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 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
 # this work for additional information regarding copyright ownership.
 # The ASF licenses this file to You under the Apache License, Version 2.0
 # (the "License"); you may not use this file except in compliance with
 # the License.  You may obtain a copy of the License at
 #
 #    http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
 
 import os
 import shutil
 import signal
 import sys
 import threading
 import warnings
 from threading import RLock
 from tempfile import NamedTemporaryFile
 
 from py4j.protocol import Py4JError
 from py4j.java_gateway import is_instance_of
 
 from pyspark import accumulators
 from pyspark.accumulators import Accumulator
 from pyspark.broadcast import Broadcast, BroadcastPickleRegistry
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway, local_connect_and_auth
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
     PairDeserializer, AutoBatchedSerializer, NoOpSerializer, ChunkedStream
 from pyspark.storagelevel import StorageLevel
 from pyspark.resource import ResourceInformation
 from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix
 from pyspark.traceback_utils import CallSite, first_spark_call
 from pyspark.status import StatusTracker
 from pyspark.profiler import ProfilerCollector, BasicProfiler
 from pyspark.util import _warn_pin_thread
 
 if sys.version > '3':
     xrange = range
 
 
 __all__ = ['SparkContext']
 
 
 # These are special default configs for PySpark, they will overwrite
 # the default ones for Spark if they are not configured by user.
 DEFAULT_CONFIGS = {
     "spark.serializer.objectStreamReset": 100,
     "spark.rdd.compress": True,
 }
 
 
 class SparkContext(object):
 
     """
     Main entry point for Spark functionality. A SparkContext represents the
     connection to a Spark cluster, and can be used to create :class:`RDD` and
     broadcast variables on that cluster.
 
     .. note:: Only one :class:`SparkContext` should be active per JVM. You must `stop()`
         the active :class:`SparkContext` before creating a new one.
 
     .. note:: :class:`SparkContext` instance is not supported to share across multiple
         processes out of the box, and PySpark does not guarantee multi-processing execution.
         Use threads instead for concurrent processing purpose.
     """
 
     _gateway = None
     _jvm = None
     _next_accum_id = 0
     _active_spark_context = None
     _lock = RLock()
     _python_includes = None  # zip and egg files that need to be added to PYTHONPATH
 
     PACKAGE_EXTENSIONS = ('.zip', '.egg', '.jar')
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
                  environment=None, batchSize=0, serializer=PickleSerializer(), conf=None,
                  gateway=None, jsc=None, profiler_cls=BasicProfiler):
         """
         Create a new SparkContext. At least the master and app name should be set,
         either through the named parameters here or through `conf`.
 
         :param master: Cluster URL to connect to
                (e.g. mesos://host:port, spark://host:port, local[4]).
         :param appName: A name for your job, to display on the cluster web UI.
         :param sparkHome: Location where Spark is installed on cluster nodes.
         :param pyFiles: Collection of .zip or .py files to send to the cluster
                and add to PYTHONPATH.  These can be paths on the local file
                system or HDFS, HTTP, HTTPS, or FTP URLs.
         :param environment: A dictionary of environment variables to set on
                worker nodes.
         :param batchSize: The number of Python objects represented as a single
                Java object. Set 1 to disable batching, 0 to automatically choose
                the batch size based on object sizes, or -1 to use an unlimited
                batch size
         :param serializer: The serializer for RDDs.
         :param conf: A :class:`SparkConf` object setting Spark properties.
         :param gateway: Use an existing gateway and JVM, otherwise a new JVM
                will be instantiated.
         :param jsc: The JavaSparkContext instance (optional).
         :param profiler_cls: A class of custom Profiler used to do profiling
                (default is pyspark.profiler.BasicProfiler).
 
 
         >>> from pyspark.context import SparkContext
         >>> sc = SparkContext('local', 'test')
 
         >>> sc2 = SparkContext('local', 'test2') # doctest: +IGNORE_EXCEPTION_DETAIL
         Traceback (most recent call last):
             ...
         ValueError:...
         """
         self._callsite = first_spark_call() or CallSite(None, None, None)
         if gateway is not None and gateway.gateway_parameters.auth_token is None:
             raise ValueError(
                 "You are trying to pass an insecure Py4j gateway to Spark. This"
                 " is not allowed as it is a security risk.")
 
         SparkContext._ensure_initialized(self, gateway=gateway, conf=conf)
         try:
             self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
                           conf, jsc, profiler_cls)
         except:
             # If an error occurs, clean up in order to allow future SparkContext creation:
             self.stop()
             raise
 
     def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
                  conf, jsc, profiler_cls):
         self.environment = environment or {}
         # java gateway must have been launched at this point.
         if conf is not None and conf._jconf is not None:
             # conf has been initialized in JVM properly, so use conf directly. This represents the
             # scenario that JVM has been launched before SparkConf is created (e.g. SparkContext is
             # created and then stopped, and we create a new SparkConf and new SparkContext again)
             self._conf = conf
         else:
             self._conf = SparkConf(_jvm=SparkContext._jvm)
             if conf is not None:
                 for k, v in conf.getAll():
                     self._conf.set(k, v)
 
         self._batchSize = batchSize  # -1 represents an unlimited batch size
         self._unbatched_serializer = serializer
         if batchSize == 0:
             self.serializer = AutoBatchedSerializer(self._unbatched_serializer)
         else:
             self.serializer = BatchedSerializer(self._unbatched_serializer,
                                                 batchSize)
 
         # Set any parameters passed directly to us on the conf
         if master:
             self._conf.setMaster(master)
         if appName:
             self._conf.setAppName(appName)
         if sparkHome:
             self._conf.setSparkHome(sparkHome)
         if environment:
             for key, value in environment.items():
                 self._conf.setExecutorEnv(key, value)
         for key, value in DEFAULT_CONFIGS.items():
             self._conf.setIfMissing(key, value)
 
         # Check that we have at least the required parameters
         if not self._conf.contains("spark.master"):
             raise Exception("A master URL must be set in your configuration")
         if not self._conf.contains("spark.app.name"):
             raise Exception("An application name must be set in your configuration")
 
         # Read back our properties from the conf in case we loaded some of them from
         # the classpath or an external config file
         self.master = self._conf.get("spark.master")
         self.appName = self._conf.get("spark.app.name")
         self.sparkHome = self._conf.get("spark.home", None)
 
         for (k, v) in self._conf.getAll():
             if k.startswith("spark.executorEnv."):
                 varName = k[len("spark.executorEnv."):]
                 self.environment[varName] = v
 
         self.environment["PYTHONHASHSEED"] = os.environ.get("PYTHONHASHSEED", "0")
 
         # Create the Java SparkContext through Py4J
         self._jsc = jsc or self._initialize_context(self._conf._jconf)
         # Reset the SparkConf to the one actually used by the SparkContext in JVM.
         self._conf = SparkConf(_jconf=self._jsc.sc().conf())
 
         # Create a single Accumulator in Java that we'll send all our updates through;
         # they will be passed back to us through a TCP server
         auth_token = self._gateway.gateway_parameters.auth_token
         self._accumulatorServer = accumulators._start_update_server(auth_token)
         (host, port) = self._accumulatorServer.server_address
         self._javaAccumulator = self._jvm.PythonAccumulatorV2(host, port, auth_token)
         self._jsc.sc().register(self._javaAccumulator)
 
         # If encryption is enabled, we need to setup a server in the jvm to read broadcast
         # data via a socket.
         # scala's mangled names w/ $ in them require special treatment.
         self._encryption_enabled = self._jvm.PythonUtils.isEncryptionEnabled(self._jsc)
 
         self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python')
         self.pythonVer = "%d.%d" % sys.version_info[:2]
 
         if sys.version_info < (3, 6):
             with warnings.catch_warnings():
                 warnings.simplefilter("once")
                 warnings.warn(
                     "Support for Python 2 and Python 3 prior to version 3.6 is deprecated as "
                     "of Spark 3.0. See also the plan for dropping Python 2 support at "
                     "https://spark.apache.org/news/plan-for-dropping-python-2-support.html.",
                     DeprecationWarning)
 
         # Broadcast's __reduce__ method stores Broadcast instances here.
         # This allows other code to determine which Broadcast instances have
         # been pickled, so it can determine which Java broadcast objects to
         # send.
         self._pickled_broadcast_vars = BroadcastPickleRegistry()
 
         SparkFiles._sc = self
         root_dir = SparkFiles.getRootDirectory()
         sys.path.insert(1, root_dir)
 
         # Deploy any code dependencies specified in the constructor
         self._python_includes = list()
         for path in (pyFiles or []):
             self.addPyFile(path)
 
         # Deploy code dependencies set by spark-submit; these will already have been added
         # with SparkContext.addFile, so we just need to add them to the PYTHONPATH
         for path in self._conf.get("spark.submit.pyFiles", "").split(","):
             if path != "":
                 (dirname, filename) = os.path.split(path)
                 try:
                     filepath = os.path.join(SparkFiles.getRootDirectory(), filename)
                     if not os.path.exists(filepath):
                         # In case of YARN with shell mode, 'spark.submit.pyFiles' files are
                         # not added via SparkContext.addFile. Here we check if the file exists,
                         # try to copy and then add it to the path. See SPARK-21945.
                         shutil.copyfile(path, filepath)
                     if filename[-4:].lower() in self.PACKAGE_EXTENSIONS:
                         self._python_includes.append(filename)
                         sys.path.insert(1, filepath)
                 except Exception:
                     warnings.warn(
                         "Failed to add file [%s] speficied in 'spark.submit.pyFiles' to "
                         "Python path:\n  %s" % (path, "\n  ".join(sys.path)),
                         RuntimeWarning)
 
         # Create a temporary directory inside spark.local.dir:
         local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf())
         self._temp_dir = \
             self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir, "pyspark") \
                 .getAbsolutePath()
 
         # profiling stats collected for each PythonRDD
         if self._conf.get("spark.python.profile", "false") == "true":
             dump_path = self._conf.get("spark.python.profile.dump", None)
             self.profiler_collector = ProfilerCollector(profiler_cls, dump_path)
         else:
             self.profiler_collector = None
 
         # create a signal handler which would be invoked on receiving SIGINT
         def signal_handler(signal, frame):
             self.cancelAllJobs()
             raise KeyboardInterrupt()
 
         # see http://stackoverflow.com/questions/23206787/
         if isinstance(threading.current_thread(), threading._MainThread):
             signal.signal(signal.SIGINT, signal_handler)
 
     def __repr__(self):
         return "<SparkContext master={master} appName={appName}>".format(
             master=self.master,
             appName=self.appName,
         )
 
     def _repr_html_(self):
         return """
         <div>
             <p><b>SparkContext</b></p>
 
             <p><a href="{sc.uiWebUrl}">Spark UI</a></p>
 
             <dl>
               <dt>Version</dt>
                 <dd><code>v{sc.version}</code></dd>
               <dt>Master</dt>
                 <dd><code>{sc.master}</code></dd>
               <dt>AppName</dt>
                 <dd><code>{sc.appName}</code></dd>
             </dl>
         </div>
         """.format(
             sc=self
         )
 
     def _initialize_context(self, jconf):
         """
         Initialize SparkContext in function to allow subclass specific initialization
         """
         return self._jvm.JavaSparkContext(jconf)
 
     @classmethod
     def _ensure_initialized(cls, instance=None, gateway=None, conf=None):
         """
         Checks whether a SparkContext is initialized or not.
         Throws error if a SparkContext is already running.
         """
         with SparkContext._lock:
             if not SparkContext._gateway:
                 SparkContext._gateway = gateway or launch_gateway(conf)
                 SparkContext._jvm = SparkContext._gateway.jvm
 
             if instance:
                 if (SparkContext._active_spark_context and
                         SparkContext._active_spark_context != instance):
                     currentMaster = SparkContext._active_spark_context.master
                     currentAppName = SparkContext._active_spark_context.appName
                     callsite = SparkContext._active_spark_context._callsite
 
                     # Raise error if there is already a running Spark context
                     raise ValueError(
                         "Cannot run multiple SparkContexts at once; "
                         "existing SparkContext(app=%s, master=%s)"
                         " created by %s at %s:%s "
                         % (currentAppName, currentMaster,
                             callsite.function, callsite.file, callsite.linenum))
                 else:
                     SparkContext._active_spark_context = instance
 
     def __getnewargs__(self):
         # This method is called when attempting to pickle SparkContext, which is always an error:
         raise Exception(
             "It appears that you are attempting to reference SparkContext from a broadcast "
             "variable, action, or transformation. SparkContext can only be used on the driver, "
             "not in code that it run on workers. For more information, see SPARK-5063."
         )
 
     def __enter__(self):
         """
         Enable 'with SparkContext(...) as sc: app(sc)' syntax.
         """
         return self
 
     def __exit__(self, type, value, trace):
         """
         Enable 'with SparkContext(...) as sc: app' syntax.
 
         Specifically stop the context on exit of the with block.
         """
         self.stop()
 
     @classmethod
     def getOrCreate(cls, conf=None):
         """
         Get or instantiate a SparkContext and register it as a singleton object.
 
         :param conf: SparkConf (optional)
         """
         with SparkContext._lock:
             if SparkContext._active_spark_context is None:
                 SparkContext(conf=conf or SparkConf())
             return SparkContext._active_spark_context
 
     def setLogLevel(self, logLevel):
         """
         Control our logLevel. This overrides any user-defined log settings.
         Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
         """
         self._jsc.setLogLevel(logLevel)
 
     @classmethod
     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)
 
     @property
     def version(self):
         """
         The version of Spark on which this application is running.
         """
         return self._jsc.version()
 
     @property
     @ignore_unicode_prefix
     def applicationId(self):
         """
         A unique identifier for the Spark application.
         Its format depends on the scheduler implementation.
 
         * in case of local spark app something like 'local-1433865536131'
         * in case of YARN something like 'application_1433865536131_34483'
 
         >>> sc.applicationId  # doctest: +ELLIPSIS
         u'local-...'
         """
         return self._jsc.sc().applicationId()
 
     @property
     def uiWebUrl(self):
         """Return the URL of the SparkUI instance started by this SparkContext"""
         return self._jsc.sc().uiWebUrl().get()
 
     @property
     def startTime(self):
         """Return the epoch time when the Spark Context was started."""
         return self._jsc.startTime()
 
     @property
     def defaultParallelism(self):
         """
         Default level of parallelism to use when not given by user (e.g. for
         reduce tasks)
         """
         return self._jsc.sc().defaultParallelism()
 
     @property
     def defaultMinPartitions(self):
         """
         Default min number of partitions for Hadoop RDDs when not given by user
         """
         return self._jsc.sc().defaultMinPartitions()
 
     def stop(self):
         """
         Shut down the SparkContext.
         """
         if getattr(self, "_jsc", None):
             try:
                 self._jsc.stop()
             except Py4JError:
                 # Case: SPARK-18523
                 warnings.warn(
                     'Unable to cleanly shutdown Spark JVM process.'
                     ' It is possible that the process has crashed,'
                     ' been killed or may also be in a zombie state.',
                     RuntimeWarning
                 )
             finally:
                 self._jsc = None
         if getattr(self, "_accumulatorServer", None):
             self._accumulatorServer.shutdown()
             self._accumulatorServer = None
         with SparkContext._lock:
             SparkContext._active_spark_context = None
 
     def emptyRDD(self):
         """
         Create an RDD that has no partitions or elements.
         """
         return RDD(self._jsc.emptyRDD(), self, NoOpSerializer())
 
     def range(self, start, end=None, step=1, numSlices=None):
         """
         Create a new RDD of int containing elements from `start` to `end`
         (exclusive), increased by `step` every element. Can be called the same
         way as python's built-in range() function. If called with a single argument,
         the argument is interpreted as `end`, and `start` is set to 0.
 
         :param start: the start value
         :param end: the end value (exclusive)
         :param step: the incremental step (default: 1)
         :param numSlices: the number of partitions of the new RDD
         :return: An RDD of int
 
         >>> sc.range(5).collect()
         [0, 1, 2, 3, 4]
         >>> sc.range(2, 4).collect()
         [2, 3]
         >>> sc.range(1, 7, 2).collect()
         [1, 3, 5]
         """
         if end is None:
             end = start
             start = 0
 
         return self.parallelize(xrange(start, end, step), numSlices)
 
     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.parallelize(xrange(0, 6, 2), 5).glom().collect()
         [[], [0], [], [2], [4]]
         """
         numSlices = int(numSlices) if numSlices is not None else self.defaultParallelism
         if isinstance(c, xrange):
             size = len(c)
             if size == 0:
                 return self.parallelize([], numSlices)
             step = c[1] - c[0] if size > 1 else 1
             start0 = c[0]
 
             def getStart(split):
                 return start0 + int((split * size / numSlices)) * step
 
             def f(split, iterator):
                 # it's an empty iterator here but we need this line for triggering the
                 # logic of signal handling in FramedSerializer.load_stream, for instance,
                 # SpecialLengths.END_OF_DATA_SECTION in _read_with_length. Since
                 # FramedSerializer.load_stream produces a generator, the control should
                 # at least be in that function once. Here we do it by explicitly converting
                 # the empty iterator to a list, thus make sure worker reuse takes effect.
                 # See more details in SPARK-26549.
                 assert len(list(iterator)) == 0
                 return xrange(getStart(split), getStart(split + 1), step)
 
             return self.parallelize([], numSlices).mapPartitionsWithIndex(f)
 
         # Make sure we distribute data evenly if it's smaller than self.batchSize
         if "__len__" not in dir(c):
             c = list(c)    # Make it a list so we can compute its length
         batchSize = max(1, min(len(c) // numSlices, self._batchSize or 1024))
         serializer = BatchedSerializer(self._unbatched_serializer, batchSize)
 
         def reader_func(temp_filename):
             return self._jvm.PythonRDD.readRDDFromFile(self._jsc, temp_filename, numSlices)
 
         def createRDDServer():
             return self._jvm.PythonParallelizeServer(self._jsc.sc(), numSlices)
 
         jrdd = self._serialize_to_jvm(c, serializer, reader_func, createRDDServer)
         return RDD(jrdd, self, serializer)
 
     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 function which takes a filename and reads in the data in the jvm and
                 returns a JavaRDD. Only used when encryption is disabled.
         :param createRDDServer:  A function which creates a PythonRDDServer in the jvm to
                accept the serialized data, for use when encryption is enabled.
         :return:
         """
         if self._encryption_enabled:
             # with encryption, we open a server in java and send the data directly
             server = createRDDServer()
             (sock_file, _) = local_connect_and_auth(server.port(), server.secret())
             chunked_out = ChunkedStream(sock_file, 8192)
             serializer.dump_stream(data, chunked_out)
             chunked_out.close()
             # this call will block until the server has read all the data and processed it (or
             # throws an exception)
             r = server.getResult()
             return r
         else:
             # without encryption, we serialize to a file, and we read the file in java and
             # parallelize from there.
             tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir)
             try:
                 try:
                     serializer.dump_stream(data, tempFile)
                 finally:
                     tempFile.close()
                 return reader_func(tempFile.name)
             finally:
                 # we eagerily reads the file so we can delete right after.
                 os.unlink(tempFile.name)
 
     def pickleFile(self, name, minPartitions=None):
         """
         Load an RDD previously saved using :meth:`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]
         """
         minPartitions = minPartitions or self.defaultMinPartitions
         return RDD(self._jsc.objectFile(name, minPartitions), self)
 
     @ignore_unicode_prefix
     def textFile(self, name, minPartitions=None, use_unicode=True):
         """
         Read a text file from HDFS, a local file system (available on all
         nodes), or any Hadoop-supported file system URI, and return it as an
         RDD of Strings.
         The text files must be encoded as UTF-8.
 
         If use_unicode is False, the strings will be kept as `str` (encoding
         as `utf-8`), which is faster and smaller than unicode. (Added in
         Spark 1.2)
 
         >>> path = os.path.join(tempdir, "sample-text.txt")
         >>> with open(path, "w") as testFile:
         ...    _ = testFile.write("Hello world!")
         >>> textFile = sc.textFile(path)
         >>> textFile.collect()
         [u'Hello world!']
         """
         minPartitions = minPartitions or min(self.defaultParallelism, 2)
         return RDD(self._jsc.textFile(name, minPartitions), self,
                    UTF8Deserializer(use_unicode))
 
     @ignore_unicode_prefix
     def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
         """
         Read a directory of text files from HDFS, a local file system
         (available on all nodes), or any  Hadoop-supported file system
         URI. Each file is read as a single record and returned in a
         key-value pair, where the key is the path of each file, the
         value is the content of each file.
         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 is faster and smaller than unicode. (Added in
         Spark 1.2)
 
         For example, if you have the following files:
 
         .. code-block:: text
 
             hdfs://a-hdfs-path/part-00000
             hdfs://a-hdfs-path/part-00001
             ...
             hdfs://a-hdfs-path/part-nnnnn
 
         Do ``rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")``,
         then ``rdd`` contains:
 
         .. code-block:: text
 
             (a-hdfs-path/part-00000, its content)
             (a-hdfs-path/part-00001, its content)
             ...
             (a-hdfs-path/part-nnnnn, its content)
 
         .. note:: Small files are preferred, as each file will be loaded
             fully in memory.
 
         >>> dirPath = os.path.join(tempdir, "files")
         >>> os.mkdir(dirPath)
         >>> with open(os.path.join(dirPath, "1.txt"), "w") as file1:
         ...    _ = file1.write("1")
         >>> with open(os.path.join(dirPath, "2.txt"), "w") as file2:
         ...    _ = file2.write("2")
         >>> textFiles = sc.wholeTextFiles(dirPath)
         >>> sorted(textFiles.collect())
         [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')]
         """
         minPartitions = minPartitions or self.defaultMinPartitions
         return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
                    PairDeserializer(UTF8Deserializer(use_unicode), UTF8Deserializer(use_unicode)))
 
     def binaryFiles(self, path, minPartitions=None):
         """
         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
         value is the content of each file.
 
         .. note:: Small files are preferred, large file is also allowable, but
             may cause bad performance.
         """
         minPartitions = minPartitions or self.defaultMinPartitions
         return RDD(self._jsc.binaryFiles(path, minPartitions), self,
                    PairDeserializer(UTF8Deserializer(), NoOpSerializer()))
 
     def binaryRecords(self, path, recordLength):
         """
         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 length at which to split the records
         """
         return RDD(self._jsc.binaryRecords(path, recordLength), self, NoOpSerializer())
 
     def _dictToJavaMap(self, d):
         jm = self._jvm.java.util.HashMap()
         if not d:
             d = {}
         for k, v in d.items():
             jm[k] = v
         return jm
 
     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 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
                and value Writable classes
             2. Serialization is attempted via Pyrolite pickling
             3. If this fails, the fallback is to call 'toString' on each key and value
             4. :class:`PickleSerializer` is used to deserialize pickled objects on the Python side
 
         :param path: path to sequncefile
         :param keyClass: fully qualified classname of key Writable class
                (e.g. "org.apache.hadoop.io.Text")
         :param valueClass: fully qualified classname of value Writable class
                (e.g. "org.apache.hadoop.io.LongWritable")
         :param keyConverter:
         :param valueConverter:
         :param minSplits: minimum splits in dataset
                (default min(2, sc.defaultParallelism))
         :param batchSize: The number of Python objects represented as a single
                Java object. (default 0, choose batchSize automatically)
         """
         minSplits = minSplits or min(self.defaultParallelism, 2)
         jrdd = self._jvm.PythonRDD.sequenceFile(self._jsc, path, keyClass, valueClass,
                                                 keyConverter, valueConverter, minSplits, batchSize)
         return RDD(jrdd, self)
 
     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), 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 converted into a
         Configuration in Java
 
         :param path: path to Hadoop file
         :param inputFormatClass: fully qualified classname of Hadoop InputFormat
                (e.g. "org.apache.hadoop.mapreduce.lib.input.TextInputFormat")
         :param keyClass: fully qualified classname of key Writable class
                (e.g. "org.apache.hadoop.io.Text")
         :param valueClass: fully qualified classname of value Writable class
                (e.g. "org.apache.hadoop.io.LongWritable")
         :param keyConverter: (None by default)
         :param valueConverter: (None by default)
         :param conf: Hadoop configuration, passed in as a dict
                (None by default)
         :param batchSize: The number of Python objects represented as a single
                Java object. (default 0, choose batchSize automatically)
         """
         jconf = self._dictToJavaMap(conf)
         jrdd = self._jvm.PythonRDD.newAPIHadoopFile(self._jsc, path, inputFormatClass, keyClass,
                                                     valueClass, keyConverter, valueConverter,
                                                     jconf, batchSize)
         return RDD(jrdd, self)
 
     def newAPIHadoopRDD(self, inputFormatClass, keyClass, valueClass, keyConverter=None,
                         valueConverter=None, conf=None, batchSize=0):
         """
         Read a 'new API' Hadoop InputFormat with arbitrary key and value class, from an arbitrary
         Hadoop configuration, which is passed in as a Python dict.
         This will be converted into a Configuration in Java.
         The mechanism is the same as for sc.sequenceFile.
 
         :param inputFormatClass: fully qualified classname of Hadoop InputFormat
                (e.g. "org.apache.hadoop.mapreduce.lib.input.TextInputFormat")
         :param keyClass: fully qualified classname of key Writable class
                (e.g. "org.apache.hadoop.io.Text")
         :param valueClass: fully qualified classname of value Writable class
                (e.g. "org.apache.hadoop.io.LongWritable")
         :param keyConverter: (None by default)
         :param valueConverter: (None by default)
         :param conf: Hadoop configuration, passed in as a dict
                (None by default)
         :param batchSize: The number of Python objects represented as a single
                Java object. (default 0, choose batchSize automatically)
         """
         jconf = self._dictToJavaMap(conf)
         jrdd = self._jvm.PythonRDD.newAPIHadoopRDD(self._jsc, inputFormatClass, keyClass,
                                                    valueClass, keyConverter, valueConverter,
                                                    jconf, batchSize)
         return RDD(jrdd, self)
 
     def hadoopFile(self, path, inputFormatClass, keyClass, valueClass, keyConverter=None,
                    valueConverter=None, conf=None, batchSize=0):
         """
         Read an 'old' 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 converted into a
         Configuration in Java.
 
         :param path: path to Hadoop file
         :param inputFormatClass: fully qualified classname of Hadoop InputFormat
                (e.g. "org.apache.hadoop.mapred.TextInputFormat")
         :param keyClass: fully qualified classname of key Writable class
                (e.g. "org.apache.hadoop.io.Text")
         :param valueClass: fully qualified classname of value Writable class
                (e.g. "org.apache.hadoop.io.LongWritable")
         :param keyConverter: (None by default)
         :param valueConverter: (None by default)
         :param conf: Hadoop configuration, passed in as a dict
                (None by default)
         :param batchSize: The number of Python objects represented as a single
                Java object. (default 0, choose batchSize automatically)
         """
         jconf = self._dictToJavaMap(conf)
         jrdd = self._jvm.PythonRDD.hadoopFile(self._jsc, path, inputFormatClass, keyClass,
                                               valueClass, keyConverter, valueConverter,
                                               jconf, batchSize)
         return RDD(jrdd, self)
 
     def hadoopRDD(self, inputFormatClass, keyClass, valueClass, keyConverter=None,
                   valueConverter=None, conf=None, batchSize=0):
         """
         Read an 'old' Hadoop InputFormat with arbitrary key and value class, from an arbitrary
         Hadoop configuration, which is passed in as a Python dict.
         This will be converted into a Configuration in Java.
         The mechanism is the same as for sc.sequenceFile.
 
         :param inputFormatClass: fully qualified classname of Hadoop InputFormat
                (e.g. "org.apache.hadoop.mapred.TextInputFormat")
         :param keyClass: fully qualified classname of key Writable class
                (e.g. "org.apache.hadoop.io.Text")
         :param valueClass: fully qualified classname of value Writable class
                (e.g. "org.apache.hadoop.io.LongWritable")
         :param keyConverter: (None by default)
         :param valueConverter: (None by default)
         :param conf: Hadoop configuration, passed in as a dict
                (None by default)
         :param batchSize: The number of Python objects represented as a single
                Java object. (default 0, choose batchSize automatically)
         """
         jconf = self._dictToJavaMap(conf)
         jrdd = self._jvm.PythonRDD.hadoopRDD(self._jsc, inputFormatClass, keyClass,
                                              valueClass, keyConverter, valueConverter,
                                              jconf, batchSize)
         return RDD(jrdd, self)
 
     def _checkpointFile(self, name, input_deserializer):
         jrdd = self._jsc.checkpointFile(name)
         return RDD(jrdd, self, input_deserializer)
 
     @ignore_unicode_prefix
     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 open(path, "w") as testFile:
         ...    _ = testFile.write("Hello")
         >>> textFile = sc.textFile(path)
         >>> textFile.collect()
         [u'Hello']
         >>> parallelized = sc.parallelize(["World!"])
         >>> sorted(sc.union([textFile, parallelized]).collect())
         [u'Hello', 'World!']
         """
         first_jrdd_deserializer = rdds[0]._jrdd_deserializer
         if any(x._jrdd_deserializer != first_jrdd_deserializer for x in rdds):
             rdds = [x._reserialize() for x in rdds]
         gw = SparkContext._gateway
         jvm = SparkContext._jvm
         jrdd_cls = jvm.org.apache.spark.api.java.JavaRDD
         jpair_rdd_cls = jvm.org.apache.spark.api.java.JavaPairRDD
         jdouble_rdd_cls = jvm.org.apache.spark.api.java.JavaDoubleRDD
         if is_instance_of(gw, rdds[0]._jrdd, jrdd_cls):
             cls = jrdd_cls
         elif is_instance_of(gw, rdds[0]._jrdd, jpair_rdd_cls):
             cls = jpair_rdd_cls
         elif is_instance_of(gw, rdds[0]._jrdd, jdouble_rdd_cls):
             cls = jdouble_rdd_cls
         else:
             cls_name = rdds[0]._jrdd.getClass().getCanonicalName()
             raise TypeError("Unsupported Java RDD class %s" % cls_name)
         jrdds = gw.new_array(cls, len(rdds))
         for i in range(0, len(rdds)):
             jrdds[i] = rdds[i]._jrdd
         return RDD(self._jsc.union(jrdds), self, rdds[0]._jrdd_deserializer)
 
     def broadcast(self, value):
         """
         Broadcast a read-only variable to the cluster, returning a :class:`Broadcast`
         object for reading it in distributed functions. The variable will
         be sent to each cluster only once.
         """
         return Broadcast(self, value, self._pickled_broadcast_vars)
 
     def accumulator(self, value, accum_param=None):
         """
         Create an :class:`Accumulator` with the given initial value, using a given
         :class:`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 AccumulatorParam can be used.
         """
         if accum_param is None:
             if isinstance(value, int):
                 accum_param = accumulators.INT_ACCUMULATOR_PARAM
             elif isinstance(value, float):
                 accum_param = accumulators.FLOAT_ACCUMULATOR_PARAM
             elif isinstance(value, complex):
                 accum_param = accumulators.COMPLEX_ACCUMULATOR_PARAM
             else:
                 raise TypeError("No default accumulator param for type %s" % type(value))
         SparkContext._next_accum_id += 1
         return Accumulator(SparkContext._next_accum_id - 1, value, accum_param)
 
     def addFile(self, path, recursive=False):
         """
         Add a file to be downloaded with this Spark job on every node.
         The `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 :meth:`SparkFiles.get` with the
         filename to find its download location.
 
         A directory can be given if the recursive option is set to True.
         Currently directories are only supported for Hadoop-supported filesystems.
 
         .. note:: A path can be added only once. Subsequent additions of the same path are ignored.
 
         >>> from pyspark import SparkFiles
         >>> path = os.path.join(tempdir, "test.txt")
         >>> with open(path, "w") as testFile:
         ...    _ = testFile.write("100")
         >>> sc.addFile(path)
         >>> def func(iterator):
         ...    with open(SparkFiles.get("test.txt")) as testFile:
         ...        fileVal = int(testFile.readline())
         ...        return [x * fileVal for x in iterator]
         >>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
         [100, 200, 300, 400]
         """
         self._jsc.sc().addFile(path, recursive)
 
     def addPyFile(self, path):
         """
         Add a .py or .zip dependency for all tasks to be executed on this
         SparkContext in the future.  The `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 additions of the same path are ignored.
         """
         self.addFile(path)
         (dirname, filename) = os.path.split(path)  # dirname may be directory or HDFS/S3 prefix
         if filename[-4:].lower() in self.PACKAGE_EXTENSIONS:
             self._python_includes.append(filename)
             # for tests in local mode
             sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename))
         if sys.version > '3':
             import importlib
             importlib.invalidate_caches()
 
     def setCheckpointDir(self, dirName):
         """
         Set the directory under which RDDs are going to be checkpointed. The
         directory must be an HDFS path if running on a cluster.
         """
         self._jsc.sc().setCheckpointDir(dirName)
 
     def _getJavaStorageLevel(self, storageLevel):
         """
         Returns a Java StorageLevel based on a pyspark.StorageLevel.
         """
         if not isinstance(storageLevel, StorageLevel):
             raise Exception("storageLevel must be of type pyspark.StorageLevel")
 
         newStorageLevel = self._jvm.org.apache.spark.storage.StorageLevel
         return newStorageLevel(storageLevel.useDisk,
                                storageLevel.useMemory,
                                storageLevel.useOffHeap,
                                storageLevel.deserialized,
                                storageLevel.replication)
 
     def setJobGroup(self, groupId, description, interruptOnCancel=False):
         """
         Assigns a group ID to all the jobs started by this thread until the group ID is set to a
         different value or cleared.
 
         Often, a unit of execution in an application consists of multiple Spark actions or jobs.
         Application programmers can use this method to group all those jobs together and give a
         group description. Once set, the Spark web UI will associate such jobs with this group.
 
         The application can use :meth:`SparkContext.cancelJobGroup` to cancel all
         running jobs in this group.
 
         >>> import threading
         >>> from time import sleep
         >>> result = "Not Set"
         >>> lock = threading.Lock()
         >>> def map_func(x):
         ...     sleep(100)
         ...     raise Exception("Task should have been cancelled")
         >>> def start_job(x):
         ...     global result
         ...     try:
         ...         sc.setJobGroup("job_to_cancel", "some description")
         ...         result = sc.parallelize(range(x)).map(map_func).collect()
         ...     except Exception as e:
         ...         result = "Cancelled"
         ...     lock.release()
         >>> def stop_job():
         ...     sleep(5)
         ...     sc.cancelJobGroup("job_to_cancel")
         >>> suppress = lock.acquire()
         >>> suppress = threading.Thread(target=start_job, args=(10,)).start()
         >>> suppress = threading.Thread(target=stop_job).start()
         >>> suppress = lock.acquire()
         >>> print(result)
         Cancelled
 
         If interruptOnCancel is set to true for the job group, then job cancellation will result
         in Thread.interrupt() being called on the job's executor threads. This is useful to help
         ensure that the tasks are actually stopped in a timely manner, but is off by default due
         to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead.
 
         .. note:: Currently, setting a group ID (set to local properties) with multiple threads
             does not properly work. Internally threads on PVM and JVM are not synced, and JVM
             thread can be reused for multiple threads on PVM, which fails to isolate local
             properties for each thread on PVM.
 
             To work around this, you can set `PYSPARK_PIN_THREAD` to
             `'true'` (see SPARK-22340). However, note that it cannot inherit the local properties
             from the parent thread although it isolates each thread on PVM and JVM with its own
             local properties.
 
             To work around this, you should manually copy and set the local
             properties from the parent thread to the child thread when you create another thread.
         """
         _warn_pin_thread("setJobGroup")
         self._jsc.setJobGroup(groupId, description, interruptOnCancel)
 
     def setLocalProperty(self, key, value):
         """
         Set a local property that affects jobs submitted from this thread, such as the
         Spark fair scheduler pool.
 
         .. note:: Currently, setting a local property with multiple threads does not properly work.
             Internally threads on PVM and JVM are not synced, and JVM thread
             can be reused for multiple threads on PVM, which fails to isolate local properties
             for each thread on PVM.
 
             To work around this, you can set `PYSPARK_PIN_THREAD` to
             `'true'` (see SPARK-22340). However, note that it cannot inherit the local properties
             from the parent thread although it isolates each thread on PVM and JVM with its own
             local properties.
 
             To work around this, you should manually copy and set the local
             properties from the parent thread to the child thread when you create another thread.
         """
         _warn_pin_thread("setLocalProperty")
         self._jsc.setLocalProperty(key, value)
 
     def getLocalProperty(self, key):
         """
         Get a local property set in this thread, or null if it is missing. See
         :meth:`setLocalProperty`.
         """
         return self._jsc.getLocalProperty(key)
 
     def setJobDescription(self, value):
         """
         Set a human readable description of the current job.
 
         .. note:: Currently, setting a job description (set to local properties) with multiple
             threads does not properly work. Internally threads on PVM and JVM are not synced,
             and JVM thread can be reused for multiple threads on PVM, which fails to isolate
             local properties for each thread on PVM.
 
             To work around this, you can set `PYSPARK_PIN_THREAD` to
             `'true'` (see SPARK-22340). However, note that it cannot inherit the local properties
             from the parent thread although it isolates each thread on PVM and JVM with its own
             local properties.
 
             To work around this, you should manually copy and set the local
             properties from the parent thread to the child thread when you create another thread.
         """
         _warn_pin_thread("setJobDescription")
         self._jsc.setJobDescription(value)
 
     def sparkUser(self):
         """
         Get SPARK_USER for user who is running SparkContext.
         """
         return self._jsc.sc().sparkUser()
 
     def cancelJobGroup(self, groupId):
         """
         Cancel active jobs for the specified group. See :meth:`SparkContext.setJobGroup`.
         for more information.
         """
         self._jsc.sc().cancelJobGroup(groupId)
 
     def cancelAllJobs(self):
         """
         Cancel all jobs that have been scheduled or are running.
         """
         self._jsc.sc().cancelAllJobs()
 
     def statusTracker(self):
         """
         Return :class:`StatusTracker` object
         """
         return StatusTracker(self._jsc.statusTracker())
 
     def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
         """
         Executes the given partitionFunc on the specified set of partitions,
         returning the result as an array of elements.
 
         If 'partitions' is not specified, this will run over all partitions.
 
         >>> myRDD = sc.parallelize(range(6), 3)
         >>> sc.runJob(myRDD, lambda part: [x * x for x in part])
         [0, 1, 4, 9, 16, 25]
 
         >>> myRDD = sc.parallelize(range(6), 3)
         >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
         [0, 1, 16, 25]
         """
         if partitions is None:
             partitions = range(rdd._jrdd.partitions().size())
 
         # Implementation note: This is implemented as a mapPartitions followed
         # by runJob() in order to avoid having to pass a Python lambda into
         # SparkContext#runJob.
         mappedRDD = rdd.mapPartitions(partitionFunc)
         sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
         return list(_load_from_socket(sock_info, mappedRDD._jrdd_deserializer))
 
     def show_profiles(self):
         """ Print the profile stats to stdout """
         if self.profiler_collector is not None:
             self.profiler_collector.show_profiles()
         else:
             raise RuntimeError("'spark.python.profile' configuration must be set "
                                "to 'true' to enable Python profile.")
 
     def dump_profiles(self, path):
         """ Dump the profile stats into directory `path`
         """
         if self.profiler_collector is not None:
             self.profiler_collector.dump_profiles(path)
         else:
             raise RuntimeError("'spark.python.profile' configuration must be set "
                                "to 'true' to enable Python profile.")
 
     def getConf(self):
         conf = SparkConf()
         conf.setAll(self._conf.getAll())
         return conf
 
     @property
     def resources(self):
         resources = {}
         jresources = self._jsc.resources()
         for x in jresources:
             name = jresources[x].name()
             jaddresses = jresources[x].addresses()
             addrs = [addr for addr in jaddresses]
             resources[name] = ResourceInformation(name, addrs)
         return resources
 
 
 def _test():
     import atexit
     import doctest
     import tempfile
     globs = globals().copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest')
     globs['tempdir'] = tempfile.mkdtemp()
     atexit.register(lambda: shutil.rmtree(globs['tempdir']))
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
         sys.exit(-1)
 
 
 if __name__ == "__main__":
     _test()

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