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  # The ASF licenses this file to You under the Apache License, Version 2.0
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  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  import sys
  
  from py4j.java_gateway import JavaClass
  
  from pyspark import RDD, since
  from pyspark.sql.column import _to_seq, _to_java_column
  from pyspark.sql.types import StructType
  from pyspark.sql import utils
  from pyspark.sql.utils import to_str
  
  __all__ = ["DataFrameReader", "DataFrameWriter"]
  
  
  class OptionUtils(object):
  
      def _set_opts(self, schema=None, **options):
          """
          Set named options (filter out those the value is None)
          """
          if schema is not None:
              self.schema(schema)
          for k, v in options.items():
              if v is not None:
                  self.option(k, v)
  
  
  class DataFrameReader(OptionUtils):
      """
      Interface used to load a :class:`DataFrame` from external storage systems
      (e.g. file systems, key-value stores, etc). Use :attr:`SparkSession.read`
      to access this.
  
      .. versionadded:: 1.4
      """
  
      def __init__(self, spark):
          self._jreader = spark._ssql_ctx.read()
          self._spark = spark
  
      def _df(self, jdf):
          from pyspark.sql.dataframe import DataFrame
          return DataFrame(jdf, self._spark)
  
      def format(self, source):
          """Specifies the input data source format.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          source : str
              string, name of the data source, e.g. 'json', 'parquet'.
  
          Examples
          --------
          >>> df = spark.read.format('json').load('python/test_support/sql/people.json')
          >>> df.dtypes
          [('age', 'bigint'), ('name', 'string')]
  
          """
          self._jreader = self._jreader.format(source)
          return self
  
      def schema(self, schema):
          """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:: 1.4.0
  
          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``).
  
          >>> s = spark.read.schema("col0 INT, col1 DOUBLE")
          """
          from pyspark.sql import SparkSession
          spark = SparkSession.builder.getOrCreate()
          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 TypeError("schema should be StructType or string")
          return self
  
      @since(1.5)
      def option(self, key, value):
          """Adds an input option for the underlying data source.
  
          You can set the following option(s) for reading files:
              * ``timeZone``: sets the string that indicates a time zone ID to be used to parse
                  timestamps in the JSON/CSV datasources or partition values. The following
                  formats of `timeZone` are supported:
  
                  * Region-based zone ID: It should have the form 'area/city', such as \
                    'America/Los_Angeles'.
                  * Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
                   '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  
                  Other short names like 'CST' are not recommended to use because they can be
                  ambiguous. If it isn't set, the current value of the SQL config
                  ``spark.sql.session.timeZone`` is used by default.
              * ``pathGlobFilter``: an optional glob pattern to only include files with paths matching
                  the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter.
                  It does not change the behavior of partition discovery.
          """
          self._jreader = self._jreader.option(key, to_str(value))
          return self
  
      @since(1.4)
      def options(self, **options):
          """Adds input options for the underlying data source.
  
          You can set the following option(s) for reading files:
              * ``timeZone``: sets the string that indicates a time zone ID to be used to parse
                  timestamps in the JSON/CSV datasources or partition values. The following
                  formats of `timeZone` are supported:
  
                  * Region-based zone ID: It should have the form 'area/city', such as \
                    'America/Los_Angeles'.
                  * Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
                   '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  
                  Other short names like 'CST' are not recommended to use because they can be
                  ambiguous. If it isn't set, the current value of the SQL config
                  ``spark.sql.session.timeZone`` is used by default.
              * ``pathGlobFilter``: an optional glob pattern to only include files with paths matching
                  the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter.
                  It does not change the behavior of partition discovery.
          """
          for k in options:
              self._jreader = self._jreader.option(k, to_str(options[k]))
          return self
  
      def load(self, path=None, format=None, schema=None, **options):
          """Loads data from a data source and returns it as a :class:`DataFrame`.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          path : str or list, optional
              optional string or a list of 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
  
          Examples
          --------
          >>> df = spark.read.format("parquet").load('python/test_support/sql/parquet_partitioned',
          ...     opt1=True, opt2=1, opt3='str')
          >>> df.dtypes
          [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
  
          >>> df = spark.read.format('json').load(['python/test_support/sql/people.json',
          ...     'python/test_support/sql/people1.json'])
          >>> df.dtypes
          [('age', 'bigint'), ('aka', 'string'), ('name', 'string')]
          """
          if format is not None:
              self.format(format)
          if schema is not None:
              self.schema(schema)
          self.options(**options)
          if isinstance(path, str):
              return self._df(self._jreader.load(path))
          elif path is not None:
              if type(path) != list:
                  path = [path]
              return self._df(self._jreader.load(self._spark._sc._jvm.PythonUtils.toSeq(path)))
          else:
              return self._df(self._jreader.load())
  
      def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
               allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
               allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
               mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
               multiLine=None, allowUnquotedControlChars=None, lineSep=None, samplingRatio=None,
               dropFieldIfAllNull=None, encoding=None, locale=None, pathGlobFilter=None,
               recursiveFileLookup=None, allowNonNumericNumbers=None):
          """
          Loads JSON files 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:: 1.4.0
  
          Parameters
          ----------
          path : str, list or :class:`RDD`
              string represents path to the JSON dataset, or a list of paths,
              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``).
          primitivesAsString : str or bool, optional
              infers all primitive values as a string type. If None is set,
              it uses the default value, ``false``.
          prefersDecimal : str or bool, optional
              infers all floating-point values as a decimal type. If the values
              do not fit in decimal, then it infers them as doubles. If None is
              set, it uses the default value, ``false``.
          allowComments : str or bool, optional
              ignores Java/C++ style comment in JSON records. If None is set,
              it uses the default value, ``false``.
          allowUnquotedFieldNames : str or bool, optional
              allows unquoted JSON field names. If None is set,
              it uses the default value, ``false``.
          allowSingleQuotes : str or bool, optional
              allows single quotes in addition to double quotes. If None is
              set, it uses the default value, ``true``.
          allowNumericLeadingZero : str or bool, optional
              allows leading zeros in numbers (e.g. 00012). If None is
              set, it uses the default value, ``false``.
          allowBackslashEscapingAnyCharacter : str or bool, optional
              allows accepting quoting of all character
              using backslash quoting mechanism. If None is
              set, it uses the default value, ``false``.
          mode : str, optional
              allows a mode for dealing with corrupt records during parsing. If None is
                       set, it uses the default value, ``PERMISSIVE``.
  
              * ``PERMISSIVE``: when it meets a corrupted record, puts the malformed string \
                into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
                fields to ``null``. To keep corrupt records, an user can set a string type \
                field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
                schema does not have the field, it drops corrupt records during parsing. \
                When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \
                field in an output schema.
              *  ``DROPMALFORMED``: ignores the whole corrupted records.
              *  ``FAILFAST``: throws an exception when it meets corrupted records.
  
          columnNameOfCorruptRecord: str, optional
              allows renaming the new field having malformed string
              created by ``PERMISSIVE`` mode. This overrides
              ``spark.sql.columnNameOfCorruptRecord``. If None is set,
              it uses the value specified in
              ``spark.sql.columnNameOfCorruptRecord``.
          dateFormat : str, optional
              sets the string that indicates a date format. Custom date formats
              follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to date type. If None is set, it uses the
              default value, ``yyyy-MM-dd``.
          timestampFormat : str, optional
              sets the string that indicates a timestamp format.
              Custom date formats follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to timestamp type. If None is set, it uses the
              default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
          multiLine : str or bool, optional
              parse one record, which may span multiple lines, per file. If None is
              set, it uses the default value, ``false``.
          allowUnquotedControlChars : str or bool, optional
              allows JSON Strings to contain unquoted control
              characters (ASCII characters with value less than 32,
              including tab and line feed characters) or not.
          encoding : str or bool, optional
              allows to forcibly set one of standard basic or extended encoding for
              the JSON files. For example UTF-16BE, UTF-32LE. If None is set,
              the encoding of input JSON will be detected automatically
              when the multiLine option is set to ``true``.
          lineSep : str, optional
              defines the line separator that should be used for parsing. If None is
              set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
          samplingRatio : str or float, optional
              defines fraction of input JSON objects used for schema inferring.
              If None is set, it uses the default value, ``1.0``.
          dropFieldIfAllNull : str or bool, optional
              whether to ignore column of all null values or empty
              array/struct during schema inference. If None is set, it
              uses the default value, ``false``.
          locale : str, optional
              sets a locale as language tag in IETF BCP 47 format. If None is set,
              it uses the default value, ``en-US``. For instance, ``locale`` is used while
              parsing dates and timestamps.
          pathGlobFilter : str or bool, optional
              an optional glob pattern to only include files with paths matching
              the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
              It does not change the behavior of
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          recursiveFileLookup : str or bool, optional
              recursively scan a directory for files. Using this option
              disables
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          allowNonNumericNumbers : str or bool
              allows JSON parser to recognize set of "Not-a-Number" (NaN)
              tokens as legal floating number values. If None is set,
              it uses the default value, ``true``.
  
                  * ``+INF``: for positive infinity, as well as alias of
                              ``+Infinity`` and ``Infinity``.
                  *  ``-INF``: for negative infinity, alias ``-Infinity``.
                  *  ``NaN``: for other not-a-numbers, like result of division by zero.
  
          Examples
          --------
          >>> df1 = spark.read.json('python/test_support/sql/people.json')
          >>> df1.dtypes
          [('age', 'bigint'), ('name', 'string')]
          >>> rdd = sc.textFile('python/test_support/sql/people.json')
          >>> df2 = spark.read.json(rdd)
          >>> df2.dtypes
          [('age', 'bigint'), ('name', 'string')]
  
          """
          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,
              samplingRatio=samplingRatio, dropFieldIfAllNull=dropFieldIfAllNull, encoding=encoding,
              locale=locale, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup,
              allowNonNumericNumbers=allowNonNumericNumbers)
          if isinstance(path, str):
              path = [path]
          if type(path) == list:
              return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
          elif isinstance(path, RDD):
              def func(iterator):
                  for x in iterator:
                      if not isinstance(x, str):
                          x = str(x)
                      if isinstance(x, str):
                          x = x.encode("utf-8")
                      yield x
              keyed = path.mapPartitions(func)
              keyed._bypass_serializer = True
              jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
              return self._df(self._jreader.json(jrdd))
          else:
              raise TypeError("path can be only string, list or RDD")
  
      def table(self, tableName):
          """Returns the specified table as a :class:`DataFrame`.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          tableName : str
              string, name of the table.
  
          Examples
          --------
          >>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
          >>> df.createOrReplaceTempView('tmpTable')
          >>> spark.read.table('tmpTable').dtypes
          [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
          """
          return self._df(self._jreader.table(tableName))
  
      def parquet(self, *paths, **options):
          """
          Loads Parquet files, returning the result as a :class:`DataFrame`.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          paths : str
  
          Other Parameters
          ----------------
          mergeSchema : str or bool, optional
              sets whether we should merge schemas collected from all
              Parquet part-files. This will override
              ``spark.sql.parquet.mergeSchema``. The default value is specified in
              ``spark.sql.parquet.mergeSchema``.
          pathGlobFilter : str or bool, optional
              an optional glob pattern to only include files with paths matching
              the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
              It does not change the behavior of
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          recursiveFileLookup : str or bool, optional
              recursively scan a directory for files. Using this option
              disables
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
  
          Examples
          --------
          >>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
          >>> df.dtypes
          [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
          """
          mergeSchema = options.get('mergeSchema', None)
          pathGlobFilter = options.get('pathGlobFilter', None)
          recursiveFileLookup = options.get('recursiveFileLookup', None)
          self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
                         recursiveFileLookup=recursiveFileLookup)
          return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
  
      def text(self, paths, wholetext=False, lineSep=None, pathGlobFilter=None,
               recursiveFileLookup=None):
          """
          Loads text files 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:: 1.6.0
  
          Parameters
          ----------
          paths : str or list
              string, or list of strings, for input path(s).
          wholetext : str or bool, optional
              if true, read each file from input path(s) as a single row.
          lineSep : str, optional
              defines the line separator that should be used for parsing. If None is
              set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
          pathGlobFilter : str or bool, optional
              an optional glob pattern to only include files with paths matching
              the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
              It does not change the behavior of
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          recursiveFileLookup : str or bool, optional
              recursively scan a directory for files. Using this option disables
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
  
          Examples
          --------
          >>> df = spark.read.text('python/test_support/sql/text-test.txt')
          >>> df.collect()
          [Row(value='hello'), Row(value='this')]
          >>> df = spark.read.text('python/test_support/sql/text-test.txt', wholetext=True)
          >>> df.collect()
          [Row(value='hello\\nthis')]
          """
          self._set_opts(
              wholetext=wholetext, lineSep=lineSep, pathGlobFilter=pathGlobFilter,
              recursiveFileLookup=recursiveFileLookup)
          if isinstance(paths, str):
              paths = [paths]
          return self._df(self._jreader.text(self._spark._sc._jvm.PythonUtils.toSeq(paths)))
  
      def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=None,
              comment=None, header=None, inferSchema=None, ignoreLeadingWhiteSpace=None,
              ignoreTrailingWhiteSpace=None, nullValue=None, nanValue=None, positiveInf=None,
              negativeInf=None, dateFormat=None, timestampFormat=None, maxColumns=None,
              maxCharsPerColumn=None, maxMalformedLogPerPartition=None, mode=None,
              columnNameOfCorruptRecord=None, multiLine=None, charToEscapeQuoteEscaping=None,
              samplingRatio=None, enforceSchema=None, emptyValue=None, locale=None, lineSep=None,
              pathGlobFilter=None, recursiveFileLookup=None):
          r"""Loads a CSV file 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``.
  
          .. versionadded:: 2.0.0
  
          Parameters
          ----------
          path : str or list
              string, or list of strings, for input path(s),
              or RDD of Strings storing CSV rows.
          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``).
          sep : str, optional
              sets a separator (one or more characters) for each field and value. If None is
              set, it uses the default value, ``,``.
          encoding : str, optional
              decodes the CSV files by the given encoding type. If None is set,
              it uses the default value, ``UTF-8``.
          quote : str, optional
              sets a single character used for escaping quoted values where the
              separator can be part of the value. If None is set, it uses the default
              value, ``"``. If you would like to turn off quotations, you need to set an
              empty string.
          escape : str, optional
              sets a single character used for escaping quotes inside an already
              quoted value. If None is set, it uses the default value, ``\``.
          comment : str, optional
              sets a single character used for skipping lines beginning with this
              character. By default (None), it is disabled.
          header : str or bool, optional
              uses the first line as names of columns. If None is set, it uses the
              default value, ``false``.
  
              .. note:: if the given path is a RDD of Strings, this header
                  option will remove all lines same with the header if exists.
  
          inferSchema : str or bool, optional
              infers the input schema automatically from data. It requires one extra
              pass over the data. If None is set, it uses the default value, ``false``.
          enforceSchema : str or bool, optional
              If it is set to ``true``, the specified or inferred schema will be
              forcibly applied to datasource files, and headers in CSV files will be
              ignored. If the option is set to ``false``, the schema will be
              validated against all headers in CSV files or the first header in RDD
              if the ``header`` option is set to ``true``. Field names in the schema
              and column names in CSV headers are checked by their positions
              taking into account ``spark.sql.caseSensitive``. If None is set,
              ``true`` is used by default. Though the default value is ``true``,
              it is recommended to disable the ``enforceSchema`` option
              to avoid incorrect results.
          ignoreLeadingWhiteSpace : str or bool, optional
              A flag indicating whether or not leading whitespaces from
              values being read should be skipped. If None is set, it
              uses the default value, ``false``.
          ignoreTrailingWhiteSpace : str or bool, optional
              A flag indicating whether or not trailing whitespaces from
              values being read should be skipped. If None is set, it
              uses the default value, ``false``.
          nullValue : str, optional
              sets the string representation of a null value. If None is set, it uses
              the default value, empty string. Since 2.0.1, this ``nullValue`` param
              applies to all supported types including the string type.
          nanValue : str, optional
              sets the string representation of a non-number value. If None is set, it
              uses the default value, ``NaN``.
          positiveInf : str, optional
              sets the string representation of a positive infinity value. If None
              is set, it uses the default value, ``Inf``.
          negativeInf : str, optional
              sets the string representation of a negative infinity value. If None
              is set, it uses the default value, ``Inf``.
          dateFormat : str, optional
              sets the string that indicates a date format. Custom date formats
              follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to date type. If None is set, it uses the
              default value, ``yyyy-MM-dd``.
          timestampFormat : str, optional
              sets the string that indicates a timestamp format.
              Custom date formats follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to timestamp type. If None is set, it uses the
              default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
          maxColumns : str or int, optional
              defines a hard limit of how many columns a record can have. If None is
              set, it uses the default value, ``20480``.
          maxCharsPerColumn : str or int, optional
              defines the maximum number of characters allowed for any given
              value being read. If None is set, it uses the default value,
              ``-1`` meaning unlimited length.
          maxMalformedLogPerPartition : str or int, optional
              this parameter is no longer used since Spark 2.2.0.
              If specified, it is ignored.
          mode : str, optional
              allows a mode for dealing with corrupt records during parsing. If None is
              set, it uses the default value, ``PERMISSIVE``. Note that Spark tries to
              parse only required columns in CSV under column pruning. Therefore, corrupt
              records can be different based on required set of fields. This behavior can
              be controlled by ``spark.sql.csv.parser.columnPruning.enabled``
              (enabled by default).
  
              * ``PERMISSIVE``: when it meets a corrupted record, puts the malformed string \
                into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
                fields to ``null``. To keep corrupt records, an user can set a string type \
                field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
                schema does not have the field, it drops corrupt records during parsing. \
                A record with less/more tokens than schema is not a corrupted record to CSV. \
                When it meets a record having fewer tokens than the length of the schema, \
                sets ``null`` to extra fields. When the record has more tokens than the \
                length of the schema, it drops extra tokens.
              * ``DROPMALFORMED``: ignores the whole corrupted records.
              * ``FAILFAST``: throws an exception when it meets corrupted records.
  
          columnNameOfCorruptRecord : str, optional
              allows renaming the new field having malformed string
              created by ``PERMISSIVE`` mode. This overrides
              ``spark.sql.columnNameOfCorruptRecord``. If None is set,
              it uses the value specified in
              ``spark.sql.columnNameOfCorruptRecord``.
          multiLine : str or bool, optional
              parse records, which may span multiple lines. If None is
              set, it uses the default value, ``false``.
          charToEscapeQuoteEscaping : str, optional
              sets a single character used for escaping the escape for
              the quote character. If None is set, the default value is
              escape character when escape and quote characters are
              different, ``\0`` otherwise.
          samplingRatio : str or float, optional
              defines fraction of rows used for schema inferring.
              If None is set, it uses the default value, ``1.0``.
          emptyValue : str, optional
              sets the string representation of an empty value. If None is set, it uses
              the default value, empty string.
          locale : str, optional
              sets a locale as language tag in IETF BCP 47 format. If None is set,
              it uses the default value, ``en-US``. For instance, ``locale`` is used while
              parsing dates and timestamps.
          lineSep : str, optional
              defines the line separator that should be used for parsing. If None is
              set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
              Maximum length is 1 character.
          pathGlobFilter : str or bool, optional
              an optional glob pattern to only include files with paths matching
              the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
              It does not change the behavior of
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          recursiveFileLookup : str or bool, optional
              recursively scan a directory for files. Using this option disables
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
  
          Examples
          --------
          >>> df = spark.read.csv('python/test_support/sql/ages.csv')
          >>> df.dtypes
          [('_c0', 'string'), ('_c1', 'string')]
          >>> rdd = sc.textFile('python/test_support/sql/ages.csv')
          >>> df2 = spark.read.csv(rdd)
          >>> df2.dtypes
          [('_c0', 'string'), ('_c1', 'string')]
          """
          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, samplingRatio=samplingRatio,
              enforceSchema=enforceSchema, emptyValue=emptyValue, locale=locale, lineSep=lineSep,
              pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup)
          if isinstance(path, str):
              path = [path]
          if type(path) == list:
              return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
          elif isinstance(path, RDD):
              def func(iterator):
                  for x in iterator:
                      if not isinstance(x, str):
                          x = str(x)
                      if isinstance(x, str):
                          x = x.encode("utf-8")
                      yield x
              keyed = path.mapPartitions(func)
              keyed._bypass_serializer = True
              jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
              # see SPARK-22112
              # There aren't any jvm api for creating a dataframe from rdd storing csv.
              # We can do it through creating a jvm dataset firstly and using the jvm api
              # for creating a dataframe from dataset storing csv.
              jdataset = self._spark._ssql_ctx.createDataset(
                  jrdd.rdd(),
                  self._spark._jvm.Encoders.STRING())
              return self._df(self._jreader.csv(jdataset))
          else:
              raise TypeError("path can be only string, list or RDD")
  
      def orc(self, path, mergeSchema=None, pathGlobFilter=None, recursiveFileLookup=None):
          """Loads ORC files, returning the result as a :class:`DataFrame`.
  
          .. versionadded:: 1.5.0
  
          Parameters
          ----------
          path : str or list
          mergeSchema : str or bool, optional
              sets whether we should merge schemas collected from all
              ORC part-files. This will override ``spark.sql.orc.mergeSchema``.
              The default value is specified in ``spark.sql.orc.mergeSchema``.
          pathGlobFilter : str or bool
              an optional glob pattern to only include files with paths matching
              the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
              It does not change the behavior of
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
          recursiveFileLookup : str or bool
              recursively scan a directory for files. Using this option
              disables
              `partition discovery <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery>`_.  # noqa
  
          Examples
          --------
          >>> df = spark.read.orc('python/test_support/sql/orc_partitioned')
          >>> df.dtypes
          [('a', 'bigint'), ('b', 'int'), ('c', 'int')]
          """
          self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
                         recursiveFileLookup=recursiveFileLookup)
          if isinstance(path, str):
              path = [path]
          return self._df(self._jreader.orc(_to_seq(self._spark._sc, path)))
  
      def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
               predicates=None, properties=None):
          """
          Construct a :class:`DataFrame` representing the database table named ``table``
          accessible via JDBC URL ``url`` and connection ``properties``.
  
          Partitions of the table will be retrieved in parallel if either ``column`` or
          ``predicates`` is specified. ``lowerBound``, ``upperBound`` and ``numPartitions``
          is needed when ``column`` is specified.
  
          If both ``column`` and ``predicates`` are specified, ``column`` will be used.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          url : str
              a JDBC URL of the form ``jdbc:subprotocol:subname``
          table : str
              the name of the table
          column : str, optional
              the name of a column of numeric, date, or timestamp type
              that will be used for partitioning;
              if this parameter is specified, then ``numPartitions``, ``lowerBound``
              (inclusive), and ``upperBound`` (exclusive) will form partition strides
              for generated WHERE clause expressions used to split the column
              ``column`` evenly
          lowerBound : str or int, optional
              the minimum value of ``column`` used to decide partition stride
          upperBound : str or int, optional
              the maximum value of ``column`` used to decide partition stride
          numPartitions : int, optional
              the number of partitions
          predicates : list, optional
              a list of expressions suitable for inclusion in WHERE clauses;
              each one defines one partition of the :class:`DataFrame`
          properties : dict, optional
              a dictionary of JDBC database connection arguments. Normally at
              least properties "user" and "password" with their corresponding values.
              For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
  
          Notes
          -----
          Don't create too many partitions in parallel on a large cluster;
          otherwise Spark might crash your external database systems.
  
          Returns
          -------
          :class:`DataFrame`
          """
          if properties is None:
              properties = dict()
          jprop = JavaClass("java.util.Properties", self._spark._sc._gateway._gateway_client)()
          for k in properties:
              jprop.setProperty(k, properties[k])
          if column is not None:
              assert lowerBound is not None, "lowerBound can not be None when ``column`` is specified"
              assert upperBound is not None, "upperBound can not be None when ``column`` is specified"
              assert numPartitions is not None, \
                  "numPartitions can not be None when ``column`` is specified"
              return self._df(self._jreader.jdbc(url, table, column, int(lowerBound), int(upperBound),
                                                 int(numPartitions), jprop))
          if predicates is not None:
              gateway = self._spark._sc._gateway
              jpredicates = utils.toJArray(gateway, gateway.jvm.java.lang.String, predicates)
              return self._df(self._jreader.jdbc(url, table, jpredicates, jprop))
          return self._df(self._jreader.jdbc(url, table, jprop))
  
  
  class DataFrameWriter(OptionUtils):
      """
      Interface used to write a :class:`DataFrame` to external storage systems
      (e.g. file systems, key-value stores, etc). Use :attr:`DataFrame.write`
      to access this.
  
      .. versionadded:: 1.4
      """
      def __init__(self, df):
          self._df = df
          self._spark = df.sql_ctx
          self._jwrite = df._jdf.write()
  
      def _sq(self, jsq):
          from pyspark.sql.streaming import StreamingQuery
          return StreamingQuery(jsq)
  
      def mode(self, saveMode):
          """Specifies the behavior when data or table already exists.
  
          Options include:
  
          * `append`: Append contents of this :class:`DataFrame` to existing data.
          * `overwrite`: Overwrite existing data.
          * `error` or `errorifexists`: Throw an exception if data already exists.
          * `ignore`: Silently ignore this operation if data already exists.
  
          .. versionadded:: 1.4.0
  
          Examples
          --------
          >>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          # At the JVM side, the default value of mode is already set to "error".
          # So, if the given saveMode is None, we will not call JVM-side's mode method.
          if saveMode is not None:
              self._jwrite = self._jwrite.mode(saveMode)
          return self
  
      def format(self, source):
          """Specifies the underlying output data source.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          source : str
              string, name of the data source, e.g. 'json', 'parquet'.
  
          Examples
          --------
          >>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self._jwrite = self._jwrite.format(source)
          return self
  
      @since(1.5)
      def option(self, key, value):
          """Adds an output option for the underlying data source.
  
          You can set the following option(s) for writing files:
              * ``timeZone``: sets the string that indicates a time zone ID to be used to format
                  timestamps in the JSON/CSV datasources or partition values. The following
                  formats of `timeZone` are supported:
  
                  * Region-based zone ID: It should have the form 'area/city', such as \
                    'America/Los_Angeles'.
                  * Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
                   '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  
                  Other short names like 'CST' are not recommended to use because they can be
                  ambiguous. If it isn't set, the current value of the SQL config
                  ``spark.sql.session.timeZone`` is used by default.
          """
          self._jwrite = self._jwrite.option(key, to_str(value))
          return self
  
      @since(1.4)
      def options(self, **options):
          """Adds output options for the underlying data source.
  
          You can set the following option(s) for writing files:
              * ``timeZone``: sets the string that indicates a time zone ID to be used to format
                  timestamps in the JSON/CSV datasources or partition values. The following
                  formats of `timeZone` are supported:
  
                  * Region-based zone ID: It should have the form 'area/city', such as \
                    'America/Los_Angeles'.
                  * Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
                   '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  
                  Other short names like 'CST' are not recommended to use because they can be
                  ambiguous. If it isn't set, the current value of the SQL config
                  ``spark.sql.session.timeZone`` is used by default.
          """
          for k in options:
              self._jwrite = self._jwrite.option(k, to_str(options[k]))
          return self
  
      def partitionBy(self, *cols):
          """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:: 1.4.0
  
          Parameters
          ----------
          cols : str or list
              name of columns
  
          Examples
          --------
          >>> df.write.partitionBy('year', 'month').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          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
  
      def bucketBy(self, numBuckets, col, *cols):
          """Buckets the output by the given columns.If specified,
          the output is laid out on the file system similar to Hive's bucketing scheme.
  
          .. versionadded:: 2.3.0
  
          Parameters
          ----------
          numBuckets : int
              the number of buckets to save
          col : str, list or tuple
              a name of a column, or a list of names.
          cols : str
              additional names (optional). If `col` is a list it should be empty.
  
          Notes
          -----
          Applicable for file-based data sources in combination with
          :py:meth:`DataFrameWriter.saveAsTable`.
  
          Examples
          --------
          >>> (df.write.format('parquet')  # doctest: +SKIP
          ...     .bucketBy(100, 'year', 'month')
          ...     .mode("overwrite")
          ...     .saveAsTable('bucketed_table'))
          """
          if not isinstance(numBuckets, int):
              raise TypeError("numBuckets should be an int, got {0}.".format(type(numBuckets)))
  
          if isinstance(col, (list, tuple)):
              if cols:
                  raise ValueError("col is a {0} but cols are not empty".format(type(col)))
  
              col, cols = col[0], col[1:]
  
          if not all(isinstance(c, str) for c in cols) or not(isinstance(col, str)):
              raise TypeError("all names should be `str`")
  
          self._jwrite = self._jwrite.bucketBy(numBuckets, col, _to_seq(self._spark._sc, cols))
          return self
  
      def sortBy(self, col, *cols):
          """Sorts the output in each bucket by the given columns on the file system.
  
          .. versionadded:: 2.3.0
  
          Parameters
          ----------
          col : str, tuple or list
              a name of a column, or a list of names.
          cols : str
              additional names (optional). If `col` is a list it should be empty.
  
          Examples
          --------
          >>> (df.write.format('parquet')  # doctest: +SKIP
          ...     .bucketBy(100, 'year', 'month')
          ...     .sortBy('day')
          ...     .mode("overwrite")
          ...     .saveAsTable('sorted_bucketed_table'))
          """
          if isinstance(col, (list, tuple)):
              if cols:
                  raise ValueError("col is a {0} but cols are not empty".format(type(col)))
  
              col, cols = col[0], col[1:]
  
          if not all(isinstance(c, str) for c in cols) or not(isinstance(col, str)):
              raise TypeError("all names should be `str`")
  
          self._jwrite = self._jwrite.sortBy(col, _to_seq(self._spark._sc, cols))
          return self
  
      def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
          """Saves 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:: 1.4.0
  
          Parameters
          ----------
          path : str, optional
              the path in a Hadoop supported file system
          format : str, optional
              the format used to save
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
          partitionBy : list, optional
              names of partitioning columns
          **options : dict
              all other string options
  
          Examples
          --------
          >>> df.write.mode("append").save(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self.mode(mode).options(**options)
          if partitionBy is not None:
              self.partitionBy(partitionBy)
          if format is not None:
              self.format(format)
          if path is None:
              self._jwrite.save()
          else:
              self._jwrite.save(path)
  
      @since(1.4)
      def insertInto(self, tableName, overwrite=None):
          """Inserts the content of the :class:`DataFrame` to the specified table.
  
          It requires that the schema of the :class:`DataFrame` is the same as the
          schema of the table.
  
          Optionally overwriting any existing data.
          """
          if overwrite is not None:
              self.mode("overwrite" if overwrite else "append")
          self._jwrite.insertInto(tableName)
  
      def saveAsTable(self, name, format=None, mode=None, partitionBy=None, **options):
          """Saves the content of the :class:`DataFrame` as the specified table.
  
          In the case the table already exists, behavior of this function depends on the
          save mode, specified by the `mode` function (default to throwing an exception).
          When `mode` is `Overwrite`, the schema of the :class:`DataFrame` does not need to be
          the same as that of the existing table.
  
          * `append`: Append contents of this :class:`DataFrame` to existing data.
          * `overwrite`: Overwrite existing data.
          * `error` or `errorifexists`: Throw an exception if data already exists.
          * `ignore`: Silently ignore this operation if data already exists.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          name : str
              the table name
          format : str, optional
              the format used to save
          mode : str, optional
              one of `append`, `overwrite`, `error`, `errorifexists`, `ignore` \
              (default: error)
          partitionBy : str or list
              names of partitioning columns
          **options : dict
              all other string options
          """
          self.mode(mode).options(**options)
          if partitionBy is not None:
              self.partitionBy(partitionBy)
          if format is not None:
              self.format(format)
          self._jwrite.saveAsTable(name)
  
      def json(self, path, mode=None, compression=None, dateFormat=None, timestampFormat=None,
               lineSep=None, encoding=None, ignoreNullFields=None):
          """Saves the content of the :class:`DataFrame` in JSON format
          (`JSON Lines text format or newline-delimited JSON <http://jsonlines.org/>`_) at the
          specified path.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          path : str
              the path in any Hadoop supported file system
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
          compression : str, optional
              compression codec to use when saving to file. This can be one of the
              known case-insensitive shorten names (none, bzip2, gzip, lz4,
              snappy and deflate).
          dateFormat : str, optional
              sets the string that indicates a date format. Custom date formats
              follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to date type. If None is set, it uses the
              default value, ``yyyy-MM-dd``.
          timestampFormat : str, optional
              sets the string that indicates a timestamp format.
              Custom date formats follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to timestamp type. If None is set, it uses the
              default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
          encoding : str, optional
              specifies encoding (charset) of saved json files. If None is set,
              the default UTF-8 charset will be used.
          lineSep : str, optional defines the line separator that should be used for writing. If None is
              set, it uses the default value, ``\\n``.
          ignoreNullFields : str or bool, optional
              Whether to ignore null fields when generating JSON objects.
              If None is set, it uses the default value, ``true``.
  
          Examples
          --------
          >>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self.mode(mode)
          self._set_opts(
              compression=compression, dateFormat=dateFormat, timestampFormat=timestampFormat,
              lineSep=lineSep, encoding=encoding, ignoreNullFields=ignoreNullFields)
          self._jwrite.json(path)
  
      def parquet(self, path, mode=None, partitionBy=None, compression=None):
          """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          path : str
              the path in any Hadoop supported file system
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
          partitionBy : str or list, optional
              names of partitioning columns
          compression : str, optional
              compression codec to use when saving to file. This can be one of the
              known case-insensitive shorten names (none, uncompressed, snappy, gzip,
              lzo, brotli, lz4, and zstd). This will override
              ``spark.sql.parquet.compression.codec``. If None is set, it uses the
              value specified in ``spark.sql.parquet.compression.codec``.
  
          Examples
          --------
          >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self.mode(mode)
          if partitionBy is not None:
              self.partitionBy(partitionBy)
          self._set_opts(compression=compression)
          self._jwrite.parquet(path)
  
      def text(self, path, compression=None, lineSep=None):
          """Saves the content of the DataFrame in a text file at the specified path.
          The text files will be encoded as UTF-8.
  
          .. versionadded:: 1.6.0
  
          Parameters
          ----------
          path : str
              the path in any Hadoop supported file system
          compression : str, optional
              compression codec to use when saving to file. This can be one of the
              known case-insensitive shorten names (none, bzip2, gzip, lz4,
              snappy and deflate).
          lineSep : str, optional
              defines the line separator that should be used for writing. If None is
              set, it uses the default value, ``\\n``.
  
          The DataFrame must have only one column that is of string type.
          Each row becomes a new line in the output file.
          """
          self._set_opts(compression=compression, lineSep=lineSep)
          self._jwrite.text(path)
  
      def csv(self, path, mode=None, compression=None, sep=None, quote=None, escape=None,
              header=None, nullValue=None, escapeQuotes=None, quoteAll=None, dateFormat=None,
              timestampFormat=None, ignoreLeadingWhiteSpace=None, ignoreTrailingWhiteSpace=None,
              charToEscapeQuoteEscaping=None, encoding=None, emptyValue=None, lineSep=None):
          r"""Saves the content of the :class:`DataFrame` in CSV format at the specified path.
  
          .. versionadded:: 2.0.0
  
          Parameters
          ----------
          path : str
              the path in any Hadoop supported file system
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
  
          compression : str, optional
              compression codec to use when saving to file. This can be one of the
              known case-insensitive shorten names (none, bzip2, gzip, lz4,
              snappy and deflate).
          sep : str, optional
              sets a separator (one or more characters) for each field and value. If None is
              set, it uses the default value, ``,``.
          quote : str, optional
              sets a single character used for escaping quoted values where the
              separator can be part of the value. If None is set, it uses the default
              value, ``"``. If an empty string is set, it uses ``u0000`` (null character).
          escape : str, optional
              sets a single character used for escaping quotes inside an already
              quoted value. If None is set, it uses the default value, ``\``
          escapeQuotes : str or bool, optional
              a flag indicating whether values containing quotes should always
              be enclosed in quotes. If None is set, it uses the default value
              ``true``, escaping all values containing a quote character.
          quoteAll : str or bool, optional
              a flag indicating whether all values should always be enclosed in
              quotes. If None is set, it uses the default value ``false``,
              only escaping values containing a quote character.
          header : str or bool, optional
              writes the names of columns as the first line. If None is set, it uses
              the default value, ``false``.
          nullValue : str, optional
              sets the string representation of a null value. If None is set, it uses
              the default value, empty string.
          dateFormat : str, optional
              sets the string that indicates a date format. Custom date formats follow
              the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to date type. If None is set, it uses the
              default value, ``yyyy-MM-dd``.
          timestampFormat : str, optional
              sets the string that indicates a timestamp format.
              Custom date formats follow the formats at
              `datetime pattern <https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html>`_.  # noqa
              This applies to timestamp type. If None is set, it uses the
              default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
          ignoreLeadingWhiteSpace : str or bool, optional
              a flag indicating whether or not leading whitespaces from
              values being written should be skipped. If None is set, it
              uses the default value, ``true``.
          ignoreTrailingWhiteSpace : str or bool, optional
              a flag indicating whether or not trailing whitespaces from
              values being written should be skipped. If None is set, it
              uses the default value, ``true``.
          charToEscapeQuoteEscaping : str, optional
              sets a single character used for escaping the escape for
              the quote character. If None is set, the default value is
              escape character when escape and quote characters are
              different, ``\0`` otherwise..
          encoding : str, optional
              sets the encoding (charset) of saved csv files. If None is set,
              the default UTF-8 charset will be used.
          emptyValue : str, optional
              sets the string representation of an empty value. If None is set, it uses
              the default value, ``""``.
          lineSep : str, optional
              defines the line separator that should be used for writing. If None is
              set, it uses the default value, ``\\n``. Maximum length is 1 character.
  
          Examples
          --------
          >>> df.write.csv(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self.mode(mode)
          self._set_opts(compression=compression, sep=sep, quote=quote, escape=escape, header=header,
                         nullValue=nullValue, escapeQuotes=escapeQuotes, quoteAll=quoteAll,
                         dateFormat=dateFormat, timestampFormat=timestampFormat,
                         ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace,
                         ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace,
                         charToEscapeQuoteEscaping=charToEscapeQuoteEscaping,
                         encoding=encoding, emptyValue=emptyValue, lineSep=lineSep)
          self._jwrite.csv(path)
  
      def orc(self, path, mode=None, partitionBy=None, compression=None):
          """Saves the content of the :class:`DataFrame` in ORC format at the specified path.
  
          .. versionadded:: 1.5.0
  
          Parameters
          ----------
          path : str
              the path in any Hadoop supported file system
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
          partitionBy : str or list, optional
              names of partitioning columns
          compression : str, optional
              compression codec to use when saving to file. This can be one of the
              known case-insensitive shorten names (none, snappy, zlib, and lzo).
              This will override ``orc.compress`` and
              ``spark.sql.orc.compression.codec``. If None is set, it uses the value
              specified in ``spark.sql.orc.compression.codec``.
  
          Examples
          --------
          >>> orc_df = spark.read.orc('python/test_support/sql/orc_partitioned')
          >>> orc_df.write.orc(os.path.join(tempfile.mkdtemp(), 'data'))
          """
          self.mode(mode)
          if partitionBy is not None:
              self.partitionBy(partitionBy)
          self._set_opts(compression=compression)
          self._jwrite.orc(path)
  
      def jdbc(self, url, table, mode=None, properties=None):
          """Saves the content of the :class:`DataFrame` to an external database table via JDBC.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          url : str
              a JDBC URL of the form ``jdbc:subprotocol:subname``
          table : str
              Name of the table in the external database.
          mode : str, optional
              specifies the behavior of the save operation when data already exists.
  
              * ``append``: Append contents of this :class:`DataFrame` to existing data.
              * ``overwrite``: Overwrite existing data.
              * ``ignore``: Silently ignore this operation if data already exists.
              * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
                  exists.
          properties : dict
              a dictionary of JDBC database connection arguments. Normally at
              least properties "user" and "password" with their corresponding values.
              For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
  
          Notes
          -----
          Don't create too many partitions in parallel on a large cluster;
          otherwise Spark might crash your external database systems.
          """
          if properties is None:
              properties = dict()
          jprop = JavaClass("java.util.Properties", self._spark._sc._gateway._gateway_client)()
          for k in properties:
              jprop.setProperty(k, properties[k])
          self.mode(mode)._jwrite.jdbc(url, table, jprop)
  
  
  class DataFrameWriterV2(object):
      """
      Interface used to write a class:`pyspark.sql.dataframe.DataFrame`
      to external storage using the v2 API.
  
      .. versionadded:: 3.1.0
      """
  
      def __init__(self, df, table):
          self._df = df
          self._spark = df.sql_ctx
          self._jwriter = df._jdf.writeTo(table)
  
      @since(3.1)
      def using(self, provider):
          """
          Specifies a provider for the underlying output data source.
          Spark's default catalog supports "parquet", "json", etc.
          """
          self._jwriter.using(provider)
          return self
  
      @since(3.1)
      def option(self, key, value):
          """
          Add a write option.
          """
          self._jwriter.option(key, to_str(value))
          return self
  
      @since(3.1)
      def options(self, **options):
          """
          Add write options.
          """
          options = {k: to_str(v) for k, v in options.items()}
          self._jwriter.options(options)
          return self
  
      @since(3.1)
      def tableProperty(self, property, value):
          """
          Add table property.
          """
          self._jwriter.tableProperty(property, value)
          return self
  
      @since(3.1)
      def partitionedBy(self, col, *cols):
          """
          Partition the output table created by `create`, `createOrReplace`, or `replace` using
          the given columns or transforms.
  
          When specified, the table data will be stored by these values for efficient reads.
  
          For example, when a table is partitioned by day, it may be stored
          in a directory layout like:
  
          * `table/day=2019-06-01/`
          * `table/day=2019-06-02/`
  
          Partitioning is one of the most widely used techniques to optimize physical data layout.
          It provides a coarse-grained index for skipping unnecessary data reads when queries have
          predicates on the partitioned columns. In order for partitioning to work well, the number
          of distinct values in each column should typically be less than tens of thousands.
  
          `col` and `cols` support only the following functions:
  
          * :py:func:`pyspark.sql.functions.years`
          * :py:func:`pyspark.sql.functions.months`
          * :py:func:`pyspark.sql.functions.days`
          * :py:func:`pyspark.sql.functions.hours`
          * :py:func:`pyspark.sql.functions.bucket`
  
          """
          col = _to_java_column(col)
          cols = _to_seq(self._spark._sc, [_to_java_column(c) for c in cols])
          return self
  
      @since(3.1)
      def create(self):
          """
          Create a new table from the contents of the data frame.
  
          The new table's schema, partition layout, properties, and other configuration will be
          based on the configuration set on this writer.
          """
          self._jwriter.create()
  
      @since(3.1)
      def replace(self):
          """
          Replace an existing table with the contents of the data frame.
  
          The existing table's schema, partition layout, properties, and other configuration will be
          replaced with the contents of the data frame and the configuration set on this writer.
          """
          self._jwriter.replace()
  
      @since(3.1)
      def createOrReplace(self):
          """
          Create a new table or replace an existing table with the contents of the data frame.
  
          The output table's schema, partition layout, properties,
          and other configuration will be based on the contents of the data frame
          and the configuration set on this writer.
          If the table exists, its configuration and data will be replaced.
          """
          self._jwriter.createOrReplace()
  
      @since(3.1)
      def append(self):
          """
          Append the contents of the data frame to the output table.
          """
          self._jwriter.append()
  
      @since(3.1)
      def overwrite(self, condition):
          """
          Overwrite rows matching the given filter condition with the contents of the data frame in
          the output table.
          """
          self._jwriter.overwrite(condition)
  
      @since(3.1)
      def overwritePartitions(self):
          """
          Overwrite all partition for which the data frame contains at least one row with the contents
          of the data frame in the output table.
  
          This operation is equivalent to Hive's `INSERT OVERWRITE ... PARTITION`, which replaces
          partitions dynamically depending on the contents of the data frame.
          """
          self._jwriter.overwritePartitions()
  
  
  def _test():
      import doctest
      import os
      import tempfile
      import py4j
      from pyspark.context import SparkContext
      from pyspark.sql import SparkSession
      import pyspark.sql.readwriter
  
      os.chdir(os.environ["SPARK_HOME"])
  
      globs = pyspark.sql.readwriter.__dict__.copy()
      sc = SparkContext('local[4]', 'PythonTest')
      try:
          spark = SparkSession.builder.getOrCreate()
      except py4j.protocol.Py4JError:
          spark = SparkSession(sc)
  
      globs['tempfile'] = tempfile
      globs['os'] = os
      globs['sc'] = sc
      globs['spark'] = spark
      globs['df'] = spark.read.parquet('python/test_support/sql/parquet_partitioned')
      (failure_count, test_count) = doctest.testmod(
          pyspark.sql.readwriter, globs=globs,
          optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
      sc.stop()
      if failure_count:
          sys.exit(-1)
  
  
  if __name__ == "__main__":
      _test()
  