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--- Diff: python/pyspark/ml/regression.py ---
@@ -705,12 +710,59 @@ def getNumTrees(self):
return self.getOrDefault(self.numTrees)
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--- Diff: python/pyspark/ml/classification.py ---
@@ -1174,9 +1165,31 @@ def trees(self):
return [DecisionTreeClassificationModel(m) for m
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--- Diff: python/pyspark/ml/classification.py ---
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--- Diff: docs/ml-clustering.md ---
@@ -265,3 +265,44 @@ Refer to the [R API
docs](api/R/spark.gaussianMixture.html) for more details.
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--- Diff: core/src/main/scala/org/apache/spark/io/CompressionCodec.scala ---
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Thanks. I will update the PR title
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Jenkins, retest this please
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--- Diff: examples/src/main/r/ml/powerIterationClustering.R ---
@@ -0,0 +1,37 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under
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--- Diff: core/src/main/scala/org/apache/spark/io/CompressionCodec.scala ---
@@ -197,4 +201,8 @@ class ZStdCompressionCodec(conf: SparkConf) extends
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--- Diff:
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@@ -33,10 +33,10 @@ import org.apache.spark.shuffle._
*
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Oh, I see. Close this one now.
Please change the title in #23160
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Seems ok to me, but there are a few silly things with this test that might
help also
* why is the `stepSize` so low at 0.01? I think it would be fine at 0.1,
but even conservatively at
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--- Diff: R/pkg/R/mllib_clustering.R ---
@@ -610,3 +616,58 @@ setMethod("write.ml", signature(object = "LDAModel",
path = "character"),
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Can one of the admins verify this patch?
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cc @vanzin @srowen Kindly review
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--- Diff: R/pkg/tests/fulltests/test_mllib_clustering.R ---
@@ -319,4 +319,18 @@ test_that("spark.posterior and spark.perplexity", {
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[SPARK-26283][CORE]When zstd compression enabled, Inprogress application in
the history server appUI showing finished job as running.
## What changes were proposed in this pull request?
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--- Diff: R/pkg/tests/fulltests/test_mllib_fpm.R ---
@@ -84,19 +84,21 @@ test_that("spark.fpGrowth", {
})
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--- Diff: R/pkg/R/mllib_clustering.R ---
@@ -610,3 +616,58 @@ setMethod("write.ml", signature(object = "LDAModel",
path = "character"),
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cc @viirya @maropu
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--- Diff:
core/src/main/scala/org/apache/spark/executor/ProcfsMetricsGetter.scala ---
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software
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lgtm
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@attilapiros
> I mean if node blacklisting in Spark would be perfectly aligned to YARN
then it would be just redundant to have it in Spark in the first place.
I'm not super
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The failure isn't related to my change. Jenkins retest this please
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Thank you, @srowen , @HyukjinKwon , @shaneknapp !
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--- Diff: docs/structured-streaming-kafka-integration.md ---
@@ -624,3 +624,199 @@ For experimenting on `spark-shell`, you can also use
`--packages` to add
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Hi @gengliangwang , It seems, this was already handled in the PR,
https://github.com/apache/spark/pull/23160
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@@ -612,11 +612,14 @@ private[yarn] class
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@@ -417,4 +426,59 @@ class YarnAllocatorSuite
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@shahidki31 @pgandhi999 @srowen
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[SPARK-26281][WebUI] Duration column of task table should be executor run
time instead of real duration
## What changes were proposed in this pull request?
In PR
Github user dongjoon-hyun commented on the issue:
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I merged all three PRs (2.4/2.3/2.2). Please close the PRs. :)
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Github user ptkool commented on the issue:
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I'm not sure how this ended up being omitted. `TransposeWindowSuite` will
be fine since it creates a simple optimizer from this rule and a few others.
The new test added to
Github user shaneknapp commented on the issue:
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shouldn't be too hard, but it will require some downtime.
On Wed, Dec 5, 2018 at 5:41 AM Sean Owen wrote:
> Ah OK, so all of them were a JVM crash. It would probably be a good
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Yes, the 3 cases I pointed that need to handle NaN and -0.0 do not change
the value in `UnsafeRow`.
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@cloud-fan what about UnsafeRow::setDouble/Float? It doesn't go through the
same flow. Is it not used?
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cc @adoron @kiszk @viirya @gatorsmile
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[SPARK-26021][SQL][followup] only deal with NaN and -0.0 in UnsafeWriter
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23043
Github user icexelloss commented on the issue:
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Hi @BryanCutler @HyukjinKwon @ueshin , mind taking another look? I think
this is in a good shape. Thanks!
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thanks, merging to master!
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thanks,@cloud-fan,@HyukjinKwon
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