我对自己的情况感到困惑。我在 pyspark 中找到序列模式。 一开始我有这样的键值RDD
p_split.take(2)
[(['A', 'B', 'C', 'D'], u'749'),
(['O', 'K', 'A'], u'162')]
比起我找到字符串的组合并加入它们:
def patterns1(text):
output = [list(combinations(text, i)) for i in range(len(text) + 1)]
output = output[2:-1]
paths = []
for item in output:
for i in range(len(item)):
paths.append('->'.join(item[i]))
return paths
p_patterns = p_split.map(lambda (x,y): (patterns1(x), y))
p_patterns.take(2)
[(['A->B',
'A->C'
'A->D',
'B->C',
'B->D',
...
u'749'), .....
使用这个 RDD p_patterns，我不能做像 count() 和 collect() 这样的操作。使用 p_split 我成功地完成了这个操作。
p_patterns.count()
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-14-75eb19776fa7> in <module>()
----> 1 p_patterns.count()
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py in count(self)
930 3
931 """
--> 932 return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
933
934 def stats(self):
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py in sum(self)
921 6.0
922 """
--> 923 return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
924
925 def count(self):
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py in reduce(self, f)
737 yield reduce(f, iterator, initial)
738
--> 739 vals = self.mapPartitions(func).collect()
740 if vals:
741 return reduce(f, vals)
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py in collect(self)
711 """
712 with SCCallSiteSync(self.context) as css:
--> 713 port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
714 return list(_load_from_socket(port, self._jrdd_deserializer))
715
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 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 0 in stage 8.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8.0 (TID 8, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/worker.py", line 101, in main
process()
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/worker.py", line 96, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 2252, in pipeline_func
return func(split, prev_func(split, iterator))
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 2252, in pipeline_func
return func(split, prev_func(split, iterator))
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 2252, in pipeline_func
return func(split, prev_func(split, iterator))
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 282, in func
return f(iterator)
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 932, in <lambda>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "/usr/local/bin/spark-1.3.1-bin-hadoop2.6/python/pyspark/rdd.py", line 932, in <genexpr>
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File "<ipython-input-12-0e1339e78f5c>", line 1, in <lambda>
File "<ipython-input-11-b71a29b24fa7>", line 7, in patterns1
MemoryError
at org.apache.spark.api.python.PythonRDD$$anon$1.read(PythonRDD.scala:135)
at org.apache.spark.api.python.PythonRDD$$anon$1.<init>(PythonRDD.scala:176)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:94)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:64)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1192)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:693)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1393)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1354)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
我的错误是什么？
请您参考如下方法:
正如@lanenok 所指出的，这是一个内存错误，考虑到 patterns1 函数内部的情况，这并不奇怪。以下语句的内存复杂度:
o = [list(combinations(text, i)) for i in range(len(text) + 1)]
大致为 O(2^N)，其中 N 是输入文本的长度。
这背后还隐藏着第二个问题。它不会让事情变得比指数复杂度更糟，但它本身就很糟糕。当您将 combinations 转换为列表时，您失去了惰性序列的所有好处，可以利用它进一步插入内存复杂性设置的限制。
我会建议尽可能使用生成器和惰性函数(toolz rocks here)。我已经提到过这种方法 here所以请看一看。例如 pattern1 可以重写如下:
from itertools import combinations
from toolz.itertoolz import concat, map
def patterns1(text):
return map(
lambda x: '->'.join(x),
concat(combinations(text, i) for i in range(2, len(text) + 1)))
显然它不会解决内存复杂性问题，但它是如何优化算法的起点。
