Question

我刚开始学习Spark编程和Python编程：你能不能帮我理解我的代码中的错误：

我在Jupyter笔记本中以交互模式运行代码。

以下测试代码工作正常，我测试了这个概念：

 rdd = sc.parallelize([('librarian', (1, [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])), ('librarian', (1, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]))])

result = rdd.reduceByKey(lambda x, y: ((x[0]+y[0]), (x[1][0]+y[1][0]), (x[1][1]+y[1][1]), (x[1][2]+y[1][2]), (x[1][3]+y[1][3]), (x[1][4]+y[1][4]), (x[1][5]+y[1][5]), (x[1][6]+y[1][6]), (x[1][7]+y[1][7]), (x[1][8]+y[1][8]), (x[1][9]+y[1][9]), (x[1][10]+y[1][10]), (x[1][11]+y[1][11])))
print (result.top(3))

输出：

[('librarian', (2, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0))]

下面也可以正常使用：

#[(movieid, genre_list)]

aggregateRDD = movieRDD.reduceByKey(lambda x, y: ((x[0]+y[0]), (x[1][0]+y[1][0]), (x[1][1]+y[1][1])))
print (aggregateRDD.top(3))

输出：

[(1682, [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (1681, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), (1680, [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])]

但是，当我在程序中使用类似的概念时，它并不接受。我做错了什么：

##############################################################################
### Analysis of Movie Ratings percentages across Occupation and Movie Genre
##############################################################################

from pyspark import SparkConf, SparkContext

conf = SparkConf().setMaster("local").setAppName("popularMovie")
sc = SparkContext(conf =conf)

###import movie ratings into RDD
ratingLines = sc.textFile("C:/SparkCourse/ml-100k/u.data")
###import user details into RDD
userLines = sc.textFile("///SparkCourse/ml-100k/u.user")
###import movie data into RDD
movieLines = sc.textFile("C:/SparkCourse/ml-100k/u.item")
###import genre data into RDD
genreLines = sc.textFile("C:/SparkCourse/ml-100k/u.genre")

###split on delimiter functions
def splitRatingTab(line):
    line = line.split('\t')
    return (int(line[0]), int(line[1]), int(line[2])) #(movieid, user, rating)
def splitUserPipe(line):
    line = line.split('|')
    return (int(line[0]), line[3]) #(user, occupation)
def splitMoviePipe(line):
    line = line.split('|')
    return (int(line[0]), list(listToIntElements(line[5:]))) #(movieid, genre_list[])


def listToIntElements(lst):
    """conver the boolean text ('0', '1') genre value to integers (0, 1)"""
    for cnt, _ in enumerate(lst):
        lst[cnt] = int(_)
    return lst

###create dictionary object for genreid and genre
def loadMovieGenre():
    """
    create dictionary object for genreid and genre
    """
    genre = {}
    with open('C:/SparkCourse/ml-100k/u.genre') as file:
        for line in file:
            #each line is of type [genere, genreid]
            line = line.split('|')
            #convert genreid to int, to remove new line '\n' at the end of string
            genre[int(line[1])] = line[0]
        return genre


### Transform to RDD as [(movieid, user, rating)] for movies, which are reviewed by viewers
ratingRDD = ratingLines.map(lambda line: splitRatingTab(line))

### Transform to RDD as [(user, occupation)]
occupationRDD = userLines.map(splitUserPipe)

### Transform to RDD as [(movieid, genre_list)], genre is boolean value, movie can be in multiple genres
movieRDD = movieLines.map(splitMoviePipe)

###join Transformed rating RDD [(movieid, (user, rating))] and movieRDD [(movieid, genre] to get all genres; 
###then Transform to [(movieid,((userid, rating), genre) )]
joinRatingMovieGenres = ratingRDD.map(lambda line: (line[0], (line[1], line[2]))).join(movieRDD)


###Transform joinRatingMovieGenres to RDD [userid, (rating, genre)] and join with occupationRDD [(userid, occupation)]
###to Transform to [(occupation, ((1, genre)))]
transRatingMovieGenres = joinRatingMovieGenres.map(lambda line: (line[1][0][0], (line[1][0][1], line[1][1])))
joinRatingGenresOccup = transRatingMovieGenres.join(occupationRDD).map(lambda line: (line[1][1], (1, line[1][0][1])))
print (joinRatingGenresOccup.take(2))


###Transform by Aggregating the ratingCount and genreCount to [(occupation, (totalRatings, {cntGenresRating}))]
totalRatingGenreCntByOccupation = joinRatingGenresOccup.reduceByKey(lambda x, y: ((x[0]+y[0]), (x[1][0]+y[1][0]), (x[1][1]+y[1][1]), (x[1][2]+y[1][12]), (x[1][3]+y[1][3])))
print (totalRatingGenreCntByOccupation.take(2))

错误：

[('librarian', (1, [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])), ('librarian', (1, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]))]
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-137-a156d8bbfde9> in <module>()
----> 1 get_ipython().run_cell_magic('time', '', '\n##############################################################################\n### Analysis of Movie Ratings percentages across Occupation and Movie Genre\n##############################################################################\n\n#import movie ratings into RDD\nratingLines = sc.textFile("C:/SparkCourse/ml-100k/u.data")\n#import user details into RDD\nuserLines = sc.textFile("///SparkCourse/ml-100k/u.user")\n#import movie data into RDD\nmovieLines = sc.textFile("C:/SparkCourse/ml-100k/u.item")\n#import genre data into RDD\ngenreLines = sc.textFile("C:/SparkCourse/ml-100k/u.genre")\n\n#split on delimiter functions\ndef splitRatingTab(line):\n    line = line.split(\'\\t\')\n    return (int(line[0]), int(line[1]), int(line[2])) #(movieid, user, rating)\ndef splitUserPipe(line):\n    line = line.split(\'|\')\n    return (int(line[0]), line[3]) #(user, occupation)\ndef splitMoviePipe(line):\n    line = line.split(\'|\')\n    return (int(line[0]), list(listToIntElements(line[5:]))) #(movieid, genre_list[])\n\n\ndef listToIntElements(lst):\n    """conver the boolean text (\'0\', \'1\') genre value to integers (0, 1)"""\n    for cnt, _ in enumerate(lst):\n        lst[cnt] = int(_)\n    return lst\n\n#create dictionary object for genreid and genre\ndef loadMovieGenre():\n    """\n    create dictionary object for genreid and genre\n    """\n    genre = {}\n    with open(\'C:/SparkCourse/ml-100k/u.genre\') as file:\n        for line in file:\n            #each line is of type [genere, genreid]\n            line = line.split(\'|\')\n            #convert genreid to int, to remove new line \'\\n\' at the end of string\n            genre[int(line[1])] = line[0]\n        return genre\n\n    \n# Transform to RDD as [(movieid, user, rating)] for movies, which are reviewed by viewers\nratingRDD = ratingLines.map(lambda line: splitRatingTab(line))\n#print (\'ratingRDD:\\n\',ratingRDD.top(5))\n\n# Transform to RDD as [(user, occupation)]\noccupationRDD = userLines.map(splitUserPipe)\n#print (\'occupationRDD:\\n\',occupationRDD.top(3))\n\n# Transform to RDD as [(movieid, genre_list)], genre is boolean value, movie can be in multiple genres\nmovieRDD = movieLines.map(splitMoviePipe)\n#print (\'movieRDD:\\n\',movieRDD.top(3))\n\n#join Transformed rating RDD [(movieid, (user, rating))] and movieRDD [(movieid, genre] to get all genres; \n#then Transform to [(movieid,((userid, rating), genre) )]\njoinRatingMovieGenres = ratingRDD.map(lambda line: (line[0], (line[1], line[2]))).join(movieRDD)\n#print (joinRatingMovieGenres.take(2))\n\n#Transform joinRatingMovieGenres to RDD [userid, (rating, genre)] and join with occupationRDD [(userid, occupation)]\n#to Transform to [(occupation, ((1, genre)))]\ntransRatingMovieGenres = joinRatingMovieGenres.map(lambda line: (line[1][0][0], (line[1][0][1], line[1][1])))\njoinRatingGenresOccup = transRatingMovieGenres.join(occupationRDD).map(lambda line: (line[1][1], (1, line[1][0][1])))\nprint (joinRatingGenresOccup.take(2))\n\n\n#Transform by Aggregating the ratingCount and genreCount to [(occupation, (totalRatings, {cntGenresRating}))]\ntotalRatingGenreCntByOccupation = joinRatingGenresOccup.reduceByKey(lambda x, y: ((x[0]+y[0]), (x[1][0]+y[1][0]), (x[1][1]+y[1][1]), (x[1][2]+y[1][12]), (x[1][3]+y[1][3])))\nprint (totalRatingGenreCntByOccupation.take(2))')

C:\Users\vmatcha\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\IPython\core\interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2113             magic_arg_s = self.var_expand(line, stack_depth)
   2114             with self.builtin_trap:
-> 2115                 result = fn(magic_arg_s, cell)
   2116             return result
   2117 

<decorator-gen-60> in time(self, line, cell, local_ns)

C:\Users\vmatcha\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\IPython\core\magic.py in <lambda>(f, *a, **k)
    186     # but it's overkill for just that one bit of state.
    187     def magic_deco(arg):
--> 188         call = lambda f, *a, **k: f(*a, **k)
    189 
    190         if callable(arg):

C:\Users\vmatcha\AppData\Local\Enthought\Canopy\edm\envs\User\lib\site-packages\IPython\core\magics\execution.py in time(self, line, cell, local_ns)
   1183         else:
   1184             st = clock2()
-> 1185             exec(code, glob, local_ns)
   1186             end = clock2()
   1187             out = None

<timed exec> in <module>()

C:\spark\python\pyspark\rdd.py in take(self, num)
   1356 
   1357             p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1358             res = self.context.runJob(self, takeUpToNumLeft, p)
   1359 
   1360             items += res

C:\spark\python\pyspark\context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
    999         # SparkContext#runJob.
   1000         mappedRDD = rdd.mapPartitions(partitionFunc)
-> 1001         port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
   1002         return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
   1003 

C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py in __call__(self, *args)
   1158         answer = self.gateway_client.send_command(command)
   1159         return_value = get_return_value(
-> 1160             answer, self.gateway_client, self.target_id, self.name)
   1161 
   1162         for temp_arg in temp_args:

C:\spark\python\pyspark\sql\utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

C:\spark\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    318                 raise Py4JJavaError(
    319                     "An error occurred while calling {0}{1}{2}.\n".
--> 320                     format(target_id, ".", name), value)
    321             else:
    322                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 239.0 failed 1 times, most recent failure: Lost task 1.0 in stage 239.0 (TID 447, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 229, in main
  File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 224, in process
  File "C:\spark\python\pyspark\rdd.py", line 2438, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "C:\spark\python\pyspark\rdd.py", line 2438, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "C:\spark\python\pyspark\rdd.py", line 362, in func
    return f(iterator)
  File "C:\spark\python\pyspark\rdd.py", line 1857, in combineLocally
    merger.mergeValues(iterator)
  File "C:\spark\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 "<timed exec>", line 73, in <lambda>
TypeError: 'int' object is not subscriptable

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:438)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:421)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1126)
    at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1132)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    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:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1587)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1586)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1586)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1820)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1769)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1758)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2027)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2048)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2067)
    at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:141)
    at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
    at sun.reflect.GeneratedMethodAccessor90.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:214)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 229, in main
  File "C:\spark\python\lib\pyspark.zip\pyspark\worker.py", line 224, in process
  File "C:\spark\python\pyspark\rdd.py", line 2438, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "C:\spark\python\pyspark\rdd.py", line 2438, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "C:\spark\python\pyspark\rdd.py", line 362, in func
    return f(iterator)
  File "C:\spark\python\pyspark\rdd.py", line 1857, in combineLocally
    merger.mergeValues(iterator)
  File "C:\spark\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 "<timed exec>", line 73, in <lambda>
TypeError: 'int' object is not subscriptable

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:438)
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:421)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1126)
    at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1132)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

Answer 1

当您reduceByKey 时，您必须返回已收到的相同结构，否则下次您将遇到相同键的值并尝试减少它时，您的功能将无效。< / p>

您只测试了两个元素，所以您还没有看到它，但是如果您尝试使用3 ...：

 rdd = sc.parallelize([('librarian', (1, [0, 0, 1, 0])), ('librarian', (1, [0, 1, 0, 0])),\
                      ('librarian', (1, [0, 1, 0, 0]))])

result = rdd.reduceByKey(lambda x, y: ((x[0]+y[0]),\
                            (x[1][0]+y[1][0]), (x[1][1]+y[1][1]), (x[1][2]+y[1][2]), (x[1][3]+y[1][3]) ))

.....文件 “/home/hado/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.py”，一行 1784年，在_mergeCombiners中 merger.mergeCombiners（iterator）File“/home/hado/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/shuffle.py”，第272行，在mergeCombiners中 d [k] = comb（d [k]，v）如果k in d else v File“”，第3行，在TypeError中： 'int'对象没有属性' getitem '......

在代码中执行reduceByKey的

正确方法是返回具有值和相同大小的列表的相同元组：

 rdd = sc.parallelize([('librarian', (1, [0, 0, 1, 0])), ('librarian', (1, [0, 1, 0, 0])),\
                      ('librarian', (1, [0, 1, 0, 0]))])

result = rdd.reduceByKey(lambda x, y: ( x[0] + y[0],\
                            [x[1][0]+y[1][0], x[1][1]+y[1][1], x[1][2]+y[1][2], x[1][3]+y[1][3] ] ))
print (result.collect())

[（'librarian'，（3，[0,2,1,0]））]

你也可以像这里解释的那样做一个combineByKey： `combineByKey`, pyspark

还要注意这个（在倒数第二行）：“（x [1] [2] + y [1] [12]）”似乎是一个错字。

分析职业和电影类型中的电影评级百分比

1 个答案: