Pyspark 错误：在数据帧上调用 count() 方法时"Py4JJavaError: An error occurred while calling o655.count."

Question

我是Spark的新手，我正在使用Pyspark 2.3.1将csv文件读入数据帧。我能够在 anaconda 环境中运行的 Jupyter 笔记本中读取文件并打印值。这是我正在使用的代码：

# Start session
spark = SparkSession 
.builder 
.appName("Embedding Models") 
.config('spark.ui.showConsoleProgress', 'true') 
.config("spark.master", "local[2]") 
.getOrCreate()
sqlContext = sql.SQLContext(spark)
schema = StructType([
StructField("Index", IntegerType(), True),
StructField("title", StringType(), True),
StructField("body", StringType(), True)])
df= sqlContext.read.csv("../data/faq_data.csv",
header=True, 
mode="DROPMALFORMED",
schema=schema)

输出：

df.show()
+-----+--------------------+--------------------+
|Index|               title|                body|
+-----+--------------------+--------------------+
|    0|What does “quantu...|Quantum theory is...|
|    1|What is a quantum...|A quantum compute...|

但是，当我在数据帧上调用.count()方法时，它会抛出以下错误

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-29-913a2f9eb5fc> in <module>()
----> 1 df.count()
~/anaconda3/envs/Community/lib/python3.6/site-packages/pyspark/sql/dataframe.py in count(self)
453         2
454         """
--> 455         return int(self._jdf.count())
456 
457     @ignore_unicode_prefix
~/anaconda3/envs/Community/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:
~/anaconda3/envs/Community/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()
~/anaconda3/envs/Community/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 o655.count.
: 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$$anonfun$collect$1.apply(RDD.scala:939)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:297)
at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2770)
at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2769)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3254)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3253)
at org.apache.spark.sql.Dataset.count(Dataset.scala:2769)
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)

我正在使用Python 3.6.5，如果这有所作为。

Answer 1

你的机器上有什么Java版本？您的问题可能与Java 9有关。

如果您下载 Java 8，异常将消失。如果您已经安装了 Java 8，只需更改JAVA_HOME即可。

Answer 2

你能试试df.repartition(1).count()和len(df.toPandas())吗？

如果它有效，那么问题很可能出在您的火花配置中。

Answer 3

在 Linux 中安装 Java 8 如下将有所帮助：

sudo apt install openjdk-8-jdk

然后使用以下方法将默认 Java 设置为版本 8：

sudo update-alternatives --config java

********* ： 2 (输入 2，当它要求您选择时( + 按回车键

Answer 4

如果无法实际看到数据，我猜这是一个架构问题。我建议尝试加载较小的数据样本，您可以在其中确保只有 3 列来测试它。

由于它是CSV，因此另一个简单的测试可能是通过新行加载和split数据，然后使用逗号来检查是否有任何破坏文件的内容。

我以前肯定见过这个，但我不记得到底出了什么问题。