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https://issues.apache.org/jira/browse/SPARK-21476?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16154849#comment-16154849
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Peng Meng commented on SPARK-21476:
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I think the performance difference between my test and yours is the number of
records in each partition.
In my test, I use:
model.transform(data)
There are many instances (e.g. 10k) in each partition, so the deserialize time
of the model is not a big part comparing with the end-to-end time.
In your cases, maybe there are very few instances in each partition, so the
deserialize time seems very long.
In my cases, when use broadcast, the end-to-end time is long comparing the
current solution.
We can share the test configuration and do more test.
> RandomForest classification model not using broadcast in transform
> ------------------------------------------------------------------
>
> Key: SPARK-21476
> URL: https://issues.apache.org/jira/browse/SPARK-21476
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Saurabh Agrawal
> Priority: Minor
>
> I notice significant task deserialization latency while running prediction
> with pipelines using RandomForestClassificationModel. While digging into the
> source, found that the transform method in RandomForestClassificationModel
> binds to its parent ProbabilisticClassificationModel and the only concrete
> definition that RandomForestClassificationModel provides and which is
> actually used in transform is that of predictRaw. Broadcasting is not being
> used in predictRaw.
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