本文介绍了什么例外：字符串的哈希的随机性应通过PYTHONHASHSEED被禁止在pyspark是什么意思？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
我想创建从pyspark列表的字典。我列出了以下列表：
I am trying to create a dictionary from a list in pyspark. I have the following list of lists:
rawPositions
给出
[[1009794, 'LPF6 Comdty', 'BC22', 'Enterprise', 3.0, 3904.125, 390412.5],
[1009794, 'LPF6 Comdty', 'BC22', 'Enterprise', 3.0, 3900.75, 390075.0],
[1009794, 'LPF6 Comdty', 'BC22', 'Enterprise', 3.0, 3882.5625, 388256.25],
[1009794, 'LPF6 Comdty', 'BC22', 'Enterprise', 3.0, 3926.25, 392625.0],
[2766232,
'CDX IG CDSI S25 V1 5Y CBBT CORP',
'BC85',
'Enterprise',
30000000.0,
-16323.2439825,
30000000.0],
[2766232,
'CDX IG CDSI S25 V1 5Y CBBT CORP',
'BC85',
'Enterprise',
30000000.0,
-16928.620101900004,
30000000.0],
[1009804, 'LPM6 Comdty', 'BC29', 'Jet', 105.0, 129596.25, 12959625.0],
[1009804, 'LPM6 Comdty', 'BC29', 'Jet', 128.0, 162112.0, 16211200.0],
[1009804, 'LPM6 Comdty', 'BC29', 'Jet', 135.0, 167146.875, 16714687.5],
[1009804, 'LPM6 Comdty', 'BC29', 'Jet', 109.0, 132884.625, 13288462.5]]
然后用我的sparkcontext变量SC我并行名单
Then using my sparkcontext variable sc I parallelize the list
i = sc.parallelize(rawPositions)
#i.collect()
然后我尝试通过各列表项的第三个元素上的GROUPBY功能，把它变成一本字典。
Then I try to turn it into a dictionary by using a groupby function on the 3rd element of each list entry.
j = i.groupBy(lambda x: x[3])
j.collect()
给出
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-143-6113a75f0a9e> in <module>()
2 #i.collect()
3 j = i.groupBy(lambda x: x[3])
----> 4 j.collect()
/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.py 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
/net/nas/uxhome/condor_ldrt-s/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:
/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/pyspark/sql/utils.py 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()
/net/nas/uxhome/condor_ldrt-s/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.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 14 in stage 50.0 failed 4 times, most recent failure: Lost task 14.3 in stage 50.0 (TID 7583, brllxhtce01.bluecrest.local): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
process()
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/serializers.py", line 133, in dump_stream
for obj in iterator:
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.py", line 1703, in add_shuffle_key
buckets[partitionFunc(k) % numPartitions].append((k, v))
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/rdd.py", line 74, in portable_hash
raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED")
Exception: Randomness of hash of string should be disabled via PYTHONHASHSEED
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:342)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:405)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.GeneratedMethodAccessor31.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
process()
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/serializers.py", line 133, in dump_stream
for obj in iterator:
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/pyspark/rdd.py", line 1703, in add_shuffle_key
buckets[partitionFunc(k) % numPartitions].append((k, v))
File "/net/nas/uxhome/condor_ldrt-s/spark-1.6.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/rdd.py", line 74, in portable_hash
raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED")
Exception: Randomness of hash of string should be disabled via PYTHONHASHSEED
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:342)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
... 1 more
我不知道这个错误是指...任何帮助将是巨大的！
I have no idea what this error refers to... any help would be great!
推荐答案
由于 STR ，的Python的3.2.3+哈希字节在Python和日期时间的对象是使用随机值prevent某些类型的拒绝服务攻击盐渍。这意味着，哈希值是单间preTER会话中一致，但脱离盘中有所不同会话。 PYTHONHASHSEED 设置RNG的种子提供会话之间的一致的值。
Since Python 3.2.3+ hash of str, byte and datetime objects in Python is salted using random value to prevent certain kinds of denial-of-service attacks. It means that hash values are consistent inside single interpreter session but differ from session to session. PYTHONHASHSEED sets RNG seed to provide a consistent value between session.
您可以轻松地在你的shell检查。如果 PYTHONHASHSEED 没有设置，你会得到一些随机值：
You can easily check this in your shell. If PYTHONHASHSEED is not set you'll get some random values:
unset PYTHONHASHSEED
for i in `seq 1 3`;
do
python3 -c "print(hash('foo'))";
done
## -7298483006336914254
## -6081529125171670673
## -3642265530762908581
但是当它被设置你会得到每一次执行相同的值：
but when it is set you'll get the same value on each execution:
export PYTHONHASHSEED=323
for i in `seq 1 3`;
do
python3 -c "print(hash('foo'))";
done
## 8902216175227028661
## 8902216175227028661
## 8902216175227028661
由于 GROUPBY 并依赖于默认分区使用散列你需要的相同值的 PYTHONHASHSEED 等操作code>在群集中的所有机器得到一致的结果。
Since groupBy and other operations which depend on default partitioner use hashing you need the same value of PYTHONHASHSEED on all machines in the cluster to get consistent results.
另请参阅：
- Python Setup and Usage » Command line and environment
- oCERT 2011-003 multiple implementations denial-of-service via hash algorithm collision
这篇关于什么例外：字符串的哈希的随机性应通过PYTHONHASHSEED被禁止在pyspark是什么意思？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！