Pyspark 错误:“Py4JJavaError:在数据帧上调用 count() 方法时调用 o655.count 时出错。

2022-09-04 02:20:59

我是Spark的新手,我正在使用Pyspark 2.3.1将csv文件读取到数据帧中。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该方法时,它会抛出以下错误.count()

    ---------------------------------------------------------------------------
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,如果这有所作为的话。


答案 1

您的机器上有什么 Java 版本?您的问题可能与 Java 9 有关。

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


答案 2

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

如果它有效,那么问题很可能出在您的火花配置中。