Question

我正在尝试从spark读取大约一百万个压缩文本文件到S3。每个文件的压缩大小介于50 MB和80 MB之间。总而言之，这是大约6.5TB的数据。

不幸的是，我遇到了内存不足的异常，我不知道如何解决。简单的事情：

raw_file_list = subprocess.Popen("aws s3 ls --recursive s3://my-bucket/export/", shell=True, stdout=subprocess.PIPE).stdout.read().strip().split('\n')
cleaned_names = ["s3://my-bucket/" + f.split()[3] for f in raw_file_list if not f.endswith('_SUCCESS')]
dat = sc.textFile(','.join(cleaned_names))
dat.count()

收率：

 ---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-22-8ce3c7d1073e> in <module>() ----> 1 dat.count()

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.pyc in count(self)
   1002         3
   1003         """
-> 1004         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1005 
   1006     def stats(self):

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.pyc in sum(self)
    993         6.0
    994         """
--> 995         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
    996 
    997     def count(self):

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.pyc in fold(self, zeroValue, op)
    867         # zeroValue provided to each partition is unique from the one provided
    868         # to the final reduce call
--> 869         vals = self.mapPartitions(func).collect()
    870         return reduce(op, vals, zeroValue)
    871 

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.pyc in collect(self)
    769         """
    770         with SCCallSiteSync(self.context) as css:
--> 771             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    772         return list(_load_from_socket(port, self._jrdd_deserializer))
    773 

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
    811         answer = self.gateway_client.send_command(command)
    812         return_value = get_return_value(
--> 813             answer, self.gateway_client, self.target_id, self.name)
    814 
    815         for temp_arg in temp_args:

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/utils.pyc in deco(*a, **kw)
     43     def deco(*a, **kw):
     44         try:
---> 45             return f(*a, **kw)
     46         except py4j.protocol.Py4JJavaError as e:
     47             s = e.java_exception.toString()

/tmp/spark-tmp-lminer/spark-1.6.1-bin-hadoop2.6/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    306                 raise Py4JJavaError(
    307                     "An error occurred while calling {0}{1}{2}.\n".
--> 308                     format(target_id, ".", name), value)
    309             else:
    310                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.lang.OutOfMemoryError: GC overhead limit exceeded

更新

部分问题似乎已通过此post解决。似乎火花难以从S3中输入如此多的文件。更新了错误，以便它现在只反映内存问题。

Answer 1

问题是文件太多了。解决方案似乎是通过读取文件子集并将它们合并为较小的数字来减少分区数量。但是，您无法使分区过大：500 - 1000 MB文件会导致问题。

从S3中将嵌套文本文件读入spark时出现内存错误

1 个答案: