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Github user WeichenXu123 commented on the issue:
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@viirya It is possible I think. A similar example is, `HasRegParam` trait,
do not put `setRegParam` in trait but moved into concrete estimator/transformer
class, should be the same reason.
Github user danielfx90 commented on a diff in the pull request:
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
---
@@ -1103,6 +1103,17 @@ class JDBCSuite extends SparkFunSuite
Github user tgravescs commented on the issue:
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It looks like zstd-jni has now been updated to pull 1.3.1
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Github user viirya commented on the issue:
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@WeichenXu123 I'm ok for that but I think adding an interface doesn't break
binary compatibility?
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@gatorsmile Dunno, but the logical operator does.
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Btw, I don't see any hint about
`df.withColumn(..).withColumn(..).withColumn(..)` can prevent the shuffle
re-using.
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Github user HyukjinKwon commented on the issue:
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Seems fine to me too as is. @maver1ck, I think you could take out `[WIP]`
and let it be merged.
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retest this please
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I don't think re-using shuffle is the reason behind the numbers. If you
looked at the previous comments, you will find that I ran the test before
without `count` after `model.transform`. Namely the
Github user jerryshao commented on the issue:
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Did you enable RM or NM recovery, can you please clarify it?
Normally, if we assume there's are 2 containers running on this NM, after
10 minutes, RM will detect the failure of NM and
Github user WeichenXu123 commented on the issue:
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@viirya Yes. But if there is some better design I will be happy to listen.
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New numbers:
numColums | Old Mean | Old Median | New Mean | New Median
-- | -- | -- | -- | --
1 | 0.1727832915997 | 0.1537169693 | 0.1687325048997 | 0.1521283075
10 |
Github user WeichenXu123 commented on the issue:
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Great! That's it. thanks!
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Updated test codes:
import org.apache.spark.ml.feature._
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types._
import spark.implicits._
Github user WeichenXu123 commented on the issue:
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@viirya I guess the reason is, the old PR version:
`df.withColumn(..).withColumn(..).withColumn(..)`, the long df chain
prevent the shuffle re-using... but now you merge them into one
Github user viirya commented on the issue:
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@WeichenXu123 Do you mean we keep both inputCol and inputCols in
`Bucketizer`?
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@WeichenXu123 Btw, the test is basically re-using the codes from
https://github.com/apache/spark/pull/18902#issuecomment-321727416. Is your
concern is specified for this?
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Github user viirya commented on the issue:
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@WeichenXu123 Sure. And I must point out that I ran this benchmark in
spark-shell under local mode. It is great if you can run the benchmark too to
verify the numbers.
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ok to test.
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ok to test
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[SPARK-22047][flaky test] HiveExternalCatalogVersionsSuite
## What changes were proposed in this pull request?
This PR tries to download Spark for each test run, to make sure each test
Github user michaelmior commented on the issue:
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Whoops. Sorry about that. I opened the PR via the CLI so I didn't see the
pointer on the web interface. I should have known better though. Updated!
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Github user klion26 commented on the issue:
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Hi @jerryshao, thank you for your reply.
# Problem
the problem is for long running jobs which run on **yarn with HA** will
request more executors than it requests.
# How to
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retest this please
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retest this please
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retest this please.
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I check with some samples and code with float can trigger errors.
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@marktab You should close merged PR. Thanks!
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Hi @cloud-fan, thanks for reviewing. The code has updated, pls take a look.
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@zsxwing @jiangxb1987 would you please help to review this PR when you have
time, thanks a lot.
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@michaelmior would you please follow the instruction
(https://spark.apache.org/contributing.html) to update PR title and create a
corresponding JIRA, thanks!
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Github user fjh100456 commented on the issue:
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Encounter two problems:
1. I tried to fix it in the order of 'compression' > 'parquet.compression'
> 'spark.sql.parquet.compression. codec', but found 'parquet.compression' may
come from a
Github user jerryshao commented on the issue:
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Hi @klion26 , sorry for the late response. Can we please understand the
problem first, would you please describe your problem in detail and how to
reproduce your issue?
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Github user original-brownbear commented on the issue:
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@srowen rebased against `master` to get the test ignore
https://github.com/apache/spark/commit/894a7561de2c2ff01fe7fcc5268378161e9e5643
, should be good to retest now :)
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LGTM, let me retest this again.
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Jenkins, retest this please.
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2142,18 +2159,26 @@ def udf(f=None, returnType=StringType()):
| 8| JOHN DOE|
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| 8| JOHN DOE|
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cc @srowen
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merging to master/2.2
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[SPARK-22047][TEST] ignore HiveExternalCatalogVersionsSuite
## What changes were proposed in this pull request?
As reported in https://issues.apache.org/jira/browse/SPARK-22047 ,
Github user cloud-fan commented on a diff in the pull request:
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---
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Github user kiszk commented on the issue:
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Can we add test code for `null` row in a column for each type?
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+/*
+ * Licensed to the
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Github user HyukjinKwon commented on the issue:
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I think it was changed back in https://github.com/apache/spark/pull/7363. I
think that avoids precision loss from Python's `float` limitation:
```python
>>> decimal.Decimal(1 / 1e6)
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retest this please
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@BryanCutler I think it's okay to rename `size` to `length` (or longer name
to avoid name-conflict like `_length_`?).
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We can split two `needConversion` for key and value only and save key
conversion or value conversion call though?
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Sure, added a JIRA for the failure here
https://issues.apache.org/jira/browse/SPARK-22047 :)
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