[GitHub] cloud-fan commented on a change in pull request #23353: [SPARK-26402][SQL] Canonicalization on GetStructField

2018-12-19 Thread GitBox
cloud-fan commented on a change in pull request #23353: [SPARK-26402][SQL] 
Canonicalization on GetStructField 
URL: https://github.com/apache/spark/pull/23353#discussion_r243179452
 
 

 ##
 File path: 
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CanonicalizeSuite.scala
 ##
 @@ -50,4 +59,44 @@ class CanonicalizeSuite extends SparkFunSuite {
 assert(range.where(arrays1).sameResult(range.where(arrays2)))
 assert(!range.where(arrays1).sameResult(range.where(arrays3)))
   }
+
+  test("SPARK-26402: GetStructField with different optional names are 
semantically equal") {
+val expId = NamedExpression.newExprId
+val qualifier = Seq.empty[String]
+val structType = StructType(
+  StructField("a", StructType(StructField("b", IntegerType, false) :: 
Nil), false) :: Nil)
+
+val fieldB1 = GetStructField(
+  AttributeReference("data1", structType, false)(expId, qualifier),
+  0, Some("b1"))
+val fieldB2 = GetStructField(
+  AttributeReference("data2", structType, false)(expId, qualifier),
+  0, Some("b2"))
+assert(fieldB1.semanticEquals(fieldB2))
+
+val fieldA1 = GetStructField(
+  GetStructField(
+AttributeReference("data1", structType, false)(expId, qualifier),
+0, Some("a1")),
+  0, Some("b1"))
+val fieldA2 = GetStructField(
+  GetStructField(
+AttributeReference("data2", structType, false)(expId, qualifier),
+0, Some("a2")),
+  0, Some("b2"))
+assert(fieldA1.semanticEquals(fieldA2))
+
+// End-to-end test case
+val testRelation = LocalRelation('a.int)
 
 Review comment:
   This is not a real end-to-end test...
   
   How about add the following test to SQLQuerySuite?
   ```
   sql("create table t (s struct) using json")
   sql("select s.I from t group by s.i")
   ```
   currently it fials with
   ```
   org.apache.spark.sql.AnalysisException: expression 'default.t.`s`' is 
neither present in the group by, nor is it an aggregate function
   ```


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[GitHub] tdas commented on issue #23324: [SPARK-26267][SS]Retry when detecting incorrect offsets from Kafka

2018-12-19 Thread GitBox
tdas commented on issue #23324: [SPARK-26267][SS]Retry when detecting incorrect 
offsets from Kafka
URL: https://github.com/apache/spark/pull/23324#issuecomment-448899155
 
 
   LGTM.


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[GitHub] AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
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[GitHub] AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
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use abstract class instead of trait for UserDefinedFunction
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[GitHub] AmplabJenkins commented on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
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[GitHub] AmplabJenkins commented on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
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[GitHub] SparkQA removed a comment on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
SparkQA removed a comment on issue #23351: [SPARK-26216][SQL][followup] use 
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URL: https://github.com/apache/spark/pull/23351#issuecomment-448833881
 
 
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[GitHub] SparkQA commented on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
SparkQA commented on issue #23351: [SPARK-26216][SQL][followup] use abstract 
class instead of trait for UserDefinedFunction
URL: https://github.com/apache/spark/pull/23351#issuecomment-448891218
 
 
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* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
 * `sealed abstract class UserDefinedFunction `


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[GitHub] AmplabJenkins removed a comment on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

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[GitHub] AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands 
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[GitHub] AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands 
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[GitHub] SparkQA removed a comment on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

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[GitHub] SparkQA commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
SparkQA commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should 
use data source if it is convertible
URL: https://github.com/apache/spark/pull/22514#issuecomment-448890147
 
 
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* This patch adds no public classes.


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[GitHub] SparkQA commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

2018-12-19 Thread GitBox
SparkQA commented on issue #23336: [SPARK-26378][SQL] Restore performance of 
queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#issuecomment-44700
 
 
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[GitHub] bersprockets commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

2018-12-19 Thread GitBox
bersprockets commented on issue #23336: [SPARK-26378][SQL] Restore performance 
of queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#issuecomment-44521
 
 
   retest this please


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[GitHub] jomach commented on issue #21033: [SPARK-19320][MESOS]allow specifying a hard limit on number of gpus required in each spark executor when running on mesos

2018-12-19 Thread GitBox
jomach commented on issue #21033: [SPARK-19320][MESOS]allow specifying a hard 
limit on number of gpus required in each spark executor when running on mesos
URL: https://github.com/apache/spark/pull/21033#issuecomment-448887982
 
 
   @susanxhuynh What about only exposing a number of gpus per executor and only 
in MesosCoarseGrainedSchedulerBackend ? At least for us would solve our problem 
to use spark with gpus at scale


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[GitHub] asfgit closed pull request #23308: [SPARK-26308][SQL] Avoid cast of decimals for ScalaUDF

2018-12-19 Thread GitBox
asfgit closed pull request #23308: [SPARK-26308][SQL] Avoid cast of decimals 
for ScalaUDF
URL: https://github.com/apache/spark/pull/23308
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git 
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
 
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
index 133fa119b7aa6..1706b3eece6d7 100644
--- 
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
+++ 
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
@@ -879,6 +879,37 @@ object TypeCoercion {
   }
 }
 e.withNewChildren(children)
+
+  case udf: ScalaUDF if udf.inputTypes.nonEmpty =>
+val children = udf.children.zip(udf.inputTypes).map { case (in, 
expected) =>
+  implicitCast(in, udfInputToCastType(in.dataType, 
expected)).getOrElse(in)
+}
+udf.withNewChildren(children)
+}
+
+private def udfInputToCastType(input: DataType, expectedType: DataType): 
DataType = {
+  (input, expectedType) match {
+// SPARK-26308: avoid casting to an arbitrary precision and scale for 
decimals. Please note
+// that precision and scale cannot be inferred properly for a ScalaUDF 
because, when it is
+// created, it is not bound to any column. So here the precision and 
scale of the input
+// column is used.
+case (in: DecimalType, _: DecimalType) => in
+case (ArrayType(dtIn, _), ArrayType(dtExp, nullableExp)) =>
+  ArrayType(udfInputToCastType(dtIn, dtExp), nullableExp)
+case (MapType(keyDtIn, valueDtIn, _), MapType(keyDtExp, valueDtExp, 
nullableExp)) =>
+  MapType(udfInputToCastType(keyDtIn, keyDtExp),
+udfInputToCastType(valueDtIn, valueDtExp),
+nullableExp)
+case (StructType(fieldsIn), StructType(fieldsExp)) =>
+  val fieldTypes =
+fieldsIn.map(_.dataType).zip(fieldsExp.map(_.dataType)).map { case 
(dtIn, dtExp) =>
+  udfInputToCastType(dtIn, dtExp)
+}
+  StructType(fieldsExp.zip(fieldTypes).map { case (field, newDt) =>
+field.copy(dataType = newDt)
+  })
+case (_, other) => other
+  }
 }
 
 /**
diff --git 
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
 
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
index fae90caebf96c..a23aaa3a0b3ef 100644
--- 
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
+++ 
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala
@@ -52,7 +52,7 @@ case class ScalaUDF(
 udfName: Option[String] = None,
 nullable: Boolean = true,
 udfDeterministic: Boolean = true)
-  extends Expression with ImplicitCastInputTypes with NonSQLExpression with 
UserDefinedExpression {
+  extends Expression with NonSQLExpression with UserDefinedExpression {
 
   // The constructor for SPARK 2.1 and 2.2
   def this(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala 
b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 20dcefa7e3cad..a26d306cff6b5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import java.math.BigDecimal
+
 import org.apache.spark.sql.api.java._
 import org.apache.spark.sql.catalyst.plans.logical.Project
 import org.apache.spark.sql.execution.QueryExecution
@@ -26,7 +28,7 @@ import 
org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationComm
 import org.apache.spark.sql.functions.{lit, udf}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.test.SQLTestData._
-import org.apache.spark.sql.types.{DataTypes, DoubleType}
+import org.apache.spark.sql.types._
 import org.apache.spark.sql.util.QueryExecutionListener
 
 
@@ -420,4 +422,32 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   checkAnswer(df, Seq(Row("null1x"), Row(null), Row("N3null")))
 }
   }
+
+  test("SPARK-26308: udf with decimal") {
+val df1 = spark.createDataFrame(
+  sparkContext.parallelize(Seq(Row(new 
BigDecimal("20110002456556",
+  StructType(Seq(StructField("col1", DecimalType(30, 0)
+val udf1 = org.apache.spark.sql.functions.udf((value: BigDecimal) => {
+  if (value == null) null else value.toBigInteger.toString
+})
+checkAnswer(df1.select(udf1(df1.col("col1"))), 
Seq(Row("20110002456556")))
+  }
+
+  test("SPARK-26308: udf with complex types of decimal") 

[GitHub] cloud-fan commented on issue #23308: [SPARK-26308][SQL] Avoid cast of decimals for ScalaUDF

2018-12-19 Thread GitBox
cloud-fan commented on issue #23308: [SPARK-26308][SQL] Avoid cast of decimals 
for ScalaUDF
URL: https://github.com/apache/spark/pull/23308#issuecomment-448886346
 
 
   thanks, merging to master!


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[GitHub] AmplabJenkins removed a comment on issue #23243: [SPARK-26288][CORE]add initRegisteredExecutorsDB

2018-12-19 Thread GitBox
AmplabJenkins removed a comment on issue #23243: [SPARK-26288][CORE]add 
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[GitHub] SparkQA commented on issue #23243: [SPARK-26288][CORE]add initRegisteredExecutorsDB

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[GitHub] asfgit closed pull request #23271: [SPARK-26318][SQL] Deprecate Row.merge

2018-12-19 Thread GitBox
asfgit closed pull request #23271: [SPARK-26318][SQL] Deprecate Row.merge
URL: https://github.com/apache/spark/pull/23271
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala 
b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index e12bf9616e2de..4f5af9ac80b10 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -57,6 +57,7 @@ object Row {
   /**
* Merge multiple rows into a single row, one after another.
*/
+  @deprecated("This method is deprecated and will be removed in future 
versions.", "3.0.0")
   def merge(rows: Row*): Row = {
 // TODO: Improve the performance of this if used in performance critical 
part.
 new GenericRow(rows.flatMap(_.toSeq).toArray)


 


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[GitHub] HyukjinKwon commented on issue #23271: [SPARK-26318][SQL] Deprecate Row.merge

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HyukjinKwon commented on issue #23271: [SPARK-26318][SQL] Deprecate Row.merge
URL: https://github.com/apache/spark/pull/23271#issuecomment-448877783
 
 
   Merged to master.


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[GitHub] AmplabJenkins removed a comment on issue #23340: [SPARK-23431][CORE] Expose the new executor memory metrics at the stage level

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[GitHub] weixiuli commented on a change in pull request #23243: [SPARK-26288][CORE]add initRegisteredExecutorsDB

2018-12-19 Thread GitBox
weixiuli commented on a change in pull request #23243: [SPARK-26288][CORE]add 
initRegisteredExecutorsDB
URL: https://github.com/apache/spark/pull/23243#discussion_r243158391
 
 

 ##
 File path: core/src/main/scala/org/apache/spark/internal/config/package.scala
 ##
 @@ -182,6 +182,12 @@ package object config {
   private[spark] val SHUFFLE_SERVICE_ENABLED =
 
ConfigBuilder("spark.shuffle.service.enabled").booleanConf.createWithDefault(false)
 
+  private[spark] val SHUFFLE_SERVICE_DB_ENABLED =
+ConfigBuilder("spark.shuffle.service.db.enabled")
 
 Review comment:
   Hi,@rxin,There is no reason for that,I also  think it's better left on by 
default and I will fix it.
   


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[GitHub] AmplabJenkins commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

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URL: https://github.com/apache/spark/pull/23336#issuecomment-448869732
 
 
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[GitHub] SparkQA commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

2018-12-19 Thread GitBox
SparkQA commented on issue #23336: [SPARK-26378][SQL] Restore performance of 
queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#issuecomment-448869262
 
 
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[GitHub] HyukjinKwon commented on a change in pull request #23352: [SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions

2018-12-19 Thread GitBox
HyukjinKwon commented on a change in pull request #23352: 
[SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions
URL: https://github.com/apache/spark/pull/23352#discussion_r243151945
 
 

 ##
 File path: 
sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
 ##
 @@ -27,4 +27,9 @@ class SerializationSuite extends SparkFunSuite with 
SharedSQLContext {
 val spark = SparkSession.builder.getOrCreate()
 new JavaSerializer(new 
SparkConf()).newInstance().serialize(spark.sqlContext)
   }
+
+  test("[SPARK-26409] SQLConf should be serializable") {
 
 Review comment:
   Yea, it should be okay as is. Honestly I want to to reformat all of them 
tho, it's obviously separate topic to discuss across the codebase.


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[GitHub] HyukjinKwon commented on issue #23349: [SPARK-26403][SQL] Support pivoting using array column for `pivot(column)` API

2018-12-19 Thread GitBox
HyukjinKwon commented on issue #23349: [SPARK-26403][SQL] Support pivoting 
using array column for `pivot(column)` API
URL: https://github.com/apache/spark/pull/23349#issuecomment-448860344
 
 
   BTW, if we give the `values` by `pivot(column, values)` API, it works as 
described in JIRA.


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[GitHub] bersprockets commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

2018-12-19 Thread GitBox
bersprockets commented on issue #23336: [SPARK-26378][SQL] Restore performance 
of queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#issuecomment-448855727
 
 
   @rxin I added the link to the description.


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[GitHub] rxin commented on issue #23336: [SPARK-26378][SQL] Restore performance of queries against wide CSV tables

2018-12-19 Thread GitBox
rxin commented on issue #23336: [SPARK-26378][SQL] Restore performance of 
queries against wide CSV tables
URL: https://github.com/apache/spark/pull/23336#issuecomment-448852774
 
 
   Do you have a link to the PR that caused the regression? We should call it 
out in your PR description.
   


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[GitHub] gengliangwang commented on a change in pull request #23352: [SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions

2018-12-19 Thread GitBox
gengliangwang commented on a change in pull request #23352: 
[SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions
URL: https://github.com/apache/spark/pull/23352#discussion_r243148690
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala
 ##
 @@ -309,13 +309,14 @@ private[sql] trait WithTestConf { self: 
BaseSessionStateBuilder =>
   def overrideConfs: Map[String, String]
 
   override protected lazy val conf: SQLConf = {
+val overrideConfigurations = overrideConfs
 val conf = parentState.map(_.conf.clone()).getOrElse {
   new SQLConf {
 clear()
 override def clear(): Unit = {
   super.clear()
   // Make sure we start with the default test configs even after clear
-  overrideConfs.foreach { case (key, value) => setConfString(key, 
value) }
+  overrideConfigurations.foreach { case (key, value) => 
setConfString(key, value) }
 
 Review comment:
   @cloud-fan agree.


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[GitHub] rxin edited a comment on issue #23208: [SPARK-25530][SQL] data source v2 API refactor (batch write)

2018-12-19 Thread GitBox
rxin edited a comment on issue #23208: [SPARK-25530][SQL] data source v2 API 
refactor (batch write)
URL: https://github.com/apache/spark/pull/23208#issuecomment-448851290
 
 
   Honestly speaking, while I feel the need to have a great API, I feel the 
same way as @cloud-fan. Things have been moving very slowly. If we question 
every single little detail, especially when one has no context, including 
designs that've been settled years ago (e.g. query id), we'd never make 
progress.


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[GitHub] gengliangwang commented on issue #23352: [SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions

2018-12-19 Thread GitBox
gengliangwang commented on issue #23352: [SPARK-26409][SQL][TESTS] SQLConf 
should be serializable in test sessions
URL: https://github.com/apache/spark/pull/23352#issuecomment-448851550
 
 
   @dongjoon-hyun I checked and the issue exists since 2.2.0. 
   
https://github.com/apache/spark/commit/ea361165e1ddce4d8aa0242ae3e878d7b39f1de2


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[GitHub] rxin edited a comment on issue #23208: [SPARK-25530][SQL] data source v2 API refactor (batch write)

2018-12-19 Thread GitBox
rxin edited a comment on issue #23208: [SPARK-25530][SQL] data source v2 API 
refactor (batch write)
URL: https://github.com/apache/spark/pull/23208#issuecomment-448851290
 
 
   Honestly speaking, while I feel the need to have a great API, I feel the 
same way as @cloud-fan. Things have been moving very slowly. If we question 
every single little detail, especially when one has no context, including ones 
that've been settled years ago (e.g. query id), we'd never make progress.


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[GitHub] rxin commented on issue #23208: [SPARK-25530][SQL] data source v2 API refactor (batch write)

2018-12-19 Thread GitBox
rxin commented on issue #23208: [SPARK-25530][SQL] data source v2 API refactor 
(batch write)
URL: https://github.com/apache/spark/pull/23208#issuecomment-448851290
 
 
   Honestly speaking, while I feel the need to have a great API, I feel the 
same way as @cloud-fan. Things have been moving very slowly. If we question 
every single little detail, including ones that've been settled years ago (e.g. 
query id), we'd never make progress.


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[GitHub] AmplabJenkins removed a comment on issue #23337: [SPARK-26019][PYSPARK] Allow insecure py4j gateways

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AmplabJenkins removed a comment on issue #23337: [SPARK-26019][PYSPARK] Allow 
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[GitHub] AmplabJenkins commented on issue #23337: [SPARK-26019][PYSPARK] Allow insecure py4j gateways

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[GitHub] SparkQA commented on issue #23337: [SPARK-26019][PYSPARK] Allow insecure py4j gateways

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SparkQA commented on issue #23337: [SPARK-26019][PYSPARK] Allow insecure py4j 
gateways
URL: https://github.com/apache/spark/pull/23337#issuecomment-448847583
 
 
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[GitHub] cloud-fan commented on a change in pull request #23352: [SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions

2018-12-19 Thread GitBox
cloud-fan commented on a change in pull request #23352: 
[SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions
URL: https://github.com/apache/spark/pull/23352#discussion_r243146082
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/internal/BaseSessionStateBuilder.scala
 ##
 @@ -309,13 +309,14 @@ private[sql] trait WithTestConf { self: 
BaseSessionStateBuilder =>
   def overrideConfs: Map[String, String]
 
   override protected lazy val conf: SQLConf = {
+val overrideConfigurations = overrideConfs
 val conf = parentState.map(_.conf.clone()).getOrElse {
   new SQLConf {
 clear()
 override def clear(): Unit = {
   super.clear()
   // Make sure we start with the default test configs even after clear
-  overrideConfs.foreach { case (key, value) => setConfString(key, 
value) }
+  overrideConfigurations.foreach { case (key, value) => 
setConfString(key, value) }
 
 Review comment:
   maybe we should move this trait to `src/test`?


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[GitHub] rdblue commented on issue #23208: [SPARK-25530][SQL] data source v2 API refactor (batch write)

2018-12-19 Thread GitBox
rdblue commented on issue #23208: [SPARK-25530][SQL] data source v2 API 
refactor (batch write)
URL: https://github.com/apache/spark/pull/23208#issuecomment-448844351
 
 
   I'm not suggesting we change the streaming design. But, where it influences 
the design of this API, I think we should consider all of our options. I also 
want to understand what those influences are. It wasn't obvious why a query ID 
would need to be passed in and it wasn't called out in the design doc. I 
naturally ask questions about things like that.


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[GitHub] AmplabJenkins removed a comment on issue #23353: [SPARK-26402][SQL] Canonicalization on GetStructField

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[GitHub] SparkQA commented on issue #23353: [SPARK-26402][SQL] Canonicalization on GetStructField

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URL: https://github.com/apache/spark/pull/23353#issuecomment-448843680
 
 
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[GitHub] gengliangwang commented on a change in pull request #23352: [SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions

2018-12-19 Thread GitBox
gengliangwang commented on a change in pull request #23352: 
[SPARK-26409][SQL][TESTS] SQLConf should be serializable in test sessions
URL: https://github.com/apache/spark/pull/23352#discussion_r243145326
 
 

 ##
 File path: 
sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
 ##
 @@ -27,4 +27,9 @@ class SerializationSuite extends SparkFunSuite with 
SharedSQLContext {
 val spark = SparkSession.builder.getOrCreate()
 new JavaSerializer(new 
SparkConf()).newInstance().serialize(spark.sqlContext)
   }
+
+  test("[SPARK-26409] SQLConf should be serializable") {
 
 Review comment:
   It is following the other test case in the test suite. I can change it to 
either way. What do you think?


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[GitHub] SparkQA commented on issue #23353: [SPARK-26402][SQL] Canonicalization on GetStructField

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[GitHub] asfgit closed pull request #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
asfgit closed pull request #22514: [SPARK-25271][SQL] Hive ctas commands should 
use data source if it is convertible
URL: https://github.com/apache/spark/pull/22514
 
 
   

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diff --git 
a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala 
b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala
index e1faecedd20ed..096481f68275d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala
@@ -820,6 +820,14 @@ object DDLUtils {
 table.provider.isDefined && table.provider.get.toLowerCase(Locale.ROOT) != 
HIVE_PROVIDER
   }
 
+  def readHiveTable(table: CatalogTable): HiveTableRelation = {
+HiveTableRelation(
+  table,
+  // Hive table columns are always nullable.
+  table.dataSchema.asNullable.toAttributes,
+  table.partitionSchema.asNullable.toAttributes)
+  }
+
   /**
* Throws a standard error for actions that require partitionProvider = hive.
*/
diff --git 
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
 
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index b304e2da6e1cf..b5cf8c9515bfb 100644
--- 
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ 
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -244,27 +244,19 @@ class FindDataSourceTable(sparkSession: SparkSession) 
extends Rule[LogicalPlan]
 })
   }
 
-  private def readHiveTable(table: CatalogTable): LogicalPlan = {
-HiveTableRelation(
-  table,
-  // Hive table columns are always nullable.
-  table.dataSchema.asNullable.toAttributes,
-  table.partitionSchema.asNullable.toAttributes)
-  }
-
   override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
 case i @ InsertIntoTable(UnresolvedCatalogRelation(tableMeta), _, _, _, _)
 if DDLUtils.isDatasourceTable(tableMeta) =>
   i.copy(table = readDataSourceTable(tableMeta))
 
 case i @ InsertIntoTable(UnresolvedCatalogRelation(tableMeta), _, _, _, _) 
=>
-  i.copy(table = readHiveTable(tableMeta))
+  i.copy(table = DDLUtils.readHiveTable(tableMeta))
 
 case UnresolvedCatalogRelation(tableMeta) if 
DDLUtils.isDatasourceTable(tableMeta) =>
   readDataSourceTable(tableMeta)
 
 case UnresolvedCatalogRelation(tableMeta) =>
-  readHiveTable(tableMeta)
+  DDLUtils.readHiveTable(tableMeta)
   }
 }
 
diff --git 
a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala 
b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 5823548a8063c..03f4b8d83e353 100644
--- 
a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ 
b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import java.util.Locale
+
 import scala.util.control.NonFatal
 
 import com.google.common.util.concurrent.Striped
@@ -29,6 +31,8 @@ import org.apache.spark.sql.catalyst.{QualifiedTableName, 
TableIdentifier}
 import org.apache.spark.sql.catalyst.catalog._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat, 
ParquetOptions}
+import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.internal.SQLConf.HiveCaseSensitiveInferenceMode._
 import org.apache.spark.sql.types._
 
@@ -113,7 +117,44 @@ private[hive] class HiveMetastoreCatalog(sparkSession: 
SparkSession) extends Log
 }
   }
 
-  def convertToLogicalRelation(
+  // Return true for Apache ORC and Hive ORC-related configuration names.
+  // Note that Spark doesn't support configurations like 
`hive.merge.orcfile.stripe.level`.
+  private def isOrcProperty(key: String) =
+key.startsWith("orc.") || key.contains(".orc.")
+
+  private def isParquetProperty(key: String) =
+key.startsWith("parquet.") || key.contains(".parquet.")
+
+  def convert(relation: HiveTableRelation): LogicalRelation = {
+val serde = 
relation.tableMeta.storage.serde.getOrElse("").toLowerCase(Locale.ROOT)
+
+// Consider table and storage properties. For properties existing in both 
sides, storage
+// properties will supersede table properties.
+if (serde.contains("parquet")) {
+  val options = 
relation.tableMeta.properties.filterKeys(isParquetProperty) ++
+relation.tableMeta.storage.properties + (ParquetOptions.MERGE_SCHEMA ->
+

[GitHub] AmplabJenkins removed a comment on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

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[GitHub] AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

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[GitHub] AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

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AmplabJenkins commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands 
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[GitHub] cloud-fan commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
cloud-fan commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands 
should use data source if it is convertible
URL: https://github.com/apache/spark/pull/22514#issuecomment-448839376
 
 
   The last commit is only updating comment, I'm merging it to master, thanks!


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[GitHub] asfgit closed pull request #23213: [SPARK-26262][SQL] Runs SQLQueryTestSuite on mixed config sets: WHOLESTAGE_CODEGEN_ENABLED and CODEGEN_FACTORY_MODE

2018-12-19 Thread GitBox
asfgit closed pull request #23213: [SPARK-26262][SQL] Runs SQLQueryTestSuite on 
mixed config sets: WHOLESTAGE_CODEGEN_ENABLED and CODEGEN_FACTORY_MODE
URL: https://github.com/apache/spark/pull/23213
 
 
   

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diff --git a/sql/core/src/test/resources/sql-tests/inputs/group-by.sql 
b/sql/core/src/test/resources/sql-tests/inputs/group-by.sql
index ec263ea70bd4a..7e81ff1aba37b 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/group-by.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/group-by.sql
@@ -141,8 +141,3 @@ SELECT every("true");
 SELECT k, v, every(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg;
 SELECT k, v, some(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg;
 SELECT k, v, any(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg;
-
--- simple explain of queries having every/some/any agregates. Optimized
--- plan should show the rewritten aggregate expression.
-EXPLAIN EXTENDED SELECT k, every(v), some(v), any(v) FROM test_agg GROUP BY k;
-
diff --git a/sql/core/src/test/resources/sql-tests/inputs/inline-table.sql 
b/sql/core/src/test/resources/sql-tests/inputs/inline-table.sql
index 41d316444ed6b..b3ec956cd178e 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/inline-table.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/inline-table.sql
@@ -49,6 +49,3 @@ select * from values ("one", count(1)), ("two", 2) as data(a, 
b);
 
 -- string to timestamp
 select * from values (timestamp('1991-12-06 00:00:00.0'), 
array(timestamp('1991-12-06 01:00:00.0'), timestamp('1991-12-06 12:00:00.0'))) 
as data(a, b);
-
--- cross-join inline tables
-EXPLAIN EXTENDED SELECT * FROM VALUES ('one', 1), ('three', null) CROSS JOIN 
VALUES ('one', 1), ('three', null);
diff --git a/sql/core/src/test/resources/sql-tests/inputs/operators.sql 
b/sql/core/src/test/resources/sql-tests/inputs/operators.sql
index 37f9cd44da7f2..ba14789d48db6 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/operators.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/operators.sql
@@ -29,27 +29,6 @@ select 2 * 5;
 select 5 % 3;
 select pmod(-7, 3);
 
--- check operator precedence.
--- We follow Oracle operator precedence in the table below that lists the 
levels of precedence
--- among SQL operators from high to low:
---
--- Operator  Operation
---
--- +, -  identity, negation
--- *, /  multiplication, division
--- +, -, ||  addition, subtraction, 
concatenation
--- =, !=, <, >, <=, >=, IS NULL, LIKE, BETWEEN, IN   comparison
--- NOT   exponentiation, logical 
negation
--- AND   conjunction
--- ORdisjunction
---
-explain select 'a' || 1 + 2;
-explain select 1 - 2 || 'b';
-explain select 2 * 4  + 3 || 'b';
-explain select 3 + 1 || 'a' || 4 / 2;
-explain select 1 == 1 OR 'a' || 'b' ==  'ab';
-explain select 'a' || 'c' == 'ac' AND 2 == 3;
-
 -- math functions
 select cot(1);
 select cot(null);
diff --git 
a/sql/core/src/test/resources/sql-tests/inputs/sql-compatibility-functions.sql 
b/sql/core/src/test/resources/sql-tests/inputs/sql-compatibility-functions.sql
index f1461032065ad..1ae49c8bfc76a 100644
--- 
a/sql/core/src/test/resources/sql-tests/inputs/sql-compatibility-functions.sql
+++ 
b/sql/core/src/test/resources/sql-tests/inputs/sql-compatibility-functions.sql
@@ -12,11 +12,6 @@ SELECT nullif(1, 2.1d), nullif(1, 1.0d);
 SELECT nvl(1, 2.1d), nvl(null, 2.1d);
 SELECT nvl2(null, 1, 2.1d), nvl2('n', 1, 2.1d);
 
--- explain for these functions; use range to avoid constant folding
-explain extended
-select ifnull(id, 'x'), nullif(id, 'x'), nvl(id, 'x'), nvl2(id, 'x', 'y')
-from range(2);
-
 -- SPARK-16730 cast alias functions for Hive compatibility
 SELECT boolean(1), tinyint(1), smallint(1), int(1), bigint(1);
 SELECT float(1), double(1), decimal(1);
diff --git a/sql/core/src/test/resources/sql-tests/inputs/string-functions.sql 
b/sql/core/src/test/resources/sql-tests/inputs/string-functions.sql
index 2effb43183d75..fbc231627e36f 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/string-functions.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/string-functions.sql
@@ -5,10 +5,6 @@ select format_string();
 -- A pipe operator for string concatenation
 select 'a' || 'b' || 'c';
 

[GitHub] SparkQA commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should use data source if it is convertible

2018-12-19 Thread GitBox
SparkQA commented on issue #22514: [SPARK-25271][SQL] Hive ctas commands should 
use data source if it is convertible
URL: https://github.com/apache/spark/pull/22514#issuecomment-448838786
 
 
   **[Test build #100330 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/100330/testReport)**
 for PR 22514 at commit 
[`839a6ce`](https://github.com/apache/spark/commit/839a6ce1732fa37b5f8ec9afa2d51730fc6ca691).


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[GitHub] cloud-fan commented on issue #23213: [SPARK-26262][SQL] Runs SQLQueryTestSuite on mixed config sets: WHOLESTAGE_CODEGEN_ENABLED and CODEGEN_FACTORY_MODE

2018-12-19 Thread GitBox
cloud-fan commented on issue #23213: [SPARK-26262][SQL] Runs SQLQueryTestSuite 
on mixed config sets: WHOLESTAGE_CODEGEN_ENABLED and CODEGEN_FACTORY_MODE
URL: https://github.com/apache/spark/pull/23213#issuecomment-448838509
 
 
   thanks, merging to master!


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[GitHub] cloud-fan commented on issue #23208: [SPARK-25530][SQL] data source v2 API refactor (batch write)

2018-12-19 Thread GitBox
cloud-fan commented on issue #23208: [SPARK-25530][SQL] data source v2 API 
refactor (batch write)
URL: https://github.com/apache/spark/pull/23208#issuecomment-448837287
 
 
   > but it affects the API because it may change the structure of the builder 
or may be a reason to use a different pattern.
   
   According to the dicussion in the doc, seems you are challenging the current 
streaming engine in Spark. I don't think we should block the API refactor 
because of it. If we do need to change something fundamental in the streaming 
engine, that's a lot of work and it doesn't matter if the API refactor is done 
before or after it.
   
   > can you put the pseudocode for Spark's invocation sequence
   
   Sure, I'll follow the read  side doc and put it in the write side doc


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[GitHub] AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] 
use abstract class instead of trait for UserDefinedFunction
URL: https://github.com/apache/spark/pull/23351#issuecomment-448834961
 
 
   Merged build finished. Test PASSed.


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[GitHub] AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] use abstract class instead of trait for UserDefinedFunction

2018-12-19 Thread GitBox
AmplabJenkins removed a comment on issue #23351: [SPARK-26216][SQL][followup] 
use abstract class instead of trait for UserDefinedFunction
URL: https://github.com/apache/spark/pull/23351#issuecomment-448834965
 
 
   Test PASSed.
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