Py4JJavaError: An error occurred while calling o26.parquet. (Reading Parquet file)

Question

正在尝试读取 PySpark 中的 Parquet 文件但得到 Py4JJavaError。我什至尝试从 spark-shell 读取它并且能够这样做。就它在 Scala 而不是 PySpark 中工作的 Python API 而言，我无法理解我在这里做错了什么；

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

堆栈跟踪：

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$$anon$$anonfun$visitMethodInsn.apply(ClosureCleaner.scala:449)
    at org.apache.spark.util.FieldAccessFinder$$anon$$anonfun$visitMethodInsn.apply(ClosureCleaner.scala:432)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach.apply(TraversableLike.scala:733)
    at scala.collection.mutable.HashMap$$anon$$anonfun$foreach.apply(HashMap.scala:103)
    at scala.collection.mutable.HashMap$$anon$$anonfun$foreach.apply(HashMap.scala:103)
    at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
    at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
    at scala.collection.mutable.HashMap$$anon.foreach(HashMap.scala:103)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
    at org.apache.spark.util.FieldAccessFinder$$anon.visitMethodInsn(ClosureCleaner.scala:432)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean.apply(ClosureCleaner.scala:262)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean.apply(ClosureCleaner.scala:261)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$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$$anonfun$collect.apply(RDD.scala:939)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.mergeSchemasInParallel(ParquetFileFormat.scala:611)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat.inferSchema(ParquetFileFormat.scala:241)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun.apply(DataSource.scala:202)
    at org.apache.spark.sql.execution.datasources.DataSource$$anonfun.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

环境信息：

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

我弄清楚到底出了什么问题。 spark-shell 使用的是 Java 1.8，而 PySpark 使用的是 Java 10.1。 Java 1.9/10 和 Spark 存在一些问题。将默认 Java 版本更改为 1.8。

Answer 2

Spark 在 Java 8 月 11 日运行。

要在 Java 版本之间切换，您可以将其添加到您的 .bashrc/.zshrc 文件中：

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

然后在您的终端中：

source .zshrc

j 1.8

java -version

这将更改系统范围内的版本。如果你只是想让它在一个应用程序中有所不同，你可以在它前面加上环境变量 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 运行时环境（build 1.8.0_275-b01） OpenJDK 64 位服务器 VM（内部版本 25.275-b01，混合模式）

Python版本： Python 3.9.5（tags/v3.9.5:0a7dcbd，2021 年 5 月 3 日，17:27:52）Win32 上的 [MSC v.1928 64 位 (AMD64)] 键入“帮助”、“版权”、“信用”或“许可”以获取更多信息。

PySpark 版本： 3.0.1 (注：此版本为key)

这个组合效果很好。

Py4JJavaError: An error occurred while calling o26.parquet. (Reading Parquet file)

Py4JJavaError: An error occurred while calling o26.parquet. (Reading Parquet file)

python-3.x

apache-spark

parquet

pyspark