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Roi Reshef edited comment on SPARK-17020 at 8/11/16 2:09 PM:
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Roi Reshef edited comment on SPARK-17020 at 8/11/16 2:09 PM:
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Roi Reshef commented on SPARK-17020:
Nevertheless, any attempt to repartition the res
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Roi Reshef commented on SPARK-17020:
The problem occurs only when calling **.rdd** on
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Roi Reshef commented on SPARK-17020:
Also note that I have just called:
*data.cache(
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Roi Reshef commented on SPARK-17020:
val ab = SomeReader.read(...) //some reader fun
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Roi Reshef commented on SPARK-17020:
[~srowen] Should there be any effect on this if
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Roi Reshef edited comment on SPARK-17020 at 8/11/16 1:13 PM:
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Roi Reshef commented on SPARK-17020:
[~srowen] I have 2 DataFrames that are generated
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Roi Reshef updated SPARK-17020:
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Affects Version/s: 2.0.0
> Materialization of RDD via DataFrame.rdd forces a poor re-distribution of
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Roi Reshef updated SPARK-17020:
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Attachment: rdd_cache.PNG
dataframe_cache.PNG
> Materialization of RDD via DataFrame.
Roi Reshef created SPARK-17020:
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Summary: Materialization of RDD via DataFrame.rdd forces a poor
re-distribution of data
Key: SPARK-17020
URL: https://issues.apache.org/jira/browse/SPARK-17020
Project: S
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Roi Reshef edited comment on SPARK-10789 at 12/29/15 11:56 AM:
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Roi Reshef commented on SPARK-10789:
Thanks [~jonathak]. That requires rebuilding spa
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Roi Reshef edited comment on SPARK-10789 at 12/29/15 11:55 AM:
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Roi Reshef commented on SPARK-10789:
Any resolution on that? Can you elaborate more o
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Roi Reshef updated SPARK-5081:
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Comment: was deleted
(was: Hi Guys,
Was this issue already solved by any chance? I'm using Spark 1.3.1 f
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Roi Reshef commented on SPARK-5081:
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Hi Guys,
Was this issue already solved by any chanc
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Roi Reshef edited comment on SPARK-5081 at 6/15/15 8:41 AM:
Hi
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