#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import sys
import warnings
import filecmp
from collections.abc import Sized
from functools import reduce, cached_property
from threading import RLock
from types import TracebackType
from typing import (
    Any,
    ClassVar,
    Dict,
    Iterable,
    List,
    Optional,
    Tuple,
    Type,
    Union,
    Set,
    cast,
    no_type_check,
    overload,
    TYPE_CHECKING,
)

from pyspark.conf import SparkConf
from pyspark.util import default_api_mode, is_remote_only
from pyspark.sql.conf import RuntimeConfig
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.functions import lit
from pyspark.sql.pandas.conversion import SparkConversionMixin
from pyspark.sql.profiler import AccumulatorProfilerCollector, Profile
from pyspark.sql.readwriter import DataFrameReader
from pyspark.sql.sql_formatter import SQLStringFormatter
from pyspark.sql.streaming import DataStreamReader
from pyspark.sql.types import (
    AtomicType,
    DataType,
    StructField,
    StructType,
    _make_type_verifier,
    _infer_schema,
    _has_nulltype,
    _merge_type,
    _create_converter,
    _from_numpy_type,
)
from pyspark.errors.exceptions.captured import install_exception_handler
from pyspark.sql.utils import (
    is_timestamp_ntz_preferred,
    to_str,
    try_remote_session_classmethod,
    remote_only,
)
from pyspark.errors import PySparkValueError, PySparkTypeError, PySparkRuntimeError

if TYPE_CHECKING:
    from py4j.java_gateway import JavaClass, JavaObject, JVMView
    import pyarrow as pa
    from pyspark.core.context import SparkContext
    from pyspark.core.rdd import RDD
    from pyspark.sql._typing import AtomicValue, RowLike, OptionalPrimitiveType
    from pyspark.sql.catalog import Catalog
    from pyspark.sql.pandas._typing import ArrayLike, DataFrameLike as PandasDataFrameLike
    from pyspark.sql.streaming import StreamingQueryManager
    from pyspark.sql.tvf import TableValuedFunction
    from pyspark.sql.udf import UDFRegistration
    from pyspark.sql.udtf import UDTFRegistration
    from pyspark.sql.datasource import DataSourceRegistration
    from pyspark.sql.dataframe import DataFrame as ParentDataFrame

    # Running MyPy type checks will always require pandas and
    # other dependencies so importing here is fine.
    from pyspark.sql.connect.client import SparkConnectClient
    from pyspark.sql.connect.shell.progress import ProgressHandler


__all__ = ["SparkSession"]


def _monkey_patch_RDD(sparkSession: "SparkSession") -> None:
    @no_type_check
    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 = spark.range(1).rdd.map(lambda x: tuple(x))
        >>> rdd.collect()
        [(0,)]
        >>> rdd.toDF().show()
        +---+
        | _1|
        +---+
        |  0|
        +---+
        """
        return sparkSession.createDataFrame(self, schema, sampleRatio)

    if not is_remote_only():
        from pyspark import RDD

        RDD.toDF = toDF  # type: ignore[method-assign]


class classproperty(property):
    """Same as Python's @property decorator, but for class attributes.

    Examples
    --------
    >>> class Builder:
    ...    def build(self):
    ...        return MyClass()
    ...
    >>> class MyClass:
    ...     @classproperty
    ...     def builder(cls):
    ...         print("instantiating new builder")
    ...         return Builder()
    ...
    >>> c1 = MyClass.builder
    instantiating new builder
    >>> c2 = MyClass.builder
    instantiating new builder
    >>> c1 == c2
    False
    >>> isinstance(c1.build(), MyClass)
    True
    """

    def __get__(self, instance: Any, owner: Any = None) -> "SparkSession.Builder":
        # The "type: ignore" below silences the following error from mypy:
        # error: Argument 1 to "classmethod" has incompatible
        # type "Optional[Callable[[Any], Any]]";
        # expected "Callable[..., Any]"  [arg-type]
        return classmethod(self.fget).__get__(None, owner)()  # type: ignore


class SparkSession(SparkConversionMixin):
    """The entry point to programming Spark with the Dataset and DataFrame API.

    A SparkSession can be used to create :class:`DataFrame`, register :class:`DataFrame` as
    tables, execute SQL over tables, cache tables, and read parquet files.
    To create a :class:`SparkSession`, use the following builder pattern:

    .. versionchanged:: 3.4.0
        Supports Spark Connect.

    .. autoattribute:: builder
       :annotation:

    Examples
    --------
    Create a Spark session.

    >>> spark = (
    ...     SparkSession.builder
    ...         .master("local")
    ...         .appName("Word Count")
    ...         .config("spark.some.config.option", "some-value")
    ...         .getOrCreate()
    ... )

    Create a Spark session with Spark Connect.

    >>> spark = (
    ...     SparkSession.builder
    ...         .remote("sc://localhost")
    ...         .appName("Word Count")
    ...         .config("spark.some.config.option", "some-value")
    ...         .getOrCreate()
    ... )  # doctest: +SKIP
    """

    class Builder:
        """Builder for :class:`SparkSession`."""

        _lock = RLock()

        def __init__(self) -> None:
            self._options: Dict[str, Any] = {}

        @overload
        def config(self, *, conf: SparkConf) -> "SparkSession.Builder":
            ...

        @overload
        def config(self, key: str, value: Any) -> "SparkSession.Builder":
            ...

        @overload
        def config(self, *, map: Dict[str, "OptionalPrimitiveType"]) -> "SparkSession.Builder":
            ...

        def config(
            self,
            key: Optional[str] = None,
            value: Optional[Any] = None,
            conf: Optional[SparkConf] = None,
            *,
            map: Optional[Dict[str, "OptionalPrimitiveType"]] = None,
        ) -> "SparkSession.Builder":
            """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

            .. versionchanged:: 3.4.0
                Supports Spark Connect.

            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`
            map: dictionary, optional
                a dictionary of configurations to set

                .. versionadded:: 3.4.0

            Returns
            -------
            :class:`SparkSession.Builder`

            See Also
            --------
            :class:`SparkConf`

            Examples
            --------
            For an existing :class:`SparkConf`, use `conf` parameter.

            >>> from pyspark.conf import SparkConf
            >>> conf = SparkConf().setAppName("example").setMaster("local")
            >>> SparkSession.builder.config(conf=conf)
            <pyspark.sql.session.SparkSession.Builder...

            For a (key, value) pair, you can omit parameter names.

            >>> SparkSession.builder.config("spark.some.config.option", "some-value")
            <pyspark.sql.session.SparkSession.Builder...

            Set multiple configurations.

            >>> SparkSession.builder.config(
            ...     "spark.some.config.number", 123).config("spark.some.config.float", 0.123)
            <pyspark.sql.session.SparkSession.Builder...

            Set multiple configurations using a dictionary.

            >>> SparkSession.builder.config(
            ...     map={"spark.some.config.number": 123, "spark.some.config.float": 0.123})
            <pyspark.sql.session.SparkSession.Builder...
            """
            with self._lock:
                if conf is not None:
                    for k, v in conf.getAll():
                        self._options[k] = v
                        self._validate_startup_urls()
                elif map is not None:
                    for k, v in map.items():  # type: ignore[assignment]
                        v = to_str(v)  # type: ignore[assignment]
                        self._options[k] = v
                        self._validate_startup_urls()
                else:
                    value = to_str(value)
                    self._options[cast(str, key)] = value
                    self._validate_startup_urls()
                return self

        def _validate_startup_urls(
            self,
        ) -> None:
            """
            Helper function that validates the combination of startup URLs and raises an exception
            if incompatible options are selected.
            """
            if ("spark.master" in self._options or "MASTER" in os.environ) and (
                "spark.remote" in self._options or "SPARK_REMOTE" in os.environ
            ):
                raise PySparkRuntimeError(
                    errorClass="CANNOT_CONFIGURE_SPARK_CONNECT_MASTER",
                    messageParameters={
                        "master_url": self._options.get("spark.master", os.environ.get("MASTER")),
                        "connect_url": self._options.get(
                            "spark.remote", os.environ.get("SPARK_REMOTE")
                        ),
                    },
                )

            if "spark.remote" in self._options:
                remote = cast(str, self._options.get("spark.remote"))
                if ("SPARK_REMOTE" in os.environ and os.environ["SPARK_REMOTE"] != remote) and (
                    "SPARK_LOCAL_REMOTE" in os.environ and not remote.startswith("local")
                ):
                    raise PySparkRuntimeError(
                        errorClass="CANNOT_CONFIGURE_SPARK_CONNECT",
                        messageParameters={
                            "existing_url": os.environ["SPARK_REMOTE"],
                            "new_url": remote,
                        },
                    )

        def master(self, master: str) -> "SparkSession.Builder":
            """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

            Returns
            -------
            :class:`SparkSession.Builder`

            Examples
            --------
            >>> SparkSession.builder.master("local")
            <pyspark.sql.session.SparkSession.Builder...
            """
            return self.config("spark.master", master)

        def remote(self, url: str) -> "SparkSession.Builder":
            """Sets the Spark remote URL to connect to, such as "sc://host:port" to run
            it via Spark Connect server.

            .. versionadded:: 3.4.0

            Parameters
            ----------
            url : str
                URL to Spark Connect server

            Returns
            -------
            :class:`SparkSession.Builder`

            Examples
            --------
            >>> SparkSession.builder.remote("sc://localhost")  # doctest: +SKIP
            <pyspark.sql.session.SparkSession.Builder...
            """
            return self.config("spark.remote", url)

        def appName(self, name: str) -> "SparkSession.Builder":
            """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

            .. versionchanged:: 3.4.0
                Supports Spark Connect.

            Parameters
            ----------
            name : str
                an application name

            Returns
            -------
            :class:`SparkSession.Builder`

            Examples
            --------
            >>> SparkSession.builder.appName("My app")
            <pyspark.sql.session.SparkSession.Builder...
            """
            return self.config("spark.app.name", name)

        def enableHiveSupport(self) -> "SparkSession.Builder":
            """Enables Hive support, including connectivity to a persistent Hive metastore, support
            for Hive SerDes, and Hive user-defined functions.

            .. versionadded:: 2.0.0

            Returns
            -------
            :class:`SparkSession.Builder`

            Examples
            --------
            >>> SparkSession.builder.enableHiveSupport()
            <pyspark.sql.session.SparkSession.Builder...
            """
            return self.config("spark.sql.catalogImplementation", "hive")

        def getOrCreate(self) -> "SparkSession":
            """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

            .. versionchanged:: 3.4.0
                Supports Spark Connect.

            Returns
            -------
            :class:`SparkSession`

            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

            The configuration of the SparkSession can be changed afterwards

            >>> s1.conf.set("k1", "v1_new")
            >>> s1.conf.get("k1") == "v1_new"
            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") == "v1_new"
            True
            >>> s1.conf.get("k2") == s2.conf.get("k2") == "v2"
            True
            """
            opts = dict(self._options)

            if is_remote_only():
                from pyspark.sql.connect.session import SparkSession as RemoteSparkSession

                url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))

                if url is None:
                    raise PySparkRuntimeError(
                        errorClass="CONNECT_URL_NOT_SET",
                        messageParameters={},
                    )

                os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
                opts["spark.remote"] = url
                return RemoteSparkSession.builder.config(map=opts).getOrCreate()  # type: ignore

            from pyspark.core.context import SparkContext

            with self._lock:
                api_mode = opts.get("spark.api.mode", os.environ.get("SPARK_API_MODE", "")).lower()
                if api_mode not in ("classic", "connect"):
                    api_mode = default_api_mode()
                is_api_mode_connect = api_mode == "connect"

                if (
                    "SPARK_CONNECT_MODE_ENABLED" in os.environ
                    or "SPARK_REMOTE" in os.environ
                    or "spark.remote" in opts
                    or is_api_mode_connect
                ):
                    with SparkContext._lock:
                        from pyspark.sql.connect.session import SparkSession as RemoteSparkSession

                        if (
                            SparkContext._active_spark_context is None
                            and SparkSession._instantiatedSession is None
                        ):
                            url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))

                            if url is None and is_api_mode_connect:
                                url = opts.get("spark.master", os.environ.get("MASTER", "local"))
                                if url.startswith("sc://"):
                                    raise PySparkRuntimeError(
                                        errorClass="MASTER_URL_INVALID",
                                        messageParameters={},
                                    )

                            if url is None:
                                raise PySparkRuntimeError(
                                    errorClass="CONNECT_URL_NOT_SET",
                                    messageParameters={},
                                )

                            if url.startswith("local") or (
                                is_api_mode_connect and not url.startswith("sc://")
                            ):
                                os.environ["SPARK_LOCAL_REMOTE"] = "1"
                                RemoteSparkSession._start_connect_server(url, opts)
                                url = "sc://localhost"

                            os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
                            opts["spark.remote"] = url
                            return cast(
                                SparkSession,
                                RemoteSparkSession.builder.config(map=opts).getOrCreate(),
                            )
                        elif "SPARK_LOCAL_REMOTE" in os.environ:
                            url = "sc://localhost"
                            os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
                            opts["spark.remote"] = url
                            return cast(
                                SparkSession,
                                RemoteSparkSession.builder.config(map=opts).getOrCreate(),
                            )
                        else:
                            raise PySparkRuntimeError(
                                errorClass="SESSION_ALREADY_EXIST",
                                messageParameters={},
                            )

                session = SparkSession._instantiatedSession
                if session is None or session._sc._jsc is None:
                    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, options=self._options)
                else:
                    module = SparkSession._get_j_spark_session_module(session._jvm)
                    module.applyModifiableSettings(session._jsparkSession, self._options)
                return session

        # Spark Connect-specific API
        @remote_only
        def create(self) -> "SparkSession":
            """Creates a new SparkSession. Can only be used in the context of Spark Connect
            and will throw an exception otherwise.

            .. versionadded:: 3.5.0

            Returns
            -------
            :class:`SparkSession`

            Notes
            -----
            This method will update the default and/or active session if they are not set.
            """
            opts = dict(self._options)
            if "SPARK_REMOTE" in os.environ or "spark.remote" in opts:
                from pyspark.sql.connect.session import SparkSession as RemoteSparkSession

                # Validate that no incompatible configuration options are selected.
                self._validate_startup_urls()

                url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))
                if url.startswith("local"):
                    raise PySparkRuntimeError(
                        errorClass="UNSUPPORTED_LOCAL_CONNECTION_STRING",
                        messageParameters={},
                    )

                # Mark this Spark Session as Spark Connect. This prevents that local PySpark is
                # used in conjunction with Spark Connect mode.
                os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
                opts["spark.remote"] = url
                return cast(SparkSession, RemoteSparkSession.builder.config(map=opts).create())
            else:
                raise PySparkRuntimeError(
                    errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
                    messageParameters={"feature": "SparkSession.builder.create"},
                )

    # SPARK-47544: Explicitly declaring this as an identifier instead of a method.
    # If changing, make sure this bug is not reintroduced.
    builder: Builder = classproperty(lambda cls: cls.Builder())  # type: ignore
    """Creates a :class:`Builder` for constructing a :class:`SparkSession`.

    .. versionchanged:: 3.4.0
        Supports Spark Connect.
    """

    _instantiatedSession: ClassVar[Optional["SparkSession"]] = None
    _activeSession: ClassVar[Optional["SparkSession"]] = None

    def __init__(
        self,
        sparkContext: "SparkContext",
        jsparkSession: Optional["JavaObject"] = None,
        options: Dict[str, Any] = {},
    ):
        self._sc = sparkContext
        self._jsc = self._sc._jsc
        self._jvm = self._sc._jvm

        assert self._jvm is not None

        jSparkSessionClass = SparkSession._get_j_spark_session_class(self._jvm)
        jSparkSessionModule = SparkSession._get_j_spark_session_module(self._jvm)

        if jsparkSession is None:
            if (
                jSparkSessionClass.getDefaultSession().isDefined()
                and not jSparkSessionClass.getDefaultSession().get().sparkContext().isStopped()
            ):
                jsparkSession = jSparkSessionClass.getDefaultSession().get()
                jSparkSessionModule.applyModifiableSettings(jsparkSession, options)
            else:
                jsparkSession = jSparkSessionClass(self._jsc.sc(), options)
        else:
            jSparkSessionModule.applyModifiableSettings(jsparkSession, options)
        self._jsparkSession = jsparkSession
        _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._should_update_active_session():
            SparkSession._instantiatedSession = self
            SparkSession._activeSession = self
            assert self._jvm is not None
            jSparkSessionClass.setDefaultSession(self._jsparkSession)
            jSparkSessionClass.setActiveSession(self._jsparkSession)

        self._profiler_collector = AccumulatorProfilerCollector()

    @staticmethod
    def _should_update_active_session() -> bool:
        return (
            SparkSession._instantiatedSession is None
            or SparkSession._instantiatedSession._sc._jsc is None
        )

    @staticmethod
    def _get_j_spark_session_class(jvm: "JVMView") -> "JavaClass":
        return getattr(jvm, "org.apache.spark.sql.classic.SparkSession")

    @staticmethod
    def _get_j_spark_session_module(jvm: "JVMView") -> "JavaObject":
        return getattr(getattr(jvm, "org.apache.spark.sql.classic.SparkSession$"), "MODULE$")

    def _repr_html_(self) -> str:
        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_(),
        )

    @property
    def _jconf(self) -> "JavaObject":
        """Accessor for the JVM SQL-specific configurations"""
        return self._jsparkSession.sessionState().conf()

    if not is_remote_only():

        def newSession(self) -> "SparkSession":
            """
            Returns a new :class:`SparkSession` as new session, that has separate SQLConf,
            registered temporary views and UDFs, but shared :class:`SparkContext` and
            table cache.

            .. versionadded:: 2.0.0

            Returns
            -------
            :class:`SparkSession`
                Spark session if an active session exists for the current thread

            Examples
            --------
            >>> spark.newSession()
            <...SparkSession object ...>
            """
            return self.__class__(self._sc, self._jsparkSession.newSession())

    @classmethod
    @try_remote_session_classmethod
    def getActiveSession(cls) -> Optional["SparkSession"]:
        """
        Returns the active :class:`SparkSession` for the current thread, returned by the builder

        .. versionadded:: 3.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Returns
        -------
        :class:`SparkSession`
            Spark session if an active session exists for the current thread

        Examples
        --------
        >>> s = SparkSession.getActiveSession()
        >>> df = s.createDataFrame([('Alice', 1)], ['name', 'age'])
        >>> df.select("age").show()
        +---+
        |age|
        +---+
        |  1|
        +---+
        """
        from pyspark import SparkContext

        sc = SparkContext._active_spark_context
        if sc is None:
            return None
        else:
            assert sc._jvm is not None
            jSparkSessionClass = SparkSession._get_j_spark_session_class(sc._jvm)
            if jSparkSessionClass.getActiveSession().isDefined():
                if SparkSession._should_update_active_session():
                    SparkSession(sc, jSparkSessionClass.getActiveSession().get())
                return SparkSession._activeSession
            else:
                return None

    @classmethod
    @try_remote_session_classmethod
    def active(cls) -> "SparkSession":
        """
        Returns the active or default :class:`SparkSession` for the current thread, returned by
        the builder.

        .. versionadded:: 3.5.0

        Returns
        -------
        :class:`SparkSession`
            Spark session if an active or default session exists for the current thread.
        """
        session = cls.getActiveSession()
        if session is None:
            session = cls._instantiatedSession
            if session is None:
                raise PySparkRuntimeError(
                    errorClass="NO_ACTIVE_OR_DEFAULT_SESSION",
                    messageParameters={},
                )
        return session

    if not is_remote_only():

        @property
        def sparkContext(self) -> "SparkContext":
            """
            Returns the underlying :class:`SparkContext`.

            .. versionadded:: 2.0.0

            Returns
            -------
            :class:`SparkContext`

            Examples
            --------
            >>> spark.sparkContext
            <SparkContext master=... appName=...>

            Create an RDD from the Spark context

            >>> rdd = spark.sparkContext.parallelize([1, 2, 3])
            >>> rdd.collect()
            [1, 2, 3]
            """
            return self._sc

    @cached_property
    def version(self) -> str:
        """
        The version of Spark on which this application is running.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        str
            the version of Spark in string.

        Examples
        --------
        >>> _ = spark.version
        """
        return self._jsparkSession.version()

    @cached_property
    def conf(self) -> RuntimeConfig:
        """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.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`pyspark.sql.conf.RuntimeConfig`

        Examples
        --------
        >>> spark.conf
        <pyspark...RuntimeConf...>

        Set a runtime configuration for the session

        >>> spark.conf.set("key", "value")
        >>> spark.conf.get("key")
        'value'
        """
        return RuntimeConfig(self._jsparkSession.conf())

    @cached_property
    def catalog(self) -> "Catalog":
        """Interface through which the user may create, drop, alter or query underlying
        databases, tables, functions, etc.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`Catalog`

        Examples
        --------
        >>> spark.catalog
        <...Catalog object ...>

        Create a temp view, show the list, and drop it.

        >>> spark.range(1).createTempView("test_view")
        >>> spark.catalog.listTables()  # doctest: +SKIP
        [Table(name='test_view', catalog=None, namespace=[], description=None, ...
        >>> _ = spark.catalog.dropTempView("test_view")
        """
        from pyspark.sql.catalog import Catalog

        return Catalog(self)

    @property
    def udf(self) -> "UDFRegistration":
        """Returns a :class:`UDFRegistration` for UDF registration.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`UDFRegistration`

        Examples
        --------
        Register a Python UDF, and use it in SQL.

        >>> strlen = spark.udf.register("strlen", lambda x: len(x))
        >>> spark.sql("SELECT strlen('test')").show()
        +------------+
        |strlen(test)|
        +------------+
        |           4|
        +------------+
        """
        from pyspark.sql.udf import UDFRegistration

        return UDFRegistration(self)

    @property
    def udtf(self) -> "UDTFRegistration":
        """Returns a :class:`UDTFRegistration` for UDTF registration.

        .. versionadded:: 3.5.0

        Returns
        -------
        :class:`UDTFRegistration`

        Notes
        -----
        Supports Spark Connect.
        """
        from pyspark.sql.udtf import UDTFRegistration

        return UDTFRegistration(self)

    @property
    def dataSource(self) -> "DataSourceRegistration":
        """Returns a :class:`DataSourceRegistration` for data source registration.

        .. versionadded:: 4.0.0

        Returns
        -------
        :class:`DataSourceRegistration`

        Notes
        -----
        This feature is experimental and unstable.
        """
        from pyspark.sql.datasource import DataSourceRegistration

        return DataSourceRegistration(self)

    @property
    def profile(self) -> Profile:
        """Returns a :class:`Profile` for performance/memory profiling.

        .. versionadded:: 4.0.0

        Returns
        -------
        :class:`Profile`

        Notes
        -----
        Supports Spark Connect.
        """
        return Profile(self._profiler_collector)

    def range(
        self,
        start: int,
        end: Optional[int] = None,
        step: int = 1,
        numPartitions: Optional[int] = None,
    ) -> DataFrame:
        """
        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

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        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).show()
        +---+
        | id|
        +---+
        |  1|
        |  3|
        |  5|
        +---+

        If only one argument is specified, it will be used as the end value.

        >>> spark.range(3).show()
        +---+
        | id|
        +---+
        |  0|
        |  1|
        |  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)

    def _inferSchemaFromList(
        self, data: Iterable[Any], names: Optional[List[str]] = None
    ) -> StructType:
        """
        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 PySparkValueError(
                errorClass="CANNOT_INFER_EMPTY_SCHEMA",
                messageParameters={},
            )
        infer_dict_as_struct = self._jconf.inferDictAsStruct()
        infer_array_from_first_element = self._jconf.legacyInferArrayTypeFromFirstElement()
        infer_map_from_first_pair = self._jconf.legacyInferMapStructTypeFromFirstItem()
        prefer_timestamp_ntz = is_timestamp_ntz_preferred()
        schema = reduce(
            _merge_type,
            (
                _infer_schema(
                    row,
                    names,
                    infer_dict_as_struct=infer_dict_as_struct,
                    infer_array_from_first_element=infer_array_from_first_element,
                    infer_map_from_first_pair=infer_map_from_first_pair,
                    prefer_timestamp_ntz=prefer_timestamp_ntz,
                )
                for row in data
            ),
        )
        if _has_nulltype(schema):
            raise PySparkValueError(
                errorClass="CANNOT_DETERMINE_TYPE",
                messageParameters={},
            )
        return schema

    def _inferSchema(
        self,
        rdd: "RDD[Any]",
        samplingRatio: Optional[float] = None,
        names: Optional[List[str]] = None,
    ) -> StructType:
        """
        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 isinstance(first, Sized) and len(first) == 0:
            raise PySparkValueError(
                errorClass="CANNOT_INFER_EMPTY_SCHEMA",
                messageParameters={},
            )

        infer_dict_as_struct = self._jconf.inferDictAsStruct()
        infer_array_from_first_element = self._jconf.legacyInferArrayTypeFromFirstElement()
        infer_map_from_first_pair = self._jconf.legacyInferMapStructTypeFromFirstItem()
        prefer_timestamp_ntz = is_timestamp_ntz_preferred()
        if samplingRatio is None:
            schema = _infer_schema(
                first,
                names=names,
                infer_dict_as_struct=infer_dict_as_struct,
                prefer_timestamp_ntz=prefer_timestamp_ntz,
            )
            if _has_nulltype(schema):
                for row in rdd.take(100)[1:]:
                    schema = _merge_type(
                        schema,
                        _infer_schema(
                            row,
                            names=names,
                            infer_dict_as_struct=infer_dict_as_struct,
                            infer_array_from_first_element=infer_array_from_first_element,
                            infer_map_from_first_pair=infer_map_from_first_pair,
                            prefer_timestamp_ntz=prefer_timestamp_ntz,
                        ),
                    )
                    if not _has_nulltype(schema):
                        break
                else:
                    raise PySparkValueError(
                        errorClass="CANNOT_DETERMINE_TYPE",
                        messageParameters={},
                    )
        else:
            if samplingRatio < 0.99:
                rdd = rdd.sample(False, float(samplingRatio))
            schema = rdd.map(
                lambda row: _infer_schema(
                    row,
                    names,
                    infer_dict_as_struct=infer_dict_as_struct,
                    infer_array_from_first_element=infer_array_from_first_element,
                    infer_map_from_first_pair=infer_map_from_first_pair,
                    prefer_timestamp_ntz=prefer_timestamp_ntz,
                )
            ).reduce(_merge_type)
        return schema

    def _createFromRDD(
        self,
        rdd: "RDD[Any]",
        schema: Optional[Union[DataType, List[str]]],
        samplingRatio: Optional[float],
    ) -> Tuple["RDD[Tuple]", StructType]:
        """
        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)
            tupled_rdd = rdd.map(converter)
            if isinstance(schema, (list, tuple)):
                for i, name in enumerate(schema):
                    struct.fields[i].name = name
                    struct.names[i] = name

        elif isinstance(schema, StructType):
            struct = schema
            tupled_rdd = rdd

        else:
            raise PySparkTypeError(
                errorClass="NOT_LIST_OR_NONE_OR_STRUCT",
                messageParameters={
                    "arg_name": "schema",
                    "arg_type": type(schema).__name__,
                },
            )

        # convert python objects to sql data
        internal_rdd = tupled_rdd.map(struct.toInternal)
        return internal_rdd, struct

    def _createFromLocal(
        self, data: Iterable[Any], schema: Optional[Union[DataType, List[str]]]
    ) -> Tuple["RDD[Tuple]", StructType]:
        """
        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)
            tupled_data: Iterable[Tuple] = map(converter, data)
            if isinstance(schema, (list, tuple)):
                for i, name in enumerate(schema):
                    struct.fields[i].name = name
                    struct.names[i] = name

        elif isinstance(schema, StructType):
            struct = schema
            tupled_data = data

        else:
            raise PySparkTypeError(
                errorClass="NOT_LIST_OR_NONE_OR_STRUCT",
                messageParameters={
                    "arg_name": "schema",
                    "arg_type": type(schema).__name__,
                },
            )

        # convert python objects to sql data
        internal_data = [struct.toInternal(row) for row in tupled_data]
        return self._sc.parallelize(internal_data), struct

    @staticmethod
    def _create_shell_session() -> "SparkSession":
        """
        Initialize a :class:`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.core.context import SparkContext

        try:
            # Try to access HiveConf, it will raise exception if Hive is not added
            conf = SparkConf()
            assert SparkContext._jvm is not None
            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._getActiveSessionOrCreate()
        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._getActiveSessionOrCreate()

    @staticmethod
    def _getActiveSessionOrCreate(**static_conf: Any) -> "SparkSession":
        """
        Returns the active :class:`SparkSession` for the current thread, returned by the builder,
        or if there is no existing one, creates a new one based on the options set in the builder.

        NOTE that 'static_conf' might not be set if there's an active or default Spark session
        running.
        """
        spark = SparkSession.getActiveSession()
        if spark is None:
            builder = SparkSession.builder
            for k, v in static_conf.items():
                builder = builder.config(k, v)
            spark = builder.getOrCreate()
        return spark

    @overload  # type: ignore[override]
    def createDataFrame(
        self,
        data: Iterable["RowLike"],
        schema: Union[List[str], Tuple[str, ...]] = ...,
        samplingRatio: Optional[float] = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: "RDD[RowLike]",
        schema: Union[List[str], Tuple[str, ...]] = ...,
        samplingRatio: Optional[float] = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: Iterable["RowLike"],
        schema: Union[StructType, str],
        *,
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: "RDD[RowLike]",
        schema: Union[StructType, str],
        *,
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: "RDD[AtomicValue]",
        schema: Union[AtomicType, str],
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: Iterable["AtomicValue"],
        schema: Union[AtomicType, str],
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self, data: "PandasDataFrameLike", samplingRatio: Optional[float] = ...
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(self, data: "pa.Table", samplingRatio: Optional[float] = ...) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: "PandasDataFrameLike",
        schema: Union[StructType, str],
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    @overload
    def createDataFrame(
        self,
        data: "pa.Table",
        schema: Union[StructType, str],
        verifySchema: bool = ...,
    ) -> DataFrame:
        ...

    def createDataFrame(  # type: ignore[misc]
        self,
        data: Union["RDD[Any]", Iterable[Any], "PandasDataFrameLike", "ArrayLike", "pa.Table"],
        schema: Optional[Union[AtomicType, StructType, str]] = None,
        samplingRatio: Optional[float] = None,
        verifySchema: bool = True,
    ) -> DataFrame:
        """
        Creates a :class:`DataFrame` from an :class:`RDD`, a list, a :class:`pandas.DataFrame`,
        a :class:`numpy.ndarray`, or a :class:`pyarrow.Table`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        .. versionchanged:: 4.0.0
            Supports :class:`pyarrow.Table`.

        Parameters
        ----------
        data : :class:`RDD` or iterable
            an RDD of any kind of SQL data representation (:class:`Row`,
            :class:`tuple`, ``int``, ``boolean``, ``dict``, etc.), or :class:`list`,
            :class:`pandas.DataFrame`, :class:`numpy.ndarray`, or :class:`pyarrow.Table`.
        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<>``.

            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.
        samplingRatio : float, optional
            the sample ratio of rows used for inferring. The first few rows will be used
            if ``samplingRatio`` is ``None``. This option is effective only when the input is
            :class:`RDD`.
        verifySchema : bool, optional
            verify data types of every row against schema. Enabled by default.
            When the input is :class:`pyarrow.Table` or when the input class is
            :class:`pandas.DataFrame` and `spark.sql.execution.arrow.pyspark.enabled` is enabled,
            this option is not effective. It follows Arrow type coercion. This option is not
            supported with Spark Connect.

            .. versionadded:: 2.1.0

        Returns
        -------
        :class:`DataFrame`

        Notes
        -----
        Usage with `spark.sql.execution.arrow.pyspark.enabled=True` is experimental.

        Examples
        --------
        Create a DataFrame from a list of tuples.

        >>> spark.createDataFrame([('Alice', 1)]).show()
        +-----+---+
        |   _1| _2|
        +-----+---+
        |Alice|  1|
        +-----+---+

        Create a DataFrame from a list of dictionaries.

        >>> d = [{'name': 'Alice', 'age': 1}]
        >>> spark.createDataFrame(d).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  1|Alice|
        +---+-----+

        Create a DataFrame with column names specified.

        >>> spark.createDataFrame([('Alice', 1)], ['name', 'age']).show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        Create a DataFrame with the explicit schema specified.

        >>> from pyspark.sql.types import *
        >>> schema = StructType([
        ...    StructField("name", StringType(), True),
        ...    StructField("age", IntegerType(), True)])
        >>> spark.createDataFrame([('Alice', 1)], schema).show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        Create a DataFrame with the schema in DDL formatted string.

        >>> spark.createDataFrame([('Alice', 1)], "name: string, age: int").show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        Create an empty DataFrame.
        When initializing an empty DataFrame in PySpark, it's mandatory to specify its schema,
        as the DataFrame lacks data from which the schema can be inferred.

        >>> spark.createDataFrame([], "name: string, age: int").show()
        +----+---+
        |name|age|
        +----+---+
        +----+---+

        Create a DataFrame from Row objects.

        >>> from pyspark.sql import Row
        >>> Person = Row('name', 'age')
        >>> df = spark.createDataFrame([Person("Alice", 1)])
        >>> df.show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        Create a DataFrame from a pandas DataFrame.

        >>> spark.createDataFrame(df.toPandas()).show()  # doctest: +SKIP
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        >>> pdf = pandas.DataFrame([[1, 2]])  # doctest: +SKIP
        >>> spark.createDataFrame(pdf).show()  # doctest: +SKIP
        +---+---+
        |  0|  1|
        +---+---+
        |  1|  2|
        +---+---+

        Create a DataFrame from a PyArrow Table.

        >>> spark.createDataFrame(df.toArrow()).show()  # doctest: +SKIP
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  1|
        +-----+---+

        >>> table = pyarrow.table({'0': [1], '1': [2]})  # doctest: +SKIP
        >>> spark.createDataFrame(table).show()  # doctest: +SKIP
        +---+---+
        |  0|  1|
        +---+---+
        |  1|  2|
        +---+---+
        """
        SparkSession._activeSession = self
        assert self._jvm is not None
        SparkSession._get_j_spark_session_class(self._jvm).setActiveSession(self._jsparkSession)
        if isinstance(data, DataFrame):
            raise PySparkTypeError(
                errorClass="INVALID_TYPE",
                messageParameters={"arg_name": "data", "arg_type": "DataFrame"},
            )

        if isinstance(schema, str):
            schema = cast(Union[AtomicType, StructType, str], self._parse_ddl(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 as pd

            has_pandas = True
        except Exception:
            has_pandas = False

        try:
            import numpy as np

            has_numpy = True
        except Exception:
            has_numpy = False

        try:
            import pyarrow as pa

            has_pyarrow = True
        except Exception:
            has_pyarrow = False

        if has_numpy and isinstance(data, np.ndarray):
            # `data` of numpy.ndarray type will be converted to a pandas DataFrame,
            # so pandas is required.
            from pyspark.sql.pandas.utils import require_minimum_pandas_version

            require_minimum_pandas_version()
            if data.ndim not in [1, 2]:
                raise PySparkValueError(
                    errorClass="INVALID_NDARRAY_DIMENSION",
                    messageParameters={"dimensions": "1 or 2"},
                )

            if data.ndim == 1 or data.shape[1] == 1:
                column_names = ["value"]
            else:
                column_names = ["_%s" % i for i in range(1, data.shape[1] + 1)]

            if schema is None and not self._jconf.arrowPySparkEnabled():
                # Construct `schema` from `np.dtype` of the input NumPy array
                # TODO: Apply the logic below when self._jconf.arrowPySparkEnabled() is True
                spark_type = _from_numpy_type(data.dtype)
                if spark_type is not None:
                    schema = StructType(
                        [StructField(name, spark_type, nullable=True) for name in column_names]
                    )

            data = pd.DataFrame(data, columns=column_names)

        if has_pandas and isinstance(data, pd.DataFrame):
            # Create a DataFrame from pandas DataFrame.
            return super(SparkSession, self).createDataFrame(  # type: ignore[call-overload]
                data, schema, samplingRatio, verifySchema
            )
        if has_pyarrow and isinstance(data, pa.Table):
            # Create a DataFrame from PyArrow Table.
            return super(SparkSession, self).createDataFrame(  # type: ignore[call-overload]
                data, schema, samplingRatio, verifySchema
            )
        return self._create_dataframe(
            data, schema, samplingRatio, verifySchema  # type: ignore[arg-type]
        )

    def _create_dataframe(
        self,
        data: Union["RDD[Any]", Iterable[Any]],
        schema: Optional[Union[DataType, List[str]]],
        samplingRatio: Optional[float],
        verifySchema: bool,
    ) -> DataFrame:
        if isinstance(schema, StructType):
            verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True

            @no_type_check
            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
            )

            @no_type_check
            def prepare(obj):
                verify_func(obj)
                return (obj,)

        else:

            def prepare(obj: Any) -> Any:
                return obj

        if not is_remote_only():
            from pyspark.core.rdd import RDD
        if not is_remote_only() and isinstance(data, RDD):
            rdd, struct = self._createFromRDD(data.map(prepare), schema, samplingRatio)
        else:
            rdd, struct = self._createFromLocal(
                map(prepare, data), schema  # type: ignore[arg-type]
            )
        assert self._jvm is not None
        jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
        jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), struct.json())
        df = DataFrame(jdf, self)
        df._schema = struct
        return df

    def sql(
        self, sqlQuery: str, args: Optional[Union[Dict[str, Any], List]] = None, **kwargs: Any
    ) -> "ParentDataFrame":
        """Returns a :class:`DataFrame` representing the result of the given query.
        When ``kwargs`` is specified, this method formats the given string by using the Python
        standard formatter. The method binds named parameters to SQL literals or
        positional parameters from `args`. It doesn't support named and positional parameters
        in the same SQL query.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect and parameterized SQL.

        .. versionchanged:: 3.5.0
            Added positional parameters.

        Parameters
        ----------
        sqlQuery : str
            SQL query string.
        args : dict or list
            A dictionary of parameter names to Python objects or a list of Python objects
            that can be converted to SQL literal expressions. See
            `Supported Data Types`_ for supported value types in Python.
            For example, dictionary keys: "rank", "name", "birthdate";
            dictionary or list values: 1, "Steven", datetime.date(2023, 4, 2).
            A value can be also a `Column` of a literal or collection constructor functions such
            as `map()`, `array()`, `struct()`, in that case it is taken as is.

            .. _Supported Data Types: https://spark.apache.org/docs/latest/sql-ref-datatypes.html

            .. versionadded:: 3.4.0

        kwargs : dict
            Other variables that the user wants to set that can be referenced in the query

            .. versionchanged:: 3.3.0
               Added optional argument ``kwargs`` to specify the mapping of variables in the query.
               This feature is experimental and unstable.

        Returns
        -------
        :class:`DataFrame`

        Notes
        -----
        In Spark Classic, a temporary view referenced in `spark.sql` is resolved immediately,
        while in Spark Connect it is lazily analyzed.
        So in Spark Connect if a view is dropped, modified or replaced after `spark.sql`, the
        execution may fail or generate different results.

        Examples
        --------
        Executing a SQL query.

        >>> spark.sql("SELECT * FROM range(10) where id > 7").show()
        +---+
        | id|
        +---+
        |  8|
        |  9|
        +---+

        Executing a SQL query with variables as Python formatter standard.

        >>> spark.sql(
        ...     "SELECT * FROM range(10) WHERE id > {bound1} AND id < {bound2}", bound1=7, bound2=9
        ... ).show()
        +---+
        | id|
        +---+
        |  8|
        +---+

        >>> mydf = spark.range(10)
        >>> spark.sql(
        ...     "SELECT {col} FROM {mydf} WHERE id IN {x}",
        ...     col=mydf.id, mydf=mydf, x=tuple(range(4))).show()
        +---+
        | id|
        +---+
        |  0|
        |  1|
        |  2|
        |  3|
        +---+

        >>> spark.sql('''
        ...   SELECT m1.a, m2.b
        ...   FROM {table1} m1 INNER JOIN {table2} m2
        ...   ON m1.key = m2.key
        ...   ORDER BY m1.a, m2.b''',
        ...   table1=spark.createDataFrame([(1, "a"), (2, "b")], ["a", "key"]),
        ...   table2=spark.createDataFrame([(3, "a"), (4, "b"), (5, "b")], ["b", "key"])).show()
        +---+---+
        |  a|  b|
        +---+---+
        |  1|  3|
        |  2|  4|
        |  2|  5|
        +---+---+

        Also, it is possible to query using class:`Column` from :class:`DataFrame`.

        >>> mydf = spark.createDataFrame([(1, 4), (2, 4), (3, 6)], ["A", "B"])
        >>> spark.sql("SELECT {df.A}, {df[B]} FROM {df}", df=mydf).show()
        +---+---+
        |  A|  B|
        +---+---+
        |  1|  4|
        |  2|  4|
        |  3|  6|
        +---+---+

        And substitute named parameters with the `:` prefix by SQL literals.

        >>> from pyspark.sql.functions import create_map, lit
        >>> spark.sql(
        ...   "SELECT *, element_at(:m, 'a') AS C FROM {df} WHERE {df[B]} > :minB",
        ...   {"minB" : 5, "m" : create_map(lit('a'), lit(1))}, df=mydf).show()
        +---+---+---+
        |  A|  B|  C|
        +---+---+---+
        |  3|  6|  1|
        +---+---+---+

        Or positional parameters marked by `?` in the SQL query by SQL literals.

        >>> from pyspark.sql.functions import array, lit
        >>> spark.sql(
        ...   "SELECT *, element_at(?, 1) AS C FROM {df} WHERE {df[B]} > ? and ? < {df[A]}",
        ...   args=[array(lit(1), lit(2), lit(3)), 5, 2], df=mydf).show()
        +---+---+---+
        |  A|  B|  C|
        +---+---+---+
        |  3|  6|  1|
        +---+---+---+
        """
        from pyspark.sql.classic.column import _to_java_column

        formatter = SQLStringFormatter(self)
        if len(kwargs) > 0:
            sqlQuery = formatter.format(sqlQuery, **kwargs)
        try:
            if isinstance(args, Dict):
                litArgs = {k: _to_java_column(lit(v)) for k, v in (args or {}).items()}
            elif args is None or isinstance(args, List):
                assert self._jvm is not None
                litArgs = self._jvm.PythonUtils.toArray(
                    [_to_java_column(lit(v)) for v in (args or [])]
                )
            else:
                raise PySparkTypeError(
                    errorClass="INVALID_TYPE",
                    messageParameters={"arg_name": "args", "arg_type": type(args).__name__},
                )
            return DataFrame(self._jsparkSession.sql(sqlQuery, litArgs), self)
        finally:
            if len(kwargs) > 0:
                formatter.clear()

    def table(self, tableName: str) -> DataFrame:
        """Returns the specified table as a :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        tableName : str
            the table name to retrieve.

        Returns
        -------
        :class:`DataFrame`

        Notes
        -----
        In Spark Classic, a temporary view referenced in `spark.table` is resolved immediately,
        while in Spark Connect it is lazily analyzed.
        So in Spark Connect if a view is dropped, modified or replaced after `spark.table`, the
        execution may fail or generate different results.

        Examples
        --------
        >>> spark.range(5).createOrReplaceTempView("table1")
        >>> spark.table("table1").sort("id").show()
        +---+
        | id|
        +---+
        |  0|
        |  1|
        |  2|
        |  3|
        |  4|
        +---+
        """
        if not isinstance(tableName, str):
            raise PySparkTypeError(
                errorClass="NOT_STR",
                messageParameters={"arg_name": "tableName", "arg_type": type(tableName).__name__},
            )

        return DataFrame(self._jsparkSession.table(tableName), self)

    @property
    def read(self) -> DataFrameReader:
        """
        Returns a :class:`DataFrameReader` that can be used to read data
        in as a :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrameReader`

        Examples
        --------
        >>> spark.read
        <...DataFrameReader object ...>

        Write a DataFrame into a JSON file and read it back.

        >>> import tempfile
        >>> with tempfile.TemporaryDirectory(prefix="read") as d:
        ...     # Write a DataFrame into a JSON file
        ...     spark.createDataFrame(
        ...         [{"age": 100, "name": "Hyukjin Kwon"}]
        ...     ).write.mode("overwrite").format("json").save(d)
        ...
        ...     # Read the JSON file as a DataFrame.
        ...     spark.read.format('json').load(d).show()
        +---+------------+
        |age|        name|
        +---+------------+
        |100|Hyukjin Kwon|
        +---+------------+
        """
        return DataFrameReader(self)

    @property
    def readStream(self) -> DataStreamReader:
        """
        Returns a :class:`DataStreamReader` that can be used to read data streams
        as a streaming :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Returns
        -------
        :class:`DataStreamReader`

        Examples
        --------
        >>> spark.readStream
        <pyspark...DataStreamReader object ...>

        The example below uses Rate source that generates rows continuously.
        After that, we operate a modulo by 3, and then write the stream out to the console.
        The streaming query stops in 3 seconds.

        >>> import time
        >>> df = spark.readStream.format("rate").load()
        >>> df = df.selectExpr("value % 3 as v")
        >>> q = df.writeStream.format("console").start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        return DataStreamReader(self)

    @cached_property
    def streams(self) -> "StreamingQueryManager":
        """Returns a :class:`StreamingQueryManager` that allows managing all the
        :class:`StreamingQuery` instances active on `this` context.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Returns
        -------
        :class:`StreamingQueryManager`

        Examples
        --------
        >>> spark.streams
        <pyspark...StreamingQueryManager object ...>

        Get the list of active streaming queries

        >>> sq = spark.readStream.format(
        ...     "rate").load().writeStream.format('memory').queryName('this_query').start()
        >>> sqm = spark.streams
        >>> [q.name for q in sqm.active]
        ['this_query']
        >>> sq.stop()
        """
        from pyspark.sql.streaming import StreamingQueryManager

        return StreamingQueryManager(self._jsparkSession.streams())

    @property
    def tvf(self) -> "TableValuedFunction":
        """
        Returns a :class:`tvf.TableValuedFunction` that can be used to call a table-valued function
        (TVF).

        .. versionadded:: 4.0.0

        Notes
        -----
        This API is evolving.

        Returns
        -------
        :class:`tvf.TableValuedFunction`

        Examples
        --------
        >>> spark.tvf
        <pyspark...TableValuedFunction object ...>

        >>> import pyspark.sql.functions as sf
        >>> spark.tvf.explode(sf.array(sf.lit(1), sf.lit(2), sf.lit(3))).show()
        +---+
        |col|
        +---+
        |  1|
        |  2|
        |  3|
        +---+
        """
        from pyspark.sql.tvf import TableValuedFunction

        return TableValuedFunction(self)

    def stop(self) -> None:
        """
        Stop the underlying :class:`SparkContext`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Examples
        --------
        >>> spark.stop()  # doctest: +SKIP
        """
        from pyspark.sql.context import SQLContext

        self._sc.stop()
        # We should clean the default session up. See SPARK-23228.
        assert self._jvm is not None
        jSparkSessionClass = SparkSession._get_j_spark_session_class(self._jvm)
        jSparkSessionClass.clearDefaultSession()
        jSparkSessionClass.clearActiveSession()
        SparkSession._instantiatedSession = None
        SparkSession._activeSession = None
        SQLContext._instantiatedContext = None

    def __enter__(self) -> "SparkSession":
        """
        Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.

        .. versionadded:: 2.0.0

        Examples
        --------
        >>> with SparkSession.builder.master("local").getOrCreate() as session:
        ...     session.range(5).show()  # doctest: +SKIP
        +---+
        | id|
        +---+
        |  0|
        |  1|
        |  2|
        |  3|
        |  4|
        +---+
        """
        return self

    def __exit__(
        self,
        exc_type: Optional[Type[BaseException]],
        exc_val: Optional[BaseException],
        exc_tb: Optional[TracebackType],
    ) -> None:
        """
        Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.

        Specifically stop the SparkSession on exit of the with block.

        .. versionadded:: 2.0.0

        Examples
        --------
        >>> with SparkSession.builder.master("local").getOrCreate() as session:
        ...     session.range(5).show()  # doctest: +SKIP
        +---+
        | id|
        +---+
        |  0|
        |  1|
        |  2|
        |  3|
        |  4|
        +---+
        """
        self.stop()

    # SparkConnect-specific API
    @property
    @remote_only
    def client(self) -> "SparkConnectClient":
        """
        Gives access to the Spark Connect client. In normal cases this is not necessary to be used
        and only relevant for testing.

        .. versionadded:: 3.4.0

        Returns
        -------
        :class:`SparkConnectClient`

        Notes
        -----
        This API is unstable, and a developer API. It returns non-API instance
        :class:`SparkConnectClient`.
        This is an API dedicated to Spark Connect client only. With regular Spark Session, it throws
        an exception.
        """
        raise PySparkRuntimeError(
            errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
            messageParameters={"feature": "SparkSession.client"},
        )

    def addArtifacts(
        self, *path: str, pyfile: bool = False, archive: bool = False, file: bool = False
    ) -> None:
        """
        Add artifact(s) to the client session. Currently only local files are supported.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Parameters
        ----------
        *path : tuple of str
            Artifact's URIs to add.
        pyfile : bool
            Whether to add them as Python dependencies such as .py, .egg, .zip or .jar files.
            The pyfiles are directly inserted into the path when executing Python functions
            in executors.
        archive : bool
            Whether to add them as archives such as .zip, .jar, .tar.gz, .tgz, or .tar files.
            The archives are unpacked on the executor side automatically.
        file : bool
            Add a file to be downloaded with this Spark job on every node.
            The ``path`` passed can only be a local file for now.
        """
        from pyspark.core.files import SparkFiles

        if sum([file, pyfile, archive]) > 1:
            raise PySparkValueError(
                errorClass="INVALID_MULTIPLE_ARGUMENT_CONDITIONS",
                messageParameters={
                    "arg_names": "'pyfile', 'archive' and/or 'file'",
                    "condition": "True together",
                },
            )
        for p in path:
            normalized_path = os.path.abspath(p)
            target_dir = os.path.join(
                SparkFiles.getRootDirectory(), os.path.basename(normalized_path)
            )

            # Check if the target path already exists
            if os.path.exists(target_dir):
                # Compare the contents of the files. If identical, skip adding this file.
                # If different, raise an exception.
                if filecmp.cmp(normalized_path, target_dir, shallow=False):
                    continue
                else:
                    raise PySparkRuntimeError(
                        errorClass="DUPLICATED_ARTIFACT",
                        messageParameters={"normalized_path": normalized_path},
                    )
        if archive:
            self._sc.addArchive(*path)
        elif pyfile:
            self._sc.addPyFile(*path)
        elif file:
            self._sc.addFile(*path)  # type: ignore[arg-type]

    addArtifact = addArtifacts

    @remote_only
    def registerProgressHandler(self, handler: "ProgressHandler") -> None:
        """
        Register a progress handler to be called when a progress update is received from the server.

        .. versionadded:: 4.0

        Parameters
        ----------
        handler : ProgressHandler
          A callable that follows the ProgressHandler interface. This handler will be called
          on every progress update.

        Examples
        --------

        >>> def progress_handler(stages, inflight_tasks, done):
        ...     print(f"{len(stages)} Stages known, Done: {done}")
        >>> spark.registerProgressHandler(progress_handler)
        >>> res = spark.range(10).repartition(1).collect()  # doctest: +SKIP
        3 Stages known, Done: False
        3 Stages known, Done: True
        >>> spark.clearProgressHandlers()
        """
        raise PySparkRuntimeError(
            errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
            messageParameters={"feature": "SparkSession.registerProgressHandler"},
        )

    @remote_only
    def removeProgressHandler(self, handler: "ProgressHandler") -> None:
        """
        Remove a progress handler that was previously registered.

        .. versionadded:: 4.0

        Parameters
        ----------
        handler : ProgressHandler
          The handler to remove if present in the list of progress handlers.
        """
        raise PySparkRuntimeError(
            errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
            messageParameters={"feature": "SparkSession.removeProgressHandler"},
        )

    @remote_only
    def clearProgressHandlers(self) -> None:
        """
        Clear all registered progress handlers.

        .. versionadded:: 4.0
        """
        raise PySparkRuntimeError(
            errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
            messageParameters={"feature": "SparkSession.clearProgressHandlers"},
        )

    @remote_only
    def copyFromLocalToFs(self, local_path: str, dest_path: str) -> None:
        """
        Copy file from local to cloud storage file system.
        If the file already exits in destination path, old file is overwritten.

        .. versionadded:: 3.5.0

        Parameters
        ----------
        local_path: str
            Path to a local file. Directories are not supported.
            The path can be either an absolute path or a relative path.
        dest_path: str
            The cloud storage path to the destination the file will
            be copied to.
            The path must be an an absolute path.

        Notes
        -----
        This API is a developer API.
        Also, this is an API dedicated to Spark Connect client only. With regular
        Spark Session, it throws an exception.
        """
        raise PySparkRuntimeError(
            errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
            messageParameters={"feature": "SparkSession.copyFromLocalToFs"},
        )

    def interruptAll(self) -> List[str]:
        """
        Interrupt all operations of this session currently running on the connected server.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Returns
        -------
        list of str
            List of operationIds of interrupted operations.

        Notes
        -----
        There is still a possibility of operation finishing just as it is interrupted.
        """
        java_list = self._jsparkSession.interruptAll()
        python_list = list()

        # Use iterator to manually iterate through Java list
        java_iterator = java_list.iterator()
        while java_iterator.hasNext():
            python_list.append(str(java_iterator.next()))

        return python_list

    def interruptTag(self, tag: str) -> List[str]:
        """
        Interrupt all operations of this session with the given operation tag.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Returns
        -------
        list of str
            List of operationIds of interrupted operations.

        Notes
        -----
        There is still a possibility of operation finishing just as it is interrupted.
        """
        java_list = self._jsparkSession.interruptTag(tag)
        python_list = list()

        # Use iterator to manually iterate through Java list
        java_iterator = java_list.iterator()
        while java_iterator.hasNext():
            python_list.append(str(java_iterator.next()))

        return python_list

    def interruptOperation(self, op_id: str) -> List[str]:
        """
        Interrupt an operation of this session with the given operationId.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Returns
        -------
        list of str
            List of operationIds of interrupted operations.

        Notes
        -----
        There is still a possibility of operation finishing just as it is interrupted.
        """
        java_list = self._jsparkSession.interruptOperation(op_id)
        python_list = list()

        # Use iterator to manually iterate through Java list
        java_iterator = java_list.iterator()
        while java_iterator.hasNext():
            python_list.append(str(java_iterator.next()))

        return python_list

    def addTag(self, tag: str) -> None:
        """
        Add a tag to be assigned to all the operations started by this thread in this session.

        Often, a unit of execution in an application consists of multiple Spark executions.
        Application programmers can use this method to group all those jobs together and give a
        group tag. The application can use :meth:`SparkSession.interruptTag` to cancel all running
        executions with this tag.

        There may be multiple tags present at the same time, so different parts of application may
        use different tags to perform cancellation at different levels of granularity.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Parameters
        ----------
        tag : str
            The tag to be added. Cannot contain ',' (comma) character or be an empty string.
        """
        self._jsparkSession.addTag(tag)

    def removeTag(self, tag: str) -> None:
        """
        Remove a tag previously added to be assigned to all the operations started by this thread in
        this session. Noop if such a tag was not added earlier.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Parameters
        ----------
        tag : list of str
            The tag to be removed. Cannot contain ',' (comma) character or be an empty string.
        """
        self._jsparkSession.removeTag(tag)

    def getTags(self) -> Set[str]:
        """
        Get the tags that are currently set to be assigned to all the operations started by this
        thread.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.

        Returns
        -------
        set of str
            Set of tags of interrupted operations.
        """
        java_set = self._jsparkSession.getTags()
        python_set = set()

        # Use iterator to manually iterate through Java Set
        java_iterator = java_set.iterator()
        while java_iterator.hasNext():
            python_set.add(str(java_iterator.next()))

        return python_set

    def clearTags(self) -> None:
        """
        Clear the current thread's operation tags.

        .. versionadded:: 3.5.0

        .. versionchanged:: 4.0.0
            Supports Spark Classic.
        """
        self._jsparkSession.clearTags()

    def _to_ddl(self, struct: StructType) -> str:
        return self._sc._to_ddl(struct)

    def _parse_ddl(self, ddl: str) -> DataType:
        return self._sc._parse_ddl(ddl)


def _test() -> None:
    import os
    import doctest
    import pyspark.sql.session

    os.chdir(os.environ["SPARK_HOME"])

    # Disable Doc Tests for Spark Connect only functions:
    pyspark.sql.session.SparkSession.registerProgressHandler.__doc__ = None
    pyspark.sql.session.SparkSession.removeProgressHandler.__doc__ = None
    pyspark.sql.session.SparkSession.clearProgressHandlers.__doc__ = None

    globs = pyspark.sql.session.__dict__.copy()
    globs["spark"] = (
        SparkSession.builder.master("local[4]").appName("sql.session tests").getOrCreate()
    )
    (failure_count, test_count) = doctest.testmod(
        pyspark.sql.session,
        globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
    )
    globs["spark"].stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()