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？ [英] What does Exception: Randomness of hash of string should be disabled via PYTHONHASHSEED mean in pyspark?

查看：3174
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IT屋！

查看全文
相关文章
其他开发最新文章
热门教程
热门工具
登录 关闭
扫码关注1秒登录
发送“验证码”获取 | 15天全站免登陆