text: #
  # 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.
  #
  
  from __future__ import print_function
  
  import os
  import shutil
  import signal
  import sys
  import threading
  import warnings
  from threading import RLock
  from tempfile import NamedTemporaryFile
  
  from pyspark import accumulators
  from pyspark.accumulators import Accumulator
  from pyspark.broadcast import Broadcast
  from pyspark.conf import SparkConf
  from pyspark.files import SparkFiles
  from pyspark.java_gateway import launch_gateway
  from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \
      PairDeserializer, AutoBatchedSerializer, NoOpSerializer
  from pyspark.storagelevel import StorageLevel
  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
  
  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 L{RDD} and
      broadcast variables on that cluster.
      """
  
      _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 C{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 L{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)
          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 represent 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)
  
          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
          if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ:
              # disable randomness of hash of string in worker, if this is not
              # launched by spark-submit
              self.environment["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
          self._accumulatorServer = accumulators._start_update_server()
          (host, port) = self._accumulatorServer.server_address
          self._javaAccumulator = self._jvm.PythonAccumulatorV2(host, port)
          self._jsc.sc().register(self._javaAccumulator)
  
          self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python')
          self.pythonVer = "%d.%d" % sys.version_info[:2]
  
          if sys.version_info < (2, 7):
              warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
  
          # 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 = set()
  
          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)
                  if filename[-4:].lower() in self.PACKAGE_EXTENSIONS:
                      self._python_includes.append(filename)
                      sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename))
  
          # 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 _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):
              self._jsc.stop()
              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):
                  return xrange(getStart(split), getStart(split + 1), step)
  
              return self.parallelize([], numSlices).mapPartitionsWithIndex(f)
          # Calling the Java parallelize() method with an ArrayList is too slow,
          # because it sends O(n) Py4J commands.  As an alternative, serialized
          # objects are written to a file and loaded through textFile().
          tempFile = NamedTemporaryFile(delete=False, dir=self._temp_dir)
          try:
              # 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)
              serializer.dump_stream(c, tempFile)
              tempFile.close()
              readRDDFromFile = self._jvm.PythonRDD.readRDDFromFile
              jrdd = readRDDFromFile(self._jsc, tempFile.name, numSlices)
          finally:
              # readRDDFromFile eagerily reads the file so we can delete right after.
              os.unlink(tempFile.name)
          return RDD(jrdd, self, serializer)
  
      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.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.
  
          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.
  
          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::
  
            hdfs://a-hdfs-path/part-00000
            hdfs://a-hdfs-path/part-00001
            ...
            hdfs://a-hdfs-path/part-nnnnn
  
          Do C{rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path")},
          then C{rdd} contains::
  
            (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):
          """
          .. 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
          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):
          """
          .. 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 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. C{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]
          first = rdds[0]._jrdd
          rest = [x._jrdd for x in rdds[1:]]
          return RDD(self._jsc.union(first, rest), self, rdds[0]._jrdd_deserializer)
  
      def broadcast(self, value):
          """
          Broadcast a read-only variable to the cluster, returning a
          L{Broadcast<pyspark.broadcast.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 L{Accumulator} with the given initial value, using a given
          L{AccumulatorParam} helper object to define how to add values of the
          data type if provided. Default AccumulatorParams are used for integers
          and floating-point numbers if you do not provide one. For other types,
          a custom 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 C{path} passed can be either a local file, a file in HDFS
          (or other Hadoop-supported filesystems), or an HTTP, HTTPS or
          FTP URI.
  
          To access the file in Spark jobs, use
          L{SparkFiles.get(fileName)<pyspark.files.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.
  
          >>> 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 C{path} passed can be either a local
          file, a file in HDFS (or other Hadoop-supported filesystems), or an
          HTTP, HTTPS or FTP URI.
          """
          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 a 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 L{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")
          >>> supress = lock.acquire()
          >>> supress = threading.Thread(target=start_job, args=(10,)).start()
          >>> supress = threading.Thread(target=stop_job).start()
          >>> supress = 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.
          """
          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.
          """
          self._jsc.setLocalProperty(key, value)
  
      def getLocalProperty(self, key):
          """
          Get a local property set in this thread, or null if it is missing. See
          L{setLocalProperty}
          """
          return self._jsc.getLocalProperty(key)
  
      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 L{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)
          port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
          return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
  
      def show_profiles(self):
          """ Print the profile stats to stdout """
          self.profiler_collector.show_profiles()
  
      def dump_profiles(self, path):
          """ Dump the profile stats into directory `path`
          """
          self.profiler_collector.dump_profiles(path)
  
      def getConf(self):
          conf = SparkConf()
          conf.setAll(self._conf.getAll())
          return conf
  
  
  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:
          exit(-1)
  
  
  if __name__ == "__main__":
      _test()
  