#
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# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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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.
        """
        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(e.g. :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()
