text: #
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  # 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 sys
  import warnings
  from functools import reduce
  from threading import RLock
  
  from pyspark import since
  from pyspark.rdd import RDD
  from pyspark.sql.conf import RuntimeConfig
  from pyspark.sql.dataframe import DataFrame
  from pyspark.sql.pandas.conversion import SparkConversionMixin
  from pyspark.sql.readwriter import DataFrameReader
  from pyspark.sql.streaming import DataStreamReader
  from pyspark.sql.types import DataType, StructType, \
      _make_type_verifier, _infer_schema, _has_nulltype, _merge_type, _create_converter, \
      _parse_datatype_string
  from pyspark.sql.utils import install_exception_handler
  
  __all__ = ["SparkSession"]
  
  
  def _monkey_patch_RDD(sparkSession):
      def toDF(self, schema=None, sampleRatio=None):
          """
          Converts current :class:`RDD` into a :class:`DataFrame`
  
          This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)``
  
          Parameters
          ----------
          schema : :class:`pyspark.sql.types.DataType`, str or list, optional
              a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
              column names, default is None.  The data type string format equals to
              :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
              omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
              ``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
              We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
          sampleRatio : float, optional
              the sample ratio of rows used for inferring
  
          Returns
          -------
          :class:`DataFrame`
  
          Examples
          --------
          >>> rdd.toDF().collect()
          [Row(name='Alice', age=1)]
          """
          return sparkSession.createDataFrame(self, schema, sampleRatio)
  
      RDD.toDF = toDF
  
  
  class SparkSession(SparkConversionMixin):
      """The entry point to programming Spark with the Dataset and DataFrame API.
  
      A SparkSession can be used create :class:`DataFrame`, register :class:`DataFrame` as
      tables, execute SQL over tables, cache tables, and read parquet files.
      To create a SparkSession, use the following builder pattern:
  
      .. autoattribute:: builder
         :annotation:
  
      Examples
      --------
      >>> spark = SparkSession.builder \\
      ...     .master("local") \\
      ...     .appName("Word Count") \\
      ...     .config("spark.some.config.option", "some-value") \\
      ...     .getOrCreate()
  
      >>> from datetime import datetime
      >>> from pyspark.sql import Row
      >>> spark = SparkSession(sc)
      >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
      ...     b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
      ...     time=datetime(2014, 8, 1, 14, 1, 5))])
      >>> df = allTypes.toDF()
      >>> df.createOrReplaceTempView("allTypes")
      >>> spark.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
      ...            'from allTypes where b and i > 0').collect()
      [Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \
          dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
      >>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
      [(1, 'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
      """
  
      class Builder(object):
          """Builder for :class:`SparkSession`.
          """
  
          _lock = RLock()
          _options = {}
          _sc = None
  
          def config(self, key=None, value=None, conf=None):
              """Sets a config option. Options set using this method are automatically propagated to
              both :class:`SparkConf` and :class:`SparkSession`'s own configuration.
  
              .. versionadded:: 2.0.0
  
              Parameters
              ----------
              key : str, optional
                  a key name string for configuration property
              value : str, optional
                  a value for configuration property
              conf : :class:`SparkConf`, optional
                  an instance of :class:`SparkConf`
  
              Examples
              --------
              For an existing SparkConf, use `conf` parameter.
  
              >>> from pyspark.conf import SparkConf
              >>> SparkSession.builder.config(conf=SparkConf())
              <pyspark.sql.session...
  
              For a (key, value) pair, you can omit parameter names.
  
              >>> SparkSession.builder.config("spark.some.config.option", "some-value")
              <pyspark.sql.session...
  
              """
              with self._lock:
                  if conf is None:
                      self._options[key] = str(value)
                  else:
                      for (k, v) in conf.getAll():
                          self._options[k] = v
                  return self
  
          def master(self, master):
              """Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]"
              to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone
              cluster.
  
              .. versionadded:: 2.0.0
  
              Parameters
              ----------
              master : str
                  a url for spark master
              """
              return self.config("spark.master", master)
  
          def appName(self, name):
              """Sets a name for the application, which will be shown in the Spark web UI.
  
              If no application name is set, a randomly generated name will be used.
  
              .. versionadded:: 2.0.0
  
              Parameters
              ----------
              name : str
                  an application name
              """
              return self.config("spark.app.name", name)
  
          @since(2.0)
          def enableHiveSupport(self):
              """Enables Hive support, including connectivity to a persistent Hive metastore, support
              for Hive SerDes, and Hive user-defined functions.
              """
              return self.config("spark.sql.catalogImplementation", "hive")
  
          def _sparkContext(self, sc):
              with self._lock:
                  self._sc = sc
                  return self
  
          def getOrCreate(self):
              """Gets an existing :class:`SparkSession` or, if there is no existing one, creates a
              new one based on the options set in this builder.
  
              .. versionadded:: 2.0.0
  
              Examples
              --------
              This method first checks whether there is a valid global default SparkSession, and if
              yes, return that one. If no valid global default SparkSession exists, the method
              creates a new SparkSession and assigns the newly created SparkSession as the global
              default.
  
              >>> s1 = SparkSession.builder.config("k1", "v1").getOrCreate()
              >>> s1.conf.get("k1") == "v1"
              True
  
              In case an existing SparkSession is returned, the config options specified
              in this builder will be applied to the existing SparkSession.
  
              >>> s2 = SparkSession.builder.config("k2", "v2").getOrCreate()
              >>> s1.conf.get("k1") == s2.conf.get("k1")
              True
              >>> s1.conf.get("k2") == s2.conf.get("k2")
              True
              """
              with self._lock:
                  from pyspark.context import SparkContext
                  from pyspark.conf import SparkConf
                  session = SparkSession._instantiatedSession
                  if session is None or session._sc._jsc is None:
                      if self._sc is not None:
                          sc = self._sc
                      else:
                          sparkConf = SparkConf()
                          for key, value in self._options.items():
                              sparkConf.set(key, value)
                          # This SparkContext may be an existing one.
                          sc = SparkContext.getOrCreate(sparkConf)
                      # Do not update `SparkConf` for existing `SparkContext`, as it's shared
                      # by all sessions.
                      session = SparkSession(sc)
                  for key, value in self._options.items():
                      session._jsparkSession.sessionState().conf().setConfString(key, value)
                  return session
  
      builder = Builder()
      """A class attribute having a :class:`Builder` to construct :class:`SparkSession` instances."""
  
      _instantiatedSession = None
      _activeSession = None
  
      def __init__(self, sparkContext, jsparkSession=None):
          from pyspark.sql.context import SQLContext
          self._sc = sparkContext
          self._jsc = self._sc._jsc
          self._jvm = self._sc._jvm
          if jsparkSession is None:
              if self._jvm.SparkSession.getDefaultSession().isDefined() \
                      and not self._jvm.SparkSession.getDefaultSession().get() \
                          .sparkContext().isStopped():
                  jsparkSession = self._jvm.SparkSession.getDefaultSession().get()
              else:
                  jsparkSession = self._jvm.SparkSession(self._jsc.sc())
          self._jsparkSession = jsparkSession
          self._jwrapped = self._jsparkSession.sqlContext()
          self._wrapped = SQLContext(self._sc, self, self._jwrapped)
          _monkey_patch_RDD(self)
          install_exception_handler()
          # If we had an instantiated SparkSession attached with a SparkContext
          # which is stopped now, we need to renew the instantiated SparkSession.
          # Otherwise, we will use invalid SparkSession when we call Builder.getOrCreate.
          if SparkSession._instantiatedSession is None \
                  or SparkSession._instantiatedSession._sc._jsc is None:
              SparkSession._instantiatedSession = self
              SparkSession._activeSession = self
              self._jvm.SparkSession.setDefaultSession(self._jsparkSession)
              self._jvm.SparkSession.setActiveSession(self._jsparkSession)
  
      def _repr_html_(self):
          return """
              <div>
                  <p><b>SparkSession - {catalogImplementation}</b></p>
                  {sc_HTML}
              </div>
          """.format(
              catalogImplementation=self.conf.get("spark.sql.catalogImplementation"),
              sc_HTML=self.sparkContext._repr_html_()
          )
  
      @since(2.0)
      def newSession(self):
          """
          Returns a new SparkSession as new session, that has separate SQLConf,
          registered temporary views and UDFs, but shared SparkContext and
          table cache.
          """
          return self.__class__(self._sc, self._jsparkSession.newSession())
  
      @classmethod
      def getActiveSession(cls):
          """
          Returns the active SparkSession for the current thread, returned by the builder
  
          .. versionadded:: 3.0.0
  
          Returns
          -------
          :class:`SparkSession`
              Spark session if an active session exists for the current thread
  
          Examples
          --------
          >>> s = SparkSession.getActiveSession()
          >>> l = [('Alice', 1)]
          >>> rdd = s.sparkContext.parallelize(l)
          >>> df = s.createDataFrame(rdd, ['name', 'age'])
          >>> df.select("age").collect()
          [Row(age=1)]
          """
          from pyspark import SparkContext
          sc = SparkContext._active_spark_context
          if sc is None:
              return None
          else:
              if sc._jvm.SparkSession.getActiveSession().isDefined():
                  SparkSession(sc, sc._jvm.SparkSession.getActiveSession().get())
                  return SparkSession._activeSession
              else:
                  return None
  
      @property
      @since(2.0)
      def sparkContext(self):
          """Returns the underlying :class:`SparkContext`."""
          return self._sc
  
      @property
      @since(2.0)
      def version(self):
          """The version of Spark on which this application is running."""
          return self._jsparkSession.version()
  
      @property
      @since(2.0)
      def conf(self):
          """Runtime configuration interface for Spark.
  
          This is the interface through which the user can get and set all Spark and Hadoop
          configurations that are relevant to Spark SQL. When getting the value of a config,
          this defaults to the value set in the underlying :class:`SparkContext`, if any.
  
          Returns
          -------
          :class:`pyspark.sql.conf.RuntimeConfig`
          """
          if not hasattr(self, "_conf"):
              self._conf = RuntimeConfig(self._jsparkSession.conf())
          return self._conf
  
      @property
      def catalog(self):
          """Interface through which the user may create, drop, alter or query underlying
          databases, tables, functions, etc.
  
          .. versionadded:: 2.0.0
  
          Returns
          -------
          :class:`Catalog`
          """
          from pyspark.sql.catalog import Catalog
          if not hasattr(self, "_catalog"):
              self._catalog = Catalog(self)
          return self._catalog
  
      @property
      def udf(self):
          """Returns a :class:`UDFRegistration` for UDF registration.
  
          .. versionadded:: 2.0.0
  
          Returns
          -------
          :class:`UDFRegistration`
          """
          from pyspark.sql.udf import UDFRegistration
          return UDFRegistration(self)
  
      def range(self, start, end=None, step=1, numPartitions=None):
          """
          Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
          ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
          step value ``step``.
  
          .. versionadded:: 2.0.0
  
          Parameters
          ----------
          start : int
              the start value
          end : int, optional
              the end value (exclusive)
          step : int, optional
              the incremental step (default: 1)
          numPartitions : int, optional
              the number of partitions of the DataFrame
  
          Returns
          -------
          :class:`DataFrame`
  
          Examples
          --------
          >>> spark.range(1, 7, 2).collect()
          [Row(id=1), Row(id=3), Row(id=5)]
  
          If only one argument is specified, it will be used as the end value.
  
          >>> spark.range(3).collect()
          [Row(id=0), Row(id=1), Row(id=2)]
          """
          if numPartitions is None:
              numPartitions = self._sc.defaultParallelism
  
          if end is None:
              jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions))
          else:
              jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions))
  
          return DataFrame(jdf, self._wrapped)
  
      def _inferSchemaFromList(self, data, names=None):
          """
          Infer schema from list of Row, dict, or tuple.
  
          Parameters
          ----------
          data : iterable
              list of Row, dict, or tuple
          names : list, optional
              list of column names
  
          Returns
          -------
          :class:`pyspark.sql.types.StructType`
          """
          if not data:
              raise ValueError("can not infer schema from empty dataset")
          schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
          if _has_nulltype(schema):
              raise ValueError("Some of types cannot be determined after inferring")
          return schema
  
      def _inferSchema(self, rdd, samplingRatio=None, names=None):
          """
          Infer schema from an RDD of Row, dict, or tuple.
  
          Parameters
          ----------
          rdd : :class:`RDD`
              an RDD of Row, dict, or tuple
          samplingRatio : float, optional
              sampling ratio, or no sampling (default)
          names : list, optional
  
          Returns
          -------
          :class:`pyspark.sql.types.StructType`
          """
          first = rdd.first()
          if not first:
              raise ValueError("The first row in RDD is empty, "
                               "can not infer schema")
  
          if samplingRatio is None:
              schema = _infer_schema(first, names=names)
              if _has_nulltype(schema):
                  for row in rdd.take(100)[1:]:
                      schema = _merge_type(schema, _infer_schema(row, names=names))
                      if not _has_nulltype(schema):
                          break
                  else:
                      raise ValueError("Some of types cannot be determined by the "
                                       "first 100 rows, please try again with sampling")
          else:
              if samplingRatio < 0.99:
                  rdd = rdd.sample(False, float(samplingRatio))
              schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type)
          return schema
  
      def _createFromRDD(self, rdd, schema, samplingRatio):
          """
          Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
          """
          if schema is None or isinstance(schema, (list, tuple)):
              struct = self._inferSchema(rdd, samplingRatio, names=schema)
              converter = _create_converter(struct)
              rdd = rdd.map(converter)
              if isinstance(schema, (list, tuple)):
                  for i, name in enumerate(schema):
                      struct.fields[i].name = name
                      struct.names[i] = name
              schema = struct
  
          elif not isinstance(schema, StructType):
              raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
  
          # convert python objects to sql data
          rdd = rdd.map(schema.toInternal)
          return rdd, schema
  
      def _createFromLocal(self, data, schema):
          """
          Create an RDD for DataFrame from a list or pandas.DataFrame, returns
          the RDD and schema.
          """
          # make sure data could consumed multiple times
          if not isinstance(data, list):
              data = list(data)
  
          if schema is None or isinstance(schema, (list, tuple)):
              struct = self._inferSchemaFromList(data, names=schema)
              converter = _create_converter(struct)
              data = map(converter, data)
              if isinstance(schema, (list, tuple)):
                  for i, name in enumerate(schema):
                      struct.fields[i].name = name
                      struct.names[i] = name
              schema = struct
  
          elif not isinstance(schema, StructType):
              raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
  
          # convert python objects to sql data
          data = [schema.toInternal(row) for row in data]
          return self._sc.parallelize(data), schema
  
      @staticmethod
      def _create_shell_session():
          """
          Initialize a SparkSession for a pyspark shell session. This is called from shell.py
          to make error handling simpler without needing to declare local variables in that
          script, which would expose those to users.
          """
          import py4j
          from pyspark.conf import SparkConf
          from pyspark.context import SparkContext
          try:
              # Try to access HiveConf, it will raise exception if Hive is not added
              conf = SparkConf()
              if conf.get('spark.sql.catalogImplementation', 'hive').lower() == 'hive':
                  SparkContext._jvm.org.apache.hadoop.hive.conf.HiveConf()
                  return SparkSession.builder\
                      .enableHiveSupport()\
                      .getOrCreate()
              else:
                  return SparkSession.builder.getOrCreate()
          except (py4j.protocol.Py4JError, TypeError):
              if conf.get('spark.sql.catalogImplementation', '').lower() == 'hive':
                  warnings.warn("Fall back to non-hive support because failing to access HiveConf, "
                                "please make sure you build spark with hive")
  
          return SparkSession.builder.getOrCreate()
  
      def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
          """
          Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
  
          When ``schema`` is a list of column names, the type of each column
          will be inferred from ``data``.
  
          When ``schema`` is ``None``, it will try to infer the schema (column names and types)
          from ``data``, which should be an RDD of either :class:`Row`,
          :class:`namedtuple`, or :class:`dict`.
  
          When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, it must match
          the real data, or an exception will be thrown at runtime. If the given schema is not
          :class:`pyspark.sql.types.StructType`, it will be wrapped into a
          :class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value".
          Each record will also be wrapped into a tuple, which can be converted to row later.
  
          If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
          rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
  
          .. versionadded:: 2.0.0
  
          .. versionchanged:: 2.1.0
             Added verifySchema.
  
          Parameters
          ----------
          data : :class:`RDD` or iterable
              an RDD of any kind of SQL data representation (:class:`Row`,
              :class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, or
              :class:`pandas.DataFrame`.
          schema : :class:`pyspark.sql.types.DataType`, str or list, optional
              a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
              column names, default is None.  The data type string format equals to
              :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
              omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
              ``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
              We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
          samplingRatio : float, optional
              the sample ratio of rows used for inferring
          verifySchema : bool, optional
              verify data types of every row against schema. Enabled by default.
  
          Returns
          -------
          :class:`DataFrame`
  
          Notes
          -----
          Usage with spark.sql.execution.arrow.pyspark.enabled=True is experimental.
  
          Examples
          --------
          >>> l = [('Alice', 1)]
          >>> spark.createDataFrame(l).collect()
          [Row(_1='Alice', _2=1)]
          >>> spark.createDataFrame(l, ['name', 'age']).collect()
          [Row(name='Alice', age=1)]
  
          >>> d = [{'name': 'Alice', 'age': 1}]
          >>> spark.createDataFrame(d).collect()
          [Row(age=1, name='Alice')]
  
          >>> rdd = sc.parallelize(l)
          >>> spark.createDataFrame(rdd).collect()
          [Row(_1='Alice', _2=1)]
          >>> df = spark.createDataFrame(rdd, ['name', 'age'])
          >>> df.collect()
          [Row(name='Alice', age=1)]
  
          >>> from pyspark.sql import Row
          >>> Person = Row('name', 'age')
          >>> person = rdd.map(lambda r: Person(*r))
          >>> df2 = spark.createDataFrame(person)
          >>> df2.collect()
          [Row(name='Alice', age=1)]
  
          >>> from pyspark.sql.types import *
          >>> schema = StructType([
          ...    StructField("name", StringType(), True),
          ...    StructField("age", IntegerType(), True)])
          >>> df3 = spark.createDataFrame(rdd, schema)
          >>> df3.collect()
          [Row(name='Alice', age=1)]
  
          >>> spark.createDataFrame(df.toPandas()).collect()  # doctest: +SKIP
          [Row(name='Alice', age=1)]
          >>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect()  # doctest: +SKIP
          [Row(0=1, 1=2)]
  
          >>> spark.createDataFrame(rdd, "a: string, b: int").collect()
          [Row(a='Alice', b=1)]
          >>> rdd = rdd.map(lambda row: row[1])
          >>> spark.createDataFrame(rdd, "int").collect()
          [Row(value=1)]
          >>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
          Traceback (most recent call last):
              ...
          Py4JJavaError: ...
          """
          SparkSession._activeSession = self
          self._jvm.SparkSession.setActiveSession(self._jsparkSession)
          if isinstance(data, DataFrame):
              raise TypeError("data is already a DataFrame")
  
          if isinstance(schema, str):
              schema = _parse_datatype_string(schema)
          elif isinstance(schema, (list, tuple)):
              # Must re-encode any unicode strings to be consistent with StructField names
              schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema]
  
          try:
              import pandas
              has_pandas = True
          except Exception:
              has_pandas = False
          if has_pandas and isinstance(data, pandas.DataFrame):
              # Create a DataFrame from pandas DataFrame.
              return super(SparkSession, self).createDataFrame(
                  data, schema, samplingRatio, verifySchema)
          return self._create_dataframe(data, schema, samplingRatio, verifySchema)
  
      def _create_dataframe(self, data, schema, samplingRatio, verifySchema):
          if isinstance(schema, StructType):
              verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
  
              def prepare(obj):
                  verify_func(obj)
                  return obj
          elif isinstance(schema, DataType):
              dataType = schema
              schema = StructType().add("value", schema)
  
              verify_func = _make_type_verifier(
                  dataType, name="field value") if verifySchema else lambda _: True
  
              def prepare(obj):
                  verify_func(obj)
                  return obj,
          else:
              prepare = lambda obj: obj
  
          if isinstance(data, RDD):
              rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
          else:
              rdd, schema = self._createFromLocal(map(prepare, data), schema)
          jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
          jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
          df = DataFrame(jdf, self._wrapped)
          df._schema = schema
          return df
  
      def sql(self, sqlQuery):
          """Returns a :class:`DataFrame` representing the result of the given query.
  
          .. versionadded:: 2.0.0
  
          Returns
          -------
          :class:`DataFrame`
  
          Examples
          --------
          >>> df.createOrReplaceTempView("table1")
          >>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1")
          >>> df2.collect()
          [Row(f1=1, f2='row1'), Row(f1=2, f2='row2'), Row(f1=3, f2='row3')]
          """
          return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  
      def table(self, tableName):
          """Returns the specified table as a :class:`DataFrame`.
  
          .. versionadded:: 2.0.0
  
          Returns
          -------
          :class:`DataFrame`
  
          Examples
          --------
          >>> df.createOrReplaceTempView("table1")
          >>> df2 = spark.table("table1")
          >>> sorted(df.collect()) == sorted(df2.collect())
          True
          """
          return DataFrame(self._jsparkSession.table(tableName), self._wrapped)
  
      @property
      def read(self):
          """
          Returns a :class:`DataFrameReader` that can be used to read data
          in as a :class:`DataFrame`.
  
          .. versionadded:: 2.0.0
  
          Returns
          -------
          :class:`DataFrameReader`
          """
          return DataFrameReader(self._wrapped)
  
      @property
      def readStream(self):
          """
          Returns a :class:`DataStreamReader` that can be used to read data streams
          as a streaming :class:`DataFrame`.
  
          .. versionadded:: 2.0.0
  
          Notes
          -----
          This API is evolving.
  
          Returns
          -------
          :class:`DataStreamReader`
          """
          return DataStreamReader(self._wrapped)
  
      @property
      def streams(self):
          """Returns a :class:`StreamingQueryManager` that allows managing all the
          :class:`StreamingQuery` instances active on `this` context.
  
          .. versionadded:: 2.0.0
  
          Notes
          -----
          This API is evolving.
  
          Returns
          -------
          :class:`StreamingQueryManager`
          """
          from pyspark.sql.streaming import StreamingQueryManager
          return StreamingQueryManager(self._jsparkSession.streams())
  
      @since(2.0)
      def stop(self):
          """Stop the underlying :class:`SparkContext`.
          """
          from pyspark.sql.context import SQLContext
          self._sc.stop()
          # We should clean the default session up. See SPARK-23228.
          self._jvm.SparkSession.clearDefaultSession()
          self._jvm.SparkSession.clearActiveSession()
          SparkSession._instantiatedSession = None
          SparkSession._activeSession = None
          SQLContext._instantiatedContext = None
  
      @since(2.0)
      def __enter__(self):
          """
          Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
          """
          return self
  
      @since(2.0)
      def __exit__(self, exc_type, exc_val, exc_tb):
          """
          Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
  
          Specifically stop the SparkSession on exit of the with block.
          """
          self.stop()
  
  
  def _test():
      import os
      import doctest
      from pyspark.context import SparkContext
      from pyspark.sql import Row
      import pyspark.sql.session
  
      os.chdir(os.environ["SPARK_HOME"])
  
      globs = pyspark.sql.session.__dict__.copy()
      sc = SparkContext('local[4]', 'PythonTest')
      globs['sc'] = sc
      globs['spark'] = SparkSession(sc)
      globs['rdd'] = rdd = sc.parallelize(
          [Row(field1=1, field2="row1"),
           Row(field1=2, field2="row2"),
           Row(field1=3, field2="row3")])
      globs['df'] = rdd.toDF()
      (failure_count, test_count) = doctest.testmod(
          pyspark.sql.session, globs=globs,
          optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
      globs['sc'].stop()
      if failure_count:
          sys.exit(-1)
  
  if __name__ == "__main__":
      _test()
  