Github user sun-rui commented on the issue:
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no, go ahead to submit one:)
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Github user shivaram commented on the issue:
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@NarineK Not as far as I know
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Github user NarineK commented on the issue:
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@shivaram, @sun-rui , I was wondering if someone created a jira for the
issue described here:
https://github.com/apache/spark/pull/12836#issuecomment-225403054
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Github user NarineK commented on the issue:
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Thanks for the quick response. I'll create one.
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Github user vectorijk commented on the issue:
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@NarineK I am not quite sure. Maybe you could create a new JIRA for
gapply's programming guide.
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@vectorijk, should I do the pull request for the same jira -
https://issues.apache.org/jira/browse/SPARK-15672, or should I create a new
jira for the programming guide?
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Github user vectorijk commented on the issue:
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@NarineK Cool~ I think it is better to open a separate PR to track `gapply`
programming guide.
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Hi @vectorijk,
Thanks for asking, i think in a separate PR. Do you think including in
#13660 would be better ?
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@NarineK Which way do you want to include programming guide for `gapply`,
in separate PR or in #13660?
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Merging this to master and branch-2.0
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@shivaram, LGTM
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Github user NarineK commented on the issue:
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Thanks, @shivaram and @sun-rui. Yes, I can work on programming guide for
gapply.
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@NarineK Thanks again for the updates to this PR and thanks @sun-rui for
reviewing. The code changes LGTM -- the refactoring of worker.R is especially
useful for readability.
I just had a
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@NarineK, there is one comment left un-addressed
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Addressed your comments @sun-rui, please let me know if you have any
comments.
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@shivaram, I think we are reaching the final version:). It would be better
that you can have a detailed review on the examples and test cases.
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yes, let's do it in a separate PR.
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Github user rxin commented on the issue:
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We can do it in a separate pr -- it'd be great to move all Python and R
methods over to a single class. Otherwise it has two major problems:
1. Those methods are public for Java.
2. It
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@rxin I think in this case we need access to grouping expression and
DataFrame from within the RelationalGroupedDataset class. One solution could be
to move the function `flatMapGroupsInR` to the h
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Github user NarineK commented on the issue:
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Thanks @liancheng and @rxin !
With respect to your point, @rxin - "private[sql] signature in public
APIs ."
dapply added that signature to `Dataset.scala `and gapply adds it to
`Rela
Github user liancheng commented on the issue:
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The SQL part of changes look generally good except for a few styling issues.
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Can we avoid adding private[sql] methods in public APIs? Those have no
effect in Java. Maybe create a helper method for all the R methods.
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Github user shivaram commented on the issue:
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Thanks @liancheng for clarification and @NarineK for implementing the
override. I just had one minor comment.
@sun-rui Can you take one final look ? Since we have not still cut RC1, we
mi
Github user liancheng commented on the issue:
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@NarineK @shivaram Sorry for the late reply. Overriding `stringArgs` is the
correct solution for this issue.
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Hi @sun-rui, hi @shivaram,
I've overwritten the stringArgs - I've pushed my changes in the following
branch. I haven't created a jira yet.
https://github.com/apache/spark/commit/939dbd
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Sure, let me try to override stringArgs and give it a try.
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Github user shivaram commented on the issue:
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I think I found the commit which causes this problem -
https://github.com/apache/spark/commit/6dde27404cb3d921d75dd6afca4b383f9df5976a
added toString to include arrays and the output we get is from
Github user NarineK commented on the issue:
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Thank you for the quick responses @sun-rui and @shivaram .
Here is how the `dataframe.queyExection.toString` printout starts with:
== Parsed Logical Plan ==
'SerializeFromObject
Github user sun-rui commented on the issue:
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I guess the byte array of the serialized R function is dumped. Let me find
which commit caused this. I guess something like overriding toString may solve
this
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I don't know what could cause this - Do we have the beginning of the string
? My guess is `MapPartitions` or one of the nodes in the plan is calling
`toString` on a byte Array that contains some R
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Do you know what exactly caused this ?
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Hi @shivaram , hi @sun-rui ,
Surprisingly the `dataframe.queyExection.toString` both for dapply and
gapply is prepended by a huge array, which I'm not able to understand. It seems
that recent co
Github user NarineK commented on the issue:
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I can print-out the query plan on scala side and see what does it look like
for that example.
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Github user shivaram commented on the issue:
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The out of memory error seems to be coming from trying to form a
`.toString` of the query plan. Is there something in the query plan for R UDFs
that might create a very large string ?
Als
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not sure why it fails. It fails for my new test case on iris dataset. The
resulting dataframe has 35x2 dimensions.
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Locally, run-tests.sh run successfully, but it fails on jenkins ...
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@shivaram, I didn't change the code, but merged with master, because prior
to this the build was failing because some pyspark tests didn't pass.
After my today's merge, when I run gapply tes
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The error was
```
1. Error: gapply() on a DataFrame
--
java.lang.OutOfMemoryJava heap space
```
@NarineK Do you think there was a
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Yeah I think we can still make this to 2.0 -- Are there any other comments
@sun-rui ?
Also pinging @davies / @rxin again for a SQL reviewer to take a look at this
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