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train | Profiler.show | Print the profile stats to stdout, id is the RDD id | python/pyspark/profiler.py | 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() | 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)
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train | Profiler.dump | Dump the profile into path, id is the RDD id | python/pyspark/profiler.py | def dump(self, id, path):
""" Dump the profile into path, id is the RDD id """
if not os.path.exists(path):
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if stats:
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stats.dump_stats(p) | def dump(self, id, path):
""" Dump the profile into path, id is the RDD id """
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stats = self.stats()
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train | BasicProfiler.profile | Runs and profiles the method to_profile passed in. A profile object is returned. | python/pyspark/profiler.py | 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()
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""" Runs and profiles the method to_profile passed in. A profile object is returned. """
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train | SQLContext.getOrCreate | Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext | python/pyspark/sql/context.py | 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(... | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
"""
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jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
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train | SQLContext.setConf | Sets the given Spark SQL configuration property. | python/pyspark/sql/context.py | def setConf(self, key, value):
"""Sets the given Spark SQL configuration property.
"""
self.sparkSession.conf.set(key, value) | def setConf(self, key, value):
"""Sets the given Spark SQL configuration property.
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train | SQLContext.getConf | 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")
... | python/pyspark/sql/context.py | 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.
>>>... | 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
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train | SQLContext.range | 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... | python/pyspark/sql/context.py | 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... | 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
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train | SQLContext.registerFunction | 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. | python/pyspark/sql/context.py | 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(
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"""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.
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train | SQLContext.registerJavaFunction | 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. | python/pyspark/sql/context.py | 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... | 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.
"""
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train | SQLContext.createDataFrame | 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... | python/pyspark/sql/context.py | 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``.
... | 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``.
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train | SQLContext.createExternalTable | 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... | python/pyspark/sql/context.py | 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``.
... | 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``.
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train | SQLContext.tables | 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... | python/pyspark/sql/context.py | 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... | def tables(self, dbName=None):
"""Returns a :class:`DataFrame` containing names of tables in the given database.
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train | SQLContext.tableNames | 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()
... | python/pyspark/sql/context.py | 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")
... | 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
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train | SQLContext.streams | Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving. | python/pyspark/sql/context.py | 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... | 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
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Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the
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"""
Converts a binary column of avro format into its corresponding catalyst value. The specified
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Note: Avro is built-in but external data ... | def from_avro(data, jsonFormatSchema, options={}):
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train | SparkFiles.get | Get the absolute path of a file added through C{SparkContext.addFile()}. | python/pyspark/files.py | def get(cls, filename):
"""
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train | SparkFiles.getRootDirectory | Get the root directory that contains files added through
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"""
Get the root directory that contains files added through
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train | OneVsRestModel.copy | Creates a copy of this instance with a randomly generated uid
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:param extra: Extra parameters to copy to the new instance
:return: Copy of this instance | python/pyspark/ml/classification.py | def copy(self, extra=None):
"""
Creates a copy of this instance with a randomly generated uid
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Creates a copy of this instance with a randomly generated uid
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train | OneVsRestModel._from_java | Given a Java OneVsRestModel, create and return a Python wrapper of it.
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train | OneVsRestModel._to_java | Transfer this instance to a Java OneVsRestModel. Used for ML persistence.
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Transfer this instance to a Java OneVsRestModel. Used for ML persistence.
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train | _exception_message | Return the message from an exception as either a str or unicode object. Supports both
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>>> msg = "Exception message"
>>> excp = Exception(msg)
>>> msg == _exception_message(excp)
True
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>>> msg == _exception_message(ex... | python/pyspark/util.py | def _exception_message(excp):
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>>> msg = "Exception message"
>>> excp = Exception(msg)
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train | _get_argspec | Get argspec of a function. Supports both Python 2 and Python 3. | python/pyspark/util.py | def _get_argspec(f):
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if sys.version_info[0] < 3:
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train | fail_on_stopiteration | 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 | python/pyspark/util.py | def fail_on_stopiteration(f):
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train | VersionUtils.majorMinorVersion | Given a Spark version string, return the (major version number, minor version number).
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>>> sparkVersion = "2.4.0"
>>> VersionUtils.majorMinorVersion(sparkVersion)
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>>> sparkVersion = "2.3.0-SNAPSHOT"
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"""
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)
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train | SparkContext._ensure_initialized | Checks whether a SparkContext is initialized or not.
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"""
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... | def _ensure_initialized(cls, instance=None, gateway=None, conf=None):
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train | SparkContext.getOrCreate | Get or instantiate a SparkContext and register it as a singleton object.
:param conf: SparkConf (optional) | python/pyspark/context.py | def getOrCreate(cls, conf=None):
"""
Get or instantiate a SparkContext and register it as a singleton object.
:param conf: SparkConf (optional)
"""
with SparkContext._lock:
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train | SparkContext.setSystemProperty | Set a Java system property, such as spark.executor.memory. This must
must be invoked before instantiating SparkContext. | python/pyspark/context.py | 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) | 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()
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train | SparkContext.stop | Shut down the SparkContext. | python/pyspark/context.py | def stop(self):
"""
Shut down the SparkContext.
"""
if getattr(self, "_jsc", None):
try:
self._jsc.stop()
except Py4JError:
# Case: SPARK-18523
warnings.warn(
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"""
Shut down the SparkContext.
"""
if getattr(self, "_jsc", None):
try:
self._jsc.stop()
except Py4JError:
# Case: SPARK-18523
warnings.warn(
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train | SparkContext.range | 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... | python/pyspark/context.py | 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
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the ... | def range(self, start, end=None, step=1, numSlices=None):
"""
Create a new RDD of int containing elements from `start` to `end`
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train | SparkContext.parallelize | 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], [... | python/pyspark/context.py | 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... | 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... | [
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train | SparkContext._serialize_to_jvm | 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. ... | python/pyspark/context.py | 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... | 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
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train | SparkContext.pickleFile | 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... | python/pyspark/context.py | 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... | 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)
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train | SparkContext.textFile | 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... | python/pyspark/context.py | 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.
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"""
Read a text file from HDFS, a local file system (available on all
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train | SparkContext.wholeTextFiles | Read a directory of text files from HDFS, a local file system
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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
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... | python/pyspark/context.py | def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
"""
Read a directory of text files from HDFS, a local file system
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URI. Each file is read as a single record and returned in a
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Read a directory of text files from HDFS, a local file system
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train | SparkContext.binaryFiles | .. 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
... | python/pyspark/context.py | 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
... | def binaryFiles(self, path, minPartitions=None):
"""
.. note:: Experimental
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train | SparkContext.binaryRecords | .. 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... | python/pyspark/context.py | def binaryRecords(self, path, recordLength):
"""
.. note:: Experimental
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bytes per record is constant.
:param path: Directo... | def binaryRecords(self, path, recordLength):
"""
.. note:: Experimental
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train | SparkContext.sequenceFile | 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
... | python/pyspark/context.py | 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... | 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,
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train | SparkContext.newAPIHadoopFile | 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... | python/pyspark/context.py | 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... | def newAPIHadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter=None,
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"""
Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS,
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train | SparkContext.union | 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:
... ... | python/pyspark/context.py | 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... | def union(self, rdds):
"""
Build the union of a list of RDDs.
This supports unions() of RDDs with different serialized formats,
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>>> path = os.path.join(tempdir, "union-text.txt")
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train | SparkContext.accumulator | Create an L{Accumulator} with the given initial value, using a given
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"""
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
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train | SparkContext.addPyFile | Add a .py or .zip dependency for all tasks to be executed on this
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Add a .py or .zip dependency for all tasks to be executed on this
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train | SparkContext._getJavaStorageLevel | Returns a Java StorageLevel based on a pyspark.StorageLevel. | python/pyspark/context.py | def _getJavaStorageLevel(self, storageLevel):
"""
Returns a Java StorageLevel based on a pyspark.StorageLevel.
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if not isinstance(storageLevel, StorageLevel):
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train | SparkContext.setJobGroup | 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
... | python/pyspark/context.py | 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
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Often, a unit of execution in an application consists of multiple Spark actions or jobs.
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Assigns a group ID to all the jobs started by this thread until the group ID is set to a
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train | SparkContext.runJob | 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])
... | python/pyspark/context.py | 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.
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train | SparkContext.dump_profiles | Dump the profile stats into directory `path` | python/pyspark/context.py | 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:
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... | 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)
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train | ALS.train | 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... | python/pyspark/mllib/recommendation.py | 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... | 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
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train | FPGrowth.train | Computes an FP-Growth model that contains frequent itemsets.
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Computes an FP-Growth model that contains frequent itemsets.
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The input data set, each element contains a transaction.
:param minSupport:
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Computes an FP-Growth model that contains frequent itemsets.
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The input data set, each element contains a transaction.
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train | PrefixSpan.train | Finds the complete set of frequent sequential patterns in the
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: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... | python/pyspark/mllib/fpm.py | 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.
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The input data set, each element contains a sequence of
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Finds the complete set of frequent sequential patterns in the
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train | KernelDensity.setSample | Set sample points from the population. Should be a RDD | python/pyspark/mllib/stat/KernelDensity.py | def setSample(self, sample):
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if not isinstance(sample, RDD):
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self._sample = sample | def setSample(self, sample):
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train | KernelDensity.estimate | Estimate the probability density at points | python/pyspark/mllib/stat/KernelDensity.py | def estimate(self, points):
"""Estimate the probability density at points"""
points = list(points)
densities = callMLlibFunc(
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return np.asarray(densities) | def estimate(self, points):
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train | _start_update_server | Start a TCP server to receive accumulator updates in a daemon thread, and returns it | python/pyspark/accumulators.py | 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()
... | def _start_update_server(auth_token):
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thread = threading.Thread(target=server.serve_forever)
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train | Accumulator.add | Adds a term to this accumulator's value | python/pyspark/accumulators.py | def add(self, term):
"""Adds a term to this accumulator's value"""
self._value = self.accum_param.addInPlace(self._value, term) | def add(self, term):
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train | GroupedData.agg | 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... | python/pyspark/sql/group.py | 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... | 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`
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train | GroupedData.pivot | Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
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"""
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train | GroupedData.apply | Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
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train | Window.partitionBy | Creates a :class:`WindowSpec` with the partitioning defined. | python/pyspark/sql/window.py | 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) | def partitionBy(*cols):
"""
Creates a :class:`WindowSpec` with the partitioning defined.
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sc = SparkContext._active_spark_context
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train | Window.rowsBetween | Creates a :class:`WindowSpec` with the frame boundaries defined,
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Both `start` and `end` are relative positions from the current row.
For example, "0" means "current row", while "-1" means the row before
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"""
Creates a :class:`WindowSpec` with the frame boundaries defined,
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Both `start` and `end` are relative positions from the current row.
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"""
Creates a :class:`WindowSpec` with the frame boundaries defined,
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Both `start` and `end` are relative positions from the current row.
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train | WindowSpec.rowsBetween | 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
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Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
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For example, "0" means "current row", while "-1" means the row before
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Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
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train | RandomRDDs.uniformRDD | 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)\
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:param sc: SparkContex... | python/pyspark/mllib/random.py | def uniformRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the
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To transform the distribution in the generated RDD from U(0.0, 1.0)
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C{RandomRDDs.uniformRDD(sc, n, p, seed... | def uniformRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the
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To transform the distribution in the generated RDD from U(0.0, 1.0)
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train | RandomRDDs.normalRDD | 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)}
... | python/pyspark/mllib/random.py | 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... | 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
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train | RandomRDDs.logNormalRDD | 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... | python/pyspark/mllib/random.py | 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... | 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.
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train | RandomRDDs.exponentialRDD | 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... | python/pyspark/mllib/random.py | 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... | 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.
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train | RandomRDDs.gammaRDD | 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
... | python/pyspark/mllib/random.py | 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... | 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.
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train | RandomRDDs.uniformVectorRDD | 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:... | python/pyspark/mllib/random.py | 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... | 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).
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train | RandomRDDs.normalVectorRDD | 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... | python/pyspark/mllib/random.py | 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.
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"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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train | RandomRDDs.logNormalVectorRDD | 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: ... | python/pyspark/mllib/random.py | 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... | def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
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train | RandomRDDs.poissonVectorRDD | Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
:param mean: Mean, or lambda, for the Poisson distribution.
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"""
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from the Poisson distribution with the input mean.
:param sc: SparkContext used to create the RDD.
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train | RandomRDDs.gammaVectorRDD | Generates an RDD comprised of vectors containing i.i.d. samples drawn
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:param sc: SparkContext used to create the RDD.
:param shape: Shape (> 0) of the Gamma distribution
:param scale: Scale (> 0) of the Gamma distribution
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"""
Generates an RDD comprised of vectors containing i.i.d. samples drawn
from the Gamma distribution.
:param sc: SparkContext used to create the RDD.
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train | SparkSession.getActiveSession | Returns the active SparkSession for the current thread, returned by the builder.
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>>> l = [('Alice', 1)]
>>> rdd = s.sparkContext.parallelize(l)
>>> df = s.createDataFrame(rdd, ['name', 'age'])
>>> df.select("age").collect()
[Row(age... | python/pyspark/sql/session.py | 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'])
... | def getActiveSession(cls):
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Returns the active SparkSession for the current thread, returned by the builder.
>>> s = SparkSession.getActiveSession()
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>>> rdd = s.sparkContext.parallelize(l)
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train | SparkSession.conf | Runtime configuration interface for Spark.
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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. | python/pyspark/sql/session.py | def conf(self):
"""Runtime configuration interface for Spark.
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this defaults to the value set in the underlying :class:`Spa... | def conf(self):
"""Runtime configuration interface for Spark.
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train | SparkSession.catalog | Interface through which the user may create, drop, alter or query underlying
databases, tables, functions etc.
:return: :class:`Catalog` | python/pyspark/sql/session.py | 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... | def catalog(self):
"""Interface through which the user may create, drop, alter or query underlying
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train | SparkSession.range | 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... | python/pyspark/sql/session.py | 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
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:param start: the sta... | def range(self, start, end=None, step=1, numPartitions=None):
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Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
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train | SparkSession._inferSchemaFromList | Infer schema from list of Row or tuple.
:param data: list of Row or tuple
:param names: list of column names
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train | SparkSession._inferSchema | Infer schema from an RDD of Row or tuple.
:param rdd: an RDD of Row or tuple
:param samplingRatio: sampling ratio, or no sampling (default)
:return: :class:`pyspark.sql.types.StructType` | python/pyspark/sql/session.py | def _inferSchema(self, rdd, samplingRatio=None, names=None):
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:param rdd: an RDD of Row or tuple
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train | SparkSession._createFromRDD | Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. | python/pyspark/sql/session.py | def _createFromRDD(self, rdd, schema, samplingRatio):
"""
Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
"""
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
convert... | def _createFromRDD(self, rdd, schema, samplingRatio):
"""
Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
"""
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
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train | SparkSession._createFromLocal | Create an RDD for DataFrame from a list or pandas.DataFrame, returns
the RDD and schema. | python/pyspark/sql/session.py | def _createFromLocal(self, data, schema):
"""
Create an RDD for DataFrame from a list or pandas.DataFrame, returns
the RDD and schema.
"""
# make sure data could consumed multiple times
if not isinstance(data, list):
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"""
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train | SparkSession._get_numpy_record_dtype | Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
the dtypes of fields in a record so they can be properly loaded into Spark.
:param rec: a numpy record to check field dtypes
:return corrected dtype for a numpy.record or None if no correction needed | python/pyspark/sql/session.py | def _get_numpy_record_dtype(self, rec):
"""
Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
the dtypes of fields in a record so they can be properly loaded into Spark.
:param rec: a numpy record to check field dtypes
:return corrected dtype ... | def _get_numpy_record_dtype(self, rec):
"""
Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
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:param rec: a numpy record to check field dtypes
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train | SparkSession._convert_from_pandas | Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
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"""
Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
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"""
if timezone is not None:
from pyspark.sql.types import _check_series_convert_timestamps_tz_local... | def _convert_from_pandas(self, pdf, schema, timezone):
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"""
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train | SparkSession._create_from_pandas_with_arrow | Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data in Pandas to Arrow conversion. | python/pyspark/sql/session.py | def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
data types will be used to coerce the data ... | def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
"""
Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
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train | SparkSession._create_shell_session | Initialize a SparkSession for a pyspark shell session. This is called from shell.py
to make error handling simpler without needing to declare local variables in that
script, which would expose those to users. | python/pyspark/sql/session.py | def _create_shell_session():
"""
Initialize a SparkSession for a pyspark shell session. This is called from shell.py
to make error handling simpler without needing to declare local variables in that
script, which would expose those to users.
"""
import py4j
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"""
Initialize a SparkSession for a pyspark shell session. This is called from shell.py
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train | SparkSession.createDataFrame | 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... | python/pyspark/sql/session.py | 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``.
... | 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`.
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train | SparkSession.sql | Returns a :class:`DataFrame` representing the result of the given query.
:return: :class:`DataFrame`
>>> df.createOrReplaceTempView("table1")
>>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1")
>>> df2.collect()
[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Ro... | python/pyspark/sql/session.py | def sql(self, sqlQuery):
"""Returns a :class:`DataFrame` representing the result of the given query.
:return: :class:`DataFrame`
>>> df.createOrReplaceTempView("table1")
>>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1")
>>> df2.collect()
[Row(f1=1, f2... | def sql(self, sqlQuery):
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train | SparkSession.table | Returns the specified table as a :class:`DataFrame`.
:return: :class:`DataFrame`
>>> df.createOrReplaceTempView("table1")
>>> df2 = spark.table("table1")
>>> sorted(df.collect()) == sorted(df2.collect())
True | python/pyspark/sql/session.py | def table(self, tableName):
"""Returns the specified table as a :class:`DataFrame`.
:return: :class:`DataFrame`
>>> df.createOrReplaceTempView("table1")
>>> df2 = spark.table("table1")
>>> sorted(df.collect()) == sorted(df2.collect())
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"""
return Dat... | def table(self, tableName):
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:return: :class:`DataFrame`
>>> df.createOrReplaceTempView("table1")
>>> df2 = spark.table("table1")
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train | SparkSession.streams | Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving.
:return: :class:`StreamingQueryManager` | python/pyspark/sql/session.py | def streams(self):
"""Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving.
:return: :class:`StreamingQueryManager`
"""
from pyspark.sql.streaming import StreamingQuer... | def streams(self):
"""Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` StreamingQueries active on `this` context.
.. note:: Evolving.
:return: :class:`StreamingQueryManager`
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train | SparkSession.stop | Stop the underlying :class:`SparkContext`. | python/pyspark/sql/session.py | def stop(self):
"""Stop the underlying :class:`SparkContext`.
"""
self._sc.stop()
# We should clean the default session up. See SPARK-23228.
self._jvm.SparkSession.clearDefaultSession()
self._jvm.SparkSession.clearActiveSession()
SparkSession._instantiatedSession ... | def stop(self):
"""Stop the underlying :class:`SparkContext`.
"""
self._sc.stop()
# We should clean the default session up. See SPARK-23228.
self._jvm.SparkSession.clearDefaultSession()
self._jvm.SparkSession.clearActiveSession()
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train | StatusTracker.getJobInfo | Returns a :class:`SparkJobInfo` object, or None if the job info
could not be found or was garbage collected. | python/pyspark/status.py | def getJobInfo(self, jobId):
"""
Returns a :class:`SparkJobInfo` object, or None if the job info
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"""
job = self._jtracker.getJobInfo(jobId)
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train | StatusTracker.getStageInfo | Returns a :class:`SparkStageInfo` object, or None if the stage
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"""
Returns a :class:`SparkStageInfo` object, or None if the stage
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"""
stage = self._jtracker.getStageInfo(stageId)
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Returns a :class:`SparkStageInfo` object, or None if the stage
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stage = self._jtracker.getStageInfo(stageId)
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train | _restore | Restore an object of namedtuple | python/pyspark/serializers.py | def _restore(name, fields, value):
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k = (name, fields)
cls = __cls.get(k)
if cls is None:
cls = collections.namedtuple(name, fields)
__cls[k] = cls
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train | _hack_namedtuple | Make class generated by namedtuple picklable | python/pyspark/serializers.py | def _hack_namedtuple(cls):
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name = cls.__name__
fields = cls._fields
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train | _hijack_namedtuple | Hack namedtuple() to make it picklable | python/pyspark/serializers.py | def _hijack_namedtuple():
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# hijack only one time
if hasattr(collections.namedtuple, "__hijack"):
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train | ArrowCollectSerializer.load_stream | Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields
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"""
Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields
a list of indices that can be used to put the RecordBatches in the correct order.
"""
# load the batches
for batch in self.serializer.load_stream(stream):
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Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields
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train | ArrowStreamPandasSerializer._create_batch | Create an Arrow record batch from the given pandas.Series or list of Series,
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:param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
:return: Arrow RecordBatch | python/pyspark/serializers.py | def _create_batch(self, series):
"""
Create an Arrow record batch from the given pandas.Series or list of Series,
with optional type.
:param series: A single pandas.Series, list of Series, or list of (series, arrow_type)
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"""
import pand... | def _create_batch(self, series):
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Create an Arrow record batch from the given pandas.Series or list of Series,
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train | ArrowStreamPandasSerializer.dump_stream | Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or
a list of series accompanied by an optional pyarrow type to coerce the data to. | python/pyspark/serializers.py | def dump_stream(self, iterator, stream):
"""
Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or
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"""
batches = (self._create_batch(series) for series in iterator)
... | def dump_stream(self, iterator, stream):
"""
Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or
a list of series accompanied by an optional pyarrow type to coerce the data to.
"""
batches = (self._create_batch(series) for series in iterator)
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train | ArrowStreamPandasSerializer.load_stream | Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series. | python/pyspark/serializers.py | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
for batch in batches:
yield [self.arrow_t... | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
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train | ArrowStreamPandasUDFSerializer.dump_stream | Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
This should be sent after creating the first record batch so in case of an error, it can
be sent back to the JVM before the Arrow stream starts. | python/pyspark/serializers.py | def dump_stream(self, iterator, stream):
"""
Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
This should be sent after creating the first record batch so in case of an error, it can
be sent back to the JVM before the Arrow stream starts.
... | def dump_stream(self, iterator, stream):
"""
Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent.
This should be sent after creating the first record batch so in case of an error, it can
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train | StreamingQuery.awaitTermination | Waits for the termination of `this` query, either by :func:`query.stop()` or by an
exception. If the query has terminated with an exception, then the exception will be thrown.
If `timeout` is set, it returns whether the query has terminated or not within the
`timeout` seconds.
If the qu... | python/pyspark/sql/streaming.py | def awaitTermination(self, timeout=None):
"""Waits for the termination of `this` query, either by :func:`query.stop()` or by an
exception. If the query has terminated with an exception, then the exception will be thrown.
If `timeout` is set, it returns whether the query has terminated or not wit... | def awaitTermination(self, timeout=None):
"""Waits for the termination of `this` query, either by :func:`query.stop()` or by an
exception. If the query has terminated with an exception, then the exception will be thrown.
If `timeout` is set, it returns whether the query has terminated or not wit... | [
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train | StreamingQuery.recentProgress | Returns an array of the most recent [[StreamingQueryProgress]] updates for this query.
The number of progress updates retained for each stream is configured by Spark session
configuration `spark.sql.streaming.numRecentProgressUpdates`. | python/pyspark/sql/streaming.py | def recentProgress(self):
"""Returns an array of the most recent [[StreamingQueryProgress]] updates for this query.
The number of progress updates retained for each stream is configured by Spark session
configuration `spark.sql.streaming.numRecentProgressUpdates`.
"""
return [jso... | def recentProgress(self):
"""Returns an array of the most recent [[StreamingQueryProgress]] updates for this query.
The number of progress updates retained for each stream is configured by Spark session
configuration `spark.sql.streaming.numRecentProgressUpdates`.
"""
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] | 618d6bff71073c8c93501ab7392c3cc579730f0b |
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