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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/17671
So now that we have the configurable worker module I think this is no
longer needed to be integrated in this way.
I'm not sure if Sentry support directly belongs in but at the very least
th
Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
@holdenk
mmm...sweet! That may work and even makes integration process more
flexible. Sentry integration wrapper would be trivial with this feature.
Thanks!
For the future reference:
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
ð
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Github user AmplabJenkins commented on the issue:
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/17671
Thanks for working on improving the PySpark debugging experience. Perhaps a
similar effect be achieved by adding a broadcast variable which lazily loads
sentry?
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Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
Hm...I'd read about broadcast variables, but never tried to use them.
However, after quick look and try, I found that this won't change things too
much.
Yes, you will be able to pass client
Github user tedmiston commented on the issue:
https://github.com/apache/spark/pull/17671
Anyone know what the status of this PR is? I'd really like to be able to
run Sentry on my PySpark code in production.
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Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
Rebased to resolve conflicts.
@holdenk could you take a look please? Is there need something else to do?
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Github user holdenk commented on the issue:
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Jenkins OK to test.
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
Thanks @holdenk. Sure, I am interested in this. Will take a look shortly
within tomorrow.
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Github user HyukjinKwon commented on the issue:
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ok to test
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Github user SparkQA commented on the issue:
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Github user SparkQA commented on the issue:
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Github user AmplabJenkins commented on the issue:
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
To me, for the similar reason -
https://github.com/apache/spark/pull/17671#pullrequestreview-81100230, I think
I am less sure of adding this.
For the workaround you described @kxepal -
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/17671
So we could do the monkey patching for UDFs but for
reduce/aggregate/combineByKey it starts to get a little rough to do.
I wonder if we could maybe make a mechanism for this that would be us
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
Yea, having a mechanism for things like this much more makes sense to me.
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Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
> If this is the reason to add the support of thirdparty library, it sounds
not quite compelling. I think you can even just simply monkey-patch udf or
UserDefinedFunction. It wouldn't be too difficul
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
What's the specific reasons then? I think it's basically a maintenance
stuff. I don't think the changes diff will be largely different when it's
handled application side.
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
Also, there are many good tools Python debuggers, coverage and etc. I think
providing a mechanism for such things is a great idea if possible but I doubt
each support should be added while worka
Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
@HyukjinKwon
The specific reason is to simplify debugging and errors understanding by
integration pyspark with one of the most popular error tracking system among
Python developers. E.g. improve
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
I am a Python developer and I use PySpark in production. @kxepal, would you
mind sending an email for this discussion in the dev mailing list?
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Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
@HyukjinKwon
Sorry, I'm a bit lost. What email? With a link to this PR to gather more
opinions?
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/17671
Ah, yes. There are good places to discuss. For example, see "Mailing List"
- https://spark.apache.org/community.html. I also want to actually see if
others think about this.
I have done
Github user kxepal commented on the issue:
https://github.com/apache/spark/pull/17671
Ok, will do. Thanks @HyukjinKwon .
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Github user AmplabJenkins commented on the issue:
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Github user tedmiston commented on the issue:
https://github.com/apache/spark/pull/17671
I checked the mailing list archives but could not find a recent thread on
Sentry. Anyone know if there is one I'm missing?
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