Question

我正在使用Ubuntu和本地 Spark安装（spark-2.0.2）。我的数据集很小，我的代码运行在我有一个小数据。如果我用更多行增加数据集（txt文件），则会出现错误。

我在Cloudera VM上尝试了完全相同的代码，其中安装了Hadoop并且工作正常。

因此，它必须是我的Ubuntu机器上的一些内存问题或限制。

还有一些类似的问题，例如：Apache Spark: pyspark crash for large dataset

但在我的情况下，它没有帮助。我没有Hadoop集群，只有Spark，python 2.7和java 1.8。它工作正常，只是当有一些更复杂的计算或数据集更大时它会崩溃。

有任何线索吗？

错误：

spark-submit myCalc.py

ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 175, in main
    process()
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 167, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 317, in func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 1792, in combineLocally
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/shuffle.py", line 238, in mergeValues
    d[k] = comb(d[k], v) if k in d else creator(v)
  File "/home/alg/Documents//Spark/code/customer_orders/myCalc.py", line 24, in <lambda>
    reduced_total = RDD_map.reduceByKey(lambda x,y: (x[1]+y[1]))
TypeError: 'float' object has no attribute '__getitem__'

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
16/12/01 23:25:51 ERROR TaskSetManager: Task 0 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
  File "/home/alg/Documents//Spark/code/customer_orders/myCalc.py", line 28, in <module>
    results = reduced_total.collect()
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 776, in collect
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 175, in main
    process()
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 167, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 317, in func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 1792, in combineLocally
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/shuffle.py", line 238, in mergeValues
    d[k] = comb(d[k], v) if k in d else creator(v)
  File "/home/alg/Documents//Spark/code/customer_orders/myCalc.py", line 24, in <lambda>
    reduced_total = RDD_map.reduceByKey(lambda x,y: (x[1]+y[1]))
TypeError: 'float' object has no attribute '__getitem__'

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1873)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1886)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1899)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1913)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:912)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:911)
    at org.apache.spark.api.python.PythonRDD$.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:498)
    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:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 175, in main
    process()
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 167, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2371, in pipeline_func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 317, in func
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 1792, in combineLocally
  File "/home/alg/programs/spark-2.0.2-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/shuffle.py", line 238, in mergeValues
    d[k] = comb(d[k], v) if k in d else creator(v)
  File "/home/alg/Documents//Spark/code/customer_orders/myCalc.py", line 24, in <lambda>
    reduced_total = RDD_map.reduceByKey(lambda x,y: (x[1]+y[1]))
TypeError: 'float' object has no attribute '__getitem__'

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:390)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

Answer 1

因此，它必须是我的（...）机器上的一些内存问题或限制。

不是。虽然您没有在正常情况下提供可重现的示例（具有明智的__add__和“ getitem ＆＃39;实现）以下功能：

lambda x, y: x[1] + y[1]

不是reduceByKey的有效有效选择。您传递给reduceByKey的功能必须为associative and commutative。显然它应该采用与返回类型相同类型的参数。

使用Python 3.5+注释：

from typing import TypeVar

T = TypeVar('T')

def (t1: T, t2: T) -> T:
    return ...

为什么使用功能并不总是失败？因为它的行为取决于数据分布。我们假设你有形状元组(string, (string, float))：

will_succeed = sc.parallelize([
  ("a", ("foo", 1.0)), ("a", ("bar", 1.0)),
  ("b", ("foo", 1.0)), ("b", ("bar", 1.0))
], 2)

will_succeed.reduceByKey(lambda x, y: x[1] + y[1]).collect()

[('b', 2.0), ('a', 2.0)]

VS：

will_fail = sc.parallelize([
  ("a", ("foo", 1.0)), ("a", ("bar", 1.0)), ("a", ("baz", 1.0)),
  ("b", ("foo", 1.0)), ("b", ("bar", 1.0))
], 2)

will_fail.reduceByKey(lambda x, y: x[1] + y[1]).collect()

TypeError: 'float' object is not subscriptable
...

在第一种情况下，密钥a的执行顺序为：

f(("foo", 1.0), ("bar", 1.0))
2.0

其中f你的功能。在第二种情况下，它将等同于（不一定按此顺序）：

f(f(("foo", 1.0),  ("bar", 1.0)), ("baz", 1.0))
f(2.0, ("baz", 1.0))
exception!

正确的解决方案可能是：

from operator import itemgetter, add

# will fail no more
will_fail.mapValues(itemgetter(1)).reduceByKey(add)

也可以使用一个combineByKey和aggregateByKey：

will_fail.combineByKey(itemgetter(1), lambda x, y: x + y[1], add)

Spark：某些数据集的pyspark崩溃 - ubuntu

1 个答案: