Question

我主要根据《 Frank Kane用Apache Spark和Python驯服大数据》一书中的说明在Windows上安装Spark。它们似乎与我在网上找到的其他说明一致。它涉及安装java，python，scala和spark，以及设置环境变量和路径。我能够运行java和python。为了运行pyspark，我必须运行pyspark.cmd（我使用Canopy命令提示符）。这确实预订了火花。

然后我运行： rdd = sc.textFile（“ README.md”）

然后

rdd.count（）

但是我得到这个错误：

>>> rdd = sc.textFile("README.md")
>>> rdd.count()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\spark\python\pyspark\rdd.py", line 1073, in count
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "C:\spark\python\pyspark\rdd.py", line 1064, in sum
    return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
  File "C:\spark\python\pyspark\rdd.py", line 935, in fold
    vals = self.mapPartitions(func).collect()
  File "C:\spark\python\pyspark\rdd.py", line 834, in collect
    sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
  File "C:\spark\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py", line 1257, in __call__
  File "C:\spark\python\pyspark\sql\utils.py", line 63, in deco
    return f(*a, **kw)
  File "C:\spark\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: 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$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
        at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:162)
        at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
        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)

我怎么了？

Answer 1

我认为您需要将README文件放入hdfs或使用具有前缀“ file：//”的文件的完整路径。

在Windows上执行Spark --rdd.count（）不起作用

1 个答案: