Github user shahidki31 commented on the issue:
https://github.com/apache/spark/pull/23016
> I am currently using it with spark 2.3 as
>
> org.apache.spark
> spark-mllib_2.11
>
> How can i get this fix?
You can cherry pick the commit from the master
Github user shahidki31 commented on the issue:
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@idlevi Actually, input and output of the prefix span are RDD. Earlier
intermediate rdd was cached, now final rdd is cached, and materialized it. So,
if you materialize the model, earlier it
Github user idlevi commented on the issue:
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I am currently using it with spark 2.3 as
org.apache.spark
spark-mllib_2.11
How can i get this fix?
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Github user idlevi commented on the issue:
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Wouldn't the added count increase the execution time? did you do a
benchmark?
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Github user shahidki31 commented on the issue:
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Thank you @srowen
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Github user srowen commented on the issue:
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Merged to master
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Github user shahidki31 commented on the issue:
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Thanks @srowen , I will update the PR
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Github user srowen commented on the issue:
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I'm not sure about that, because the returned PrefixSpanModel has an RDD
that depends on that RDD. We could cache the final RDD instead and materialize
it; that could make more sense.
In
Github user shahidki31 commented on the issue:
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cc @srowen Kindly review
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Can one of the admins verify this patch?
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Can one of the admins verify this patch?
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