Apache spark Spark:初始作业未接受任何资源

我在4台不同的机器上安装了一个Spark群集。每台机器都有7.7gb内存和8核i7处理器。我正在使用Pyspark并尝试将5个numpy阵列（每个2.9gb）加载到集群中。它们都是我在另一台机器上生成的更大的14gb numpy阵列的一部分。我试图在第一个rdd上运行一个简单的计数函数，以确保集群正常运行。我在执行时收到以下警告：

>>> import numpy as np
>>> gen1 = sc.parallelize(np.load('/home/hduser/gen1.npy'),512)
>>> gen1.count()
[Stage 0:>                                                        (0 + 0) / 512]
17/01/28 13:07:07 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
17/01/28 13:07:22 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
17/01/28 13:07:37 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
[Stage 0:>                                                        (0 + 0) / 512]
17/01/28 13:07:52 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
^C
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/spark/python/pyspark/rdd.py", line 1008, in count
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "/opt/spark/python/pyspark/rdd.py", line 999, in sum
    return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
  File "/opt/spark/python/pyspark/rdd.py", line 873, in fold
    vals = self.mapPartitions(func).collect()
  File "/opt/spark/python/pyspark/rdd.py", line 776, in collect
    port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
  File "/opt/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 931, in __call__
  File "/opt/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 695, in send_command
  File "/opt/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 828, in send_command
  File "/home/hduser/anaconda2/lib/python2.7/socket.py", line 451, in readline
    data = self._sock.recv(self._rbufsize)
  File "/opt/spark/python/pyspark/context.py", line 223, in signal_handler
    raise KeyboardInterrupt()
KeyboardInterrupt

这些设置在每台工作机器上都是相同的

spark-defaults.conf（主）中的我的设置：

每个辅助进程只有

spark.master

和

spark.serializer

配置选项设置如上所述

我还需要弄清楚如何优化我的内存管理，因为在这个问题出现之前，我本应该有足够的内存，但Java堆空间异常却被左右抛出。但也许我会把它留给另一个问题

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请帮忙

如果您可以在web UI中找到spark Slave，但它们不接受作业，则很可能是防火墙阻止了通信

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您可以像我的另一个答案中那样进行测试：

如果您可以在web UI中找到spark Slave，但他们不接受作业，那么很有可能防火墙正在阻止通信

你可以像我的另一个答案一样做一个测试：

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SPARK_MASTER_IP=192.168.1.2

spark.master    spark://lebron:7077
spark.serializer    org.apache.spark.serializer.KryoSerializer
spark.driver.memory 5g
spark.dynamicAllocation.enabled true
spark.shuffle.service.enabled   true
spark.kryoserializer.buffer.max 128m