apache-spark - Py4JJavaError : An error occurred while calling z:org. apache.spark.api.python.PythonRDD.collectAndServe

Question

            import os 
        import sys 
        os.chdir("/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/bin")
        os.curdir
        if 'SPARK_HOME' not in os.environ:
            os.environ['SPARK_HOME'] = '/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7'
        SPARK_HOME = os.environ['SPARK_HOME']
        sys.path.insert(0,os.path.join(SPARK_HOME,"python"))
        sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib"))
        sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib","pyspark.zip"))
        sys.path.insert(0,os.path.join(SPARK_HOME,"python","lib","py4j-0.9-src.zip"))
    from pyspark import SparkContext
    from pyspark import SparkConf

    # Optionally configure Spark Settings
    conf=SparkConf()
    conf.set("spark.executor.memory", "1g")
    conf.set("spark.cores.max", "2")

    conf.setAppName("V2 Maestros")

    ## Initialize SparkContext. Run only once. Otherwise you get multiple 
    #Context Error.
    sc = SparkContext('local', conf=conf)

    #Test to make sure everything works.
    lines=sc.textFile("auto-data.csv")
    lines.count()

这是发生的错误。这是一个计算文件输入次数的简单程序，但出现了这个错误。我将文件保存在代码中提到的两个位置，即使结果相同。

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-6-5c9242495358> in <module>()
      1 lines = sc.textFile("auto-save.csv")
----> 2 lines.count()

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.pyc in count(self)
   1006         3
   1007         """
-> 1008         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1009 
   1010     def stats(self):

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.pyc in sum(self)
    997         6.0
    998         """
--> 999         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1000 
   1001     def count(self):

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.pyc in fold(self, zeroValue, op)
    871         # zeroValue provided to each partition is unique from the one provided
    872         # to the final reduce call
--> 873         vals = self.mapPartitions(func).collect()
    874         return reduce(op, vals, zeroValue)
    875 

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/pyspark/rdd.pyc in collect(self)
    774         """
    775         with SCCallSiteSync(self.context) as css:
--> 776             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    777         return list(_load_from_socket(port, self._jrdd_deserializer))
    778 

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
    931         answer = self.gateway_client.send_command(command)
    932         return_value = get_return_value(
--> 933             answer, self.gateway_client, self.target_id, self.name)
    934 
    935         for temp_arg in temp_args:

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc 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()

/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    310                 raise Py4JJavaError(
    311                     "An error occurred while calling {0}{1}{2}.\n".
--> 312                     format(target_id, ".", name), value)
    313             else:
    314                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/hp/Downloads/spark-2.0.0-bin-hadoop2.7/auto-save.csv
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:287)
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:200)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
    at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:53)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:893)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:892)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:280)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:211)
    at java.lang.Thread.run(Thread.java:745)

Answer 1

我遇到了同样的错误，我解决了。如果我们将 Spark 上下文配置为工作内核数超过系统支持的内核数。就像我有 3 个核心系统，但在我的代码中，当我提到下面的代码时，它不会工作，因为我没有第 4 个核心。

我收到 Py4JJavaerror 的不支持的 Spark 上下文配置代码:

from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("Collinear Points").setMaster("local[4]") #Initialize spark context using 4 local cores as workers
sc = SparkContext(conf=conf)    
from pyspark.rdd import RDD

支持所有类型系统的 SparkContext 配置代码，因为在下面我们没有将内核显式初始化为工作线程。

from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("Collinear Points")
sc = SparkContext('local',conf=conf)    
from pyspark.rdd import RDD