#
# 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 re
import sys
from collections.abc import Iterator
from typing import cast, overload, Any, Callable, List, Optional, TYPE_CHECKING, Union

from pyspark.sql.readwriter import OptionUtils, to_str
from pyspark.sql.streaming.query import StreamingQuery
from pyspark.sql.types import Row, StructType
from pyspark.sql.utils import ForeachBatchFunction
from pyspark.errors import (
    PySparkTypeError,
    PySparkValueError,
    PySparkAttributeError,
    PySparkRuntimeError,
)

if TYPE_CHECKING:
    from py4j.java_gateway import JavaObject
    from pyspark.sql.session import SparkSession
    from pyspark.sql._typing import SupportsProcess, OptionalPrimitiveType
    from pyspark.sql.dataframe import DataFrame

__all__ = ["DataStreamReader", "DataStreamWriter"]


class DataStreamReader(OptionUtils):
    """
    Interface used to load a streaming :class:`DataFrame <pyspark.sql.DataFrame>` from external
    storage systems (e.g. file systems, key-value stores, etc).
    Use :attr:`SparkSession.readStream <pyspark.sql.SparkSession.readStream>` to access this.

    .. versionadded:: 2.0.0

    .. versionchanged:: 3.5.0
        Supports Spark Connect.

    Notes
    -----
    This API is evolving.

    Examples
    --------
    >>> spark.readStream
    <...streaming.readwriter.DataStreamReader object ...>

    The example below uses Rate source that generates rows continuously.
    After that, we operate a modulo by 3, and then writes 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()
    """

    def __init__(self, spark: "SparkSession") -> None:
        self._jreader = spark._jsparkSession.readStream()
        self._spark = spark

    def _df(self, jdf: "JavaObject") -> "DataFrame":
        from pyspark.sql.dataframe import DataFrame

        return DataFrame(jdf, self._spark)

    def format(self, source: str) -> "DataStreamReader":
        """Specifies the input data source format.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        source : str
            name of the data source, e.g. 'json', 'parquet'.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> spark.readStream.format("text")
        <...streaming.readwriter.DataStreamReader object ...>

        This API allows to configure other sources to read. The example below writes a small text
        file, and reads it back via Text source.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="format") as d:
        ...     # Write a temporary text file to read it.
        ...     spark.createDataFrame(
        ...         [("hello",), ("this",)]).write.mode("overwrite").format("text").save(d)
        ...
        ...     # Start a streaming query to read the text file.
        ...     q = spark.readStream.format("text").load(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._jreader = self._jreader.format(source)
        return self

    def schema(self, schema: Union[StructType, str]) -> "DataStreamReader":
        """Specifies the input schema.

        Some data sources (e.g. JSON) can infer the input schema automatically from data.
        By specifying the schema here, the underlying data source can skip the schema
        inference step, and thus speed up data loading.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        schema : :class:`pyspark.sql.types.StructType` or str
            a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string
            (For example ``col0 INT, col1 DOUBLE``).

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> from pyspark.sql.types import StructField, StructType, StringType
        >>> spark.readStream.schema(StructType([StructField("data", StringType(), True)]))
        <...streaming.readwriter.DataStreamReader object ...>
        >>> spark.readStream.schema("col0 INT, col1 DOUBLE")
        <...streaming.readwriter.DataStreamReader object ...>

        The example below specifies a different schema to CSV file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="schema") as d:
        ...     # Start a streaming query to read the CSV file.
        ...     spark.readStream.schema("col0 INT, col1 STRING").format("csv").load(d).printSchema()
        root
         |-- col0: integer (nullable = true)
         |-- col1: string (nullable = true)
        """
        from pyspark.sql import SparkSession

        spark = SparkSession._getActiveSessionOrCreate()
        if isinstance(schema, StructType):
            jschema = spark._jsparkSession.parseDataType(schema.json())
            self._jreader = self._jreader.schema(jschema)
        elif isinstance(schema, str):
            self._jreader = self._jreader.schema(schema)
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "schema",
                    "expected_type": "str or struct type",
                    "arg_type": type(schema).__name__,
                },
            )
        return self

    def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamReader":
        """Adds an input option for the underlying data source.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> spark.readStream.option("x", 1)
        <...streaming.readwriter.DataStreamReader object ...>

        The example below specifies 'rowsPerSecond' option to Rate source in order to generate
        10 rows every second.

        >>> import time
        >>> q = spark.readStream.format(
        ...     "rate").option("rowsPerSecond", 10).load().writeStream.format("console").start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        self._jreader = self._jreader.option(key, to_str(value))
        return self

    def options(self, **options: "OptionalPrimitiveType") -> "DataStreamReader":
        """Adds input options for the underlying data source.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> spark.readStream.options(x="1", y=2)
        <...streaming.readwriter.DataStreamReader object ...>

        Specify options in a dictionary.

        >>> spark.readStream.options(**{"k1": "v1", "k2": "v2"})
        <...streaming.readwriter.DataStreamReader object ...>

        The example below specifies 'rowsPerSecond' and 'numPartitions' options to
        Rate source in order to generate 10 rows with 10 partitions every second.

        >>> import time
        >>> q = spark.readStream.format("rate").options(
        ...    rowsPerSecond=10, numPartitions=10
        ... ).load().writeStream.format("console").start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        for k in options:
            self._jreader = self._jreader.option(k, to_str(options[k]))
        return self

    def name(self, source_name: str) -> "DataStreamReader":
        """Specifies a name for the streaming source.

        This name is used to identify the source in checkpoint metadata and enables
        stable checkpoint locations for source evolution.

        .. versionadded:: 4.2.0

        Parameters
        ----------
        source_name : str
            the name to assign to this streaming source. Must contain only ASCII letters,
            digits, and underscores.

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

        Notes
        -----
        This API is experimental.

        Examples
        --------
        >>> spark.readStream.format("rate").name("my_source")  # doctest: +SKIP
        <...streaming.readwriter.DataStreamReader object ...>
        """
        if not isinstance(source_name, str):
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "source_name",
                    "expected_type": "str",
                    "arg_type": type(source_name).__name__,
                },
            )

        # Validate that source_name contains only ASCII letters, digits, and underscores
        if not re.match(r"^[a-zA-Z0-9_]+$", source_name):
            raise PySparkValueError(
                errorClass="INVALID_STREAMING_SOURCE_NAME",
                messageParameters={"source_name": source_name},
            )

        self._jreader = self._jreader.name(source_name)
        return self

    def load(
        self,
        path: Optional[str] = None,
        format: Optional[str] = None,
        schema: Optional[Union[StructType, str]] = None,
        **options: "OptionalPrimitiveType",
    ) -> "DataFrame":
        """Loads a data stream from a data source and returns it as a
        :class:`DataFrame <pyspark.sql.DataFrame>`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        path : str, optional
            optional string for file-system backed data sources.
        format : str, optional
            optional string for format of the data source. Default to 'parquet'.
        schema : :class:`pyspark.sql.types.StructType` or str, optional
            optional :class:`pyspark.sql.types.StructType` for the input schema
            or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
        **options : dict
            all other string options

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Load a data stream from a temporary JSON file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="load") as d:
        ...     # Write a temporary JSON file to read it.
        ...     spark.createDataFrame(
        ...         [(100, "Hyukjin Kwon"),], ["age", "name"]
        ...     ).write.mode("overwrite").format("json").save(d)
        ...
        ...     # Start a streaming query to read the JSON file.
        ...     q = spark.readStream.schema(
        ...         "age INT, name STRING"
        ...     ).format("json").load(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        if format is not None:
            self.format(format)
        if schema is not None:
            self.schema(schema)
        self.options(**options)
        if path is not None:
            if not isinstance(path, str) or len(path.strip()) == 0:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_NON_EMPTY_STR",
                    messageParameters={"arg_name": "path", "arg_value": str(path)},
                )
            return self._df(self._jreader.load(path))
        else:
            return self._df(self._jreader.load())

    def json(
        self,
        path: str,
        schema: Optional[Union[StructType, str]] = None,
        primitivesAsString: Optional[Union[bool, str]] = None,
        prefersDecimal: Optional[Union[bool, str]] = None,
        allowComments: Optional[Union[bool, str]] = None,
        allowUnquotedFieldNames: Optional[Union[bool, str]] = None,
        allowSingleQuotes: Optional[Union[bool, str]] = None,
        allowNumericLeadingZero: Optional[Union[bool, str]] = None,
        allowBackslashEscapingAnyCharacter: Optional[Union[bool, str]] = None,
        mode: Optional[str] = None,
        columnNameOfCorruptRecord: Optional[str] = None,
        dateFormat: Optional[str] = None,
        timestampFormat: Optional[str] = None,
        multiLine: Optional[Union[bool, str]] = None,
        allowUnquotedControlChars: Optional[Union[bool, str]] = None,
        lineSep: Optional[str] = None,
        locale: Optional[str] = None,
        dropFieldIfAllNull: Optional[Union[bool, str]] = None,
        encoding: Optional[str] = None,
        pathGlobFilter: Optional[Union[bool, str]] = None,
        recursiveFileLookup: Optional[Union[bool, str]] = None,
        allowNonNumericNumbers: Optional[Union[bool, str]] = None,
        useUnsafeRow: Optional[Union[bool, str]] = None,
    ) -> "DataFrame":
        """
        Loads a JSON file stream and returns the results as a :class:`DataFrame`.

        `JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
        For JSON (one record per file), set the ``multiLine`` parameter to ``true``.

        If the ``schema`` parameter is not specified, this function goes
        through the input once to determine the input schema.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        path : str
            string represents path to the JSON dataset,
            or RDD of Strings storing JSON objects.
        schema : :class:`pyspark.sql.types.StructType` or str, optional
            an optional :class:`pyspark.sql.types.StructType` for the input schema
            or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
            in the version you use.

            .. # noqa

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Load a data stream from a temporary JSON file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="json") as d:
        ...     # Write a temporary JSON file to read it.
        ...     spark.createDataFrame(
        ...         [(100, "Hyukjin Kwon"),], ["age", "name"]
        ...     ).write.mode("overwrite").format("json").save(d)
        ...
        ...     # Start a streaming query to read the JSON file.
        ...     q = spark.readStream.schema(
        ...         "age INT, name STRING"
        ...     ).json(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            schema=schema,
            primitivesAsString=primitivesAsString,
            prefersDecimal=prefersDecimal,
            allowComments=allowComments,
            allowUnquotedFieldNames=allowUnquotedFieldNames,
            allowSingleQuotes=allowSingleQuotes,
            allowNumericLeadingZero=allowNumericLeadingZero,
            allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
            mode=mode,
            columnNameOfCorruptRecord=columnNameOfCorruptRecord,
            dateFormat=dateFormat,
            timestampFormat=timestampFormat,
            multiLine=multiLine,
            allowUnquotedControlChars=allowUnquotedControlChars,
            lineSep=lineSep,
            locale=locale,
            dropFieldIfAllNull=dropFieldIfAllNull,
            encoding=encoding,
            pathGlobFilter=pathGlobFilter,
            recursiveFileLookup=recursiveFileLookup,
            allowNonNumericNumbers=allowNonNumericNumbers,
            useUnsafeRow=useUnsafeRow,
        )
        if isinstance(path, str):
            return self._df(self._jreader.json(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def orc(
        self,
        path: str,
        mergeSchema: Optional[bool] = None,
        pathGlobFilter: Optional[Union[bool, str]] = None,
        recursiveFileLookup: Optional[Union[bool, str]] = None,
    ) -> "DataFrame":
        """Loads a ORC file stream, returning the result as a :class:`DataFrame`.

        .. versionadded:: 2.3.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option>`_
            in the version you use.

            .. # noqa

        Examples
        --------
        Load a data stream from a temporary ORC file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="orc") as d:
        ...     # Write a temporary ORC file to read it.
        ...     spark.range(10).write.mode("overwrite").format("orc").save(d)
        ...
        ...     # Start a streaming query to read the ORC file.
        ...     q = spark.readStream.schema("id LONG").orc(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            mergeSchema=mergeSchema,
            pathGlobFilter=pathGlobFilter,
            recursiveFileLookup=recursiveFileLookup,
        )
        if isinstance(path, str):
            return self._df(self._jreader.orc(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def parquet(
        self,
        path: str,
        mergeSchema: Optional[bool] = None,
        pathGlobFilter: Optional[Union[bool, str]] = None,
        recursiveFileLookup: Optional[Union[bool, str]] = None,
        datetimeRebaseMode: Optional[Union[bool, str]] = None,
        int96RebaseMode: Optional[Union[bool, str]] = None,
    ) -> "DataFrame":
        """
        Loads a Parquet file stream, returning the result as a :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        path : str
            the path in any Hadoop supported file system

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option>`_.
            in the version you use.

            .. # noqa

        Examples
        --------
        Load a data stream from a temporary Parquet file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="parquet") as d:
        ...     # Write a temporary Parquet file to read it.
        ...     spark.range(10).write.mode("overwrite").format("parquet").save(d)
        ...
        ...     # Start a streaming query to read the Parquet file.
        ...     q = spark.readStream.schema(
        ...         "id LONG").parquet(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            mergeSchema=mergeSchema,
            pathGlobFilter=pathGlobFilter,
            recursiveFileLookup=recursiveFileLookup,
            datetimeRebaseMode=datetimeRebaseMode,
            int96RebaseMode=int96RebaseMode,
        )
        if isinstance(path, str):
            return self._df(self._jreader.parquet(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def text(
        self,
        path: str,
        wholetext: bool = False,
        lineSep: Optional[str] = None,
        pathGlobFilter: Optional[Union[bool, str]] = None,
        recursiveFileLookup: Optional[Union[bool, str]] = None,
    ) -> "DataFrame":
        """
        Loads a text file stream and returns a :class:`DataFrame` whose schema starts with a
        string column named "value", and followed by partitioned columns if there
        are any.
        The text files must be encoded as UTF-8.

        By default, each line in the text file is a new row in the resulting DataFrame.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        path : str
            string for input path.

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option>`_
            in the version you use.

            .. # noqa

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Load a data stream from a temporary text file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="text") as d:
        ...     # Write a temporary text file to read it.
        ...     spark.createDataFrame(
        ...         [("hello",), ("this",)]).write.mode("overwrite").format("text").save(d)
        ...
        ...     # Start a streaming query to read the text file.
        ...     q = spark.readStream.text(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            wholetext=wholetext,
            lineSep=lineSep,
            pathGlobFilter=pathGlobFilter,
            recursiveFileLookup=recursiveFileLookup,
        )
        if isinstance(path, str):
            return self._df(self._jreader.text(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def csv(
        self,
        path: str,
        schema: Optional[Union[StructType, str]] = None,
        sep: Optional[str] = None,
        encoding: Optional[str] = None,
        quote: Optional[str] = None,
        escape: Optional[str] = None,
        comment: Optional[str] = None,
        header: Optional[Union[bool, str]] = None,
        inferSchema: Optional[Union[bool, str]] = None,
        ignoreLeadingWhiteSpace: Optional[Union[bool, str]] = None,
        ignoreTrailingWhiteSpace: Optional[Union[bool, str]] = None,
        nullValue: Optional[str] = None,
        nanValue: Optional[str] = None,
        positiveInf: Optional[str] = None,
        negativeInf: Optional[str] = None,
        dateFormat: Optional[str] = None,
        timestampFormat: Optional[str] = None,
        maxColumns: Optional[Union[int, str]] = None,
        maxCharsPerColumn: Optional[Union[int, str]] = None,
        maxMalformedLogPerPartition: Optional[Union[int, str]] = None,
        mode: Optional[str] = None,
        columnNameOfCorruptRecord: Optional[str] = None,
        multiLine: Optional[Union[bool, str]] = None,
        charToEscapeQuoteEscaping: Optional[Union[bool, str]] = None,
        enforceSchema: Optional[Union[bool, str]] = None,
        emptyValue: Optional[str] = None,
        locale: Optional[str] = None,
        lineSep: Optional[str] = None,
        pathGlobFilter: Optional[Union[bool, str]] = None,
        recursiveFileLookup: Optional[Union[bool, str]] = None,
        unescapedQuoteHandling: Optional[str] = None,
    ) -> "DataFrame":
        r"""Loads a CSV file stream and returns the result as a :class:`DataFrame`.

        This function will go through the input once to determine the input schema if
        ``inferSchema`` is enabled. To avoid going through the entire data once, disable
        ``inferSchema`` option or specify the schema explicitly using ``schema``.

        Parameters
        ----------
        path : str
            string for input path.
        schema : :class:`pyspark.sql.types.StructType` or str, optional
            an optional :class:`pyspark.sql.types.StructType` for the input schema
            or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
            in the version you use.

            .. # noqa

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Load a data stream from a temporary CSV file.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="csv") as d:
        ...     # Write a temporary text file to read it.
        ...     spark.createDataFrame([(1, "2"),]).write.mode("overwrite").format("csv").save(d)
        ...
        ...     # Start a streaming query to read the CSV file.
        ...     q = spark.readStream.schema(
        ...         "col0 INT, col1 STRING"
        ...     ).format("csv").load(d).writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            schema=schema,
            sep=sep,
            encoding=encoding,
            quote=quote,
            escape=escape,
            comment=comment,
            header=header,
            inferSchema=inferSchema,
            ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace,
            ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace,
            nullValue=nullValue,
            nanValue=nanValue,
            positiveInf=positiveInf,
            negativeInf=negativeInf,
            dateFormat=dateFormat,
            timestampFormat=timestampFormat,
            maxColumns=maxColumns,
            maxCharsPerColumn=maxCharsPerColumn,
            maxMalformedLogPerPartition=maxMalformedLogPerPartition,
            mode=mode,
            columnNameOfCorruptRecord=columnNameOfCorruptRecord,
            multiLine=multiLine,
            charToEscapeQuoteEscaping=charToEscapeQuoteEscaping,
            enforceSchema=enforceSchema,
            emptyValue=emptyValue,
            locale=locale,
            lineSep=lineSep,
            pathGlobFilter=pathGlobFilter,
            recursiveFileLookup=recursiveFileLookup,
            unescapedQuoteHandling=unescapedQuoteHandling,
        )
        if isinstance(path, str):
            return self._df(self._jreader.csv(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def xml(
        self,
        path: str,
        rowTag: Optional[str] = None,
        schema: Optional[Union[StructType, str]] = None,
        excludeAttribute: Optional[Union[bool, str]] = None,
        attributePrefix: Optional[str] = None,
        valueTag: Optional[str] = None,
        ignoreSurroundingSpaces: Optional[Union[bool, str]] = None,
        rowValidationXSDPath: Optional[str] = None,
        ignoreNamespace: Optional[Union[bool, str]] = None,
        wildcardColName: Optional[str] = None,
        encoding: Optional[str] = None,
        inferSchema: Optional[Union[bool, str]] = None,
        nullValue: Optional[str] = None,
        dateFormat: Optional[str] = None,
        timestampFormat: Optional[str] = None,
        mode: Optional[str] = None,
        columnNameOfCorruptRecord: Optional[str] = None,
        multiLine: Optional[Union[bool, str]] = None,
        samplingRatio: Optional[Union[float, str]] = None,
        locale: Optional[str] = None,
    ) -> "DataFrame":
        r"""Loads a XML file stream and returns the result as a :class:`DataFrame`.

        If the ``schema`` parameter is not specified, this function goes
        through the input once to determine the input schema.

        Parameters
        ----------
        path : str
            string for input path.
        schema : :class:`pyspark.sql.types.StructType` or str, optional
            an optional :class:`pyspark.sql.types.StructType` for the input schema
            or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).

        .. versionadded:: 4.0.0

        Other Parameters
        ----------------
        Extra options
            For the extra options, refer to
            `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
            in the version you use.

            .. # noqa

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Write a DataFrame into a XML file and read it back.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="xml") as d:
        ...     # Write a DataFrame into a XML file
        ...     spark.createDataFrame(
        ...         [{"age": 100, "name": "Hyukjin Kwon"}]
        ...     ).write.mode("overwrite").option("rowTag", "person").xml(d)
        ...
        ...     # Start a streaming query to read the XML file.
        ...     q = spark.readStream.schema(
        ...         "age INT, name STRING"
        ...     ).xml(d, rowTag="person").writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q.stop()
        """
        self._set_opts(
            rowTag=rowTag,
            schema=schema,
            excludeAttribute=excludeAttribute,
            attributePrefix=attributePrefix,
            valueTag=valueTag,
            ignoreSurroundingSpaces=ignoreSurroundingSpaces,
            rowValidationXSDPath=rowValidationXSDPath,
            ignoreNamespace=ignoreNamespace,
            wildcardColName=wildcardColName,
            encoding=encoding,
            inferSchema=inferSchema,
            nullValue=nullValue,
            dateFormat=dateFormat,
            timestampFormat=timestampFormat,
            mode=mode,
            columnNameOfCorruptRecord=columnNameOfCorruptRecord,
            multiLine=multiLine,
            samplingRatio=samplingRatio,
            locale=locale,
        )
        if isinstance(path, str):
            return self._df(self._jreader.xml(path))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "path",
                    "expected_type": "str",
                    "arg_type": type(path).__name__,
                },
            )

    def table(self, tableName: str) -> "DataFrame":
        """Define a Streaming DataFrame on a Table. The DataSource corresponding to the table should
        support streaming mode.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        tableName : str
            string, for the name of the table.

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

        Notes
        -----
        This API is evolving.

        Examples
        --------
        Load a data stream from a table.

        >>> import tempfile
        >>> import time
        >>> _ = spark.sql("DROP TABLE IF EXISTS my_table")
        >>> with tempfile.TemporaryDirectory(prefix="table") as d:
        ...     # Create a table with Rate source.
        ...     q1 = spark.readStream.format("rate").load().writeStream.toTable(
        ...         "my_table", checkpointLocation=d)
        ...
        ...     # Read the table back and print out in the console.
        ...     q2 = spark.readStream.table("my_table").writeStream.format("console").start()
        ...     time.sleep(3)
        ...     q1.stop()
        ...     q2.stop()
        ...     _ = spark.sql("DROP TABLE my_table")
        """
        if isinstance(tableName, str):
            return self._df(self._jreader.table(tableName))
        else:
            raise PySparkTypeError(
                errorClass="NOT_EXPECTED_TYPE",
                messageParameters={
                    "arg_name": "tableName",
                    "expected_type": "str",
                    "arg_type": type(tableName).__name__,
                },
            )

    def changes(self, tableName: str) -> "DataFrame":
        """Returns the row-level changes (Change Data Capture) from the specified table
        as a streaming :class:`DataFrame`. Currently this API is only supported for
        Data Source V2 tables whose catalog implements ``TableCatalog.loadChangelog()``.

        Use :meth:`option` to specify the starting version/timestamp and processing options.

        .. versionadded:: 4.2.0

        Parameters
        ----------
        tableName : str
            string, name of the table.

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

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> spark.readStream.option("startingVersion", "10").changes(
        ...     "my_table"
        ... )  # doctest: +SKIP
        """
        if isinstance(tableName, str):
            return self._df(self._jreader.changes(tableName))
        else:
            raise PySparkTypeError(
                errorClass="NOT_STR",
                messageParameters={"arg_name": "tableName", "arg_type": type(tableName).__name__},
            )


class DataStreamWriter:
    """
    Interface used to write a streaming :class:`DataFrame <pyspark.sql.DataFrame>` to external
    storage systems (e.g. file systems, key-value stores, etc).
    Use :attr:`DataFrame.writeStream <pyspark.sql.DataFrame.writeStream>`
    to access this.

    .. versionadded:: 2.0.0

    .. versionchanged:: 3.5.0
        Supports Spark Connect.

    Notes
    -----
    This API is evolving.

    Examples
    --------
    The example below uses Rate source that generates rows continuously.
    After that, we operate a modulo by 3, and then writes 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()
    """

    def __init__(self, df: "DataFrame") -> None:
        self._df = df
        self._spark = df.sparkSession
        self._jwrite = df._jdf.writeStream()

    def _sq(self, jsq: "JavaObject") -> StreamingQuery:
        return StreamingQuery(jsq)

    def outputMode(self, outputMode: str) -> "DataStreamWriter":
        """Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Options include:

        * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to
           the sink
        * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the sink
           every time these are some updates
        * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be
           written to the sink every time there are some updates. If the query doesn't contain
           aggregations, it will be equivalent to `append` mode.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.outputMode('append')
        <...streaming.readwriter.DataStreamWriter object ...>

        The example below uses Complete mode that the entire aggregated counts are printed out.

        >>> import time
        >>> df = spark.readStream.format("rate").option("rowsPerSecond", 10).load()
        >>> df = df.groupby().count()
        >>> q = df.writeStream.outputMode("complete").format("console").start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        if not outputMode or not isinstance(outputMode, str) or len(outputMode.strip()) == 0:
            raise PySparkValueError(
                errorClass="VALUE_NOT_NON_EMPTY_STR",
                messageParameters={"arg_name": "outputMode", "arg_value": str(outputMode)},
            )
        self._jwrite = self._jwrite.outputMode(outputMode)
        return self

    def format(self, source: str) -> "DataStreamWriter":
        """Specifies the underlying output data source.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        source : str
            string, name of the data source, which for now can be 'parquet'.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.format("text")
        <...streaming.readwriter.DataStreamWriter object ...>

        This API allows to configure the source to write. The example below writes a CSV
        file from Rate source in a streaming manner.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="format1") as d:
        ...     with tempfile.TemporaryDirectory(prefix="format2") as cp:
        ...         df = spark.readStream.format("rate").load()
        ...         q = df.writeStream.format("csv").option("checkpointLocation", cp).start(d)
        ...         time.sleep(5)
        ...         q.stop()
        ...         spark.read.schema("timestamp TIMESTAMP, value STRING").csv(d).show()
        +...---------+-----+
        |...timestamp|value|
        +...---------+-----+
        ...
        """
        self._jwrite = self._jwrite.format(source)
        return self

    def option(self, key: str, value: "OptionalPrimitiveType") -> "DataStreamWriter":
        """Adds an output option for the underlying data source.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.option("x", 1)
        <...streaming.readwriter.DataStreamWriter object ...>

        The example below specifies 'numRows' option to Console source in order to print
        3 rows for every batch.

        >>> import time
        >>> q = spark.readStream.format(
        ...     "rate").option("rowsPerSecond", 10).load().writeStream.format(
        ...         "console").option("numRows", 3).start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        self._jwrite = self._jwrite.option(key, to_str(value))
        return self

    def options(self, **options: "OptionalPrimitiveType") -> "DataStreamWriter":
        """Adds output options for the underlying data source.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.option("x", 1)
        <...streaming.readwriter.DataStreamWriter object ...>

        Specify options in a dictionary.

        >>> df.writeStream.options(**{"k1": "v1", "k2": "v2"})
        <...streaming.readwriter.DataStreamWriter object ...>

        The example below specifies 'numRows' and 'truncate' options to Console source in order
        to print 3 rows for every batch without truncating the results.

        >>> import time
        >>> q = spark.readStream.format(
        ...     "rate").option("rowsPerSecond", 10).load().writeStream.format(
        ...         "console").options(numRows=3, truncate=False).start()
        >>> time.sleep(3)
        >>> q.stop()
        """
        for k in options:
            self._jwrite = self._jwrite.option(k, to_str(options[k]))
        return self

    @overload
    def partitionBy(self, *cols: str) -> "DataStreamWriter": ...

    @overload
    def partitionBy(self, __cols: List[str]) -> "DataStreamWriter": ...

    def partitionBy(self, *cols: str) -> "DataStreamWriter":  # type: ignore[misc]
        """Partitions the output by the given columns on the file system.

        If specified, the output is laid out on the file system similar
        to Hive's partitioning scheme.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : str or list
            name of columns

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.partitionBy("value")
        <...streaming.readwriter.DataStreamWriter object ...>

        Partition-by timestamp column from Rate source.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="partitionBy1") as d:
        ...     with tempfile.TemporaryDirectory(prefix="partitionBy2") as cp:
        ...         df = spark.readStream.format("rate").option("rowsPerSecond", 10).load()
        ...         q = df.writeStream.partitionBy(
        ...             "timestamp").format("parquet").option("checkpointLocation", cp).start(d)
        ...         time.sleep(5)
        ...         q.stop()
        ...         spark.read.schema(df.schema).parquet(d).show()
        +...---------+-----+
        |...timestamp|value|
        +...---------+-----+
        ...
        """
        from pyspark.sql.classic.column import _to_seq

        if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
            cols = cols[0]
        self._jwrite = self._jwrite.partitionBy(_to_seq(self._spark._sc, cols))
        return self

    @overload
    def clusterBy(self, *cols: str) -> "DataStreamWriter": ...

    @overload
    def clusterBy(self, __cols: List[str]) -> "DataStreamWriter": ...

    def clusterBy(self, *cols: str) -> "DataStreamWriter":  # type: ignore[misc]
        """Clusters the output by the given columns.

        If specified, the output is laid out such that records with similar values on the clustering
        column(s) are grouped together in the same file.

        Clustering improves query efficiency by allowing queries with predicates on the clustering
        columns to skip unnecessary data. Unlike partitioning, clustering can be used on very high
        cardinality columns.

        .. versionadded:: 4.0.0

        Parameters
        ----------
        cols : str or list
            name of columns

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.writeStream.clusterBy("value")
        <...streaming.readwriter.DataStreamWriter object ...>

        Cluster-by timestamp column from Rate source.

        >>> import tempfile
        >>> import time
        >>> with tempfile.TemporaryDirectory(prefix="partitionBy1") as d:
        ...     with tempfile.TemporaryDirectory(prefix="partitionBy2") as cp:
        ...         df = spark.readStream.format("rate").option("rowsPerSecond", 10).load()
        ...         q = df.writeStream.clusterBy(
        ...             "timestamp").format("parquet").option("checkpointLocation", cp).start(d)
        ...         time.sleep(5)
        ...         q.stop()
        ...         spark.read.schema(df.schema).parquet(d).show()
        +...---------+-----+
        |...timestamp|value|
        +...---------+-----+
        ...
        """
        from pyspark.sql.classic.column import _to_seq

        if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
            cols = cols[0]
        self._jwrite = self._jwrite.clusterBy(_to_seq(self._spark._sc, cols))
        return self

    def queryName(self, queryName: str) -> "DataStreamWriter":
        """Specifies the name of the :class:`StreamingQuery` that can be started with
        :func:`start`. This name must be unique among all the currently active queries
        in the associated SparkSession.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        queryName : str
            unique name for the query

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> import time
        >>> df = spark.readStream.format("rate").load()
        >>> q = df.writeStream.queryName("streaming_query").format("console").start()
        >>> q.stop()
        >>> q.name
        'streaming_query'
        """
        if not queryName or not isinstance(queryName, str) or len(queryName.strip()) == 0:
            raise PySparkValueError(
                errorClass="VALUE_NOT_NON_EMPTY_STR",
                messageParameters={"arg_name": "queryName", "arg_value": str(queryName)},
            )
        self._jwrite = self._jwrite.queryName(queryName)
        return self

    @overload
    def trigger(self, *, processingTime: str) -> "DataStreamWriter": ...

    @overload
    def trigger(self, *, once: bool) -> "DataStreamWriter": ...

    @overload
    def trigger(self, *, continuous: str) -> "DataStreamWriter": ...

    @overload
    def trigger(self, *, availableNow: bool) -> "DataStreamWriter": ...

    def trigger(
        self,
        *,
        processingTime: Optional[str] = None,
        once: Optional[bool] = None,
        continuous: Optional[str] = None,
        availableNow: Optional[bool] = None,
        realTime: Optional[str] = None,
    ) -> "DataStreamWriter":
        """Set the trigger for the stream query. If this is not set it will run the query as fast
        as possible, which is equivalent to setting the trigger to ``processingTime='0 seconds'``.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        processingTime : str, optional
            a processing time interval as a string, e.g. '5 seconds', '1 minute'.
            Set a trigger that runs a microbatch query periodically based on the
            processing time. Only one trigger can be set.
        once : bool, optional
            if set to True, set a trigger that processes only one batch of data in a
            streaming query then terminates the query. Only one trigger can be set.
        continuous : str, optional
            a time interval as a string, e.g. '5 seconds', '1 minute'.
            Set a trigger that runs a continuous query with a given checkpoint
            interval. Only one trigger can be set.
        availableNow : bool, optional
            if set to True, set a trigger that processes all available data in multiple
            batches then terminates the query. Only one trigger can be set.
        realTime : str, optional
            a batch duration as a string, e.g. '5 seconds', '1 minute'.
            Set a trigger that runs a real time mode query with
            batch at the specified duration. Only one trigger can be set.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()

        Trigger the query for execution every 5 seconds

        >>> df.writeStream.trigger(processingTime='5 seconds')
        <...streaming.readwriter.DataStreamWriter object ...>

        Trigger the query for execution every 5 seconds

        >>> df.writeStream.trigger(continuous='5 seconds')
        <...streaming.readwriter.DataStreamWriter object ...>

        Trigger the query for reading all available data with multiple batches

        >>> df.writeStream.trigger(availableNow=True)
        <...streaming.readwriter.DataStreamWriter object ...>

        Trigger the query for real time mode execution every 5 seconds.

        >>> df.writeStream.trigger(realTime='5 seconds')
        <...streaming.readwriter.DataStreamWriter object ...>
        """
        params = [processingTime, once, continuous, availableNow, realTime]

        if params.count(None) == 5:
            raise PySparkValueError(
                errorClass="ONLY_ALLOW_SINGLE_TRIGGER",
                messageParameters={},
            )
        elif params.count(None) < 4:
            raise PySparkValueError(
                errorClass="ONLY_ALLOW_SINGLE_TRIGGER",
                messageParameters={},
            )

        jTrigger = None
        assert self._spark._sc._jvm is not None
        if processingTime is not None:
            if not isinstance(processingTime, str) or len(processingTime.strip()) == 0:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_NON_EMPTY_STR",
                    messageParameters={
                        "arg_name": "processingTime",
                        "arg_value": str(processingTime),
                    },
                )
            interval = processingTime.strip()
            jTrigger = getattr(
                self._spark._sc._jvm, "org.apache.spark.sql.streaming.Trigger"
            ).ProcessingTime(interval)

        elif once is not None:
            if once is not True:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_TRUE",
                    messageParameters={"arg_name": "once", "arg_value": str(once)},
                )

            jTrigger = getattr(
                self._spark._sc._jvm, "org.apache.spark.sql.streaming.Trigger"
            ).Once()

        elif continuous is not None:
            if not isinstance(continuous, str) or len(continuous.strip()) == 0:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_NON_EMPTY_STR",
                    messageParameters={"arg_name": "continuous", "arg_value": str(continuous)},
                )
            interval = continuous.strip()
            jTrigger = getattr(
                self._spark._sc._jvm, "org.apache.spark.sql.streaming.Trigger"
            ).Continuous(interval)
        elif realTime is not None:
            if not isinstance(realTime, str) or len(realTime.strip()) == 0:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_NON_EMPTY_STR",
                    messageParameters={"arg_name": "realTime", "arg_value": str(realTime)},
                )
            batch_duration = realTime.strip()
            jTrigger = getattr(
                self._spark._sc._jvm, "org.apache.spark.sql.streaming.Trigger"
            ).RealTime(batch_duration)
        else:
            if availableNow is not True:
                raise PySparkValueError(
                    errorClass="VALUE_NOT_TRUE",
                    messageParameters={"arg_name": "availableNow", "arg_value": str(availableNow)},
                )
            jTrigger = getattr(
                self._spark._sc._jvm, "org.apache.spark.sql.streaming.Trigger"
            ).AvailableNow()

        self._jwrite = self._jwrite.trigger(jTrigger)
        return self

    @staticmethod
    def _construct_foreach_function(
        f: Union[Callable[[Row], None], "SupportsProcess"],
    ) -> Callable[[Any, Iterator], Iterator]:
        from pyspark.taskcontext import TaskContext

        if callable(f):
            # The provided object is a callable function that is supposed to be called on each row.
            # Construct a function that takes an iterator and calls the provided function on each
            # row.
            def func_without_process(_: Any, iterator: Iterator) -> Iterator:
                for x in iterator:
                    f(x)
                return iter([])

            return func_without_process

        else:
            # The provided object is not a callable function. Then it is expected to have a
            # 'process(row)' method, and optional 'open(partition_id, epoch_id)' and
            # 'close(error)' methods.

            if not hasattr(f, "process"):
                raise PySparkAttributeError(
                    errorClass="ATTRIBUTE_NOT_CALLABLE",
                    messageParameters={"attr_name": "process", "obj_name": "f"},
                )

            if not callable(getattr(f, "process")):
                raise PySparkAttributeError(
                    errorClass="ATTRIBUTE_NOT_CALLABLE",
                    messageParameters={"attr_name": "process", "obj_name": "f"},
                )

            def doesMethodExist(method_name: str) -> bool:
                exists = hasattr(f, method_name)
                if exists and not callable(getattr(f, method_name)):
                    raise PySparkAttributeError(
                        errorClass="ATTRIBUTE_NOT_CALLABLE",
                        messageParameters={"attr_name": method_name, "obj_name": "f"},
                    )
                return exists

            open_exists = doesMethodExist("open")
            close_exists = doesMethodExist("close")

            def func_with_open_process_close(partition_id: Any, iterator: Iterator) -> Iterator:
                epoch_id = cast(TaskContext, TaskContext.get()).getLocalProperty(
                    "streaming.sql.batchId"
                )
                if epoch_id:
                    int_epoch_id = int(epoch_id)
                else:
                    raise PySparkRuntimeError(
                        errorClass="CANNOT_GET_BATCH_ID",
                        messageParameters={"obj_name": "TaskContext"},
                    )

                # Check if the data should be processed
                should_process = True
                if open_exists:
                    should_process = f.open(  # type: ignore[attr-defined]
                        partition_id, int_epoch_id
                    )

                error = None

                try:
                    if should_process:
                        for x in iterator:
                            f.process(x)
                except Exception as ex:
                    error = ex
                finally:
                    if close_exists:
                        f.close(error)  # type: ignore[attr-defined]
                    if error:
                        raise error

                return iter([])

            return func_with_open_process_close

    @overload
    def foreach(self, f: Callable[[Row], None]) -> "DataStreamWriter": ...

    @overload
    def foreach(self, f: "SupportsProcess") -> "DataStreamWriter": ...

    def foreach(self, f: Union[Callable[[Row], None], "SupportsProcess"]) -> "DataStreamWriter":
        """
        Sets the output of the streaming query to be processed using the provided writer ``f``.
        This is often used to write the output of a streaming query to arbitrary storage systems.
        The processing logic can be specified in two ways.

        #. A **function** that takes a row as input.
            This is a simple way to express your processing logic. Note that this does
            not allow you to deduplicate generated data when failures cause reprocessing of
            some input data. That would require you to specify the processing logic in the next
            way.

        #. An **object** with a ``process`` method and optional ``open`` and ``close`` methods.
            The object can have the following methods.

            * ``open(partition_id, epoch_id)``: *Optional* method that initializes the processing
                (for example, open a connection, start a transaction, etc). Additionally, you can
                use the `partition_id` and `epoch_id` to deduplicate regenerated data
                (discussed later).

            * ``process(row)``: *Non-optional* method that processes each :class:`Row`.

            * ``close(error)``: *Optional* method that finalizes and cleans up (for example,
                close connection, commit transaction, etc.) after all rows have been processed.

            The object will be used by Spark in the following way.

            * A single copy of this object is responsible of all the data generated by a
                single task in a query. In other words, one instance is responsible for
                processing one partition of the data generated in a distributed manner.

            * This object must be serializable because each task will get a fresh
                serialized-deserialized copy of the provided object. Hence, it is strongly
                recommended that any initialization for writing data (e.g. opening a
                connection or starting a transaction) is done after the `open(...)`
                method has been called, which signifies that the task is ready to generate data.

            * The lifecycle of the methods are as follows.

                For each partition with ``partition_id``:

                ... For each batch/epoch of streaming data with ``epoch_id``:

                ....... Method ``open(partitionId, epochId)`` is called.

                ....... If ``open(...)`` returns true, for each row in the partition and
                        batch/epoch, method ``process(row)`` is called.

                ....... Method ``close(errorOrNull)`` is called with error (if any) seen while
                        processing rows.

            Important points to note:

            * The `partitionId` and `epochId` can be used to deduplicate generated data when
                failures cause reprocessing of some input data. This depends on the execution
                mode of the query. If the streaming query is being executed in the micro-batch
                mode, then every partition represented by a unique tuple (partition_id, epoch_id)
                is guaranteed to have the same data. Hence, (partition_id, epoch_id) can be used
                to deduplicate and/or transactionally commit data and achieve exactly-once
                guarantees. However, if the streaming query is being executed in the continuous
                mode, then this guarantee does not hold and therefore should not be used for
                deduplication.

            * The ``close()`` method (if exists) will be called if `open()` method exists and
                returns successfully (irrespective of the return value), except if the Python
                crashes in the middle.

        .. versionadded:: 2.4.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> import time
        >>> df = spark.readStream.format("rate").load()

        Print every row using a function

        >>> def print_row(row):
        ...     print(row)
        ...
        >>> q = df.writeStream.foreach(print_row).start()
        >>> time.sleep(3)
        >>> q.stop()

        Print every row using a object with process() method

        >>> class RowPrinter:
        ...     def open(self, partition_id, epoch_id):
        ...         print("Opened %d, %d" % (partition_id, epoch_id))
        ...         return True
        ...
        ...     def process(self, row):
        ...         print(row)
        ...
        ...     def close(self, error):
        ...         print("Closed with error: %s" % str(error))
        ...
        >>> q = df.writeStream.foreach(RowPrinter()).start()
        >>> time.sleep(3)
        >>> q.stop()
        """

        from pyspark.core.rdd import _wrap_function
        from pyspark.serializers import CPickleSerializer, AutoBatchedSerializer

        func = self._construct_foreach_function(f)
        serializer = AutoBatchedSerializer(CPickleSerializer())
        wrapped_func = _wrap_function(self._spark._sc, func, serializer, serializer)
        assert self._spark._sc._jvm is not None
        jForeachWriter = getattr(
            self._spark._sc._jvm,
            "org.apache.spark.sql.execution.python.streaming.PythonForeachWriter",
        )(wrapped_func, self._df._jdf.schema())
        self._jwrite.foreach(jForeachWriter)
        return self

    def foreachBatch(self, func: Callable[["DataFrame", int], None]) -> "DataStreamWriter":
        """
        Sets the output of the streaming query to be processed using the provided
        function. This is supported only the in the micro-batch execution modes (that is, when the
        trigger is not continuous). In every micro-batch, the provided function will be called in
        every micro-batch with (i) the output rows as a DataFrame and (ii) the batch identifier.
        The batchId can be used deduplicate and transactionally write the output
        (that is, the provided Dataset) to external systems. The output DataFrame is guaranteed
        to exactly same for the same batchId (assuming all operations are deterministic in the
        query).

        .. versionadded:: 2.4.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.
        This function behaves differently in Spark Connect mode. See examples.
        In Connect, the provided function doesn't have access to variables defined outside of it.

        Examples
        --------
        >>> import time
        >>> df = spark.readStream.format("rate").load()
        >>> my_value = -1
        >>> def func(batch_df, batch_id):
        ...     global my_value
        ...     my_value = 100
        ...     batch_df.collect()
        ...
        >>> q = df.writeStream.foreachBatch(func).start()
        >>> time.sleep(3)
        >>> q.stop()
        >>> # if in Spark Connect, my_value = -1, else my_value = 100
        """
        from py4j.java_gateway import java_import
        from pyspark.java_gateway import ensure_callback_server_started

        gw = self._spark._sc._gateway
        assert gw is not None
        java_import(gw.jvm, "org.apache.spark.sql.execution.streaming.sources.*")

        wrapped_func = ForeachBatchFunction(self._spark, func)
        gw.jvm.PythonForeachBatchHelper.callForeachBatch(self._jwrite, wrapped_func)
        ensure_callback_server_started(gw)
        return self

    def start(
        self,
        path: Optional[str] = None,
        format: Optional[str] = None,
        outputMode: Optional[str] = None,
        partitionBy: Optional[Union[str, List[str]]] = None,
        queryName: Optional[str] = None,
        **options: "OptionalPrimitiveType",
    ) -> "StreamingQuery":
        """Streams the contents of the :class:`DataFrame` to a data source.

        The data source is specified by the ``format`` and a set of ``options``.
        If ``format`` is not specified, the default data source configured by
        ``spark.sql.sources.default`` will be used.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        path : str, optional
            the path in a Hadoop supported file system
        format : str, optional
            the format used to save
        outputMode : str, optional
            specifies how data of a streaming DataFrame/Dataset is written to a
            streaming sink.

            * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to the
              sink
            * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the
              sink every time these are some updates
            * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be
              written to the sink every time there are some updates. If the query doesn't contain
              aggregations, it will be equivalent to `append` mode.
        partitionBy : str or list, optional
            names of partitioning columns
        queryName : str, optional
            unique name for the query
        **options : dict
            All other string options. You may want to provide a `checkpointLocation`
            for most streams, however it is not required for a `memory` stream.

        Notes
        -----
        This API is evolving.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()

        Basic example.

        >>> q = df.writeStream.format('memory').queryName('this_query').start()
        >>> q.isActive
        True
        >>> q.name
        'this_query'
        >>> q.stop()
        >>> q.isActive
        False

        Example with using other parameters with a trigger.

        >>> q = df.writeStream.trigger(processingTime='5 seconds').start(
        ...     queryName='that_query', outputMode="append", format='memory')
        >>> q.name
        'that_query'
        >>> q.isActive
        True
        >>> q.stop()
        """
        self.options(**options)
        if outputMode is not None:
            self.outputMode(outputMode)
        if partitionBy is not None:
            self.partitionBy(partitionBy)
        if format is not None:
            self.format(format)
        if queryName is not None:
            self.queryName(queryName)
        if path is None:
            return self._sq(self._jwrite.start())
        else:
            return self._sq(self._jwrite.start(path))

    def toTable(
        self,
        tableName: str,
        format: Optional[str] = None,
        outputMode: Optional[str] = None,
        partitionBy: Optional[Union[str, List[str]]] = None,
        queryName: Optional[str] = None,
        **options: "OptionalPrimitiveType",
    ) -> "StreamingQuery":
        """
        Starts the execution of the streaming query, which will continually output results to the
        given table as new data arrives.

        The returned :class:`StreamingQuery` object can be used to interact with the stream.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        tableName : str
            string, for the name of the table.
        format : str, optional
            the format used to save.
        outputMode : str, optional
            specifies how data of a streaming DataFrame/Dataset is written to a
            streaming sink.

            * `append`: Only the new rows in the streaming DataFrame/Dataset will be written to the
              sink
            * `complete`: All the rows in the streaming DataFrame/Dataset will be written to the
              sink every time these are some updates
            * `update`: only the rows that were updated in the streaming DataFrame/Dataset will be
              written to the sink every time there are some updates. If the query doesn't contain
              aggregations, it will be equivalent to `append` mode.
        partitionBy : str or list, optional
            names of partitioning columns
        queryName : str, optional
            unique name for the query
        **options : dict
            All other string options. You may want to provide a `checkpointLocation`.

        Notes
        -----
        This API is evolving.

        For v1 table, partitioning columns provided by `partitionBy` will be respected no matter
        the table exists or not. A new table will be created if the table not exists.

        For v2 table, `partitionBy` will be ignored if the table already exists. `partitionBy` will
        be respected only if the v2 table does not exist. Besides, the v2 table created by this API
        lacks some functionalities (e.g., customized properties, options, and serde info). If you
        need them, please create the v2 table manually before the execution to avoid creating a
        table with incomplete information.

        Examples
        --------
        Save a data stream to a table.

        >>> import tempfile
        >>> import time
        >>> _ = spark.sql("DROP TABLE IF EXISTS my_table2")
        >>> with tempfile.TemporaryDirectory(prefix="toTable") as d:
        ...     # Create a table with Rate source.
        ...     q = spark.readStream.format("rate").option(
        ...         "rowsPerSecond", 10).load().writeStream.toTable(
        ...             "my_table2",
        ...             queryName='that_query',
        ...             outputMode="append",
        ...             format='parquet',
        ...             checkpointLocation=d)
        ...     time.sleep(3)
        ...     q.stop()
        ...     spark.read.table("my_table2").show()
        ...     _ = spark.sql("DROP TABLE my_table2")
        +...---------+-----+
        |...timestamp|value|
        +...---------+-----+
        ...
        """
        self.options(**options)
        if outputMode is not None:
            self.outputMode(outputMode)
        if partitionBy is not None:
            self.partitionBy(partitionBy)
        if format is not None:
            self.format(format)
        if queryName is not None:
            self.queryName(queryName)
        return self._sq(self._jwrite.toTable(tableName))


def _test() -> None:
    import doctest
    import os
    from pyspark.sql import SparkSession
    import pyspark.sql.streaming.readwriter

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

    globs = pyspark.sql.streaming.readwriter.__dict__.copy()
    globs["spark"] = (
        SparkSession.builder.master("local[4]")
        .appName("sql.streaming.readwriter tests")
        .getOrCreate()
    )

    failure_count, test_count = doctest.testmod(
        pyspark.sql.streaming.readwriter,
        globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF,
    )
    globs["spark"].stop()

    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()
