class_docstr: 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()
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docstr: 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() 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()
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code: 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() 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(
              error_class="NOT_STR",
              message_parameters={
                  "arg_name": "path",
                  "arg_type": type(path).__name__,
              },
          )
  
imports: import sys
  from collections.abc import Iterator
  from typing import cast, overload, Any, Callable, List, Optional, TYPE_CHECKING, Union
  from py4j.java_gateway import java_import, JavaObject
  from pyspark.sql.column import _to_seq
  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
label_desc: Use image node_id 10 for calling the DataStreamReader obj's underlying member method code with example usage: obj.parquet(path, mergeSchema, pathGlobFilter, recursiveFileLookup, datetimeRebaseMode, int96RebaseMode) and returns: self