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Pysparkエラー：「Py4JJavaError：o655.countの呼び出し中にエラーが発生しました。」データフレームでcount（）メソッドを呼び出すとき

私はSparkを初めて使用し、CSVファイルをデータフレームに読み込むためにPyspark 2.3.1を使用しています。ファイルを読み込んで、Jupyterノートブックで値を印刷できますアナコンダ環境内で実行されているこれは私が使用しているコードです：

_# 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)
_

出力：

_df.show()

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

しかし、データフレームで.count()メソッドを呼び出すと、以下のエラーがスローされます

_    ---------------------------------------------------------------------------
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)
_

Python 3.6.5を使用しています。

pythondataframepyspark
6
Kaushik

Javaお使いのマシンのバージョンは何ですか？問題はおそらくJava 9.に関連しています。

Java 8をダウンロードすると、例外は消えます。すでにJava 8がインストールされている場合は、Java_HOMEに。

4
luminousmen

df.repartition(1).count()とlen(df.toPandas())を試していただけませんか？

それが機能する場合、問題はおそらくspark構成にあります。

1
Borislav Aymaliev

Linuxでは、次のようにJava 8をインストールすると役立ちます。

Sudo apt install openjdk-8-jdk

次に、デフォルトのJavaをバージョン8に設定します。

Sudo update-alternatives --config Java

*****************：2（選択を求められたら2を入力）+ Enterを押します

0
Rene B.