def load(self, path=None, format=None, schema=None, **options):
        """Loads a data stream from a data source and returns it as a :class`DataFrame`.

        .. note:: Evolving.

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

        >>> json_sdf = spark.readStream.format("json") \\
        ...     .schema(sdf_schema) \\
        ...     .load(tempfile.mkdtemp())
        >>> json_sdf.isStreaming
        True
        >>> json_sdf.schema == sdf_schema
        True
        """
        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 type(path) != str or len(path.strip()) == 0:
                raise ValueError("If the path is provided for stream, it needs to be a " +
                                 "non-empty string. List of paths are not supported.")
            return self._df(self._jreader.load(path))
        else:
            return self._df(self._jreader.load())
def load ( self , path = None , format = None , schema = None , ** options ) : 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 type ( path ) != str or len ( path . strip ( ) ) == 0 : raise ValueError ( "If the path is provided for stream, it needs to be a " + "non-empty string. List of paths are not supported." ) return self . _df ( self . _jreader . load ( path ) ) else : return self . _df ( self . _jreader . load ( ) )
Loads a data stream from a data source and returns it as a :class`DataFrame`.

        .. note:: Evolving.

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

        >>> json_sdf = spark.readStream.format("json") \\
        ...     .schema(sdf_schema) \\
        ...     .load(tempfile.mkdtemp())
        >>> json_sdf.isStreaming
        True
        >>> json_sdf.schema == sdf_schema
        True