If use_unic... |
Read a directory of text files from HDFS, a local file system
(available on all nodes), or any Hadoop-supported file system
URI. Each file is read as a single record and returned in a
key-value pair, where the key is the path of each file, the
value is the content of each file.
... | def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
"""
Read a directory of text files from HDFS, a local file system
(available on all nodes), or any Hadoop-supported file system
URI. Each file is read as a single record and returned in a
key-value pair, where... |
.. note:: Experimental
Read a directory of binary files from HDFS, a local file system
(available on all nodes), or any Hadoop-supported file system URI
as a byte array. Each file is read as a single record and returned
in a key-value pair, where the key is the path of each file, the
... | def binaryFiles(self, path, minPartitions=None):
"""
.. note:: Experimental
Read a directory of binary files from HDFS, a local file system
(available on all nodes), or any Hadoop-supported file system URI
as a byte array. Each file is read as a single record and returned
... |
.. note:: Experimental
Load data from a flat binary file, assuming each record is a set of numbers
with the specified numerical format (see ByteBuffer), and the number of
bytes per record is constant.
:param path: Directory to the input data files
:param recordLength: The lengt... | def binaryRecords(self, path, recordLength):
"""
.. note:: Experimental
Load data from a flat binary file, assuming each record is a set of numbers
with the specified numerical format (see ByteBuffer), and the number of
bytes per record is constant.
:param path: Directo... |
Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,
a local file system (available on all nodes), or any Hadoop-supported file system URI.
The mechanism is as follows:
1. A Java RDD is created from the SequenceFile or other InputFormat, and the key
... | def sequenceFile(self, path, keyClass=None, valueClass=None, keyConverter=None,
valueConverter=None, minSplits=None, batchSize=0):
"""
Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,
a local file system (available on all nodes), or any Hadoo... |
Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS,
a local file system (available on all nodes), or any Hadoop-supported file system URI.
The mechanism is the same as for sc.sequenceFile.
A Hadoop configuration can be passed in as a Python dict. This will be conve... | def newAPIHadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter=None,
valueConverter=None, conf=None, batchSize=0):
"""
Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS,
a local file system (available on all nodes), o... |
Build the union of a list of RDDs.
This supports unions() of RDDs with different serialized formats,
although this forces them to be reserialized using the default
serializer:
>>> path = os.path.join(tempdir, "union-text.txt")
>>> with open(path, "w") as testFile:
... ... | def union(self, rdds):
"""
Build the union of a list of RDDs.
This supports unions() of RDDs with different serialized formats,
although this forces them to be reserialized using the default
serializer:
>>> path = os.path.join(tempdir, "union-text.txt")
>>> with... |
Create an L{Accumulator} with the given initial value, using a given
L{AccumulatorParam} helper object to define how to add values of the
data type if provided. Default AccumulatorParams are used for integers
and floating-point numbers if you do not provide one. For other types,
a custom... | def accumulator(self, value, accum_param=None):
"""
Create an L{Accumulator} with the given initial value, using a given
L{AccumulatorParam} helper object to define how to add values of the
data type if provided. Default AccumulatorParams are used for integers
and floating-point ... |
Add a file to be downloaded with this Spark job on every node.
The C{path} passed can be either a local file, a file in HDFS
(or other Hadoop-supported filesystems), or an HTTP, HTTPS or
FTP URI.
To access the file in Spark jobs, use
L{SparkFiles.get(fileName)<pyspark.files.Spar... | def addFile(self, path, recursive=False):
"""
Add a file to be downloaded with this Spark job on every node.
The C{path} passed can be either a local file, a file in HDFS
(or other Hadoop-supported filesystems), or an HTTP, HTTPS or
FTP URI.
To access the file in Spark j... |
Add a .py or .zip dependency for all tasks to be executed on this
SparkContext in the future. The C{path} passed can be either a local
file, a file in HDFS (or other Hadoop-supported filesystems), or an
HTTP, HTTPS or FTP URI.
.. note:: A path can be added only once. Subsequent additio... | def addPyFile(self, path):
"""
Add a .py or .zip dependency for all tasks to be executed on this
SparkContext in the future. The C{path} passed can be either a local
file, a file in HDFS (or other Hadoop-supported filesystems), or an
HTTP, HTTPS or FTP URI.
.. note:: A ... |
Returns a Java StorageLevel based on a pyspark.StorageLevel. | def _getJavaStorageLevel(self, storageLevel):
"""
Returns a Java StorageLevel based on a pyspark.StorageLevel.
"""
if not isinstance(storageLevel, StorageLevel):
raise Exception("storageLevel must be of type pyspark.StorageLevel")
newStorageLevel = self._jvm.org.apac... |
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
different value or cleared.
Often, a unit of execution in an application consists of multiple Spark actions or jobs.
Application programmers can use this method to group all those jobs together and give a
... | def setJobGroup(self, groupId, description, interruptOnCancel=False):
"""
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
different value or cleared.
Often, a unit of execution in an application consists of multiple Spark actions or jobs.
... |
Executes the given partitionFunc on the specified set of partitions,
returning the result as an array of elements.
If 'partitions' is not specified, this will run over all partitions.
>>> myRDD = sc.parallelize(range(6), 3)
>>> sc.runJob(myRDD, lambda part: [x * x for x in part])
... | def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
"""
Executes the given partitionFunc on the specified set of partitions,
returning the result as an array of elements.
If 'partitions' is not specified, this will run over all partitions.
>>> myRDD = sc.pa... |
Dump the profile stats into directory `path` | def dump_profiles(self, path):
""" Dump the profile stats into directory `path`
"""
if self.profiler_collector is not None:
self.profiler_collector.dump_profiles(path)
else:
raise RuntimeError("'spark.python.profile' configuration must be set "
... |
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The ratings matrix is approximated as the
product of two lower-rank matrices of a given rank (number of
features). To solve for these features, ALS is run iteratively with
a configurable level o... | def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
seed=None):
"""
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The ratings matrix is approximated as the
product of two lower-rank matrices... |
Computes an FP-Growth model that contains frequent itemsets.
:param data:
The input data set, each element contains a transaction.
:param minSupport:
The minimal support level.
(default: 0.3)
:param numPartitions:
The number of partitions used by parallel... | def train(cls, data, minSupport=0.3, numPartitions=-1):
"""
Computes an FP-Growth model that contains frequent itemsets.
:param data:
The input data set, each element contains a transaction.
:param minSupport:
The minimal support level.
(default: 0.3)
... |
Finds the complete set of frequent sequential patterns in the
input sequences of itemsets.
:param data:
The input data set, each element contains a sequence of
itemsets.
:param minSupport:
The minimal support level of the sequential pattern, any
pattern t... | def train(cls, data, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000):
"""
Finds the complete set of frequent sequential patterns in the
input sequences of itemsets.
:param data:
The input data set, each element contains a sequence of
itemsets.
... |
Set sample points from the population. Should be a RDD | def setSample(self, sample):
"""Set sample points from the population. Should be a RDD"""
if not isinstance(sample, RDD):
raise TypeError("samples should be a RDD, received %s" % type(sample))
self._sample = sample |
Estimate the probability density at points | def estimate(self, points):
"""Estimate the probability density at points"""
points = list(points)
densities = callMLlibFunc(
"estimateKernelDensity", self._sample, self._bandwidth, points)
return np.asarray(densities) |
Start a TCP server to receive accumulator updates in a daemon thread, and returns it | def _start_update_server(auth_token):
"""Start a TCP server to receive accumulator updates in a daemon thread, and returns it"""
server = AccumulatorServer(("localhost", 0), _UpdateRequestHandler, auth_token)
thread = threading.Thread(target=server.serve_forever)
thread.daemon = True
thread.start()
... |
Adds a term to this accumulator's value | def add(self, term):
"""Adds a term to this accumulator's value"""
self._value = self.accum_param.addInPlace(self._value, term) |
Compute aggregates and returns the result as a :class:`DataFrame`.
The available aggregate functions can be:
1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`
2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functions.pandas_udf`
.. note... | def agg(self, *exprs):
"""Compute aggregates and returns the result as a :class:`DataFrame`.
The available aggregate functions can be:
1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`
2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functio... |
Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
There are two versions of pivot function: one that requires the caller to specify the list
of distinct values to pivot on, and one that does not. The latter is more concise but less
efficient, because Spark ... | def pivot(self, pivot_col, values=None):
"""
Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
There are two versions of pivot function: one that requires the caller to specify the list
of distinct values to pivot on, and one that does not. The latt... |
Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
as a `DataFrame`.
The user-defined function should take a `pandas.DataFrame` and return another
`pandas.DataFrame`. For each group, all columns are passed together as a `pandas.DataFrame`
to the ... | def apply(self, udf):
"""
Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
as a `DataFrame`.
The user-defined function should take a `pandas.DataFrame` and return another
`pandas.DataFrame`. For each group, all columns are passed togeth... |
Creates a :class:`WindowSpec` with the partitioning defined. | def partitionBy(*cols):
"""
Creates a :class:`WindowSpec` with the partitioning defined.
"""
sc = SparkContext._active_spark_context
jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))
return WindowSpec(jspec) |
Creates a :class:`WindowSpec` with the frame boundaries defined,
from `start` (inclusive) to `end` (inclusive).
Both `start` and `end` are relative positions from the current row.
For example, "0" means "current row", while "-1" means the row before
the current row, and "5" means the fi... | def rowsBetween(start, end):
"""
Creates a :class:`WindowSpec` with the frame boundaries defined,
from `start` (inclusive) to `end` (inclusive).
Both `start` and `end` are relative positions from the current row.
For example, "0" means "current row", while "-1" means the row bef... |
Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
Both `start` and `end` are relative positions from the current row.
For example, "0" means "current row", while "-1" means the row before
the current row, and "5" means the fifth row after the current row.
We ... | def rowsBetween(self, start, end):
"""
Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
Both `start` and `end` are relative positions from the current row.
For example, "0" means "current row", while "-1" means the row before
the current row, and "5" ... |
Generates an RDD comprised of i.i.d. samples from the
uniform distribution U(0.0, 1.0).
To transform the distribution in the generated RDD from U(0.0, 1.0)
to U(a, b), use
C{RandomRDDs.uniformRDD(sc, n, p, seed)\
.map(lambda v: a + (b - a) * v)}
:param sc: SparkContex... | def uniformRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the
uniform distribution U(0.0, 1.0).
To transform the distribution in the generated RDD from U(0.0, 1.0)
to U(a, b), use
C{RandomRDDs.uniformRDD(sc, n, p, seed... |
Generates an RDD comprised of i.i.d. samples from the standard normal
distribution.
To transform the distribution in the generated RDD from standard normal
to some other normal N(mean, sigma^2), use
C{RandomRDDs.normal(sc, n, p, seed)\
.map(lambda v: mean + sigma * v)}
... | def normalRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the standard normal
distribution.
To transform the distribution in the generated RDD from standard normal
to some other normal N(mean, sigma^2), use
C{RandomRDDs... |
Generates an RDD comprised of i.i.d. samples from the log normal
distribution with the input mean and standard distribution.
:param sc: SparkContext used to create the RDD.
:param mean: mean for the log Normal distribution
:param std: std for the log Normal distribution
:param s... | def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the log normal
distribution with the input mean and standard distribution.
:param sc: SparkContext used to create the RDD.
:param mean: mean for the log No... |
Generates an RDD comprised of i.i.d. samples from the Exponential
distribution with the input mean.
:param sc: SparkContext used to create the RDD.
:param mean: Mean, or 1 / lambda, for the Exponential distribution.
:param size: Size of the RDD.
:param numPartitions: Number of p... | def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the Exponential
distribution with the input mean.
:param sc: SparkContext used to create the RDD.
:param mean: Mean, or 1 / lambda, for the Exponential distri... |
Generates an RDD comprised of i.i.d. samples from the Gamma
distribution with the input shape and scale.
:param sc: SparkContext used to create the RDD.
:param shape: shape (> 0) parameter for the Gamma distribution
:param scale: scale (> 0) parameter for the Gamma distribution
... | def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the Gamma
distribution with the input shape and scale.
:param sc: SparkContext used to create the RDD.
:param shape: shape (> 0) parameter for the Gamma dis... |
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the uniform distribution U(0.0, 1.0).
:param sc: SparkContext used to create the RDD.
:param numRows: Number of Vectors in the RDD.
:param numCols: Number of elements in each Vector.
:param numPartitions:... | def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the uniform distribution U(0.0, 1.0).
:param sc: SparkContext used to create the RDD.
:param numRows: Number of Vectors in th... |
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the standard normal distribution.
:param sc: SparkContext used to create the RDD.
:param numRows: Number of Vectors in the RDD.
:param numCols: Number of elements in each Vector.
:param numPartitions: Num... | def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the standard normal distribution.
:param sc: SparkContext used to create the RDD.
:param numRows: Number of Vectors in the RDD... |
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the log normal distribution.
:param sc: SparkContext used to create the RDD.
:param mean: Mean of the log normal distribution
:param std: Standard Deviation of the log normal distribution
:param numRows: ... | def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the log normal distribution.
:param sc: SparkContext used to create the RDD.
:param mean: Mean of the log normal... |
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Poisson distribution with the input mean.
:param sc: SparkContext used to create the RDD.
:param mean: Mean, or lambda, for the Poisson distribution.
:param numRows: Number of Vectors in the RDD.
:par... | def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Poisson distribution with the input mean.
:param sc: SparkContext used to create the RDD.
:param mean: Mean, or lam... |
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Gamma distribution.
:param sc: SparkContext used to create the RDD.
:param shape: Shape (> 0) of the Gamma distribution
:param scale: Scale (> 0) of the Gamma distribution
:param numRows: Number of Ve... | def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Gamma distribution.
:param sc: SparkContext used to create the RDD.
:param shape: Shape (> 0) of the Gamma di... |
Returns the active SparkSession for the current thread, returned by the builder.
>>> s = SparkSession.getActiveSession()
>>> l = [('Alice', 1)]
>>> rdd = s.sparkContext.parallelize(l)
>>> df = s.createDataFrame(rdd, ['name', 'age'])
>>> df.select("age").collect()
[Row(age... | def getActiveSession(cls):
"""
Returns the active SparkSession for the current thread, returned by the builder.
>>> s = SparkSession.getActiveSession()
>>> l = [('Alice', 1)]
>>> rdd = s.sparkContext.parallelize(l)
>>> df = s.createDataFrame(rdd, ['name', 'age'])
... |
Runtime configuration interface for Spark.
This is the interface through which the user can get and set all Spark and Hadoop
configurations that are relevant to Spark SQL. When getting the value of a config,
this defaults to the value set in the underlying :class:`SparkContext`, if any. | def conf(self):
"""Runtime configuration interface for Spark.
This is the interface through which the user can get and set all Spark and Hadoop
configurations that are relevant to Spark SQL. When getting the value of a config,
this defaults to the value set in the underlying :class:`Spa... |
Interface through which the user may create, drop, alter or query underlying
databases, tables, functions etc.
:return: :class:`Catalog` | def catalog(self):
"""Interface through which the user may create, drop, alter or query underlying
databases, tables, functions etc.
:return: :class:`Catalog`
"""
from pyspark.sql.catalog import Catalog
if not hasattr(self, "_catalog"):
self._catalog = Catalo... |
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the start value
:param end: the end value (exclusive)
:param step: the in... | def range(self, start, end=None, step=1, numPartitions=None):
"""
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
:param start: the sta... |
Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:param names: list of column names
:return: :class:`pyspark.sql.types.StructType` | def _inferSchemaFromList(self, data, names=None):
"""
Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:param names: list of column names
:return: :class:`pyspark.sql.types.StructType`
"""
if not data:
raise ValueError("can no... |
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