[GitHub] [spark] AmplabJenkins removed a comment on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] AmplabJenkins removed a comment on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] AmplabJenkins commented on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] SparkQA removed a comment on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] AmplabJenkins commented on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest 
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URL: https://github.com/apache/spark/pull/27982#issuecomment-602417833
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

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[GitHub] [spark] AmplabJenkins removed a comment on issue #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

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[GitHub] [spark] AmplabJenkins commented on issue #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

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[GitHub] [spark] AmplabJenkins commented on issue #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

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[GitHub] [spark] SparkQA commented on issue #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

2020-03-22 Thread GitBox
SparkQA commented on issue #27984: [SPARK-31224][SQL] Add view support to SHOW 
CREATE TABLE
URL: https://github.com/apache/spark/pull/27984#issuecomment-602414660
 
 
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[GitHub] [spark] viirya opened a new pull request #27984: [SPARK-31224][SQL] Add view support to SHOW CREATE TABLE

2020-03-22 Thread GitBox
viirya opened a new pull request #27984: [SPARK-31224][SQL] Add view support to 
SHOW CREATE TABLE
URL: https://github.com/apache/spark/pull/27984
 
 
   
   
   ### What changes were proposed in this pull request?
   
   
   For now `SHOW CREATE TABLE` command doesn't support views, but `SHOW CREATE 
TABLE AS SERDE` supports it. Since the views syntax are the same between Hive 
DDL and Spark DDL, we should be able to support views in both two commands.
   
   This patch proposes to add views support to `SHOW CREATE TABLE`.
   
   ### Why are the changes needed?
   
   
   To extend the view support of `SHOW CREATE TABLE`, so users can use `SHOW 
CREATE TABLE` to show Spark DDL for views.
   
   ### Does this PR introduce any user-facing change?
   
   
   Yes. `SHOW CREATE TABLE` can be used to show Spark DDL for views.
   
   ### How was this patch tested?
   
   
   Unit tests.


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[GitHub] [spark] SparkQA commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

2020-03-22 Thread GitBox
SparkQA commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils
URL: https://github.com/apache/spark/pull/27617#issuecomment-602409929
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

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[GitHub] [spark] AmplabJenkins removed a comment on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

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[GitHub] [spark] cloud-fan closed pull request #27974: [SPARK-31211][SQL] Fix rebasing of 29 February of Julian leap years

2020-03-22 Thread GitBox
cloud-fan closed pull request #27974: [SPARK-31211][SQL] Fix rebasing of 29 
February of Julian leap years 
URL: https://github.com/apache/spark/pull/27974
 
 
   


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

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[GitHub] [spark] AmplabJenkins commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

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[GitHub] [spark] AmplabJenkins removed a comment on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

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[GitHub] [spark] AmplabJenkins commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

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[GitHub] [spark] AmplabJenkins commented on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

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[GitHub] [spark] AmplabJenkins commented on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

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[GitHub] [spark] SparkQA removed a comment on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

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[GitHub] [spark] MaxGekk commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

2020-03-22 Thread GitBox
MaxGekk commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils
URL: https://github.com/apache/spark/pull/27617#issuecomment-602407687
 
 
   jenkins, retest this, please


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[GitHub] [spark] SparkQA commented on issue #27937: [SPARK-30127][SQL] Support case class parameter for typed Scala UDF

2020-03-22 Thread GitBox
SparkQA commented on issue #27937: [SPARK-30127][SQL] Support case class 
parameter for typed Scala UDF
URL: https://github.com/apache/spark/pull/27937#issuecomment-602407564
 
 
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[GitHub] [spark] MaxGekk commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils

2020-03-22 Thread GitBox
MaxGekk commented on issue #27617: [SPARK-30865][SQL] Refactor DateTimeUtils
URL: https://github.com/apache/spark/pull/27617#issuecomment-602407299
 
 
   The issue will be fixed by https://github.com/apache/spark/pull/27980. I am 
going to re-trigger build, and hope that a leap year in Julian (and not leap 
year in Gregorian) will be not generated in tests.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core

2020-03-22 Thread GitBox
AmplabJenkins removed a comment on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] 
Implement ScriptTransformation in sql/core
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   Can one of the admins verify this patch?


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[GitHub] [spark] AmplabJenkins commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core

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AmplabJenkins commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] 
Implement ScriptTransformation in sql/core
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[GitHub] [spark] AmplabJenkins commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] 
Implement ScriptTransformation in sql/core
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[GitHub] [spark] AngersZhuuuu commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core

2020-03-22 Thread GitBox
AngersZh commented on issue #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement 
ScriptTransformation in sql/core
URL: https://github.com/apache/spark/pull/27983#issuecomment-602405307
 
 
   @gatorsmile @tejasapatil @wangyum 
   
   I have restart implement ScriptTransform in sql/core now,
   
   Rewrite origin pr in current code. Hope for your review and suggestion


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[GitHub] [spark] AngersZhuuuu commented on a change in pull request #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core (part 1)

2020-03-22 Thread GitBox
AngersZh commented on a change in pull request #27983: 
[SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core (part 
1) 
URL: https://github.com/apache/spark/pull/27983#discussion_r396228748
 
 

 ##
 File path: 
sql/core/src/test/scala/org/apache/spark/sql/execution/script/ScriptTrnasformationExecSuite.scala
 ##
 @@ -0,0 +1,195 @@
+/*
+ * 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.sql.execution.script
+
+import java.sql.{Date, Timestamp}
+
+import org.scalatest.Assertions._
+import org.scalatest.BeforeAndAfterEach
+import org.scalatest.exceptions.TestFailedException
+
+import org.apache.spark.{SparkException, TaskContext, TestUtils}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, 
AttributeReference}
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest, UnaryExecNode}
+import org.apache.spark.sql.test.{SharedSparkSession, SQLTestUtils}
+import org.apache.spark.sql.types.StringType
+
+class ScriptTransformationSuite extends SparkPlanTest with SharedSparkSession
+  with BeforeAndAfterEach {
+  import testImplicits._
+
+  private val noSerdeIOSchema = new ScriptTransformIOSchema(
+inputRowFormat = Seq.empty,
+outputRowFormat = Seq.empty,
+inputSerdeClass = None,
+outputSerdeClass = None,
+inputSerdeProps = Seq.empty,
+outputSerdeProps = Seq.empty,
+recordReaderClass = None,
+recordWriterClass = None,
+schemaLess = false
+  )
+
+  private var defaultUncaughtExceptionHandler: Thread.UncaughtExceptionHandler 
= _
+
+  private val uncaughtExceptionHandler = new TestUncaughtExceptionHandler
+
+  protected override def beforeAll(): Unit = {
+super.beforeAll()
+defaultUncaughtExceptionHandler = Thread.getDefaultUncaughtExceptionHandler
+Thread.setDefaultUncaughtExceptionHandler(uncaughtExceptionHandler)
+  }
+
+  protected override def afterAll(): Unit = {
+super.afterAll()
+Thread.setDefaultUncaughtExceptionHandler(defaultUncaughtExceptionHandler)
+  }
+
+  override protected def afterEach(): Unit = {
+super.afterEach()
+uncaughtExceptionHandler.cleanStatus()
+  }
+
+  test("cat without SerDe") {
+assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+checkAnswer(
+  rowsDf,
+  (child: SparkPlan) => new ScriptTransformationExec(
+input = Seq(rowsDf.col("a").expr),
+script = "cat",
+output = Seq(AttributeReference("a", StringType)()),
+child = child,
+ioschema = noSerdeIOSchema
+  ),
+  rowsDf.collect())
+assert(uncaughtExceptionHandler.exception.isEmpty)
+  }
+
+  test("script transformation should not swallow errors from upstream 
operators (no serde)") {
+assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+val e = intercept[TestFailedException] {
+  checkAnswer(
+rowsDf,
+(child: SparkPlan) => new ScriptTransformationExec(
+  input = Seq(rowsDf.col("a").expr),
+  script = "cat",
+  output = Seq(AttributeReference("a", StringType)()),
+  child = ExceptionInjectingOperator(child),
+  ioschema = noSerdeIOSchema
+),
+rowsDf.collect())
+}
+assert(e.getMessage().contains("intentional exception"))
+// Before SPARK-25158, uncaughtExceptionHandler will catch 
IllegalArgumentException
+assert(uncaughtExceptionHandler.exception.isEmpty)
+  }
+
+
+  test("SPARK-14400 script transformation should fail for bad script command") 
{
+assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+
+val e = intercept[SparkException] {
+  val plan =
+new ScriptTransformationExec(
+  input = Seq(rowsDf.col("a").expr),
+  script = "some_non_existent_command",
+  output = Seq(AttributeReference("a", StringType)()),
+  child = rowsDf.queryExecutio

[GitHub] [spark] AngersZhuuuu opened a new pull request #27983: [SPARK-15694][SQL][FOLLOW-UP] Implement ScriptTransformation in sql/core (part 1)

2020-03-22 Thread GitBox
AngersZh opened a new pull request #27983: [SPARK-15694][SQL][FOLLOW-UP] 
Implement ScriptTransformation in sql/core (part 1) 
URL: https://github.com/apache/spark/pull/27983
 
 
   ### What changes were proposed in this pull request?
   
* Renamed `hive/execution/ScriptTransformationExec` to 
`hive/execution/script/HiveScriptTransformationExec`
* Added ScriptTransformationExec which would run script operator in SQL 
mode (without Hive).
   The output of script would be read as a string and column values are 
extracted by using a delimiter (default : tab character)
* `ScriptTransformBase` has common code used across 
`ScriptTransformationExec` and `HiveScriptTransformationExec`
* For thread writing data to script, ScriptTransformationWriterThread has 
the core logic. HiveScriptTransformationWriterThread extends that for Hive 
specific stuff.
* `ScriptTransformationWriterThread` will be used for Spark SQL. It only 
supports writing data to script process by serializing column values as tab 
delimited string
* `HiveScriptTransformationWriterThread` additionally supports Hive serde
* Added a Strategy named Scripts which would emit ScriptTransformationExec 
in physical plans. This would be used in non-Hive mode.
   
   Todo List;
   
   - For Hive, by default only serde's must be used, and for without hive can't 
use serde
   - Cleanup past hacks that are observed (and people suggest / report)
  -  support use transform with aggregation 
[SPARK-28227](https://issues.apache.org/jira/browse/SPARK-28227)
  - support array/map as transform's input 
[SPARK-22435](https://issues.apache.org/jira/browse/SPARK-22435)
   - Use code-gen projection to serialize rows to output stream()
   
   ### Why are the changes needed?
   Support run transform in SQL mode without hive
   
   
   ### Does this PR introduce any user-facing change?
   Yes
   
   
   ### How was this patch tested?
   Added UT
   


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[GitHub] [spark] cloud-fan commented on issue #27974: [SPARK-31211][SQL] Fix rebasing of 29 February of Julian leap years

2020-03-22 Thread GitBox
cloud-fan commented on issue #27974: [SPARK-31211][SQL] Fix rebasing of 29 
February of Julian leap years 
URL: https://github.com/apache/spark/pull/27974#issuecomment-602403292
 
 
   This makes me realize that we can't be fully compatible, and picking the 
closest valid date in Proleptic Gregorian calendar makes sense to me.
   
   Thanks, merging to master/3.0!


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

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[GitHub] [spark] SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

2020-03-22 Thread GitBox
SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest 
Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982#issuecomment-602398991
 
 
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[GitHub] [spark] MaxGekk commented on issue #27980: [SPARK-31221][SQL] Rebase any date-times in conversions to/from Java types

2020-03-22 Thread GitBox
MaxGekk commented on issue #27980: [SPARK-31221][SQL] Rebase any date-times in 
conversions to/from Java types
URL: https://github.com/apache/spark/pull/27980#issuecomment-602398253
 
 
   @cloud-fan @HyukjinKwon Please, review this PR.


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[GitHub] [spark] zhengruifeng commented on a change in pull request #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

2020-03-22 Thread GitBox
zhengruifeng commented on a change in pull request #27982: [SPARK-31222][ML] 
Make ANOVATest Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982#discussion_r396222712
 
 

 ##
 File path: mllib/src/test/scala/org/apache/spark/ml/stat/ANOVATestSuite.scala
 ##
 @@ -144,22 +144,30 @@ class ANOVATestSuite
   }
 
   test("test DataFrame with sparse vector") {
-val df = spark.createDataFrame(Seq(
-  (3, Vectors.sparse(6, Array((0, 6.0), (1, 7.0), (3, 7.0), (4, 6.0,
-  (1, Vectors.sparse(6, Array((1, 9.0), (2, 6.0), (4, 5.0), (5, 9.0,
-  (3, Vectors.sparse(6, Array((1, 9.0), (2, 3.0), (4, 5.0), (5, 5.0,
-  (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0))),
-  (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0))),
-  (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0)))
-)).toDF("label", "features")
+val data = Seq(
+  (3, Vectors.dense(Array(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0))),
+  (1, Vectors.dense(Array(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0))),
+  (3, Vectors.dense(Array(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0))),
+  (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0))),
+  (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0))),
+  (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0, 0.0
 
-val anovaResult = ANOVATest.test(df, "features", "label")
-val (pValues: Vector, fValues: Vector) =
-  anovaResult.select("pValues", "fValues")
-.as[(Vector, Vector)].head()
-assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 
0.45824059, 0.84633632,
-  0.15673368) relTol 1e-6)
-assert(fValues ~== Vectors.dense(0.375, 0.375, 1.5625, 1.02364865, 
0.17647059,
-  3.66) relTol 1e-6)
+val df1 = spark.createDataFrame(data.map(t => (t._1, t._2.toDense)))
+  .toDF("label", "features")
+val df2 = spark.createDataFrame(data.map(t => (t._1, t._2.toSparse)))
+  .toDF("label", "features")
+val df3 = spark.createDataFrame(data.map(t => (t._1, t._2.compressed)))
+  .toDF("label", "features")
+
+Seq(df1, df2, df3).foreach { df =>
+  val anovaResult = ANOVATest.test(df, "features", "label")
+  val (pValues: Vector, fValues: Vector) =
+anovaResult.select("pValues", "fValues")
+  .as[(Vector, Vector)].head()
+  assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 
0.45824059, 0.84633632,
+0.15673368, Double.NaN) relTol 1e-6)
 
 Review comment:
   for column only containing zero values, sklearn also returns `nan`:
   ```python
   X = np.zeros([3,5])
   
   y = [1,2,3]
   
   f_classif(X, y)
   
/home/zrf/Applications/anaconda3/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:110:
 RuntimeWarning: invalid value encountered in true_divide
 msw = sswn / float(dfwn)
   Out[24]: (array([nan, nan, nan, nan, nan]), array([nan, nan, nan, nan, nan]))
   =
   ```


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[GitHub] [spark] SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

2020-03-22 Thread GitBox
SparkQA commented on issue #27982: [SPARK-31222][ML] Make ANOVATest 
Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982#issuecomment-602396898
 
 
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[GitHub] [spark] cloud-fan commented on a change in pull request #27972: [SPARK-31207][CORE] Ensure the total number of blocks to fetch equals to the sum of local/hostLocal/remote blocks

2020-03-22 Thread GitBox
cloud-fan commented on a change in pull request #27972: [SPARK-31207][CORE] 
Ensure the total number of blocks to fetch equals to the sum of 
local/hostLocal/remote blocks
URL: https://github.com/apache/spark/pull/27972#discussion_r396222718
 
 

 ##
 File path: 
core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
 ##
 @@ -312,19 +310,57 @@ final class ShuffleBlockFetcherIterator(
 hostLocalBlocks ++= blocksForAddress.map(info => (info._1, info._3))
 hostLocalBlockBytes += mergedBlockInfos.map(_.size).sum
   } else {
-numRemoteBlocks += blockInfos.size
 remoteBlockBytes += blockInfos.map(_._2).sum
 collectFetchRequests(address, blockInfos, collectedRemoteRequests)
   }
 }
+val numRemoteBlocks = collectedRemoteRequests.map(_.blocks.size).sum
 val totalBytes = localBlockBytes + remoteBlockBytes + hostLocalBlockBytes
+assert(numBlocksToFetch == localBlocks.size + hostLocalBlocks.size + 
numRemoteBlocks,
+  s"The number of non-empty blocks $numBlocksToFetch doesn't equal to the 
number of local " +
+s"blocks ${localBlocks.size} + the number of host-local blocks 
${hostLocalBlocks.size} " +
+s"+ the number of remote blocks ${numRemoteBlocks}.")
 logInfo(s"Getting $numBlocksToFetch (${Utils.bytesToString(totalBytes)}) 
non-empty blocks " +
   s"including ${localBlocks.size} 
(${Utils.bytesToString(localBlockBytes)}) local and " +
   s"${hostLocalBlocks.size} (${Utils.bytesToString(hostLocalBlockBytes)}) 
" +
   s"host-local and $numRemoteBlocks 
(${Utils.bytesToString(remoteBlockBytes)}) remote blocks")
 collectedRemoteRequests
   }
 
+  def createFetchRequest(
+  blocks: Seq[FetchBlockInfo],
+  address: BlockManagerId,
+  curRequestSize: Long): FetchRequest = {
+logDebug(s"Creating fetch request of $curRequestSize at $address "
+  + s"with ${blocks.size} blocks")
+FetchRequest(address, blocks)
+  }
+
+  def createFetchRequests(
+  curBlocks: ArrayBuffer[FetchBlockInfo],
 
 Review comment:
   does this parameter have to be an `ArrayBuffer` here? We don't mutate it in 
this method.


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[GitHub] [spark] zhengruifeng commented on a change in pull request #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

2020-03-22 Thread GitBox
zhengruifeng commented on a change in pull request #27982: [SPARK-31222][ML] 
Make ANOVATest Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982#discussion_r396222712
 
 

 ##
 File path: mllib/src/test/scala/org/apache/spark/ml/stat/ANOVATestSuite.scala
 ##
 @@ -144,22 +144,30 @@ class ANOVATestSuite
   }
 
   test("test DataFrame with sparse vector") {
-val df = spark.createDataFrame(Seq(
-  (3, Vectors.sparse(6, Array((0, 6.0), (1, 7.0), (3, 7.0), (4, 6.0,
-  (1, Vectors.sparse(6, Array((1, 9.0), (2, 6.0), (4, 5.0), (5, 9.0,
-  (3, Vectors.sparse(6, Array((1, 9.0), (2, 3.0), (4, 5.0), (5, 5.0,
-  (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0))),
-  (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0))),
-  (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0)))
-)).toDF("label", "features")
+val data = Seq(
+  (3, Vectors.dense(Array(6.0, 7.0, 0.0, 7.0, 6.0, 0.0, 0.0))),
+  (1, Vectors.dense(Array(0.0, 9.0, 6.0, 0.0, 5.0, 9.0, 0.0))),
+  (3, Vectors.dense(Array(0.0, 9.0, 3.0, 0.0, 5.0, 5.0, 0.0))),
+  (2, Vectors.dense(Array(0.0, 9.0, 8.0, 5.0, 6.0, 4.0, 0.0))),
+  (2, Vectors.dense(Array(8.0, 9.0, 6.0, 5.0, 4.0, 4.0, 0.0))),
+  (3, Vectors.dense(Array(8.0, 9.0, 6.0, 4.0, 0.0, 0.0, 0.0
 
-val anovaResult = ANOVATest.test(df, "features", "label")
-val (pValues: Vector, fValues: Vector) =
-  anovaResult.select("pValues", "fValues")
-.as[(Vector, Vector)].head()
-assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 
0.45824059, 0.84633632,
-  0.15673368) relTol 1e-6)
-assert(fValues ~== Vectors.dense(0.375, 0.375, 1.5625, 1.02364865, 
0.17647059,
-  3.66) relTol 1e-6)
+val df1 = spark.createDataFrame(data.map(t => (t._1, t._2.toDense)))
+  .toDF("label", "features")
+val df2 = spark.createDataFrame(data.map(t => (t._1, t._2.toSparse)))
+  .toDF("label", "features")
+val df3 = spark.createDataFrame(data.map(t => (t._1, t._2.compressed)))
+  .toDF("label", "features")
+
+Seq(df1, df2, df3).foreach { df =>
+  val anovaResult = ANOVATest.test(df, "features", "label")
+  val (pValues: Vector, fValues: Vector) =
+anovaResult.select("pValues", "fValues")
+  .as[(Vector, Vector)].head()
+  assert(pValues ~== Vectors.dense(0.71554175, 0.71554175, 0.34278574, 
0.45824059, 0.84633632,
+0.15673368, Double.NaN) relTol 1e-6)
 
 Review comment:
   for column only containing zero values, sklearn will return nan:
   ```python
   X = np.zeros([3,5])
   
   y = [1,2,3]
   
   f_classif(X, y)
   
/home/zrf/Applications/anaconda3/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:110:
 RuntimeWarning: invalid value encountered in true_divide
 msw = sswn / float(dfwn)
   Out[24]: (array([nan, nan, nan, nan, nan]), array([nan, nan, nan, nan, nan]))
   =
   ```


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[GitHub] [spark] zhengruifeng opened a new pull request #27982: [SPARK-31222][ML] Make ANOVATest Sparsity-Aware

2020-03-22 Thread GitBox
zhengruifeng opened a new pull request #27982: [SPARK-31222][ML] Make ANOVATest 
Sparsity-Aware
URL: https://github.com/apache/spark/pull/27982
 
 
   ### What changes were proposed in this pull request?
   when input dataset is sparse, make `ANOVATest` only process non-zero value
   
   
   ### Why are the changes needed?
   for performance
   
   
   ### Does this PR introduce any user-facing change?
   No
   
   
   ### How was this patch tested?
   existing testsuites
   


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27959: [SPARK-31190][SQL] ScalaReflection should erasure non user defined AnyVal type

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[GitHub] [spark] SparkQA removed a comment on issue #27959: [SPARK-31190][SQL] ScalaReflection should erasure non user defined AnyVal type

2020-03-22 Thread GitBox
SparkQA removed a comment on issue #27959: [SPARK-31190][SQL] ScalaReflection 
should erasure non user defined AnyVal type
URL: https://github.com/apache/spark/pull/27959#issuecomment-602314748
 
 
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[GitHub] [spark] cloud-fan commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
cloud-fan commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move 
rebase tests to `AvroSuite` and check the rebase flag out of function bodies
URL: https://github.com/apache/spark/pull/27977#issuecomment-602393765
 
 
   thanks!


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[GitHub] [spark] SparkQA commented on issue #27959: [SPARK-31190][SQL] ScalaReflection should erasure non user defined AnyVal type

2020-03-22 Thread GitBox
SparkQA commented on issue #27959: [SPARK-31190][SQL] ScalaReflection should 
erasure non user defined AnyVal type
URL: https://github.com/apache/spark/pull/27959#issuecomment-602393783
 
 
   **[Test build #120172 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120172/testReport)**
 for PR 27959 at commit 
[`ceb2ce6`](https://github.com/apache/spark/commit/ceb2ce6eb49a39ab82568522af369b4e5c3ecd13).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
 * `// `case class Foo(i: Int) extends AnyVal` will return type `Int` 
instead of `Foo`.`


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[GitHub] [spark] cloud-fan commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
cloud-fan commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move 
rebase tests to `AvroSuite` and check the rebase flag out of function bodies
URL: https://github.com/apache/spark/pull/27977#issuecomment-602393675
 
 
   thanks, merging to 3.0!


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[GitHub] [spark] cloud-fan removed a comment on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
cloud-fan removed a comment on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] 
Move rebase tests to `AvroSuite` and check the rebase flag out of function 
bodies
URL: https://github.com/apache/spark/pull/27977#issuecomment-602393675
 
 
   thanks, merging to 3.0!


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[GitHub] [spark] dongjoon-hyun commented on issue #27185: [SPARK-30494][SQL] Fix cached data leakage during replacing an existing view

2020-03-22 Thread GitBox
dongjoon-hyun commented on issue #27185: [SPARK-30494][SQL] Fix cached data 
leakage during replacing an existing view
URL: https://github.com/apache/spark/pull/27185#issuecomment-602392216
 
 
   Hi, @LantaoJin . Could you make a backport PR against `branch-2.4`?


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[GitHub] [spark] dongjoon-hyun closed pull request #27185: [SPARK-30494][SQL] Fix cached data leakage during replacing an existing view

2020-03-22 Thread GitBox
dongjoon-hyun closed pull request #27185: [SPARK-30494][SQL] Fix cached data 
leakage during replacing an existing view
URL: https://github.com/apache/spark/pull/27185
 
 
   


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[GitHub] [spark] dongjoon-hyun commented on a change in pull request #27185: [SPARK-30494][SQL] Fix the leak of cached data when replace an existing temp view

2020-03-22 Thread GitBox
dongjoon-hyun commented on a change in pull request #27185: [SPARK-30494][SQL] 
Fix the leak of cached data when replace an existing temp view
URL: https://github.com/apache/spark/pull/27185#discussion_r396217365
 
 

 ##
 File path: sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
 ##
 @@ -1122,4 +1122,47 @@ class CachedTableSuite extends QueryTest with 
SQLTestUtils
   assert(!spark.catalog.isCached("t1"))
 }
   }
+
+  test("SPARK-30494 avoid duplicated cached RDD when replace an existing 
view") {
+withTempView("tempView") {
+  spark.catalog.clearCache()
+  sql("create or replace temporary view tempView as select 1")
+  sql("cache table tempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isDefined)
+  sql("create or replace temporary view tempView as select 1, 2")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+  sql("cache table tempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 1, 
2")).isDefined)
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+}
+
+withGlobalTempView("tempGlobalTempView") {
+  spark.catalog.clearCache()
+  sql("create or replace global temporary view tempGlobalTempView as 
select 1")
+  sql("cache table global_temp.tempGlobalTempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isDefined)
+  sql("create or replace global temporary view tempGlobalTempView as 
select 1, 2")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+  sql("cache table global_temp.tempGlobalTempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 1, 
2")).isDefined)
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+}
+
+withView("view1") {
+  spark.catalog.clearCache()
+  sql("create or replace view view1 as select 1")
+  sql("cache table view1")
+  sql("create or replace view view1 as select 1, 2")
+  sql("cache table view1")
+  // the cached plan of persisted view likes below,
+  // so we cannot use the same assertion of temp view.
+  // SubqueryAlias
+  //|
+  //+ View
+  //|
+  //+ Project[1 AS 1]
+  spark.sharedState.cacheManager.uncacheQuery(spark.table("view1"), 
cascade = false)
+  assert(spark.sharedState.cacheManager.isEmpty)
 
 Review comment:
   Then, please remove this misleading test case between line 1149 and 1165.
   > no cached data leak for persisted view


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[GitHub] [spark] dongjoon-hyun commented on a change in pull request #27185: [SPARK-30494][SQL] Fix the leak of cached data when replace an existing temp view

2020-03-22 Thread GitBox
dongjoon-hyun commented on a change in pull request #27185: [SPARK-30494][SQL] 
Fix the leak of cached data when replace an existing temp view
URL: https://github.com/apache/spark/pull/27185#discussion_r396217365
 
 

 ##
 File path: sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
 ##
 @@ -1122,4 +1122,47 @@ class CachedTableSuite extends QueryTest with 
SQLTestUtils
   assert(!spark.catalog.isCached("t1"))
 }
   }
+
+  test("SPARK-30494 avoid duplicated cached RDD when replace an existing 
view") {
+withTempView("tempView") {
+  spark.catalog.clearCache()
+  sql("create or replace temporary view tempView as select 1")
+  sql("cache table tempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isDefined)
+  sql("create or replace temporary view tempView as select 1, 2")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+  sql("cache table tempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 1, 
2")).isDefined)
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+}
+
+withGlobalTempView("tempGlobalTempView") {
+  spark.catalog.clearCache()
+  sql("create or replace global temporary view tempGlobalTempView as 
select 1")
+  sql("cache table global_temp.tempGlobalTempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isDefined)
+  sql("create or replace global temporary view tempGlobalTempView as 
select 1, 2")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+  sql("cache table global_temp.tempGlobalTempView")
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 1, 
2")).isDefined)
+  assert(spark.sharedState.cacheManager.lookupCachedData(sql("select 
1")).isEmpty)
+}
+
+withView("view1") {
+  spark.catalog.clearCache()
+  sql("create or replace view view1 as select 1")
+  sql("cache table view1")
+  sql("create or replace view view1 as select 1, 2")
+  sql("cache table view1")
+  // the cached plan of persisted view likes below,
+  // so we cannot use the same assertion of temp view.
+  // SubqueryAlias
+  //|
+  //+ View
+  //|
+  //+ Project[1 AS 1]
+  spark.sharedState.cacheManager.uncacheQuery(spark.table("view1"), 
cascade = false)
+  assert(spark.sharedState.cacheManager.isEmpty)
 
 Review comment:
   Then, please remove this misleading test case between line 1149 and 1165.
   > no cached data leak for persisted view


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[GitHub] [spark] AmplabJenkins commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to work with TABLE.COLUMN syntax

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[GitHub] [spark] AmplabJenkins commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to work with TABLE.COLUMN syntax

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27916: [SPARK-30532] DataFrameStatFunctions 
to work with TABLE.COLUMN syntax
URL: https://github.com/apache/spark/pull/27916#issuecomment-602383432
 
 
   Merged build finished. Test PASSed.


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[GitHub] [spark] SparkQA commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to work with TABLE.COLUMN syntax

2020-03-22 Thread GitBox
SparkQA commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to work 
with TABLE.COLUMN syntax
URL: https://github.com/apache/spark/pull/27916#issuecomment-602383174
 
 
   **[Test build #120176 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120176/testReport)**
 for PR 27916 at commit 
[`37f441e`](https://github.com/apache/spark/commit/37f441ed424d2ae2088565cf0ad6ddc68f039b85).


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[GitHub] [spark] HyukjinKwon commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to work with TABLE.COLUMN syntax

2020-03-22 Thread GitBox
HyukjinKwon commented on issue #27916: [SPARK-30532] DataFrameStatFunctions to 
work with TABLE.COLUMN syntax
URL: https://github.com/apache/spark/pull/27916#issuecomment-602381874
 
 
   retest this please


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[GitHub] [spark] dongjoon-hyun commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
dongjoon-hyun commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move 
rebase tests to `AvroSuite` and check the rebase flag out of function bodies
URL: https://github.com/apache/spark/pull/27977#issuecomment-602380901
 
 
   Thank you, @MaxGekk and @HyukjinKwon !


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[GitHub] [spark] nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] Refactor how we track Python test/build dependencies

2020-03-22 Thread GitBox
nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] 
Refactor how we track Python test/build dependencies
URL: https://github.com/apache/spark/pull/27928#discussion_r396197611
 
 

 ##
 File path: dev/requirements.txt
 ##
 @@ -1,5 +1,10 @@
-flake8==3.5.0
+flake8==3.7.*
 
 Review comment:
   I mentioned it elsewhere but I'll mention it again here: Linters like flake8 
and pycodestyle introduce new checks in minor/feature releases. There is very 
high chance that every new check they introduce will flag new problems and fail 
the build.
   
   In fact, we saw exactly that behavior with pydocstyle just before we removed 
it. And I [experienced 
this](https://github.com/nchammas/flintrock/commit/9157b25d735ff6ef690cfdfb761f336bf999fc82)
 with pycodestyle in Flintrock before [pinning the 
version](https://github.com/nchammas/flintrock/commit/6c6d9562673b430d142d9062a99e9ac1c87366b8).
   
   I don't understand the point of waiting for the build to break before 
pinning or severely limiting the versions for libraries like these.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27248: [WIP][SPARK-30538][SQL] Control spark sql output small file by merge small partition

2020-03-22 Thread GitBox
AmplabJenkins removed a comment on issue #27248: [WIP][SPARK-30538][SQL] 
Control spark sql output small file by merge small partition
URL: https://github.com/apache/spark/pull/27248#issuecomment-575452587
 
 
   Can one of the admins verify this patch?


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[GitHub] [spark] AmplabJenkins commented on issue #27248: [WIP][SPARK-30538][SQL] Control spark sql output small file by merge small partition

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27248: [WIP][SPARK-30538][SQL] Control spark 
sql output small file by merge small partition
URL: https://github.com/apache/spark/pull/27248#issuecomment-602376013
 
 
   Can one of the admins verify this patch?


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[GitHub] [spark] nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] Refactor how we track Python test/build dependencies

2020-03-22 Thread GitBox
nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] 
Refactor how we track Python test/build dependencies
URL: https://github.com/apache/spark/pull/27928#discussion_r396197611
 
 

 ##
 File path: dev/requirements.txt
 ##
 @@ -1,5 +1,10 @@
-flake8==3.5.0
+flake8==3.7.*
 
 Review comment:
   I mentioned it elsewhere but I'll mention it again here: Linters like flake8 
and pycodestyle introduce new checks in minor/feature releases. There is very 
high chance that every new check they introduce will flag new problems and fail 
the build. (In fact, we saw exactly that behavior with pydocstyle just before 
we removed it. And I experienced this with flake8 in Flintrock before [pinning 
the 
version](https://github.com/nchammas/flintrock/blob/52c6c84c9a1845b0ce89ca138172a6ec4cf0d632/requirements/developer.in#L4).)
   
   I don't understand the point of waiting for the build to break before 
pinning or severely limiting the versions for libraries like these.


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[GitHub] [spark] nchammas commented on issue #27928: [SPARK-31167][BUILD] Refactor how we track Python test/build dependencies

2020-03-22 Thread GitBox
nchammas commented on issue #27928: [SPARK-31167][BUILD] Refactor how we track 
Python test/build dependencies
URL: https://github.com/apache/spark/pull/27928#issuecomment-602375329
 
 
   > With this change, we will have to maintain and keep `dev/requirements.txt` 
up to date. 
   
   Maybe this is the disconnect between our points of view, because so far I 
haven't really been following your objections to pinning. Assuming we pin every 
library, why do we have to keep `dev/requirements.txt` up-to-date?
   
   As long as we can build the docs, run tests, and do whatever else we need to 
do as part of regular development, that file can remain frozen as-is for years.
   
   It's only when we specifically want to use some new feature of, say, Sphinx, 
that we need to bump versions. But that will happen very rarely, I imagine not 
more than once every couple of years.
   
   Does that address your concern? Why do you think we'd need to touch that 
file more often than once in a long while?
   
   > We shouldn't pin `numpy` to encourage people to test the highest versions. 
It should ideally be `numpy>=1.7` according to `setup.py`.
   > 
   > * `numpy` is an explicit dependency for ML/MLlib in PySpark.
   
   But the specification of numpy in `dev/requirements.txt` is so that we can 
build our docs. (It seems strange, but yes, numpy is a requirement to build our 
Python API docs.)
   
   Maybe we can improve this by replacing numpy in `dev/requirements.txt` with 
a reference to `setup.py`. That way we can track PySpark dependencies (whether 
for building the docs or for general execution) in one place. This will also 
pick up the Pandas requirement. How does that sound?
   
   A separate issue I raised earlier is that, if we want to not pin our 
build/test dependencies, we need to  figure out what to do about the Spark 
Docker image and CI. Either those will also source the unpinned requirements 
from the same file, or we go back to having the requirements specified in 
duplicate--with pinned versions for Docker and CI, and without pinned versions 
for developers.
   
   Obviously, I'd prefer to pin everything and keep it in one place, but if you 
want to go one of these routes I guess I'll do that. I just want to understand 
and try to address your objections before going there.


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[GitHub] [spark] liupc edited a comment on issue #27871: [SPARK-31105][CORE]Respect sql execution id for FIFO scheduling mode

2020-03-22 Thread GitBox
liupc edited a comment on issue #27871: [SPARK-31105][CORE]Respect sql 
execution id for FIFO scheduling mode
URL: https://github.com/apache/spark/pull/27871#issuecomment-602373911
 
 
   Thanks @dongjoon-hyun , let's spill the scopes, and add an option to respect 
`jobGroup` level priority in the `core` module. 
   And I think even in current approach, the congestion issue is serious, so 
this PR is not about to solve it, but I proposed another PR for this: 
https://github.com/apache/spark/pull/27862
   I really think this is helpful for OLAP senarios, and we test this in real 
workloads in xiaomi.


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[GitHub] [spark] HyukjinKwon commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
HyukjinKwon commented on issue #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move 
rebase tests to `AvroSuite` and check the rebase flag out of function bodies
URL: https://github.com/apache/spark/pull/27977#issuecomment-602374308
 
 
   Merged to branch-3.0.


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[GitHub] [spark] HyukjinKwon closed pull request #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move rebase tests to `AvroSuite` and check the rebase flag out of function bodies

2020-03-22 Thread GitBox
HyukjinKwon closed pull request #27977: [SPARK-31183][SQL][FOLLOWUP][3.0] Move 
rebase tests to `AvroSuite` and check the rebase flag out of function bodies
URL: https://github.com/apache/spark/pull/27977
 
 
   


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[GitHub] [spark] liupc edited a comment on issue #27871: [SPARK-31105][CORE]Respect sql execution id for FIFO scheduling mode

2020-03-22 Thread GitBox
liupc edited a comment on issue #27871: [SPARK-31105][CORE]Respect sql 
execution id for FIFO scheduling mode
URL: https://github.com/apache/spark/pull/27871#issuecomment-602373911
 
 
   Thanks @dongjoon-hyun , let's spill the scopes, and add an option to respect 
`jobGroup` level priority in the `core` module. 
   I really think this is helpful for OLAP senarios, and that's what we do in 
xiaomi.


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[GitHub] [spark] liupc commented on issue #27871: [SPARK-31105][CORE]Respect sql execution id for FIFO scheduling mode

2020-03-22 Thread GitBox
liupc commented on issue #27871: [SPARK-31105][CORE]Respect sql execution id 
for FIFO scheduling mode
URL: https://github.com/apache/spark/pull/27871#issuecomment-602373911
 
 
   Thanks @dongjoon-hyun , let's spill the scopes, and add an option to respect 
`jobGroup` level priority in the `core` module. 
   I really think this is helpful for OLAP senarios.


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[GitHub] [spark] HyukjinKwon closed pull request #27898: [SPARK-31141][DSTREAMS][DOC] Add version information to the configuration of Dstreams

2020-03-22 Thread GitBox
HyukjinKwon closed pull request #27898: [SPARK-31141][DSTREAMS][DOC] Add 
version information to the configuration of Dstreams
URL: https://github.com/apache/spark/pull/27898
 
 
   


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[GitHub] [spark] HyukjinKwon commented on issue #27898: [SPARK-31141][DSTREAMS][DOC] Add version information to the configuration of Dstreams

2020-03-22 Thread GitBox
HyukjinKwon commented on issue #27898: [SPARK-31141][DSTREAMS][DOC] Add version 
information to the configuration of Dstreams
URL: https://github.com/apache/spark/pull/27898#issuecomment-602372894
 
 
   Merged to master.


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[GitHub] [spark] nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] Refactor how we track Python test/build dependencies

2020-03-22 Thread GitBox
nchammas commented on a change in pull request #27928: [SPARK-31167][BUILD] 
Refactor how we track Python test/build dependencies
URL: https://github.com/apache/spark/pull/27928#discussion_r396197611
 
 

 ##
 File path: dev/requirements.txt
 ##
 @@ -1,5 +1,10 @@
-flake8==3.5.0
+flake8==3.7.*
 
 Review comment:
   I mentioned it elsewhere but I'll mention it again here: Linters like flake8 
and pycodestyle introduce new checks in minor/feature releases. There is very 
high chance that every new check they introduce will flag new problems and fail 
the build. (In fact, we saw exactly that behavior with pydocstyle just before 
we removed it.)
   
   I don't understand the point of waiting for the build to break before 
pinning or severely limiting the versions for libraries like these.


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[GitHub] [spark] beliefer commented on issue #27497: [SPARK-30245][SQL][FOLLOWUP] Improve regex expression when pattern not changed

2020-03-22 Thread GitBox
beliefer commented on issue #27497: [SPARK-30245][SQL][FOLLOWUP] Improve regex 
expression when pattern not changed
URL: https://github.com/apache/spark/pull/27497#issuecomment-602364756
 
 
   @HyukjinKwon With pleasure do it.


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[GitHub] [spark] SparkQA commented on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
SparkQA commented on issue #27981: [SPARK-31215][SQL][DOC] Add version 
information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602364594
 
 
   **[Test build #120175 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120175/testReport)**
 for PR 27981 at commit 
[`86d4eff`](https://github.com/apache/spark/commit/86d4eff3d3f8d5ba37d22eed81de912d8929309b).


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[GitHub] [spark] beliefer commented on a change in pull request #27898: [SPARK-31141][DSTREAMS][DOC] Add version information to the configuration of Dstreams

2020-03-22 Thread GitBox
beliefer commented on a change in pull request #27898: 
[SPARK-31141][DSTREAMS][DOC] Add version information to the configuration of 
Dstreams
URL: https://github.com/apache/spark/pull/27898#discussion_r396195734
 
 

 ##
 File path: docs/configuration.md
 ##
 @@ -2483,6 +2483,7 @@ Spark subsystems.
 spark.streaming.receiver.maxRate and 
spark.streaming.kafka.maxRatePerPartition
 
 Review comment:
   Thanks for your explanation.


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[GitHub] [spark] beliefer commented on issue #27898: [SPARK-31141][DSTREAMS][DOC] Add version information to the configuration of Dstreams

2020-03-22 Thread GitBox
beliefer commented on issue #27898: [SPARK-31141][DSTREAMS][DOC] Add version 
information to the configuration of Dstreams
URL: https://github.com/apache/spark/pull/27898#issuecomment-602363897
 
 
   @HyukjinKwon OK. I updated them.


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[GitHub] [spark] zhengruifeng commented on issue #27979: [SPARK-31138][ML][FOLLOWUP] ANOVA optimization

2020-03-22 Thread GitBox
zhengruifeng commented on issue #27979: [SPARK-31138][ML][FOLLOWUP] ANOVA 
optimization
URL: https://github.com/apache/spark/pull/27979#issuecomment-602363561
 
 
   Merged to master, thanks @srowen @huaxingao 


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
AmplabJenkins removed a comment on issue #27981: [SPARK-31215][SQL][DOC] Add 
version information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602363292
 
 
   Merged build finished. Test PASSed.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
AmplabJenkins removed a comment on issue #27981: [SPARK-31215][SQL][DOC] Add 
version information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602363298
 
 
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[GitHub] [spark] zhengruifeng closed pull request #27979: [SPARK-31138][ML][FOLLOWUP] ANOVA optimization

2020-03-22 Thread GitBox
zhengruifeng closed pull request #27979: [SPARK-31138][ML][FOLLOWUP] ANOVA 
optimization
URL: https://github.com/apache/spark/pull/27979
 
 
   


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[GitHub] [spark] AmplabJenkins commented on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27981: [SPARK-31215][SQL][DOC] Add version 
information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602363292
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27981: [SPARK-31215][SQL][DOC] Add version 
information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602363298
 
 
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[GitHub] [spark] beliefer commented on issue #27931: [SPARK-31002][CORE][DOC][FOLLOWUP] Add version information to the configuration of Core

2020-03-22 Thread GitBox
beliefer commented on issue #27931: [SPARK-31002][CORE][DOC][FOLLOWUP] Add 
version information to the configuration of Core
URL: https://github.com/apache/spark/pull/27931#issuecomment-602362761
 
 
   @HyukjinKwon Thanks!


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[GitHub] [spark] beliefer commented on issue #27981: [SPARK-31215][SQL][DOC] Add version information to the static configuration of SQL

2020-03-22 Thread GitBox
beliefer commented on issue #27981: [SPARK-31215][SQL][DOC] Add version 
information to the static configuration of SQL
URL: https://github.com/apache/spark/pull/27981#issuecomment-602362943
 
 
   retest this please


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[GitHub] [spark] AmplabJenkins removed a comment on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

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AmplabJenkins removed a comment on issue #27861: [SPARK-30707][SQL]Window 
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URL: https://github.com/apache/spark/pull/27861#issuecomment-602333577
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

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AmplabJenkins removed a comment on issue #27861: [SPARK-30707][SQL]Window 
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[GitHub] [spark] AmplabJenkins commented on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27861: [SPARK-30707][SQL]Window function set 
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URL: https://github.com/apache/spark/pull/27861#issuecomment-602333612
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

2020-03-22 Thread GitBox
AmplabJenkins commented on issue #27861: [SPARK-30707][SQL]Window function set 
partitionSpec as order spec when orderSpec is empty
URL: https://github.com/apache/spark/pull/27861#issuecomment-602333577
 
 
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[GitHub] [spark] SparkQA commented on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

2020-03-22 Thread GitBox
SparkQA commented on issue #27861: [SPARK-30707][SQL]Window function set 
partitionSpec as order spec when orderSpec is empty
URL: https://github.com/apache/spark/pull/27861#issuecomment-602332620
 
 
   **[Test build #120174 has 
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[GitHub] [spark] HyukjinKwon commented on issue #27861: [SPARK-30707][SQL]Window function set partitionSpec as order spec when orderSpec is empty

2020-03-22 Thread GitBox
HyukjinKwon commented on issue #27861: [SPARK-30707][SQL]Window function set 
partitionSpec as order spec when orderSpec is empty
URL: https://github.com/apache/spark/pull/27861#issuecomment-602330738
 
 
   retest this please


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[GitHub] [spark] HyukjinKwon commented on a change in pull request #27690: [SPARK-21514][SQL] Added a new option to use non-blobstore storage when writing into blobstore storage

2020-03-22 Thread GitBox
HyukjinKwon commented on a change in pull request #27690: [SPARK-21514][SQL] 
Added a new option to use non-blobstore storage when writing into blobstore 
storage
URL: https://github.com/apache/spark/pull/27690#discussion_r396186608
 
 

 ##
 File path: 
sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/SaveAsHiveFile.scala
 ##
 @@ -125,10 +163,22 @@ private[hive] trait SaveAsHiveFile extends 
DataWritingCommand {
 val stagingDir = hadoopConf.get("hive.exec.stagingdir", ".hive-staging")
 val scratchDir = hadoopConf.get("hive.exec.scratchdir", "/tmp/hive")
 
+// Hive sets session_path as 
HDFS_SESSION_PATH_KEY(_hive.hdfs.session.path) in hive config
+val sessionScratchDir = 
externalCatalog.unwrapped.asInstanceOf[HiveExternalCatalog]
+  .client.getConf("_hive.hdfs.session.path", "")
 
 Review comment:
   Thanks, @moomindani. How does Hive behaves when `_hive.hdfs.session.path` is 
not set?


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