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Github user viirya commented on the pull request:
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e.g., in `BisectingKMeans`, this patch changes
val data = dataset.select(col($(featuresCol))).rdd.map { case
Row(point: Vector) => point }
to
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Github user hvanhovell commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
---
@@ -337,6 +337,16 @@ case class
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Github user hvanhovell commented on the pull request:
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This is an example that will fail if we pull out predicates in the
optimizer (`min(b.value)` does not resolve):
```sql
select b.key, min(b.value)
from
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Github user hhbyyh commented on the pull request:
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Documents updated and remove an unit test.
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Github user suyanNone commented on the pull request:
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as I know, duplicate stage occurs:
stage 2 and stage 3 all depends on stage 1
stage 4 depends on stage 2 and stage 3
So, if we get
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Github user MLnick commented on the pull request:
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@viirya can you provide an example of how this works for use here in this
PR?
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Github user gatorsmile commented on the pull request:
https://github.com/apache/spark/pull/12736#issuecomment-215327600
Like `INTERSECT ALL`, `EXCEPT ALL` can be done by aggregation of the union
of the two tables. We can augment one table with a new column of constant `1`,
and the
Github user viirya commented on the pull request:
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encoder now supports UDTs. You just need to declare one before you want to
use it.
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Github user MLnick commented on the pull request:
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@viirya yeah, sorry I meant we should create an encoder that can be used in
`ml`... whether an implicit or explicit.
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Github user zhengruifeng commented on a diff in the pull request:
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/Predictor.scala ---
@@ -121,9 +121,9 @@ abstract class Predictor[
* and put it in an RDD with
Github user viirya commented on the pull request:
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We have no implicit encoder for vector udt. But we can explicitly create it.
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/Predictor.scala ---
@@ -121,9 +121,9 @@ abstract class Predictor[
* and put it in an RDD with
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
---
@@ -398,6 +398,66 @@ class DataFrameSuite extends QueryTest with
Github user jasonmoore2k commented on the pull request:
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@andrewor14 @kayousterhout
Would appreciate your thoughts on this change (or anybody else who you
recommend with some experience with the task scheduler).
Github user gatorsmile commented on a diff in the pull request:
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--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
---
@@ -488,14 +488,6 @@ class
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Github user MLnick commented on the pull request:
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ping @jkbradley @mengxr @srowen - thoughts on my suggestion above?
I think the overall gist of this PR is fine, but it seems nicer to me to
actually put a trait
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Can one of the admins verify this patch?
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Github user MLnick commented on the pull request:
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ping @srowen @yanboliang @jkbradley any comments?
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[SPARK-14915] [CORE] Don't re-queue a task if another attempt has already
succeeded
## What changes were proposed in this pull request?
Don't re-queue a task if another attempt has
Github user devaraj-kavali closed the pull request at:
https://github.com/apache/spark/pull/11778
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Github user MLnick commented on the pull request:
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We should definitely have an encoder for vector udts... cc @dbtsai @viirya
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Github user yinxusen commented on the pull request:
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@jkbradley Another bug found:
The `CrossValidator` and `TrainValidationSplit` miss the `seed` when saving
and loading. I'd prefer to create a JIRA and fix
Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12493#discussion_r61377331
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1981,6 +1982,23 @@ class Dataset[T] private[sql](
}
/**
Github user mengxr commented on the pull request:
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Had an offline discussion with @cloud-fan and we will try converting
from/to UnsafeArrayData directly using memory copy and test its performance.
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--- Diff: R/pkg/R/DataFrame.R ---
@@ -1426,11 +1426,11 @@ setMethod("withColumn",
#' Mutate
#'
-#' Return a new SparkDataFrame with the
Github user hvanhovell commented on the pull request:
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@davies we only move pulling out the predicates into the analyzer. The
actual planning (conversion into Semi Joins) still takes place in the
Optimizer. There are a
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