pyspark - реализовать помощник в rdd.map (...) - PullRequest
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pyspark - реализовать помощник в rdd.map (...)

0 голосов
/ 14 января 2020

У меня есть rdd с парами точек. 

[(((2, 1), 0.5), ((4, 2), (6, 3))),
(((2, 1), -0.6), (-3, 4)),
(((-3, 4), -0.2857142857142857), (4, 2)),
(((-3, 4), -0.6), (2, 1)),
(((-3, 4), -0.1111111111111111), (6, 3)),
(((4, 2), 0.5), ((2, 1), (6, 3))),
(((4, 2), -0.2857142857142857), (-3, 4)),
(((6, 3), 0.5), ((4, 2), (2, 1))),
(((6, 3), -0.1111111111111111), (-3, 4))]

Целевой rdd будет выглядеть так: 

[((2, 1), (4, 2), (6, 3)),
((2, 1), (-3, 4)),
((-3, 4), (4, 2)),
((-3, 4), (2, 1)),
((-3, 4), (6, 3)),
((4, 2), (2, 1), (6, 3)),
((4, 2), (-3, 4)),
((6, 3), (4, 2), (2, 1)),
((6, 3), (-3, 4))]

Чтобы попасть туда, меня попросили использовать вспомогательную функцию : 

def format_result(x):
    x[1].append(x[0][0])
    return tuple(x[1])

Будучи искрометным новичком, я пытался сделать это: 

rbb.map(lambda x: (format_result(x[0][0])))

Само собой разумеется, это не работает.

Может Пожалуйста, помогите мне внедрить помощника в мою карту (если карта - лучший метод).

Вот мой трассировка: 

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-954-dfb7f775ddfb> in <module>
      1 #trythis.map(lambda x: ((x[0][0]) + (x[1]))).collect()
----> 2 trythis.map(lambda x: (format_result(x[0][0]))).collect()

/usr/local/opt/apache-spark/libexec/python/pyspark/rdd.py in collect(self)
    814         """
    815         with SCCallSiteSync(self.context) as css:
--> 816             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    817         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    818 

/usr/local/opt/apache-spark/libexec/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/usr/local/opt/apache-spark/libexec/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

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 3 in stage 1387.0 failed 1 times, most recent failure: Lost task 3.0 in stage 1387.0 (TID 6426, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/serializers.py", line 393, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args, **kwargs)
  File "<ipython-input-954-dfb7f775ddfb>", line 2, in <lambda>
  File "<ipython-input-926-98b4fd321335>", line 2, in format_result
AttributeError: 'int' object has no attribute 'append'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
    at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
    at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
    at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    at sun.reflect.GeneratedMethodAccessor45.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/serializers.py", line 393, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/usr/local/opt/apache-spark/libexec/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
    return f(*args, **kwargs)
  File "<ipython-input-954-dfb7f775ddfb>", line 2, in <lambda>
  File "<ipython-input-926-98b4fd321335>", line 2, in format_result
AttributeError: 'int' object has no attribute 'append'

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$class.foreach(Iterator.scala:891)
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
    at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
    at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
    at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

1 Ответ

0 голосов
/ 15 января 2020

Предположим, 

data = [
    (((2, 1), 0.5), ((4, 2), (6, 3))),
    (((2, 1), -0.6), (-3, 4)),
    (((-3, 4), -0.2857142857142857), (4, 2)),
    (((-3, 4), -0.6), (2, 1)),
    (((-3, 4), -0.1111111111111111), (6, 3)),
    (((4, 2), 0.5), ((2, 1), (6, 3))),
    (((4, 2), -0.2857142857142857), (-3, 4)),
    (((6, 3), 0.5), ((4, 2), (2, 1))),
    (((6, 3), -0.1111111111111111), (-3, 4))
]

Небольшое изменение вашей функции 

def format_result(x):
    if type(x[1][0]) == tuple:
        return tuple([x[0][0]] + [*x[1]])
    elif type(x[1][0]) == int:
        return tuple([x[0][0]] + [x[1]])

И, к вашему lambda в map

data_rdd = spark.sparkContext.parallelize(data). \
    map(lambda k: format_result(k))

Вы пытались добавить значения к tuple или int в зависимости от значения в строке. Я просто изменил его на list и добавил значения. И передал весь ряд data[i] в функцию. 

# [((2, 1), (4, 2), (6, 3)),
#  ((2, 1), (-3, 4)),
#  ((-3, 4), (4, 2)),
#  ((-3, 4), (2, 1)),
#  ((-3, 4), (6, 3)),
#  ((4, 2), (2, 1), (6, 3)),
#  ((4, 2), (-3, 4)),
#  ((6, 3), (4, 2), (2, 1)),
#  ((6, 3), (-3, 4))]
...