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@dongjoon-hyun thanks for trigging the build. The python test script was
only looking for scala 2.11 jars resulting python test failures. I just fixed
it in the latest push. Let's see how it goes.
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Github user dbtsai commented on a diff in the pull request:
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--- Diff: pom.xml ---
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*:*_2.11
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I am merging this in - maybe I am rushing it but please allow me to go
ahead since it's going to block other PySpark PRs.
At worst case, I am willing to revert and propose this again if
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Ah .. right makes sense to me. Thanks @shaneknapp. +1
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@dongjoon-hyun sure.
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[SPARK-26055][CORE] InterfaceStability annotations should be retained at
runtime
## What changes were proposed in this pull request?
It's good to have annotations available at runtime, s
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LGTM
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Github user gss2002 commented on the issue:
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@vanzin can you review the latest PR. This seems like it works without
issue. I ran this on my home cluster and my larger clusters at work where the
issue was first discovered and it seems to solve
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--- Diff: dev/checkstyle.xml ---
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+
+
+
--- End
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MLli
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[SPARK-26053][SQL] Enhance LikeSimplification
## What changes were proposed in this pull request?
This PR enhance `LikeSimplification` in 2 cases:
1. null like col -> null
2.
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Github user shaneknapp commented on the issue:
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TL;DR: let's go w/deprecation.
still TL;DR: if i never have to install or manage R again, i will be a
happy person!
@HyukjinKwon upgrading R is easy. getting the right mix of
Github user zhengruifeng commented on the issue:
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@srowen Yes, this is the problem. I have to register `Param*` before any
prediction model, but there are too many anonymous classes in
`ParamValidators` and other places, and I have not found
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adding @holdenk, @ueshin and @icexelloss as well.
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adding @icexelloss as well.
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> Did you test on python3 as well?
Of course!
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--- Diff: R/pkg/R/SQLContext.R ---
@@ -189,19 +238,67 @@ createDataFrame <- function(data, schema = NULL,
samplingRatio = 1.0,
x
Github user HyukjinKwon commented on the issue:
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@shaneknapp, do you roughly know how difficult it is (and do you have some
time shortly) to upgrade R from 3.1 to 3.4? I am asking this because I had some
difficulties when I tried to manually u
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--- Diff: docs/index.md ---
@@ -31,7 +31,8 @@ Spark runs on both Windows and UNIX-like systems (e.g.
Linux, Mac OS). It's easy
locally on one machi
Github user felixcheung commented on the issue:
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Hey shane I donât think we are saying to test multiple R version at all.
In fact quite the opposite, just the new(er) version at some point in the
future.
(We donât have a better so
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}
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@HyukjinKwon No problem, I'll give a follow up PR to address all your
comments and rewrite the UT in to a separate class.
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> Could you add some descriptions to run a single test file or a single
test case if exists?
Done!
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nah, it's not urgent at all.
i also tested this locally by breaking various python scripts and
confirming that it caught errors as expected.
On Tue, Nov 13, 2018 at 5:03 PM Hy
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--- Diff: python/pyspark/taskcontext.py ---
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@classmethod
def _getOrCreate(cls):
"
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Yup!
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I haven't taken a look super closely but the idea looks itself okay. Is it
urgent? if yes, yup. I don't object to go ahead right away. Otherwise, might be
good to leave open for few days for rev
Github user rxin commented on the issue:
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One thing - I would put âpandasâ right after test_ so you get the
natural
logical grouping with sorting by file name.
On Tue, Nov 13, 2018 at 4:58 PM Hyukjin Kwon
wrote:
>
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I am going to push after testing and double checking. The line counts would
look like this
```
54 ./test_utils.py
199 ./test_catalog.py
503 ./test_grouped_agg
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Retest this please.
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GitHub user wangyum opened a pull request:
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[SPARK-26049][SQL][TEST] Add InMemoryTable filter benchmark to
FilterPushdownBenchmark
## What changes were proposed in this pull request?
Add InMemoryTable filter benchmark to `FilterPush
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