Gerard Casas Saez created SPARK-32079:
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Summary: PySpark <> Beam pickling issues for collections.namedtuple
Key: SPARK-32079
URL: https://issues.apache.org/jira/browse/SPARK-32079
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 3.0.0
Reporter: Gerard Casas Saez
PySpark monkeypatching namedtuple makes it difficult/impossible to depickle
collections.namedtuple instances from outside of a pyspark environment.
When PySpark has been loaded into the environment, any time that you try to
pickle a namedtuple, you are only able to unpickle it from an environment where
the
[hijack|https://github.com/apache/spark/blob/master/python/pyspark/serializers.py#L385]
has been applied.
This conflicts directly when trying to use Beam from a non-Spark environment
(namingly Flink or Dataflow) making it impossible to use the pipeline if it has
a namedtuple loaded somewhere.
{code:python}
import collections
import dill
ColumnInfo = collections.namedtuple(
"ColumnInfo",
[
"name", # type: ColumnName # pytype: disable=ignored-type-comment
"type", # type: Optional[ColumnType] # pytype:
disable=ignored-type-comment
])
dill.dumps(ColumnInfo('test', int))
{code}
{{b'\x80\x03cdill._dill\n_create_namedtuple\nq\x00X\n\x00\x00\x00ColumnInfoq\x01X\x04\x00\x00\x00nameq\x02X\x04\x00\x00\x00typeq\x03\x86q\x04X\x08\x00\x00\x00__main__q\x05\x87q\x06Rq\x07X\x04\x00\x00\x00testq\x08cdill._dill\n_load_type\nq\tX\x03\x00\x00\x00intq\n\x85q\x0bRq\x0c\x86q\r\x81q\x0e.'}}
{code:python}
import pyspark
import collections
import dill
ColumnInfo = collections.namedtuple(
"ColumnInfo",
[
"name", # type: ColumnName # pytype: disable=ignored-type-comment
"type", # type: Optional[ColumnType] # pytype:
disable=ignored-type-comment
])
dill.dumps(ColumnInfo('test', int))
{code}
{{b'\x80\x03cpyspark.serializers\n_restore\nq\x00X\n\x00\x00\x00ColumnInfoq\x01X\x04\x00\x00\x00nameq\x02X\x04\x00\x00\x00typeq\x03\x86q\x04X\x04\x00\x00\x00testq\x05cdill._dill\n_load_type\nq\x06X\x03\x00\x00\x00intq\x07\x85q\x08Rq\t\x86q\n\x87q\x0bRq\x0c.'}}
Second pickled object can only be used from an environment with PySpark.
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