Github user vanzin commented on the issue:
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LGTM, merging to master.
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retest this please
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Thanks for the updates @zjffdu, just 2 minor comments otherwise LGTM!
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Github user holdenk commented on the issue:
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Awesome, thanks for updating. I'm at PyData this weekend so will be a bit
slow on my end.
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@vanzin @holdenk @BryanCutler PR is updated, please help review.
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I'd also love to see this updated so we can finish the review and make it
easier for people to use PySpark this way :)
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@zjffdu could you update the PR? Thanks!
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I added a few comments @zjffdu. I also tested this PR out and looks good
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Github user vanzin commented on the issue:
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LGTM. Let's see if others have any comments.
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I just fix the unti test, @vanzin @davies @holdenk Mind to help review it
? Thanks
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Are these tests flaky or is the failure related to this change? (Other PRs
seem to be passing, so probably the latter?)
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Github user zjffdu commented on the issue:
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```
The internal SparkConf of the context will not be the same instance as conf.
```
This is the existing implementation that python is different from scala.
But I think it is correct. I
Github user vanzin commented on the issue:
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It means that if you do this:
val conf = new SparkConf()
val sc = new SparkContext(conf)
The internal SparkConf of the context will not be the same instance as
`conf`.
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@vanzin Thanks for your reviews. I just update the PR, but don't get your
following statement mean. Can you explain it ? Thanks
```
Especially since the Scala SparkContext clones the original
Github user vanzin commented on the issue:
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Also someone else more familiar with pyspark (I know Holden has already
looked), maybe @davies, should take a look.
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The conf code looks kinda nasty with the checks for whether a JVM has been
set or not... I guess part of it is mandatory because otherwise this wouldn't
work, but in particular, I'm not so sure the
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