我使用这段简单的代码从一个目录中读取json文件流。该代码在databricks笔记本上运行正常，但是在本地运行时抛出一个错误。我使用databricks connect（版本8.1）连接并通过集群运行脚本。

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ProcessSensorData").getOrCreate()

userschema = StructType().add("ID", "string").add("Created", "string").add("Data", "string").add("DeviceID", "string").add("Size", "string")
df = spark.readStream.schema(userschema).json("dbfs:/mnt/")
df.writeStream.format("parquet").option("checkpointLocation", "dbfs:/mnt/parquet/demo_checkpoint1").option("path", "dbfs:/mnt/parquet/demo_parquet1").start()

当我使用“read”而不是“readstream”时，上面的代码可以在本地正常工作。我尝试过使用不同的方式来readstream，包括选项、格式，还确认了我与databricks集群的连接。我有pyspark版本3.1.1和java8。我总是会遇到以下错误：

21/04/21 09:10:44 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
21/04/21 09:10:45 WARN MetricsSystem: Using default name SparkStatusTracker for source because neither spark.metrics.namespace nor spark.app.id is set.
Traceback (most recent call last):
  File "/Users/dir/spark_process.py", line 6, in <module>
    df = spark.readStream.schema(userschema).json("dbfs:/mnt/")
  File "/Users/dir/venv/lib/python3.9/site-packages/pyspark/sql/streaming.py", line 631, in json
    return self._df(self._jreader.json(path))
  File "/Users/dir/venv/lib/python3.9/site-packages/py4j/java_gateway.py", line 1304, in __call__
    return_value = get_return_value(
  File "/Users/dir/venv/lib/python3.9/site-packages/pyspark/sql/utils.py", line 110, in deco
    return f(*a,**kw)
  File "/Users/dir/venv/lib/python3.9/site-packages/py4j/protocol.py", line 326, in get_return_value
    raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling o31.json.
: java.lang.UnsupportedOperationException
    at com.databricks.sql.transaction.directory.DirectoryAtomicReadProtocol$.filterDirectoryListing(DirectoryAtomicReadProtocol.scala:28)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.listLeafFiles(InMemoryFileIndex.scala:375)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.$anonfun$bulkListLeafFiles$2(InMemoryFileIndex.scala:282)
    at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at scala.collection.TraversableLike.map(TraversableLike.scala:238)
    at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
    at scala.collection.AbstractTraversable.map(Traversable.scala:108)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.bulkListLeafFiles(InMemoryFileIndex.scala:274)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.listLeafFiles(InMemoryFileIndex.scala:139)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:102)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:74)
    at org.apache.spark.sql.execution.datasources.DataSource.createInMemoryFileIndex(DataSource.scala:620)
    at org.apache.spark.sql.execution.datasources.DataSource.$anonfun$sourceSchema$2(DataSource.scala:296)
    at org.apache.spark.sql.execution.datasources.DataSource.tempFileIndex$lzycompute$1(DataSource.scala:183)
    at org.apache.spark.sql.execution.datasources.DataSource.tempFileIndex$1(DataSource.scala:183)
    at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:188)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceSchema(DataSource.scala:288)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo$lzycompute(DataSource.scala:137)
    at org.apache.spark.sql.execution.datasources.DataSource.sourceInfo(DataSource.scala:137)
    at org.apache.spark.sql.execution.streaming.StreamingRelation$.apply(StreamingRelation.scala:33)
    at org.apache.spark.sql.streaming.DataStreamReader.loadInternal(DataStreamReader.scala:264)
    at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:280)
    at org.apache.spark.sql.streaming.DataStreamReader.json(DataStreamReader.scala:361)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
    at py4j.Gateway.invoke(Gateway.java:295)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:251)
    at java.lang.Thread.run(Thread.java:748)

Process finished with exit code 1

如果有人能帮我解决这个问题，那将是一个很大的帮助，谢谢！