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https://issues.apache.org/jira/browse/SPARK-19348?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15850705#comment-15850705
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Bryan Cutler commented on SPARK-19348:
--------------------------------------
The problem here is with the @keyword_only decorator that is used in the
Pipeline constructor (and every other ML class also). It relies on saving the
params, stages in this case, to a static class variable. When multiple threads
call the wrapped constructor, it becomes a race condition to read from that
static variable before it is over-written by another thread. I can put up a
potential fix, but it affects all of PySpark ML so it would need to be checked
out carefully.
As a workaround, you could just protect the construction of {{Pipeline}} with a
shared lock. Other calls to {{fit}} etc, should be ok since they don't use
that keyword_only decorator.
> 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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