Test build #42289 has finished for PR 8707 at commit d7ef6ce.

• This patch fails PySpark unit tests.
• This patch merges cleanly.
• This patch adds no public classes.

Test build #42293 has finished for PR 8707 at commit 6b9095b.

• This patch fails PySpark unit tests.
• This patch merges cleanly.
• This patch adds no public classes.

Test build #42295 has finished for PR 8707 at commit 1d766aa.

• This patch fails PySpark unit tests.
• This patch merges cleanly.
• This patch adds no public classes.

Test build #42298 has finished for PR 8707 at commit a2f9f36.

• This patch fails PySpark unit tests.
• This patch merges cleanly.
• This patch adds no public classes.

Test build #1739 has finished for PR 8707 at commit a2f9f36.

• This patch fails PySpark unit tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class SparkHadoopWriter(jobConf: JobConf)
  • class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])
  • class ZippedWithIndexRDD[T: ClassTag](prev: RDD[T]) extends RDD[(T, Long)](prev)
  • case class Instance(w: Double, a: Vector, b: Double)
  • class DefaultSource extends RelationProvider with DataSourceRegister
  • class WeibullGenerator(
  • class IndexToString(JavaTransformer, HasInputCol, HasOutputCol):
  • class Checkpoint(ssc: StreamingContext, val checkpointTime: Time)
  • abstract class InputDStream[T: ClassTag] (ssc_ : StreamingContext)
  • abstract class ReceiverInputDStream[T: ClassTag](ssc_ : StreamingContext)

Test build #42304 has finished for PR 8707 at commit 9326697.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds no public classes.

Over at https://issues.apache.org/jira/browse/SPARK-10544, someone commented to mention that other types of built-in types do not seem to be pickleable in 1.5. For instance, here's the example that they gave:

sc.parallelize(["the red", "Fox Runs", "FAST"]).map(str.lower).count()

However, this specific example also seems to fail in 1.3.1, so I don't think that this is a regression. Just wanted to mention this discussion here to make sure you were aware of it.

Do you have any intuition for why this worked prior to 1.5 without the changes implemented here?

Actually, one point of confusion: it looks like python/pyspark/serializers.py had some methods for serializing namedtuple classes with its _restore and _hack_namedtuple(cls) methods, but this patch also adds logic to cloudpickle.py to handle pickling of these instances. Do we need both of these methods? Are some only needed by certain versions of Python? It would be good to add some comments to explain some of this stuff, since it's not obvious from reading the code.

The HACK in serializers.py is used for cPickler, not cloudpickle.

Before 1.5, the old way work in CPython, but not PyPy (we don't have a unit test for it).

@JoshRosen BTW, this patch introduce a special case for namedtuple, it should be safe to merge into branch-1.5.

Empirically, this seems to work, so unless you think that we should investigate the root cause any further I'm fine with giving this an LGTM and merging to 1.5. Feel free to merge yourself, or I can do it.

I tried to find the root cause, but it seems hard to work in all Python versions (you can see them in the older commit), finally switch to current approach. merging this into master and 1.5 branch, thanks!

FYI, this works for us @ NinthDecimal. Thanks for the fix, it was a stumper!

Python 2.7.6
Spark-1.5 Branch @ 4c4a9ba28d9052fad45caca9a1eba9ef9db309d5

Hi! I am not sure if this is related but is I look for this issue everything points me here basically. I'm getting

  ...
    f(self, obj) # Call unbound method with explicit self
  File "/usr/lib/python2.7/pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "/usr/lib/python2.7/pickle.py", line 681, in _batch_setitems
    save(v)
  File "/usr/lib/python2.7/pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "/opt/spark/python/lib/pyspark.zip/pyspark/cloudpickle.py", line 315, in save_builtin_function
    return self.save_function(obj)
  File "/opt/spark/python/lib/pyspark.zip/pyspark/cloudpickle.py", line 191, in save_function
    if islambda(obj) or obj.__code__.co_filename == '<stdin>' or themodule is None:
AttributeError: 'builtin_function_or_method' object has no attribute '__code__'

When trying to create a data frame from an RDD:

rdd = self.sc.textFile(self.input_file_path).map(lambda line: self.process_line(line))

schema = StructType([StructField(u'Variable', StringType(), nullable=False),
                     StructField(u'Time', TimestampType(), nullable=False),
                     StructField(u'Value', FloatType(), nullable=False)])

return sql_context.createDataFrame(rdd, schema)

I am on PySpark 1.6.0 - any ideas what I'm doing wrong here?

I also get this error when using namedtuples

n = namedtuple('test', ['a', 'b'])
cloudpickle.dumps(n(1,2))