我正在从O'Reilly Learning Spark教科书中复制并粘贴确切的代码，但出现错误：org.apache.spark.SparkException：由于阶段失败，作业中止了
我试图了解这段代码在做什么，但是由于无法运行而难以理解：
nums = sc.parallelize([1, 2, 3, 4])
sumCount = nums.combineByKey((lambda x: (x,1)),
(lambda x, y: (x[0] + y, x[1] + 1)),
(lambda x, y: (x[0] + y[0], x[1] + y[1])))
sumCount.map(lambda key, xy: (key, xy[0]/xy[1])).collectAsMap()
下面是完整错误，您有何见解？
Job aborted due to stage failure: Task 3 in stage 26.0 failed 1 times, most recent failure: Lost task 3.0 in stage 26.0 (TID 73, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/databricks/spark/python/pyspark/worker.py", line 480, in main
process()
File "/databricks/spark/python/pyspark/worker.py", line 470, in process
out_iter = func(split_index, iterator)
File "/databricks/spark/python/pyspark/rdd.py", line 2543, in pipeline_func
return func(split, prev_func(split, iterator))
File "/databricks/spark/python/pyspark/rdd.py", line 353, in func
return f(iterator)
File "/databricks/spark/python/pyspark/rdd.py", line 1905, in combineLocally
merger.mergeValues(iterator)
File "/databricks/spark/python/pyspark/shuffle.py", line 238, in mergeValues
for k, v in iterator:
TypeError: 'int' object is not iterable
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:514)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:650)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:633)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:468)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1124)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1130)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:139)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1526)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:503)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
答案 0 :(得分：1)
好吧。
假设上面的数字不是那么好，因为它不是（K，V）元组，则假定代码如下：
data = sc.parallelize( [(0, 2.), (0, 4.), (1, 0.), (1, 10.), (1, 20.)] )
sumCount = data.combineByKey(lambda value: (value, 1),
lambda x, value: (x[0] + value, x[1] + 1),
lambda x, y: (x[0] + y[0], x[1] + y[1]))
averageByKey = sumCount.map(lambda (label, (value_sum, count)): (label, value_sum / count))
print averageByKey.collectAsMap()
在Spark下，使用python2（pyspark），以上代码可以正常运行。
在Spark下，使用python3（pyspark），以上代码在以下位置生成错误：
averageByKey = sumCount.map(lambda (label, (value_sum, count)): (label, value_sum / count))
https://www.python.org/dev/peps/pep-3113/解释了为什么在Python 3中删除了“元组参数解包”功能。这似乎让我有些失望。
解决该问题的简单方法是将上述代码在线传递到https://www.pythonconverter.com/中并运行代码转换器。这是：
data = sc.parallelize( [(0, 2.), (0, 4.), (1, 0.), (1, 10.), (1, 20.)] )
sumCount = data.combineByKey(lambda value: (value, 1),
lambda x, value: (x[0] + value, x[1] + 1),
lambda x, y: (x[0] + y[0], x[1] + y[1]))
averageByKey = sumCount.map(lambda label_value_sum_count: (label_value_sum_count[0], label_value_sum_count[1][0] / label_value_sum_count[1][1]))
print(averageByKey.collectAsMap())
正确返回：
{0: 3.0, 1: 10.0}
averageByKey现在有一个不同的声明。您需要学习和阅读该链接，并熟悉Python 2 to 3 Converter。节省一些时间，您可以轻松地进入它。尊敬的SO会员保险柜与此也有一些问题，所以就在那里，不是那么简单。