[GitHub] spark pull request #21563: [SPARK-24557][ML] ClusteringEvaluator support arr...

2018-06-14 Thread zhengruifeng
GitHub user zhengruifeng opened a pull request:

https://github.com/apache/spark/pull/21563

[SPARK-24557][ML] ClusteringEvaluator support array input

## What changes were proposed in this pull request?
ClusteringEvaluator support array input

## How was this patch tested?
added tests

You can merge this pull request into a Git repository by running:

$ git pull https://github.com/zhengruifeng/spark clu_eval_support_array

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/21563.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #21563


commit b126bd4f410ab4a01bbe7a980042704ea7420c6f
Author: 郑瑞峰 
Date:   2018-06-14T08:15:43Z

init pr




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[GitHub] spark issue #21563: [SPARK-24557][ML] ClusteringEvaluator support array inpu...

2018-06-14 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/21563
  
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[`b126bd4`](https://github.com/apache/spark/commit/b126bd4f410ab4a01bbe7a980042704ea7420c6f).


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[GitHub] spark issue #21564: [SPARK-24556][SQL] ReusedExchange should rewrite output ...

2018-06-14 Thread AmplabJenkins
Github user AmplabJenkins commented on the issue:

https://github.com/apache/spark/pull/21564
  
Can one of the admins verify this patch?


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[GitHub] spark pull request #21366: [SPARK-24248][K8S] Use level triggering and state...

2018-06-14 Thread skonto
Github user skonto commented on a diff in the pull request:

https://github.com/apache/spark/pull/21366#discussion_r195350385
  
--- Diff: 
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsAllocator.scala
 ---
@@ -0,0 +1,141 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.scheduler.cluster.k8s
+
+import java.util.concurrent.atomic.{AtomicInteger, AtomicLong}
+
+import io.fabric8.kubernetes.api.model.PodBuilder
+import io.fabric8.kubernetes.client.KubernetesClient
+import scala.collection.mutable
+
+import org.apache.spark.{SparkConf, SparkException}
+import org.apache.spark.deploy.k8s.Config._
+import org.apache.spark.deploy.k8s.Constants._
+import org.apache.spark.deploy.k8s.KubernetesConf
+import org.apache.spark.internal.Logging
+import org.apache.spark.util.{Clock, Utils}
+
+private[spark] class ExecutorPodsAllocator(
+conf: SparkConf,
+executorBuilder: KubernetesExecutorBuilder,
+kubernetesClient: KubernetesClient,
+snapshotsStore: ExecutorPodsSnapshotsStore,
+clock: Clock) extends Logging {
+
+  private val EXECUTOR_ID_COUNTER = new AtomicLong(0L)
+
+  private val totalExpectedExecutors = new AtomicInteger(0)
+
+  private val podAllocationSize = 
conf.get(KUBERNETES_ALLOCATION_BATCH_SIZE)
+
+  private val podAllocationDelay = 
conf.get(KUBERNETES_ALLOCATION_BATCH_DELAY)
+
+  private val podCreationTimeout = math.max(podAllocationDelay * 5, 6)
+
+  private val kubernetesDriverPodName = conf
+.get(KUBERNETES_DRIVER_POD_NAME)
+.getOrElse(throw new SparkException("Must specify the driver pod 
name"))
+
+  private val driverPod = kubernetesClient.pods()
+.withName(kubernetesDriverPodName)
+.get()
+
+  // Executor IDs that have been requested from Kubernetes but have not 
been detected in any
+  // snapshot yet. Mapped to the timestamp when they were created.
+  private val newlyCreatedExecutors = mutable.Map.empty[Long, Long]
+
+  def start(applicationId: String): Unit = {
+snapshotsStore.addSubscriber(podAllocationDelay) {
+  onNewSnapshots(applicationId, _)
+}
+  }
+
+  def setTotalExpectedExecutors(total: Int): Unit = 
totalExpectedExecutors.set(total)
+
+  private def onNewSnapshots(applicationId: String, snapshots: 
Seq[ExecutorPodsSnapshot]): Unit = {
+newlyCreatedExecutors --= snapshots.flatMap(_.executorPods.keys)
+// For all executors we've created against the API but have not seen 
in a snapshot
+// yet - check the current time. If the current time has exceeded some 
threshold,
+// assume that the pod was either never created (the API server never 
properly
+// handled the creation request), or the API server created the pod 
but we missed
+// both the creation and deletion events. In either case, delete the 
missing pod
+// if possible, and mark such a pod to be rescheduled below.
+newlyCreatedExecutors.foreach { case (execId, timeCreated) =>
+  if (clock.getTimeMillis() - timeCreated > podCreationTimeout) {
+logWarning(s"Executor with id $execId was not detected in the 
Kubernetes" +
+  s" cluster after $podCreationTimeout milliseconds despite the 
fact that a" +
+  " previous allocation attempt tried to create it. The executor 
may have been" +
+  " deleted but the application missed the deletion event.")
+Utils.tryLogNonFatalError {
+  kubernetesClient
+.pods()
+.withLabel(SPARK_EXECUTOR_ID_LABEL, execId.toString)
+.delete()
--- End diff --

Shouldn't deleteFromSpark called here as well? Couldn't be the case that 
the executor exists at a higher level but K8s backend missed it?


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[GitHub] spark pull request #21564: [SPARK-24556][SQL] ReusedExchange should rewrite ...

2018-06-14 Thread yucai
Github user yucai commented on a diff in the pull request:

https://github.com/apache/spark/pull/21564#discussion_r195354829
  
--- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala
 ---
@@ -170,6 +170,8 @@ case class InMemoryTableScanExec(
   override def outputPartitioning: Partitioning = {
 relation.cachedPlan.outputPartitioning match {
   case h: HashPartitioning => 
updateAttribute(h).asInstanceOf[HashPartitioning]
+  case r: RangePartitioning =>
+r.copy(ordering = 
r.ordering.map(updateAttribute(_).asInstanceOf[SortOrder]))
--- End diff --

Good suggestion, thanks @mgaido91.

@viirya Do we need consider below:
`PartitioningCollection` in `InMemoryTableScanExec.outputPartitioning`, 
which is also `Expression`?
`PartitioningCollection` and `BroadcastPartitioning` in 
`ReusedExchangeExec.outputPartitioning`?


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[GitHub] spark pull request #21566: [Python] Fix typo in serializer exception

2018-06-14 Thread rberenguel
GitHub user rberenguel opened a pull request:

https://github.com/apache/spark/pull/21566

[Python] Fix typo in serializer exception

## What changes were proposed in this pull request?

Fix typo in exception raised in Python serializer

## How was this patch tested?

No code changes

Please review http://spark.apache.org/contributing.html before opening a 
pull request.


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/rberenguel/spark fix_typo_pyspark_serializers

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/21566.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #21566


commit bd8627c879131a4af364ef667f4ac1209ea6909a
Author: Ruben Berenguel Montoro 
Date:   2018-06-14T11:11:14Z

Fix typo in serializer exception




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[GitHub] spark pull request #21563: [SPARK-24557][ML] ClusteringEvaluator support arr...

2018-06-14 Thread mgaido91
Github user mgaido91 commented on a diff in the pull request:

https://github.com/apache/spark/pull/21563#discussion_r195389157
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala 
---
@@ -107,15 +106,18 @@ class ClusteringEvaluator @Since("2.3.0") 
(@Since("2.3.0") override val uid: Str
 
   @Since("2.3.0")
   override def evaluate(dataset: Dataset[_]): Double = {
-SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new 
VectorUDT)
+SchemaUtils.validateVectorCompatibleColumn(dataset.schema, 
$(featuresCol))
 SchemaUtils.checkNumericType(dataset.schema, $(predictionCol))
 
+val vectorCol = DatasetUtils.columnToVector(dataset, $(featuresCol))
+val df = dataset.select(col($(predictionCol)),
--- End diff --

not sure this is the right way. Probably we can face the same issue 
everywhere we are using `DatasetUtils.columnToVector`. Probably it is better to 
fix the problem there.


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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

2018-06-14 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/21441
  
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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

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[GitHub] spark pull request #21366: [SPARK-24248][K8S] Use level triggering and state...

2018-06-14 Thread skonto
Github user skonto commented on a diff in the pull request:

https://github.com/apache/spark/pull/21366#discussion_r195401593
  
--- Diff: 
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsPollingSnapshotSource.scala
 ---
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.scheduler.cluster.k8s
+
+import java.util.concurrent.{Future, ScheduledExecutorService, TimeUnit}
+
+import io.fabric8.kubernetes.client.KubernetesClient
+import scala.collection.JavaConverters._
+
+import org.apache.spark.SparkConf
+import org.apache.spark.deploy.k8s.Config._
+import org.apache.spark.deploy.k8s.Constants._
+import org.apache.spark.util.ThreadUtils
+
+private[spark] class ExecutorPodsPollingSnapshotSource(
+conf: SparkConf,
+kubernetesClient: KubernetesClient,
+snapshotsStore: ExecutorPodsSnapshotsStore,
+pollingExecutor: ScheduledExecutorService) {
--- End diff --

Could you add some debug logging here. In general it would be good to be 
able to trace what is happening in case of a an issue with debug mode, this 
applies to all classes introduced for both watching and polling.


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[GitHub] spark issue #21561: [SPARK-24555][ML] logNumExamples in KMeans/BiKM/GMM/AFT/...

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[GitHub] spark issue #21561: [SPARK-24555][ML] logNumExamples in KMeans/BiKM/GMM/AFT/...

2018-06-14 Thread SparkQA
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 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #21561: [SPARK-24555][ML] logNumExamples in KMeans/BiKM/GMM/AFT/...

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[GitHub] spark issue #21547: [SPARK-24538][SQL] ByteArrayDecimalType support push dow...

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[GitHub] spark issue #21547: [SPARK-24538][SQL] ByteArrayDecimalType support push dow...

2018-06-14 Thread SparkQA
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[GitHub] spark issue #21547: [SPARK-24538][SQL] ByteArrayDecimalType support push dow...

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[GitHub] spark pull request #21321: [SPARK-24268][SQL] Use datatype.simpleString in e...

2018-06-14 Thread mgaido91
Github user mgaido91 commented on a diff in the pull request:

https://github.com/apache/spark/pull/21321#discussion_r195344259
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.scala ---
@@ -208,7 +208,8 @@ class FeatureHasher(@Since("2.3.0") override val uid: 
String) extends Transforme
   require(dataType.isInstanceOf[NumericType] ||
 dataType.isInstanceOf[StringType] ||
 dataType.isInstanceOf[BooleanType],
-s"FeatureHasher requires columns to be of NumericType, BooleanType 
or StringType. " +
+s"FeatureHasher requires columns to be of 
${NumericType.simpleString}, " +
--- End diff --

I think this PR rewrites always constant type referenced. I am not sure why 
you are saying it is not. If I missed some places, then it was just because I 
haven't seen them.


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[GitHub] spark issue #21550: [SPARK-24543][SQL] Support any type as DDL string for fr...

2018-06-14 Thread SparkQA
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https://github.com/apache/spark/pull/21550
  
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[GitHub] spark issue #16171: [SPARK-18739][ML][PYSPARK] Classification and regression...

2018-06-14 Thread zhengruifeng
Github user zhengruifeng commented on the issue:

https://github.com/apache/spark/pull/16171
  
It is out of date, and I will close it


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[GitHub] spark pull request #21366: [SPARK-24248][K8S] Use level triggering and state...

2018-06-14 Thread skonto
Github user skonto commented on a diff in the pull request:

https://github.com/apache/spark/pull/21366#discussion_r195379060
  
--- Diff: 
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterManager.scala
 ---
@@ -56,17 +58,44 @@ private[spark] class KubernetesClusterManager extends 
ExternalClusterManager wit
   Some(new File(Config.KUBERNETES_SERVICE_ACCOUNT_TOKEN_PATH)),
   Some(new File(Config.KUBERNETES_SERVICE_ACCOUNT_CA_CRT_PATH)))
 
-val allocatorExecutor = ThreadUtils
-  .newDaemonSingleThreadScheduledExecutor("kubernetes-pod-allocator")
 val requestExecutorsService = ThreadUtils.newDaemonCachedThreadPool(
   "kubernetes-executor-requests")
+
+val bufferSnapshotsExecutor = ThreadUtils
+  
.newDaemonSingleThreadScheduledExecutor("kubernetes-executor-snapshots-buffer")
+val snapshotsStore = new 
ExecutorPodsSnapshotsStoreImpl(bufferSnapshotsExecutor)
+val removedExecutorsCache = CacheBuilder.newBuilder()
+  .expireAfterWrite(3, TimeUnit.MINUTES)
--- End diff --

Why 3 minutes? Should this be configurable?


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[GitHub] spark issue #21567: [SPARK-24560][SS][MESOS] Fix some getTimeAsMs as getTime...

2018-06-14 Thread AmplabJenkins
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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

2018-06-14 Thread SparkQA
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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

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[GitHub] spark pull request #21564: [SPARK-24556][SQL] ReusedExchange should rewrite ...

2018-06-14 Thread mgaido91
Github user mgaido91 commented on a diff in the pull request:

https://github.com/apache/spark/pull/21564#discussion_r195349283
  
--- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala
 ---
@@ -170,6 +170,8 @@ case class InMemoryTableScanExec(
   override def outputPartitioning: Partitioning = {
 relation.cachedPlan.outputPartitioning match {
   case h: HashPartitioning => 
updateAttribute(h).asInstanceOf[HashPartitioning]
+  case r: RangePartitioning =>
+r.copy(ordering = 
r.ordering.map(updateAttribute(_).asInstanceOf[SortOrder]))
--- End diff --

why not just `updateAttribute(r)`?

Moreover, in order to avoid the same issue in the future with other cases, 
have you considered doing something like:
```
updateAttribute(relation.cachedPlan.outputPartitioning)
``
?


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[GitHub] spark issue #21537: [SPARK-24505][SQL] Convert strings in codegen to blocks:...

2018-06-14 Thread viirya
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retest this please.


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[GitHub] spark issue #21566: [Python] Fix typo in serializer exception

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[GitHub] spark issue #21550: [SPARK-24543][SQL] Support any type as DDL string for fr...

2018-06-14 Thread HyukjinKwon
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retest this please


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[GitHub] spark issue #21462: [SPARK-24428][K8S] Fix unused code

2018-06-14 Thread skonto
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@felixcheung gentle ping.


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[GitHub] spark issue #21566: [Python] Fix typo in serializer exception

2018-06-14 Thread HyukjinKwon
Github user HyukjinKwon commented on the issue:

https://github.com/apache/spark/pull/21566
  
@rberenguel, it's okay but mind taking another look around here and see if 
there are more typos while we are here? I am pretty sure there are more.


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[GitHub] spark pull request #21564: [SPARK-24556][SQL] ReusedExchange should rewrite ...

2018-06-14 Thread yucai
GitHub user yucai opened a pull request:

https://github.com/apache/spark/pull/21564

[SPARK-24556][SQL] ReusedExchange should rewrite output partitioning also 
when child's partitioning is RangePartitioning

## What changes were proposed in this pull request?

Currently, ReusedExchange would rewrite output partitioning if child's 
partitioning is HashPartitioning, but it does not do the same when child's 
partitioning is RangePartitioning, sometimes, it could introduce extra shuffle, 
see:

```
val df = Seq(1 -> "a", 3 -> "c", 2 -> "b").toDF("i", "j")
val df1 = df.as("t1")
val df2 = df.as("t2")
val t = df1.orderBy("j").join(df2.orderBy("j"), $"t1.i" === $"t2.i", 
"right")
t.cache.orderBy($"t2.j").explain
```
Before:
```
== Physical Plan ==
*(1) Sort [j#14 ASC NULLS FIRST], true, 0
+- Exchange rangepartitioning(j#14 ASC NULLS FIRST, 200)
   +- InMemoryTableScan [i#5, j#6, i#13, j#14]
 +- InMemoryRelation [i#5, j#6, i#13, j#14], CachedRDDBuilder...
   +- *(2) BroadcastHashJoin [i#5], [i#13], RightOuter, 
BuildLeft
  :- BroadcastExchange 
HashedRelationBroadcastMode(List(cast(input[0, int, false] as...
  :  +- *(1) Sort [j#6 ASC NULLS FIRST], true, 0
  : +- Exchange rangepartitioning(j#6 ASC NULLS FIRST, 
200)
  :+- LocalTableScan [i#5, j#6]
  +- *(2) Sort [j#14 ASC NULLS FIRST], true, 0
 +- ReusedExchange [i#13, j#14], Exchange 
rangepartitioning(j#6 ASC NULLS FIRST, 200)
```
Better plan should avoid ```Exchange rangepartitioning(j#14 ASC NULLS 
FIRST, 200)```, like:
```
== Physical Plan ==
*(1) Sort [j#14 ASC NULLS FIRST], true, 0
+- InMemoryTableScan [i#5, j#6, i#13, j#14]
  +- InMemoryRelation [i#5, j#6, i#13, j#14], CachedRDDBuilder...
+- *(2) BroadcastHashJoin [i#5], [i#13], RightOuter, BuildLeft
   :- BroadcastExchange 
HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
   :  +- *(1) Sort [j#6 ASC NULLS FIRST], true, 0
   : +- Exchange rangepartitioning(j#6 ASC NULLS FIRST, 200)
   :+- LocalTableScan [i#5, j#6]
   +- *(2) Sort [j#14 ASC NULLS FIRST], true, 0
  +- ReusedExchange [i#13, j#14], Exchange 
rangepartitioning(j#6 ASC NULLS FIRST, 200)
```

## How was this patch tested?

Add new tests.


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/yucai/spark SPARK-24556

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/21564.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #21564






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[GitHub] spark issue #21366: [SPARK-24248][K8S] Use level triggering and state reconc...

2018-06-14 Thread skonto
Github user skonto commented on the issue:

https://github.com/apache/spark/pull/21366
  
@mccheah could you add a design doc for future reference and so that new 
contributors can understand better the rationale behind this. There is some 
description in the JIRA ticket but not enough to describe the final solution. 



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[GitHub] spark pull request #21564: [SPARK-24556][SQL] ReusedExchange should rewrite ...

2018-06-14 Thread yucai
Github user yucai commented on a diff in the pull request:

https://github.com/apache/spark/pull/21564#discussion_r195361725
  
--- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala
 ---
@@ -170,6 +170,8 @@ case class InMemoryTableScanExec(
   override def outputPartitioning: Partitioning = {
 relation.cachedPlan.outputPartitioning match {
   case h: HashPartitioning => 
updateAttribute(h).asInstanceOf[HashPartitioning]
+  case r: RangePartitioning =>
+r.copy(ordering = 
r.ordering.map(updateAttribute(_).asInstanceOf[SortOrder]))
--- End diff --

`BroadcastPartitioning`'s `BroadcastMode` contains `Expression`?


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[GitHub] spark pull request #21564: [SPARK-24556][SQL] ReusedExchange should rewrite ...

2018-06-14 Thread viirya
Github user viirya commented on a diff in the pull request:

https://github.com/apache/spark/pull/21564#discussion_r19535
  
--- Diff: 
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala
 ---
@@ -170,6 +170,8 @@ case class InMemoryTableScanExec(
   override def outputPartitioning: Partitioning = {
 relation.cachedPlan.outputPartitioning match {
   case h: HashPartitioning => 
updateAttribute(h).asInstanceOf[HashPartitioning]
+  case r: RangePartitioning =>
+r.copy(ordering = 
r.ordering.map(updateAttribute(_).asInstanceOf[SortOrder]))
--- End diff --

Oh, like `HashedRelationBroadcastMode`.


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[GitHub] spark pull request #21565: wrong Idle Timeout value is used in case of the c...

2018-06-14 Thread sandeep-katta
GitHub user sandeep-katta opened a pull request:

https://github.com/apache/spark/pull/21565

wrong Idle Timeout value is used in case of the cacheBlock.

It is corrected as per the configuration.

## What changes were proposed in this pull request?
IdleTimeout info used to print in the logs is taken based on the 
cacheBlock. If it is cacheBlock then cachedExecutorIdleTimeoutS is considered 
else executorIdleTimeoutS 

## How was this patch tested?
Manual Test
spark-sql> cache table sample;
2018-05-15 14:44:02 INFO  DAGScheduler:54 - Submitting 3 missing tasks from 
ShuffleMapStage 0 (MapPartitionsRDD[8] at processCmd at CliDriver.java:376) 
(first 15 tasks are for partitions Vector(0, 1, 2))
2018-05-15 14:44:02 INFO  YarnScheduler:54 - Adding task set 0.0 with 3 
tasks
2018-05-15 14:44:03 INFO  ExecutorAllocationManager:54 - Requesting 1 new 
executor because tasks are backlogged (new desired total will be 1)
...
...
2018-05-15 14:46:10 INFO  YarnClientSchedulerBackend:54 - Actual list of 
executor(s) to be killed is 1
2018-05-15 14:46:10 INFO  **ExecutorAllocationManager:54 - Removing 
executor 1 because it has been idle for 120 seconds (new desired total will be 
0)**
2018-05-15 14:46:11 INFO  YarnSchedulerBackend$YarnDriverEndpoint:54 - 
Disabling executor 1.
2018-05-15 14:46:11 INFO  DAGScheduler:54 - Executor lost: 1 (epoch 1)


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/sandeep-katta/spark loginfoBug

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/21565.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #21565


commit 30fcef650ee2bd2873bf402448652acba055f989
Author: sandeep-katta 
Date:   2018-06-14T09:56:59Z

wrong Idle Timeout value is used in case of the cacheBlock.
It is corrected as per the configuration.




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[GitHub] spark issue #21565: wrong Idle Timeout value is used in case of the cacheBlo...

2018-06-14 Thread AmplabJenkins
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[GitHub] spark issue #21565: wrong Idle Timeout value is used in case of the cacheBlo...

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[GitHub] spark issue #21109: [SPARK-24020][SQL] Sort-merge join inner range optimizat...

2018-06-14 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/21109
  
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 * This patch **fails Spark unit tests**.
 * This patch **does not merge cleanly**.
 * This patch adds no public classes.


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[GitHub] spark issue #21379: [SPARK-24327][SQL] Verify and normalize a partition colu...

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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

2018-06-14 Thread SparkQA
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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

2018-06-14 Thread mgaido91
Github user mgaido91 commented on the issue:

https://github.com/apache/spark/pull/21529
  
retest this please


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[GitHub] spark issue #21537: [SPARK-24505][SQL] Convert strings in codegen to blocks:...

2018-06-14 Thread SparkQA
Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/21537
  
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[`b592e66`](https://github.com/apache/spark/commit/b592e66c030ba7c2d260c3be48c3b15139f40e5b).
 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

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[GitHub] spark issue #21529: [SPARK-24495][SQL] EnsureRequirement returns wrong plan ...

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[GitHub] spark issue #21563: [SPARK-24557][ML] ClusteringEvaluator support array inpu...

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[GitHub] spark issue #21564: [SPARK-24556][SQL] ReusedExchange should rewrite output ...

2018-06-14 Thread SparkQA
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[GitHub] spark issue #21564: [SPARK-24556][SQL] ReusedExchange should rewrite output ...

2018-06-14 Thread AmplabJenkins
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[GitHub] spark issue #19084: [SPARK-20711][ML]MultivariateOnlineSummarizer/Summarizer...

2018-06-14 Thread zhengruifeng
Github user zhengruifeng commented on the issue:

https://github.com/apache/spark/pull/19084
  
@srowen Could you please give a final review? Thanks


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[GitHub] spark issue #20611: [SPARK-23425][SQL]Support wildcard in HDFS path for load...

2018-06-14 Thread SparkQA
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[GitHub] spark pull request #16171: [SPARK-18739][ML][PYSPARK] Classification and reg...

2018-06-14 Thread zhengruifeng
Github user zhengruifeng closed the pull request at:

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[GitHub] spark pull request #21366: [SPARK-24248][K8S] Use level triggering and state...

2018-06-14 Thread skonto
Github user skonto commented on a diff in the pull request:

https://github.com/apache/spark/pull/21366#discussion_r195380019
  
--- Diff: 
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsLifecycleManager.scala
 ---
@@ -0,0 +1,164 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.scheduler.cluster.k8s
+
+import com.google.common.cache.Cache
+import io.fabric8.kubernetes.api.model.Pod
+import io.fabric8.kubernetes.client.KubernetesClient
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.SparkConf
+import org.apache.spark.deploy.k8s.Config._
+import org.apache.spark.scheduler.ExecutorExited
+import org.apache.spark.util.Utils
+
+private[spark] class ExecutorPodsLifecycleManager(
+conf: SparkConf,
+executorBuilder: KubernetesExecutorBuilder,
+kubernetesClient: KubernetesClient,
+snapshotsStore: ExecutorPodsSnapshotsStore,
+// Use a best-effort to track which executors have been removed 
already. It's not generally
+// job-breaking if we remove executors more than once but it's ideal 
if we make an attempt
+// to avoid doing so. Expire cache entries so that this data structure 
doesn't grow beyond
+// bounds.
+removedExecutorsCache: Cache[java.lang.Long, java.lang.Long]) {
+
+  import ExecutorPodsLifecycleManager._
+
+  private val eventProcessingInterval = 
conf.get(KUBERNETES_EXECUTOR_EVENT_PROCESSING_INTERVAL)
+
+  def start(schedulerBackend: KubernetesClusterSchedulerBackend): Unit = {
+snapshotsStore.addSubscriber(eventProcessingInterval) {
+  onNewSnapshots(schedulerBackend, _)
+}
+  }
+
+  private def onNewSnapshots(
+  schedulerBackend: KubernetesClusterSchedulerBackend,
+  snapshots: Seq[ExecutorPodsSnapshot]): Unit = {
+val execIdsRemovedInThisRound = mutable.HashSet.empty[Long]
+snapshots.foreach { snapshot =>
+  snapshot.executorPods.foreach { case (execId, state) =>
+state match {
+  case deleted@PodDeleted(pod) =>
+removeExecutorFromSpark(schedulerBackend, deleted, execId)
+execIdsRemovedInThisRound += execId
+  case failed@PodFailed(pod) =>
+onFinalNonDeletedState(failed, execId, schedulerBackend, 
execIdsRemovedInThisRound)
+  case succeeded@PodSucceeded(pod) =>
--- End diff --

same as above.


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[GitHub] spark pull request #21366: [SPARK-24248][K8S] Use level triggering and state...

2018-06-14 Thread skonto
Github user skonto commented on a diff in the pull request:

https://github.com/apache/spark/pull/21366#discussion_r195379975
  
--- Diff: 
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsLifecycleManager.scala
 ---
@@ -0,0 +1,164 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.scheduler.cluster.k8s
+
+import com.google.common.cache.Cache
+import io.fabric8.kubernetes.api.model.Pod
+import io.fabric8.kubernetes.client.KubernetesClient
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.SparkConf
+import org.apache.spark.deploy.k8s.Config._
+import org.apache.spark.scheduler.ExecutorExited
+import org.apache.spark.util.Utils
+
+private[spark] class ExecutorPodsLifecycleManager(
+conf: SparkConf,
+executorBuilder: KubernetesExecutorBuilder,
+kubernetesClient: KubernetesClient,
+snapshotsStore: ExecutorPodsSnapshotsStore,
+// Use a best-effort to track which executors have been removed 
already. It's not generally
+// job-breaking if we remove executors more than once but it's ideal 
if we make an attempt
+// to avoid doing so. Expire cache entries so that this data structure 
doesn't grow beyond
+// bounds.
+removedExecutorsCache: Cache[java.lang.Long, java.lang.Long]) {
+
+  import ExecutorPodsLifecycleManager._
+
+  private val eventProcessingInterval = 
conf.get(KUBERNETES_EXECUTOR_EVENT_PROCESSING_INTERVAL)
+
+  def start(schedulerBackend: KubernetesClusterSchedulerBackend): Unit = {
+snapshotsStore.addSubscriber(eventProcessingInterval) {
+  onNewSnapshots(schedulerBackend, _)
+}
+  }
+
+  private def onNewSnapshots(
+  schedulerBackend: KubernetesClusterSchedulerBackend,
+  snapshots: Seq[ExecutorPodsSnapshot]): Unit = {
+val execIdsRemovedInThisRound = mutable.HashSet.empty[Long]
+snapshots.foreach { snapshot =>
+  snapshot.executorPods.foreach { case (execId, state) =>
+state match {
+  case deleted@PodDeleted(pod) =>
+removeExecutorFromSpark(schedulerBackend, deleted, execId)
+execIdsRemovedInThisRound += execId
+  case failed@PodFailed(pod) =>
--- End diff --

same as above.


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[GitHub] spark pull request #21567: [SPARK-24560][SS][MESOS] Fix some getTimeAsMs as ...

2018-06-14 Thread xueyumusic
GitHub user xueyumusic opened a pull request:

https://github.com/apache/spark/pull/21567

[SPARK-24560][SS][MESOS] Fix some getTimeAsMs as getTimeAsSeconds

## What changes were proposed in this pull request?

This PR replaces some "getTimeAsMs" with "getTimeAsSeconds". This will 
return a wrong value when the user specifies a value without a time unit.

## How was this patch tested?
manual test

Please review http://spark.apache.org/contributing.html before opening a 
pull request.


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/xueyumusic/spark fixGetTimeAs

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/21567.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #21567


commit 10bf41ec86c0af59a791fa02b5efaedc7a164a3c
Author: xueyu <278006819@...>
Date:   2018-06-14T11:01:29Z

fix getTimeAs method




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[GitHub] spark issue #21441: [DO-NOT-MERGE] Run tests against hadoop-3.1 to see the t...

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[GitHub] spark issue #21564: [SPARK-24556][SQL] ReusedExchange should rewrite output ...

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[GitHub] spark issue #21564: [SPARK-24556][SQL] ReusedExchange should rewrite output ...

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