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https://issues.apache.org/jira/browse/SPARK-19348?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15859737#comment-15859737
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Peter D Kirchner commented on SPARK-19348:
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Per the above, perhaps a fix could address both threadsafety and the orphaned
modifications to variables in the wrapped function bodies. For instance, in
Pipeline.__init__() and setParams() the fix could remove references to
_input_kwargs and instead invoke setParams(stages=stages) within __init__() and
_set(stages=stages) within setParams(), respectively, Parallel changes would
be needed in all wrapped functions but the resulting code would be functional,
readable, and threadsafe.
A fix for Pipeline alone is insufficient, because multiple pipelines could have
stages consisting of instances of the same class, e.g. LogisticRegression. All
the classes using @keyword_only need to be addressed by whatever fix is decided
upon.
> pyspark.ml.Pipeline gets corrupted under multi threaded use
> -----------------------------------------------------------
>
> Key: SPARK-19348
> URL: https://issues.apache.org/jira/browse/SPARK-19348
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 1.6.0, 2.0.0, 2.1.0
> Reporter: Vinayak Joshi
> Attachments: pyspark_pipeline_threads.py
>
>
> When pyspark.ml.Pipeline objects are constructed concurrently in separate
> python threads, it is observed that the stages used to construct a pipeline
> object get corrupted i.e the stages supplied to a Pipeline object in one
> thread appear inside a different Pipeline object constructed in a different
> thread.
> Things work fine if construction of pyspark.ml.Pipeline objects is
> serialized, so this looks like a thread safety problem with
> pyspark.ml.Pipeline object construction.
> Confirmed that the problem exists with Spark 1.6.x as well as 2.x.
> While the corruption of the Pipeline stages is easily caught, we need to know
> if performing other pipeline operations, such as pyspark.ml.pipeline.fit( )
> are also affected by the underlying cause of this problem. That is, whether
> other pipeline operations like pyspark.ml.pipeline.fit( ) may be performed
> in separate threads (on distinct pipeline objects) concurrently without any
> cross contamination between them.
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