Kevin Van Lieshout created SPARK-32534:
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Summary: Cannot load a Pipeline Model on a stopped Spark Context
Key: SPARK-32534
URL: https://issues.apache.org/jira/browse/SPARK-32534
Project: Spark
Issue Type: Bug
Components: Deploy, Kubernetes
Affects Versions: 2.4.6
Reporter: Kevin Van Lieshout
I am running Spark in a Kubernetes cluster than is running Spark NLP using the
Pyspark ML Pipeline Model class to load the model and then transform on the
spark dataframe. We run this within a docker container that starts up a spark
context, mounts volumes, spins up executors, etc and then does it
transformations, udfs, etc and then closes down the spark context. The first
time I load the model when my service has just been started, everything is
fine. If I run my application for a second time without resetting my service,
even though the context is entirely stopped from the previous run and a new one
is started up, the Pipeline Model has some attribute in one of its base classes
that thinks the context its running on is closed, so then I get a : cannot call
a function on a stopped spark context when I try and load the model in my
service again. I have to shut down my service each time if I want consecutive
runs through my spark pipeline, which is not ideal, so I was wondering if this
was a common issue amongst fellow pyspark users that use Pipeline Model, or is
there a common work around to resetting all spark contexts or whether the
pipeline model caches a spark context of some sort. Any help is very useful.
cls.pipeline = PipelineModel.read().load(NLP_MODEL)
is how I load the model. And our spark context is very similar to a typical
kubernetes/spark setup. Nothing special there
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