尝试一个简单的例子，为了方便起见，我将使用方便的 toolz

  import sys 
 import base64 

 if sys.version_info<（3，）：
将cPickle导入为pickle 
 else：
从toolz.functoolz导入pickle 


导入组合

 rdd = sc.parallelize（[（1，{foo：bar}），（2，{bar：foo}）]）
 

现在，您的代码现在不是完全可移植的。 code>返回 str ，而在Python 3中它返回 tes 。让我们来说明一下：

• Python 2

    type（base64.b64encode（pickle.dumps（{foo：bar}）））
   ## str 
   




• Python 3 b $ b

    type（base64.b64encode（pickle.dumps（{foo：bar}）））
   ## bytes 
  
  
  
   $ b $ p $所以让我们将解码添加到流水线中：
  
   ＃相当于
  ＃def pickle_and_b64（x）：
  ＃return base64.b64encode（pickle.dumps（x））。decode （ascii）
  
   pickle_and_b64 =撰写（
   lambda x：x.decode（ascii），
   base64.b64encode，
   pickle.dumps 
  
   $ / code> 
  请注意，这不会假设任何特定形状的数据。因此，我们将使用 mapValues 来仅序列化键：
    serialized = rdd.mapValues（pickle_and_b64）
   serialized.first（）
   ## 1，u'KGRwMApTJ2ZvbycKcDEKUydiYXInCnAyCnMu'）
   
  现在我们可以按照简单格式进行操作并保存： 
  
  
    from tempfile import mkdtemp 
   import os 
  
   outdir = os.path.join（mkdtemp（），foo）
  
   serialized.map（lambda x：{0} \格式（* x））。saveAsTextFile（outdir）
   
  读取文件我们逆过程：
  
  
   ＃相当于
  ＃def b64_and_unpickle（x）：
  ＃return pickle .loads（base64.b64decode（x））
  
   b64_and_unpickle = compose（
   pickle.loads，
   base64.b64decode 
  ）
  
  解码=（sc.textFile（outdir）
   .map（lambda x：x.split（\ t））＃在Python 3中，我们可以简单地使用str.split 
   .mapValues（b64_and_unpickle） ）
  
   de coded.first（）
   ##（u'1'，{'foo'：'bar'}）
   
   
  
  Inspired from this question, I wrote some code to store an RDD (which was read from a Parquet file), with a Schema of (photo_id, data), in pairs, delimited by tabs, and just as a detail base 64 encode it, like this:
  def do_pipeline(itr):
     ...
     item_id = x.photo_id
  
  def toTabCSVLine(data):
    return '\t'.join(str(d) for d in data)
  
  serialize_vec_b64pkl = lambda x: (x[0], base64.b64encode(cPickle.dumps(x[1])))
  
  def format(data):
      return toTabCSVLine(serialize_vec_b64pkl(data))
  
  dataset = sqlContext.read.parquet('mydir')
  lines = dataset.map(format)
  lines.saveAsTextFile('outdir')
  
  So now, the point of interest: How to read that dataset and print for example its deserialized data?
  
  I am using Python 2.6.6.
  
  
  My attempt lies here, where for just verifying that everything can be done, I wrote this code:
  deserialize_vec_b64pkl = lambda x: (x[0], cPickle.loads(base64.b64decode(x[1])))
  
  base64_dataset = sc.textFile('outdir')
  collected_base64_dataset = base64_dataset.collect()
  print(deserialize_vec_b64pkl(collected_base64_dataset[0].split('\t')))
  
  which calls collect(), which for testing is OK, but in a real-world scenario would struggle...
  
  
  Edit:
  
  When I tried zero323's suggestion:
  foo = (base64_dataset.map(str.split).map(deserialize_vec_b64pkl)).collect()
  
  I got this error, which boils down to this:
  PythonRDD[2] at RDD at PythonRDD.scala:43
  16/08/04 18:32:30 WARN TaskSetManager: Lost task 4.0 in stage 0.0 (TID 4, gsta31695.tan.ygrid.yahoo.com): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
    File "/grid/0/tmp/yarn-local/usercache/gsamaras/appcache/application_1470212406507_56888/container_e04_1470212406507_56888_01_000009/pyspark.zip/pyspark/worker.py", line 98, in main
      command = pickleSer._read_with_length(infile)
    File "/grid/0/tmp/yarn-local/usercache/gsamaras/appcache/application_1470212406507_56888/container_e04_1470212406507_56888_01_000009/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
      return self.loads(obj)
    File "/grid/0/tmp/yarn-local/usercache/gsamaras/appcache/application_1470212406507_56888/container_e04_1470212406507_56888_01_000009/pyspark.zip/pyspark/serializers.py", line 422, in loads
      return pickle.loads(obj)
  UnpicklingError: NEWOBJ class argument has NULL tp_new
  
      at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
      at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
      at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
      at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
      at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
      at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
      at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
      at org.apache.spark.scheduler.Task.run(Task.scala:89)
      at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
      at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
      at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
      at java.lang.Thread.run(Thread.java:745)
  
  16/08/04 18:32:30 ERROR TaskSetManager: Task 12 in stage 0.0 failed 4 times; aborting job
  16/08/04 18:32:31 WARN TaskSetManager: Lost task 14.3 in stage 0.0 (TID 38, gsta31695.tan.ygrid.yahoo.com): TaskKilled (killed intentionally)
  16/08/04 18:32:31 WARN TaskSetManager: Lost task 13.3 in stage 0.0 (TID 39, gsta31695.tan.ygrid.yahoo.com): TaskKilled (killed intentionally)
  16/08/04 18:32:31 WARN TaskSetManager: Lost task 16.3 in stage 0.0 (TID 42, gsta31695.tan.ygrid.yahoo.com): TaskKilled (killed intentionally)
  ---------------------------------------------------------------------------
  Py4JJavaError                             Traceback (most recent call last)
  /homes/gsamaras/code/read_and_print.py in <module>()
       17     print(base64_dataset.map(str.split).map(deserialize_vec_b64pkl))
       18 
  ---> 19     foo = (base64_dataset.map(str.split).map(deserialize_vec_b64pkl)).collect()
       20     print(foo)
  
  /home/gs/spark/current/python/lib/pyspark.zip/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 
  
  /home/gs/spark/current/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:
  
  /home/gs/spark/current/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.
  
  
  
  
  Let's try a simple example. For convenience I'll be using handy toolz library but it is not really required here.
  import sys
  import base64
  
  if sys.version_info < (3, ):
      import cPickle as pickle
  else:
      import pickle
  
  
  from toolz.functoolz import compose
  
  rdd = sc.parallelize([(1, {"foo": "bar"}), (2, {"bar": "foo"})])
  
  Now, your code is not exactly portable right now. In Python 2 base64.b64encode returns str, while in Python 3 it returns bytes. Lets illustrate that:
  
  
  
  Python 2
  type(base64.b64encode(pickle.dumps({"foo": "bar"})))
  ## str
  
  
  
  
  Python 3
  type(base64.b64encode(pickle.dumps({"foo": "bar"})))
  ## bytes
  
  
  
  
  So lets add decoding to the pipeline:
  # Equivalent to 
  # def pickle_and_b64(x):
  #     return base64.b64encode(pickle.dumps(x)).decode("ascii")
  
  pickle_and_b64 = compose(
      lambda x: x.decode("ascii"),
      base64.b64encode,
      pickle.dumps
  )
  
  Please note that this doesn't assume any particular shape of the data. Because of that, we'll use mapValues to serialize only keys:
  serialized = rdd.mapValues(pickle_and_b64)
  serialized.first()
  ## 1, u'KGRwMApTJ2ZvbycKcDEKUydiYXInCnAyCnMu')
  
  Now we can follow it with simple format and save:
  from tempfile import mkdtemp
  import os
  
  outdir = os.path.join(mkdtemp(), "foo")
  
  serialized.map(lambda x: "{0}\t{1}".format(*x)).saveAsTextFile(outdir)
  
  To read the file we reverse the process:
  # Equivalent to
  # def  b64_and_unpickle(x):
  #     return pickle.loads(base64.b64decode(x))
  
  b64_and_unpickle = compose(
      pickle.loads,
      base64.b64decode
  )
  
  decoded = (sc.textFile(outdir)
      .map(lambda x: x.split("\t"))  # In Python 3 we could simply use str.split
      .mapValues(b64_and_unpickle))
  
  decoded.first()
  ## (u'1', {'foo': 'bar'})