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Github user viirya commented on a diff in the pull request:
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@@ -585,34 +589,6 @@ private[hive] class HiveMetastoreCatalog(hive:
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GitHub user vanzin opened a pull request:
https://github.com/apache/spark/pull/5018
[SPARK-6325] [core,yarn] Disallow reducing executor count past running c...
...ount.
The dynamic execution code has two ways to reduce the number of executors:
one
where it reduces the
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jenkins, test this please
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Merged into master and branch-1.3.
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Github user JoshRosen commented on the pull request:
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I have now cherry-picked this into `branch-1.3` (1.3.1).
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Github user viirya commented on a diff in the pull request:
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@@ -557,8 +557,11 @@
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-79250025
Also quadraticMinimizer keeps it's own workspace...The idea is to construct
ALS.QuadraticSolver once and keep re-using it..this is specially useful for LSA
Github user dragos commented on the pull request:
https://github.com/apache/spark/pull/5000#issuecomment-79251406
Thanks for the pointer, I'll have a look in Mesos scheduler backends.
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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/5015#issuecomment-79242721
LGTM, straightforward doc update.
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Github user mengxr commented on the pull request:
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@debasish83 Let's first implement breeze-based solvers as new solvers
instead of replacing old ones. So we can easily compare the performance and
accuracy. For example,
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GitHub user viirya opened a pull request:
https://github.com/apache/spark/pull/5017
[SPARK-6326][SQL] Improve castStruct to be faster
Current `castStruct` should be very slow. This pr slightly improves it.
You can merge this pull request into a Git repository by running:
$ git
Github user harishreedharan commented on the pull request:
https://github.com/apache/spark/pull/4964#issuecomment-79228838
Looks like the tests passed, but the build has been stuck in MiMa for a
while with lock not being available on the ivy cache:
`Waiting for lock on
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/4828#issuecomment-79272515
LGTM! Will merge asap.
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--- Diff:
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@@ -138,4 +126,36 @@ class GaussianMixtureSuite extends
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Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/5009#issuecomment-79265762
Note: After conferring with @mengxr I'll make the enum-like types private
for now and only expose an interface taking basic types (String).
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Merged into master. Thanks!
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/cc @srowen too as you this is a, seemingly trivial, doc change
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Github user viirya commented on a diff in the pull request:
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-79234218
@mengxr breeze NNLS solver is exactly same as mllib optimization
NNLS...breeze QuadraticMinimizer default is Cholesky and it supports all the
constraints that we have
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Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/4986#issuecomment-79350101
The number of clusters won't be very large. Flattening an
`Array[Array[Double]]` doesn't copy the data, so there is no overhead. The
content of parquet file is easy to
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Github user MechCoder commented on a diff in the pull request:
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--- Diff:
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@@ -83,5 +95,87 @@ class GaussianMixtureModel(
Github user CodingCat commented on a diff in the pull request:
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--- Diff:
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@@ -123,12 +133,30 @@ private[spark] class DiskBlockObjectWriter(
Github user aarondav commented on a diff in the pull request:
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--- Diff:
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@@ -262,6 +262,7 @@ private[sql] case class ParquetRelation2(
Github user MechCoder commented on the pull request:
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@mengxr I am not sure if we should flatten it or not, would it be worth if
the number of clusters is large? Also I think it would be better if we deal
with MatrixUDT
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[SPARK-6330] Fix filesystem bug in newParquet relation
If I'm running this locally and my path points to S3, this would currently
error out because of incorrect FS.
I tested this in a
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GitHub user davies opened a pull request:
https://github.com/apache/spark/pull/5019
[SPARK-6327] [PySpark] fix launch spark-submit from python
SparkSubmit should be launched without setting PYSPARK_SUBMIT_ARGS
cc @JoshRosen , this mode is actually used by python unit test,
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Talked to @sryza offline and we decided to go with a simpler fix; after
some thinking, the only bad effect of this approach is that if the frontend (=
driver) kills
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Github user liancheng commented on the pull request:
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@srowen Thanks for the review! Gonna merge this into master and branch-1.3
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Github user liancheng commented on the pull request:
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Actually I'm not quite sure whether this should backported to branch-1.3 at
this moment, merged to master only for safe. @pwendell?
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