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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... |
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(aut... |
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, authentic... |
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... |
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 ... |
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... |
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... |
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... |
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 :... |
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.
... |
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.
... |
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:
... |
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 =... |
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 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="")... |
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... |
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... |
>>> _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,... |
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_pro... |
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... |
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... |
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)
stat... |
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 # m... |
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:
... |
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")
... |
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... |
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.UDF... |
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:`spa... |
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... |
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... |
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... |
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()
... |
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` StreamingQuer... |
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... |
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... |
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
... |
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 datase... |
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(... |
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.getFeaturesCo... |
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.
"""
... |
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... |
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 w... |
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 '... |
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... |
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.
... |
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 SparkCont... |
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.
... |
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 ... |
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... |
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], [... |
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. ... |
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... |
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... |
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.
... |
.. 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
... |
.. 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... |
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
... |
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... |
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:
... ... |
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... |
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... |
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... |
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 pyspar... |
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
... |
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])
... |
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' c... |
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... |
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... |
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... |
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 = thre... |
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... |
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 ... |
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 ... |
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(col... |
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... |
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 ... |
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... |
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)}
... |
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... |
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... |
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
... |
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:... |
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... |
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: ... |
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... |
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... |
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... |
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 co... |
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... |
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... |
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 o... |
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