Github user mateiz commented on the issue:
https://github.com/apache/spark/pull/8318
Something like this would be great IMO. A few questions though:
* How will it work if users want to run a different version of PySpark from
a different version of Spark (maybe something they
Github user praveendareddy21 commented on the issue:
https://github.com/apache/spark/pull/14375
@holdenk
The work on this feature is done. This PR is reopen of PR on 2.0 branch.
refer https://github.com/apache/spark/pull/13248
I waiting for admins to verify and merge.
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/14375
Ok so you are blocked on reviewers, but still active on the PR then yes?
You might want to try pinging (by using @[username] some of the reviewers from
your previous PR
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Github user rxin commented on the issue:
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LGTM - we should double check whether the synchronized block is sufficient,
but we can fix that in a follow-up pr if needed.
Merging in master.
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Github user hvanhovell commented on the issue:
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Merging to master. Thanks for commenting.
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Github user holdenk commented on the issue:
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Just pinging @davies or @marmbrus to see if this is something you might
have bandwith to review (in which case we can ask the author if they have the
time to update to the latest master).
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--- Diff: core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
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@@ -344,15 +338,20 @@ private[spark] class BlockStatusesAccumulator
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jenkins retest this please
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Github user tgravescs commented on a diff in the pull request:
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--- Diff:
core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala ---
@@ -96,16 +96,15 @@ private[spark] class ReliableCheckpointRDD[T:
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Github user tgravescs commented on the issue:
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If you still believe this to complex can you please clarify why so we can
discuss. This is basically 5 lines of code (yes lines of code aren't good
complexity indicator but..), 3 of them to
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Github user srowen commented on the issue:
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The central problem with the formatter is just that it doesn't output
padding, and I don't see a reason to make the lexical ordering no longer match
numerical, as it does now. You can make it output
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Github user marmbrus commented on the issue:
https://github.com/apache/spark/pull/9766
+1 to this functionality, but also to the request to add more tests and
documentation. It would also to be good to comment on the idea of using SQL as
a more general way to implement this.
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GitHub user sethah opened a pull request:
https://github.com/apache/spark/pull/15394
[SPARK-17749][ML] One pass solver for Weighted Least Squares with ElasticNet
## What changes were proposed in this pull request?
1. Make a pluggable solver interface for
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Github user zsxwing commented on the issue:
https://github.com/apache/spark/pull/15371
@seyfe I'm taking my words back. Yea, `BlockStatusesAccumulator.merge` will
be called in driver.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/12691
Thanks for working on this @bkpathak :) Are you still interested in working
on this? If so can you update this to the latest master and then we can try and
find a committer to take a more thorough
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Github user asfgit closed the pull request at:
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Github user gatorsmile commented on the issue:
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retest this please
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/12968
@burakkose is this something you are still working on? If so can you update
it to master and look at @mengxr's comments - if not interested in working on
it anymore no worries.
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Github user zsxwing commented on the issue:
https://github.com/apache/spark/pull/15387
> but polling ordinarily consumes messages and adjusts position.
Even if `enable.auto.commit` is `false`? In the doc, it says `automatically
set as the last committed offset`, so I guess
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Github user ajbozarth commented on the issue:
https://github.com/apache/spark/pull/15366
Jenkins, retest this please
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Github user koeninger commented on the issue:
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I set auto commit to false, and still recreated the test failure.
That makes sense to me, consumer position should still be getting updated
in memory even if it isn't saved to storage
Github user felixcheung commented on the issue:
https://github.com/apache/spark/pull/15391
LGTM
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Github user felixcheung commented on the issue:
https://github.com/apache/spark/pull/15353
it looks like the "after" image still has a \n in socket text stream?
https://cloud.githubusercontent.com/assets/3925641/19097318/a8edc9de-8a58-11e6-8c37-30271b761284.png
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Github user holdenk commented on the issue:
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Hi @yinxusen thanks for working on this - can you update it against the
latest master? I'm all for switching our examples to be more include based and
the improved error messages during doc builds
Github user zasdfgbnm commented on the issue:
https://github.com/apache/spark/pull/14198
I'd love to help
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Github user zasdfgbnm commented on the issue:
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Something to mention is, there is still one problem that I'm not sure
whether I solve it correctly: in python's array, unsigned types are supported,
but unsigned types are not supported in JVM.
Github user sethah commented on the issue:
https://github.com/apache/spark/pull/15394
cc @yanboliang @dbtsai
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Github user yinxusen commented on the issue:
https://github.com/apache/spark/pull/9
@dbtsai @sethah I updated the code. Now we check the equivalence of K when
setting initialModel if K is set previously. We also check the equivalence when
fitting a model.
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/13656
I'm not sure if this is something that would still be considered since we
aren't doing new development for MLlib anymore. It might make more sense to
work on
Github user seyfe commented on a diff in the pull request:
https://github.com/apache/spark/pull/15371#discussion_r82468147
--- Diff: core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
---
@@ -344,15 +338,20 @@ private[spark] class BlockStatusesAccumulator
Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/15389#discussion_r82468103
--- Diff: python/pyspark/rdd.py ---
@@ -2029,7 +2030,11 @@ def coalesce(self, numPartitions, shuffle=False):
>>> sc.parallelize([1, 2, 3,
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/8384
I think its safe to say that this isn't something that there is committer
interest on bringing into Spark. Especially now that we can use DataFrames to
write out to other formats from Python its
Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/7963
Now that its past the 2.0 release should we maybe take another look @MLNick
/ @davies?
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/12888
So to trigger the test we will need one of the committers - e.g. @davies is
one of the more active Python committers (although he has been busy lately) so
we can also check with @MLnick . It would
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Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r82472226
--- Diff: python/pyspark/mllib/linalg/distributed.py ---
@@ -303,6 +303,121 @@ def tallSkinnyQR(self, computeQ=False):
R = decomp.call("R")
Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r82471915
--- Diff: docs/mllib-dimensionality-reduction.md ---
@@ -84,6 +84,25 @@ quick-start guide. Be sure to also include *spark-mllib*
to your build file as
a
Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/7963#discussion_r82472791
--- Diff: python/pyspark/mllib/linalg/distributed.py ---
@@ -303,6 +303,121 @@ def tallSkinnyQR(self, computeQ=False):
R = decomp.call("R")
Github user NarineK commented on the issue:
https://github.com/apache/spark/pull/10162
I'd be happy to update to the latest master if we want to review this now.
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Github user yinxusen commented on the issue:
https://github.com/apache/spark/pull/13794
Thanks @holdenk Yes, I am still interested in this. @jkbradley Do we still
need the PR to support meta-algorithms in PySpark?
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Github user ajbozarth commented on the issue:
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It's failed twice now on the same issue but it's completely unrelated to my
change. Trying one more time, but is this failure a known issue?
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Github user kayousterhout commented on the issue:
https://github.com/apache/spark/pull/15249
@tgravescs @mridulm To avoid being stuck in analysis paralysis for this
feature, I'd propose the following:
(1) We merge this PR. I think we're mostly in agreement that the behavior
Github user davies commented on the issue:
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@zsxwing
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Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/13762#discussion_r82465493
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala ---
@@ -196,8 +196,13 @@ final class OneVsRestModel private[ml] (
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Github user holdenk commented on the issue:
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@yinxusen - is this something you are still interested in? If so updating
it to master would be good as well as making sure the unit tests pass in
jenkins (a lot of reviwers just skip PRs which are
Github user yinxusen commented on the issue:
https://github.com/apache/spark/pull/14006
@holdenk I'll update a version ASAP
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Github user holdenk commented on a diff in the pull request:
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--- Diff: python/pyspark/ml/clustering.py ---
@@ -56,8 +56,83 @@ def gaussiansDF(self):
"""
return self._call_java("gaussiansDF")
Github user dusenberrymw commented on a diff in the pull request:
https://github.com/apache/spark/pull/15389#discussion_r82468575
--- Diff: python/pyspark/rdd.py ---
@@ -2029,7 +2030,11 @@ def coalesce(self, numPartitions, shuffle=False):
>>> sc.parallelize([1, 2, 3,
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--- Diff: python/pyspark/ml/tests.py ---
@@ -1070,6 +1070,21 @@ def test_logistic_regression_summary(self):
sameSummary = model.evaluate(df)
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GitHub user davies opened a pull request:
https://github.com/apache/spark/pull/15395
[SPARK-17738] [TEST] Fix flaky test in ColumnTypeSuite
## What changes were proposed in this pull request?
The default buffer size is not big enough for randomly generated MapType.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/15393
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Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/15333
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/12337
So I'm curious what is the intention of this versus the existing isnull
function? It seems like this might not be needed in which case we should
consider closing the PR & JR (of course if there is
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