Py4JJavaError：調用 o26.parquet 時發生錯誤。 （閱讀 Parquet 文件）

Question

試圖在 PySpark 中讀取Parquet文件但得到Py4JJavaError 。 我什至嘗試從spark-shell讀取它並且能夠這樣做。 我無法理解我在這里做錯了什么，因為它在 Scala 中而不是在 PySpark 中工作的 Python API；

spark = SparkSession.builder.master("local").appName("test-read").getOrCreate()
sdf = spark.read.parquet("game_logs.parquet")

堆棧跟蹤：

Py4JJavaError                             Traceback (most recent call last)
<timed exec> in <module>()

~/pyenv/pyenv/lib/python3.6/site-packages/pyspark/sql/readwriter.py in parquet(self, *paths)
    301         [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
    302         """
--> 303         return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
    304 
    305     @ignore_unicode_prefix

~/pyenv/pyenv/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

~/pyenv/pyenv/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

~/pyenv/pyenv/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o26.parquet.
: java.lang.IllegalArgumentException
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
    at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
    at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
    at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:432)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:262)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:261)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:261)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2299)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2073)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.mergeSchemasInParallel(ParquetFileFormat.scala:611)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.inferSchema(ParquetFileFormat.scala:241)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:202)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:202)
    at scala.Option.orElse(Option.scala:289)
    at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:201)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:392)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
    at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:622)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:564)
    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.base/java.lang.Thread.run(Thread.java:844

環境信息：

Spark version 2.3.1     
Using Scala version 2.11.8, 
Java HotSpot(TM) 64-Bit Server VM, 1.8.0_172
Python 3.6.5
PySpark 2.3.1

Answer 1

我弄清楚出了什么問題。 spark-shell使用的是Java 1.8 ，但PySpark使用的是Java 10.1 。 Java 1.9/10 和 Spark 存在一些問題。 將默認 Java 版本更改為 1.8。

Answer 2

Spark 在 Java 8/11 上運行。

要在 Java 版本之間切換，您可以將其添加到 .bashrc/.zshrc 文件中：

alias j='f(){ export JAVA_HOME=$(/usr/libexec/java_home -v $1) };f'

然后在您的終端中：

source .zshrc

j 1.8

java -version

這將在系統范圍內更改版本。 如果你只是想讓一個應用程序不同，你可以在它前面加上環境變量 JAVA_HOME

JAVA_HOME=$(/usr/libexec/java_home -v 1.8) jupyter notebook

%env JAVA_HOME {path}

Answer 3

Java 版本：openjdk 版本“1.8.0_275” OpenJDK 運行時環境（構建 1.8.0_275-b01） OpenJDK 64 位服務器 VM（構建 25.275-b01，混合模式）

Python 版本：Python 3.9.5 (tags/v3.9.5:0a7dcbd, May 3 2021, 17:27:52) [MSC v.1928 64 位 (AMD64)] on win32 輸入“help”、“copyright”、“credits” ”或“許可證”了解更多信息。

PySpark 版本：3.0.1（注：這個版本是關鍵）

這種組合效果很好。