星火不能咸菜method_descriptor [英] Spark can't pickle method_descriptor
本文介绍了星火不能咸菜method_descriptor的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我得到这个奇怪的错误消息
I get this weird error message
15/01/26 13:05:12 INFO spark.SparkContext: Created broadcast 0 from wholeTextFiles at NativeMethodAccessorImpl.java:-2
Traceback (most recent call last):
File "/home/user/inverted-index.py", line 78, in <module>
print sc.wholeTextFiles(data_dir).flatMap(update).top(10)#groupByKey().map(store)
File "/home/user/spark2/python/pyspark/rdd.py", line 1045, in top
return self.mapPartitions(topIterator).reduce(merge)
File "/home/user/spark2/python/pyspark/rdd.py", line 715, in reduce
vals = self.mapPartitions(func).collect()
File "/home/user/spark2/python/pyspark/rdd.py", line 676, in collect
bytesInJava = self._jrdd.collect().iterator()
File "/home/user/spark2/python/pyspark/rdd.py", line 2107, in _jrdd
pickled_command = ser.dumps(command)
File "/home/user/spark2/python/pyspark/serializers.py", line 402, in dumps
return cloudpickle.dumps(obj, 2)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 816, in dumps
cp.dump(obj)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 133, in dump
return pickle.Pickler.dump(self, obj)
File "/usr/lib/python2.7/pickle.py", line 224, in dump
self.save(obj)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 562, in save_tuple
save(element)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 254, in save_function
self.save_function_tuple(obj, [themodule])
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 304, in save_function_tuple
save((code, closure, base_globals))
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 548, in save_tuple
save(element)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 600, in save_list
self._batch_appends(iter(obj))
File "/usr/lib/python2.7/pickle.py", line 633, in _batch_appends
save(x)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 254, in save_function
self.save_function_tuple(obj, [themodule])
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 304, in save_function_tuple
save((code, closure, base_globals))
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 548, in save_tuple
save(element)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 600, in save_list
self._batch_appends(iter(obj))
File "/usr/lib/python2.7/pickle.py", line 633, in _batch_appends
save(x)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 254, in save_function
self.save_function_tuple(obj, [themodule])
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 304, in save_function_tuple
save((code, closure, base_globals))
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 548, in save_tuple
save(element)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 600, in save_list
self._batch_appends(iter(obj))
File "/usr/lib/python2.7/pickle.py", line 636, in _batch_appends
save(tmp[0])
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 249, in save_function
self.save_function_tuple(obj, modList)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 309, in save_function_tuple
save(f_globals)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 650, in save_reduce
save(state)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 650, in save_reduce
save(state)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 650, in save_reduce
save(state)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 547, in save_inst
self.save_inst_logic(obj)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 537, in save_inst_logic
save(stuff)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 547, in save_inst
self.save_inst_logic(obj)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 537, in save_inst_logic
save(stuff)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 616, in save_reduce
save(cls)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 467, in save_global
d),obj=obj)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 631, in save_reduce
save(args)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib/python2.7/pickle.py", line 548, in save_tuple
save(element)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 174, in save_dict
pickle.Pickler.save_dict(self, obj)
File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
save(v)
File "/usr/lib/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 616, in save_reduce
save(cls)
File "/usr/lib/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/home/user/spark2/python/pyspark/cloudpickle.py", line 442, in save_global
raise pickle.PicklingError("Can't pickle builtin %s" % obj)
pickle.PicklingError: Can't pickle builtin <type 'method_descriptor'>
我的更新FUNC返回类型的元列表（键，（值1，值2））键，所有的人都串如下所示：
My update func returns a list of tuples of type (key, (value1, value2)) and all of them are strings as seen below:
def update(doc):
doc_id = doc[0][path_len:-ext_len] #actual file name
content = doc[1].lower()
new_fi = regex.split(content)
old_fi = fi_table.row(doc_id)
fi_table.put(doc_id, {'cf:col': ",".join(new_fi)})
if not old_fi:
return [(term, ('add', doc_id)) for term in new_fi]
else:
new_fi = set(new_fi)
old_fi = set(old_fi['cf:col'].split(','))
return [(term, ('add', doc_id)) for term in new_fi - old_fi] + \
[(term, ('del', doc_id)) for term in old_fi - new_fi]
编辑：
问题在于这些2 HBase的功能，行和看跌期权。当我评论他们两个的code ++工程（设置old_fi为空字典），但如果他们中的一个运行时，它产生的上述错误。我用happybase在python运行HBase的。有人可以解释我什么不顺心？
The problem lies on these 2 hbase functions, the row and the put. When I comment them both the code works (setting the old_fi as an empty dictionary) but if one of them runs, it produces the above error. I use happybase to operate hbase in python. Can someone explain me what goes wrong?
推荐答案
星火试图序列化对象的连接，因此它可以执行人内使用，这必将失败，因为反序列化的数据库连接对象不能授予读/写权限另一个范围（甚至计算机）。该问题可以通过尝试以广播连接对象被再现。对于这种情况下出现了一个问题上的序列化I / O对象。
Spark tries to serialize the connect object so it can be used inside the executors, which will surely fail because a deserialized db connect object can't grant read/write permission to another scope (or even computer). The problem can be reproduced by trying to broadcast the connect object. For this instance there was a problem on serializing an i/o object.
问题部分被连接到地图功能数据库里面解决。由于将有在地图中的每个功能单元RDD太多的连接，我不得不切换到分区处理，以减少约8-64（基于分区数）从20K的数据库连接。星火开发者应当考虑为执行人初始化函数/脚本，以避免这些类型的死胡同问题。
The problem was partly solved by connecting to the database inside the map functions. Since there will be too many connections for each RDD element in the map function, I had to switch to partition processing to reduce the db connections from 20k to about 8-64 (based on number of partitions). Spark developers should consider creating an initialization function/script for the executors to avoid these kind of dead end problems.
所以我们可以说我被每个节点执行该初始化函数，那么每一个节点将连接到数据库中（部分康恩池，或者单独的动物园管理员节点），因为初始化功能和地图功能将共享相同的范围，那么问题就消失了，所以你写得更快code比我找到了解决办法。在执行火花年底将释放/卸载这些定义的变量，程序将结束。
So let's say I got this init function executed by every node, then every node will be connected to the database (some conn pool, or separate zookeeper nodes) because the init function and the map functions will share the same scope, and then the problem is gone, so you write faster code than the workaround I found. At the end of the execution spark will free/unload these defined variables and the program will end.
这篇关于星火不能咸菜method_descriptor的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
