我可以在常规Spark映射操作中使用Spark DataFrame吗? [英] Can I use Spark DataFrame inside regular Spark map operation?
问题描述
我试图通过如下所示的常规Spark映射操作使用在Spark DataFrame之前定义的内容:
I tried to use defined before Spark DataFrame from a regular Spark map operation like below:
businessJSON = os.path.join(targetDir, 'business.json')
businessDF = sqlContext.read.json(businessJSON)
reviewsJSON = os.path.join(targetDir, 'review.json')
reviewsDF = sqlContext.read.json(reviewsJSON)
contains = udf(lambda xs, val: val in xs, BooleanType())
def selectReviews(category):
businessesByCategory = businessDF[contains(businessDF.categories, lit(category))]
selectedReviewsDF = reviewsDF.join(businessesByCategory,\
businessesByCategory.business_id == reviewsDF.business_id)
return selectedReviewsDF.select("text").map(lambda x: x.text)
categories = ['category1', 'category2']
rdd = (sc.parallelize(cuisines)
.map(lambda c: (c, selectReviews(c)))
)
rdd.take(1)
并且我收到一条巨大的错误消息:
and I've got a huge error message:
Py4JError Traceback (most recent call last)
<ipython-input-346-051af5183a76> in <module>()
12 )
13
---> 14 rdd.take(1)
/usr/local/Cellar/apache-spark/1.4.1/libexec/python/pyspark/rdd.pyc in take(self, num)
1275
1276 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1277 res = self.context.runJob(self, takeUpToNumLeft, p, True)
1278
1279 items += res
/usr/local/Cellar/apache-spark/1.4.1/libexec/python/pyspark/context.pyc in runJob(self, rdd, partitionFunc, partitions, allowLocal)
894 # SparkContext#runJob.
895 mappedRDD = rdd.mapPartitions(partitionFunc)
--> 896 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions,
897 allowLocal)
898 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
/usr/local/Cellar/apache-spark/1.4.1/libexec/python/pyspark/rdd.pyc in _jrdd(self)
2361 command = (self.func, profiler, self._prev_jrdd_deserializer,
2362 self._jrdd_deserializer)
-> 2363 pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self.ctx, command, self)
2364 python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(),
2365 bytearray(pickled_cmd),
/usr/local/Cellar/apache-spark/1.4.1/libexec/python/pyspark/rdd.pyc in _prepare_for_python_RDD(sc, command, obj)
2281 # the serialized command will be compressed by broadcast
2282 ser = CloudPickleSerializer()
-> 2283 pickled_command = ser.dumps(command)
2284 if len(pickled_command) > (1 << 20): # 1M
2285 # The broadcast will have same life cycle as created PythonRDD
...
/Users/igorsokolov/anaconda/lib/python2.7/pickle.pyc in save(self, obj)
304 reduce = getattr(obj, "__reduce_ex__", None)
305 if reduce:
--> 306 rv = reduce(self.proto)
307 else:
308 reduce = getattr(obj, "__reduce__", None)
/usr/local/Cellar/apache-spark/1.4.1/libexec/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/Cellar/apache-spark/1.4.1/libexec/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
302 raise Py4JError(
303 'An error occurred while calling {0}{1}{2}. Trace:\n{3}\n'.
--> 304 format(target_id, '.', name, value))
305 else:
306 raise Py4JError(
Py4JError: An error occurred while calling o96495.__getnewargs__. Trace:
py4j.Py4JException: Method __getnewargs__([]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:333)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:342)
at py4j.Gateway.invoke(Gateway.java:252)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
我进行了一些调查，以了解哪一行确切地导致了此错误，并且我发现获得此错误的最低代码是:
I make some investigation to understand which line exactly leads to this error and I found out that the minimum code to get this error is:
def selectReviews(category):
return reviewsDF.select("text")
rdd = (sc.parallelize(categories)
.map(lambda c: (c, selectReviews(c)))
)
rdd.take(1)
因此，我得出的结论是我以某种方式使用了错误的DataFrame，但从Spark文档中还不清楚.我怀疑reviewDF应该分布在群集中的所有计算机上，但是我猜想，因为我已经使用SqlContext创建了它，所以它应该已经在Spark Context中了.
Thus I make conclusion I use somehow wrong DataFrame, but what exactly is not clear from the Spark documentation. I have suspicious that reviewsDF should be distributed across all machines in a cluster, but I guess since I've created using SqlContext this should be already in the Spark Context.
谢谢.
推荐答案
火花不是可重入的.具体来说，工作人员无法在另一个动作或转换的步骤内执行新的RDD动作或转换.
Spark is not re-entrant. Specifically, workers can not execute new RDD actions or transformations within a step of another action or transformation.
当在工作节点上发生的映射lambda函数中调用selectReviews时，会发生此问题，因为selectReviews要求在RDD后备reviewsDF上执行.select().
This issue occurs when selectReviews is called in a map's lambda function, which is occuring on a worker node, as selectReviews requires executing .select() on the RDD backing reviewsDF.
解决方法是在本地执行的简单的for循环或类似的替换categories的地方替换sc.parallelize.在每次调用selectReviews时发生的数据帧过滤中仍会涉及从spark加速.
The workaround is to replace sc.parallelize with a simple for loop or similar, over categories, executed locally. Speedup from spark will still be involved in the data frame filtering that occurs in each call to selectReviews.
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