javadba javadba - 1 year ago 113
Python Question

How to convert a DataFrame back to normal RDD in pyspark?

I need to use the

(rdd.)partitionBy(npartitions, custom_partitioner)

method that is not available on the DataFrame. All of the DataFrame methods refer only to DataFrame results. So then how to create an RDD from the DataFrame data?

Note: this is a change (in 1.3.0) from 1.2.0.

Update from the answer from @dpangmao: the method is .rdd. I was interested to understand if (a) it were public and (b) what are the performance implications.

Well (a) is yes and (b) - well you can see here that there are significant perf implications: a new RDD must be created by invoking mapPartitions :

In (note the file name changed as well (was

def rdd(self):
Return the content of the :class:`DataFrame` as an :class:`RDD`
of :class:`Row` s.
if not hasattr(self, '_lazy_rdd'):
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema

def applySchema(it):
cls = _create_cls(schema)
return itertools.imap(cls, it)

self._lazy_rdd = rdd.mapPartitions(applySchema)

return self._lazy_rdd

Answer Source

Use the method .rdd like this:

rdd = df.rdd
Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download