我遠程連接到資料湖，使用 Hive 直線終端處理存盤在 Hadoop 集群中的資料湖上的資料，并將資料以orc格式存盤在 HDFS 上。然后我將這個 orc 檔案傳輸到沒有任何 Hadoop HDFS 設定的本地系統。我在本地機器上安裝了 spark 并使用 pyspark 讀取了 orc 檔案的一個資料部分。
我的 orc 檔案在我的本地機器上具有以下檔案結構：
orc_table--
Partition-01--
1.000000_0
Partition-02--
1.000000_0
2.000000_0
Partition-03--
1.000000_0
我可以通過使用以下代碼在 spark 資料幀中提供帶有擴展名 (000000_0) 的完整檔案路徑來讀取單個部分（如 1.000000_0）
import findspark
findspark.init()
from pyspark.sql import SparkSession
from pyspark import SparkContext
sc = SparkContext("local", "first_app")
spark = SparkSession(sc)
df_spark = spark.read.format("orc").option("inferSchema", "true").orc("C:/orc_table/Partition-01/1.000000_0")
但是，我想orc_table在 spark 資料框中一次讀取存盤在不同檔案夾中的所有磁區檔案。我給出了orc_table檔案夾的路徑（如“C：/orc_table”）。我收到以下錯誤。
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
Input In [8], in <cell line: 1>()
----> 1 df_spark = spark.read.format("orc").option("inferSchema", "true").orc("C:\orc_table\Partition-01")
File C:\Spark\spark-3.1.2-bin-hadoop3.2\python\pyspark\sql\readwriter.py:803, in DataFrameReader.orc(self, path, mergeSchema, pathGlobFilter, recursiveFileLookup, modifiedBefore, modifiedAfter)
801 if isinstance(path, str):
802 path = [path]
--> 803 return self._df(self._jreader.orc(_to_seq(self._spark._sc, path)))
File C:\Spark\spark-3.1.2-bin-hadoop3.2\python\lib\py4j-0.10.9-src.zip\py4j\java_gateway.py:1304, in JavaMember.__call__(self, *args)
1298 command = proto.CALL_COMMAND_NAME \
1299 self.command_header \
1300 args_command \
1301 proto.END_COMMAND_PART
1303 answer = self.gateway_client.send_command(command)
-> 1304 return_value = get_return_value(
1305 answer, self.gateway_client, self.target_id, self.name)
1307 for temp_arg in temp_args:
1308 temp_arg._detach()
File C:\Spark\spark-3.1.2-bin-hadoop3.2\python\pyspark\sql\utils.py:111, in capture_sql_exception.<locals>.deco(*a, **kw)
109 def deco(*a, **kw):
110 try:
--> 111 return f(*a, **kw)
112 except py4j.protocol.Py4JJavaError as e:
113 converted = convert_exception(e.java_exception)
File C:\Spark\spark-3.1.2-bin-hadoop3.2\python\lib\py4j-0.10.9-src.zip\py4j\protocol.py:326, in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
331 "An error occurred while calling {0}{1}{2}. Trace:\n{3}\n".
332 format(target_id, ".", name, value))
Py4JJavaError: An error occurred while calling o30.orc.
: java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
at org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Native Method)
at org.apache.hadoop.io.nativeio.NativeIO$Windows.access(NativeIO.java:645)
at org.apache.hadoop.fs.FileUtil.canRead(FileUtil.java:1230)
at org.apache.hadoop.fs.FileUtil.list(FileUtil.java:1435)
at org.apache.hadoop.fs.RawLocalFileSystem.listStatus(RawLocalFileSystem.java:493)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1868)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1910)
at org.apache.hadoop.fs.ChecksumFileSystem.listStatus(ChecksumFileSystem.java:678)
at org.apache.spark.util.HadoopFSUtils$.listLeafFiles(HadoopFSUtils.scala:225)
at org.apache.spark.util.HadoopFSUtils$.$anonfun$parallelListLeafFilesInternal$1(HadoopFSUtils.scala:95)
at scala.collection.TraversableLike.$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$(TraversableLike.scala:231)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFilesInternal(HadoopFSUtils.scala:85)
at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFiles(HadoopFSUtils.scala:69)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.bulkListLeafFiles(InMemoryFileIndex.scala:158)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.listLeafFiles(InMemoryFileIndex.scala:131)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:94)
at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:66)
at org.apache.spark.sql.execution.datasources.DataSource.createInMemoryFileIndex(DataSource.scala:581)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:417)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:325)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:307)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:307)
at org.apache.spark.sql.DataFrameReader.orc(DataFrameReader.scala:872)
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:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
星火版本：3.1.2；斯卡拉版本：2.12.10；Java 版本：“1.8.0_131”
任何幫助表示贊賞。
uj5u.com熱心網友回復：
我終于解決了這個問題。我在沒有安裝 Hadoop 的情況下運行 spark。因此，如果我們不安裝 Hadoop，那么我們必須將“winutils.exe”放到 C:\hadoop\bin 檔案夾中。但就我而言，我不得不將 hadoop.dll 和 winutils.exe 一起放入，我的問題就解決了。您可以根據您使用的 spark 版本在此處下載這兩個檔案。 https://github.com/cdarlint/winutils
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