我使用的是ubuntu和一个本地spark安装(spark-2.0.2)。我的数据集非常小,我的代码运行在一个很小的数据集中。如果我增加了数据集(txt文件)和几行更多的错误occours。
我在cloudera虚拟机上尝试了完全相同的代码,在那里安装了hadoop,效果很好。
所以,这一定是我的ubuntu机器的内存问题或限制。
还有一些类似的问题,如:apachespark:pyspark-crash-for-large-dataset
但对我来说没有帮助。我没有hadoop集群,只有spark、python2.7和java1.8。它工作得很好,只是当有一些更复杂的计算或数据集更大时,它崩溃了。
有什么线索吗?
错误:
spark提交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
1条答案
按热度按时间9rbhqvlz1#
所以,它一定是一些内存问题或限制我的(…)机器。
事实并非如此。而你没有提供一个可复制的例子在正常条件下(与明智的)
__add__
和“getitem”实现)以下函数:不是的有效选择
reduceByKey
. 传递给的函数reduceByKey
必须是结合的和交换的。显然,它应该采用与返回类型相同类型的参数。使用python 3.5+注解:
为什么你使用的函数并不总是失败?因为它的行为取决于数据分布。假设你有元组的形状
(string, (string, float))
:与:
在第一种情况下,键的执行顺序
a
将:哪里
f
你的职能。在第二种情况下,其等同于(不一定按此顺序):正确的解决方案可以是:
也可以使用一个
combineByKey
以及aggregateByKey
: