Question

我使用tar文件方法在3台机器上设置了spark。我没有做任何高级配置，我编辑了slaves文件并启动了master和worker。我能在8080端口看到sparkUI。现在我想在spark集群上运行简单的python脚本。

import sys
from random import random
from operator import add

from pyspark import SparkContext


if __name__ == "__main__":
    """
        Usage: pi [partitions]
    """
    sc = SparkContext(appName="PythonPi")
    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
    n = 100000 * partitions

    def f(_):
        x = random() * 2 - 1
        y = random() * 2 - 1
        return 1 if x ** 2 + y ** 2 < 1 else 0

    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
    print "Pi is roughly %f" % (4.0 * count / n)

    sc.stop()

我正在运行此命令

spark-submit --master spark：// IP：7077 pi.py 1

但是得到以下错误

14/12/22 18:31:23 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
14/12/22 18:31:38 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:31:43 INFO client.AppClient$ClientActor: Connecting to master spark://10.77.36.243:7077...
14/12/22 18:31:53 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:32:03 INFO client.AppClient$ClientActor: Connecting to master spark://10.77.36.243:7077...
14/12/22 18:32:08 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:32:23 ERROR cluster.SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up.
14/12/22 18:32:23 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
14/12/22 18:32:23 INFO scheduler.TaskSchedulerImpl: Cancelling stage 0
14/12/22 18:32:23 INFO scheduler.DAGScheduler: Failed to run reduce at /opt/pi.py:21
Traceback (most recent call last):
  File "/opt/pi.py", line 21, in <module>
    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
  File "/usr/local/spark/python/pyspark/rdd.py", line 759, in reduce
    vals = self.mapPartitions(func).collect()
  File "/usr/local/spark/python/pyspark/rdd.py", line 723, in collect
    bytesInJava = self._jrdd.collect().iterator()
  File "/usr/local/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/usr/local/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect.
: org.apache.spark.SparkException: Job aborted due to stage failure: All masters are unresponsive! Giving up.
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)

是否有人面临同样的问题。 Plz帮忙。

Answer 1

此：

WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

表明群集没有任何可用资源。

检查群集的状态并检查核心和RAM（http://www.datastax.com/dev/blog/common-spark-troubleshooting）。

另外，请仔细检查您的IP地址。

有关更多提示：Running a Job on Spark 0.9.0 throws error

如何在spark作业中运行python脚本？

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