[GitHub] [spark] SparkQA commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

2019-12-16 Thread GitBox
SparkQA commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1
URL: https://github.com/apache/spark/pull/26915#issuecomment-566350525
 
 
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[`d540e68`](https://github.com/apache/spark/commit/d540e68facff08b9f8eefba0070c232af1d3c8ed).


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[GitHub] [spark] SparkQA commented on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size field for interval column cache

2019-12-16 Thread GitBox
SparkQA commented on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size 
field for interval column cache
URL: https://github.com/apache/spark/pull/26906#issuecomment-566350550
 
 
   **[Test build #115420 has 
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[GitHub] [spark] AmplabJenkins commented on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size field for interval column cache

2019-12-16 Thread GitBox
AmplabJenkins commented on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove 
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URL: https://github.com/apache/spark/pull/26906#issuecomment-566350917
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

2019-12-16 Thread GitBox
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URL: https://github.com/apache/spark/pull/26915#issuecomment-566350861
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size field for interval column cache

2019-12-16 Thread GitBox
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URL: https://github.com/apache/spark/pull/26906#issuecomment-566350924
 
 
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[GitHub] [spark] jiangxb1987 opened a new pull request #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases

2019-12-16 Thread GitBox
jiangxb1987 opened a new pull request #26916: [SPARK-25100][TEST][FOLLOWUP] 
Refactor test cases
URL: https://github.com/apache/spark/pull/26916
 
 
   ### What changes were proposed in this pull request?
   
   Refactor test cases added by https://github.com/apache/spark/pull/26714, to 
improve code compactness.
   
   ### How was this patch tested?
   
   Tested locally.
   


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[GitHub] [spark] AmplabJenkins commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

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URL: https://github.com/apache/spark/pull/26915#issuecomment-566350858
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size field for interval column cache

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AmplabJenkins removed a comment on issue #26906: [SPARK-30066][SQL][FOLLOWUP] 
Remove size field for interval column cache
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

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[GitHub] [spark] wangyum commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

2019-12-16 Thread GitBox
wangyum commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1
URL: https://github.com/apache/spark/pull/26915#issuecomment-566350961
 
 
   Sorry @srowen @HyukjinKwon @dongjoon-hyun @cloud-fan 
   It should not add commits to master after 
https://github.com/apache/spark/pull/26879. Such as: 
[3.0.0-preview2-rc1](https://github.com/apache/spark/releases/tag/v3.0.0-preview2-rc1).
   
   But I forgot to check out the latest code. I'm very sorry about that.


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[GitHub] [spark] jiangxb1987 commented on a change in pull request #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
jiangxb1987 commented on a change in pull request #26916: 
[SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and 
`KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#discussion_r358569006
 
 

 ##
 File path: core/src/test/scala/org/apache/spark/FileSuite.scala
 ##
 @@ -702,32 +702,39 @@ class FileSuite extends SparkFunSuite with 
LocalSparkContext {
 assert(collectRDDAndDeleteFileBeforeCompute(true).isEmpty)
   }
 
-  test("SPARK-25100: Using KryoSerializer and" +
-  "setting registrationRequired true can lead job failed") {
-val inputFile = new File(tempDir, "/input").getAbsolutePath
-val textFileOutputDir = new File(tempDir, "/out1").getAbsolutePath
-val dataSetDir = new File(tempDir, "/out2").getAbsolutePath
-
-Utils.tryWithResource(new PrintWriter(new File(inputFile))) { writer =>
-  for (i <- 1 to 100) {
+  test("SPARK-25100: Support commit tasks when Kyro registration is required") 
{
+// Prepare the input file
+val inputFilePath = new File(tempDir, "/input").getAbsolutePath
+Utils.tryWithResource(new PrintWriter(new File(inputFilePath))) { writer =>
+  for (i <- 1 to 3) {
 writer.print(i)
 writer.write('\n')
   }
 }
 
-val conf = new SparkConf(false).setMaster("local").
-  set("spark.kryo.registrationRequired", "true").setAppName("test")
-conf.set("spark.serializer", classOf[KryoSerializer].getName)
+// Start a new SparkContext
+val conf = new SparkConf(false)
+  .setMaster("local")
+  .setAppName("test")
+  .set("spark.kryo.registrationRequired", "true")
+  .set("spark.serializer", classOf[KryoSerializer].getName)
+sc = new SparkContext(conf)
+
+// Prepare the input RDD
+val pairRDD = sc.textFile(inputFilePath).map(x => (x, x))
 
+// Test saveAsTextFile()
+val outputFilePath1 = new File(tempDir, "/out1").getAbsolutePath
+pairRDD.saveAsTextFile(outputFilePath1)
+assert(sc.textFile(outputFilePath1).collect() === Array("(1,1)", "(2,2)", 
"(3,3)"))
 
 Review comment:
   We should ensure the content in the output file is correct.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

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[GitHub] [spark] AmplabJenkins removed a comment on issue #26906: [SPARK-30066][SQL][FOLLOWUP] Remove size field for interval column cache

2019-12-16 Thread GitBox
AmplabJenkins removed a comment on issue #26906: [SPARK-30066][SQL][FOLLOWUP] 
Remove size field for interval column cache
URL: https://github.com/apache/spark/pull/26906#issuecomment-566350924
 
 
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[GitHub] [spark] jiangxb1987 commented on a change in pull request #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
jiangxb1987 commented on a change in pull request #26916: 
[SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and 
`KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#discussion_r358569339
 
 

 ##
 File path: 
core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
 ##
 @@ -363,16 +363,14 @@ class KryoSerializerSuite extends SparkFunSuite with 
SharedSparkContext {
 val conf = new SparkConf(false)
 conf.set(KRYO_REGISTRATION_REQUIRED, true)
 
-val ser = new KryoSerializer(conf).newInstance()
-// In HadoopMapReduceCommitProtocol#commitTask
-val addedAbsPathFiles: mutable.Map[String, String] = mutable.Map()
-addedAbsPathFiles.put("test1", "test1")
-addedAbsPathFiles.put("test2", "test2")
+// HadoopMapReduceCommitProtocol.commitTask() returns a TaskCommitMessage 
containing a complex
+// structure.
 
-val partitionPaths: mutable.Set[String] = mutable.Set()
-partitionPaths.add("test3")
+val ser = new KryoSerializer(conf).newInstance()
+val addedAbsPathFiles = Map("test1" -> "test1", "test2" -> "test2")
+val partitionPaths = Set("test3")
 
-val taskCommitMessage1 = new TaskCommitMessage(addedAbsPathFiles.toMap -> 
partitionPaths.toSet)
+val taskCommitMessage1 = new TaskCommitMessage(addedAbsPathFiles -> 
partitionPaths)
 
 Review comment:
   We only need to test the object returned by 
`HadoopMapReduceCommitProtocol.commitTask()`, thus no need to create a mutable 
map/set then convert it.


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[GitHub] [spark] HyukjinKwon commented on a change in pull request #26733: [SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream

2019-12-16 Thread GitBox
HyukjinKwon commented on a change in pull request #26733: 
[SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream
URL: https://github.com/apache/spark/pull/26733#discussion_r358569445
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
 ##
 @@ -132,6 +132,30 @@ final class DataStreamWriter[T] private[sql](ds: 
Dataset[T]) {
 this
   }
 
+  def parquet(path: String): StreamingQuery = {
+format("parquet")
+  .option("path", path)
+  .start()
+  }
+
+  def json(path: String): StreamingQuery = {
+format("json")
+  .option("path", path)
+  .start()
+  }
+
+  def csv(path: String): StreamingQuery = {
+format("csv")
+  .option("path", path)
+  .start()
+  }
+
+  def text(path: String): StreamingQuery = {
+format("text")
+  .option("path", path)
+  .start()
+  }
 
 Review comment:
   Yesh


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[GitHub] [spark] HyukjinKwon commented on a change in pull request #26733: [SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream

2019-12-16 Thread GitBox
HyukjinKwon commented on a change in pull request #26733: 
[SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream
URL: https://github.com/apache/spark/pull/26733#discussion_r358569519
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
 ##
 @@ -132,6 +132,30 @@ final class DataStreamWriter[T] private[sql](ds: 
Dataset[T]) {
 this
   }
 
+  def parquet(path: String): StreamingQuery = {
+format("parquet")
+  .option("path", path)
+  .start()
+  }
+
+  def json(path: String): StreamingQuery = {
+format("json")
+  .option("path", path)
+  .start()
+  }
+
+  def csv(path: String): StreamingQuery = {
+format("csv")
+  .option("path", path)
+  .start()
+  }
+
+  def text(path: String): StreamingQuery = {
+format("text")
+  .option("path", path)
+  .start()
+  }
 
 Review comment:
   Yeah, thanks for closing.


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[GitHub] [spark] jiangxb1987 commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
jiangxb1987 commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor 
test cases in `FileSuite` and `KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#issuecomment-566351638
 
 
   cc @deshanxiao @HeartSaVioR @dongjoon-hyun Please take a look when you have 
time, thanks!


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[GitHub] [spark] HyukjinKwon commented on a change in pull request #26733: [SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream

2019-12-16 Thread GitBox
HyukjinKwon commented on a change in pull request #26733: 
[SPARK-30097][SS][SQL] - Add suport for core sinks in writeStream
URL: https://github.com/apache/spark/pull/26733#discussion_r358569445
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamWriter.scala
 ##
 @@ -132,6 +132,30 @@ final class DataStreamWriter[T] private[sql](ds: 
Dataset[T]) {
 this
   }
 
+  def parquet(path: String): StreamingQuery = {
+format("parquet")
+  .option("path", path)
+  .start()
+  }
+
+  def json(path: String): StreamingQuery = {
+format("json")
+  .option("path", path)
+  .start()
+  }
+
+  def csv(path: String): StreamingQuery = {
+format("csv")
+  .option("path", path)
+  .start()
+  }
+
+  def text(path: String): StreamingQuery = {
+format("text")
+  .option("path", path)
+  .start()
+  }
 
 Review comment:
   Yesh


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[GitHub] [spark] SparkQA commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
SparkQA commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test 
cases in `FileSuite` and `KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#issuecomment-566352458
 
 
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started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115421/testReport)**
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[`559c45c`](https://github.com/apache/spark/commit/559c45c697ff7b207275e6549c6b8f6396a8e32b).


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[GitHub] [spark] AmplabJenkins commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
AmplabJenkins commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor 
test cases in `FileSuite` and `KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#issuecomment-566352757
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

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AmplabJenkins removed a comment on issue #26916: [SPARK-25100][TEST][FOLLOWUP] 
Refactor test cases in `FileSuite` and `KryoSerializerSuite`
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Refactor test cases in `FileSuite` and `KryoSerializerSuite`
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[GitHub] [spark] AmplabJenkins commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

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[GitHub] [spark] xuanyuanking opened a new pull request #26917: [SPARK-30278][SQL][DOC] Update Spark SQL document menu for new changes

2019-12-16 Thread GitBox
xuanyuanking opened a new pull request #26917: [SPARK-30278][SQL][DOC] Update 
Spark SQL document menu for new changes
URL: https://github.com/apache/spark/pull/26917
 
 
   ### What changes were proposed in this pull request?
   Update the Spark SQL document menu and join strategy hints.
   
   ### Why are the changes needed?
   - Several new changes in the Spark SQL document didn't change the 
menu-sql.yaml correspondingly.
   - Update the demo code for join strategy hints. 
   
   
   ### Does this PR introduce any user-facing change?
   No.
   
   
   ### How was this patch tested?
   Document change only.


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[GitHub] [spark] xuanyuanking commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark SQL document menu for new changes

2019-12-16 Thread GitBox
xuanyuanking commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark 
SQL document menu for new changes
URL: https://github.com/apache/spark/pull/26917#issuecomment-566355307
 
 
   cc @cloud-fan @gatorsmile 


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[GitHub] [spark] SparkQA commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark SQL document menu for new changes

2019-12-16 Thread GitBox
SparkQA commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark SQL 
document menu for new changes
URL: https://github.com/apache/spark/pull/26917#issuecomment-566356040
 
 
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[GitHub] [spark] dongjoon-hyun commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

2019-12-16 Thread GitBox
dongjoon-hyun commented on issue #26915: [INFRA] Reverts commit 56dcd79 and 
c216ef1
URL: https://github.com/apache/spark/pull/26915#issuecomment-566356920
 
 
   You can merge this, @wangyum .


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[GitHub] [spark] SparkQA commented on issue #26838: [SPARK-30144][ML][PySpark] Make MultilayerPerceptronClassificationModel extend MultilayerPerceptronParams

2019-12-16 Thread GitBox
SparkQA commented on issue #26838: [SPARK-30144][ML][PySpark] Make 
MultilayerPerceptronClassificationModel extend MultilayerPerceptronParams
URL: https://github.com/apache/spark/pull/26838#issuecomment-566357719
 
 
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[GitHub] [spark] wangyum closed pull request #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

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wangyum closed pull request #26915: [INFRA] Reverts commit 56dcd79 and c216ef1
URL: https://github.com/apache/spark/pull/26915
 
 
   


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[GitHub] [spark] wangyum commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1

2019-12-16 Thread GitBox
wangyum commented on issue #26915: [INFRA] Reverts commit 56dcd79 and c216ef1
URL: https://github.com/apache/spark/pull/26915#issuecomment-566357813
 
 
   Thank you all.


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[GitHub] [spark] dongjoon-hyun commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor test cases in `FileSuite` and `KryoSerializerSuite`

2019-12-16 Thread GitBox
dongjoon-hyun commented on issue #26916: [SPARK-25100][TEST][FOLLOWUP] Refactor 
test cases in `FileSuite` and `KryoSerializerSuite`
URL: https://github.com/apache/spark/pull/26916#issuecomment-566358336
 
 
   Thank you for making a followup and pinging me, @jiangxb1987 !


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[GitHub] [spark] SparkQA commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark SQL document menu for new changes

2019-12-16 Thread GitBox
SparkQA commented on issue #26917: [SPARK-30278][SQL][DOC] Update Spark SQL 
document menu for new changes
URL: https://github.com/apache/spark/pull/26917#issuecomment-566358598
 
 
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* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.


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[GitHub] [spark] srowen commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
srowen commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid 
overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358578200
 
 

 ##
 File path: 
sql/core/src/test/scala/org/apache/spark/sql/ApproximatePercentileQuerySuite.scala
 ##
 @@ -182,7 +182,7 @@ class ApproximatePercentileQuerySuite extends QueryTest 
with SharedSparkSession
 spark.sql(
   s"""SELECT
  |  key,
- |  percentile_approx(null, 0.5)
+ |  percentile_approx(cast(null as int), 0.5)
 
 Review comment:
   It doesn't make much sense, yeah. I am not sure whether we generally return 
null or an error in a case like this. Following Hive seems like a reasonable 
guide. It could be OK to change behavior if there's a release note attached to 
the JIRA, and if we think it's more bug fix than anything


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[GitHub] [spark] cloud-fan closed pull request #26894: [SPARK-30094][SQL] Apply current namespace for the single-part table name

2019-12-16 Thread GitBox
cloud-fan closed pull request #26894: [SPARK-30094][SQL] Apply current 
namespace for the single-part table name
URL: https://github.com/apache/spark/pull/26894
 
 
   


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[GitHub] [spark] cloud-fan commented on issue #26894: [SPARK-30094][SQL] Apply current namespace for the single-part table name

2019-12-16 Thread GitBox
cloud-fan commented on issue #26894: [SPARK-30094][SQL] Apply current namespace 
for the single-part table name
URL: https://github.com/apache/spark/pull/26894#issuecomment-566361014
 
 
   thanks, merging to master!


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[GitHub] [spark] yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid 
overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358579502
 
 

 ##
 File path: 
sql/core/src/test/scala/org/apache/spark/sql/ApproximatePercentileQuerySuite.scala
 ##
 @@ -182,7 +182,7 @@ class ApproximatePercentileQuerySuite extends QueryTest 
with SharedSparkSession
 spark.sql(
   s"""SELECT
  |  key,
- |  percentile_approx(null, 0.5)
+ |  percentile_approx(cast(null as int), 0.5)
 
 Review comment:
   Agreed. I'll updated this change in the PR description. And I guess the 
author's original purpose of these tests here are to verify the null handling 
of the non-null types, not `NullType`


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[GitHub] [spark] cloud-fan commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
cloud-fan commented on a change in pull request #26905: [SPARK-30266][SQL] 
Avoid overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358580862
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
 ##
 @@ -83,32 +83,37 @@ case class ApproximatePercentile(
   }
 
   // Mark as lazy so that accuracyExpression is not evaluated during tree 
transformation.
-  private lazy val accuracy: Int = accuracyExpression.eval().asInstanceOf[Int]
-
-  override def inputTypes: Seq[AbstractDataType] = {
-// Support NumericType, DateType and TimestampType since their internal 
types are all numeric,
-// and can be easily cast to double for processing.
-Seq(TypeCollection(NumericType, DateType, TimestampType),
-  TypeCollection(DoubleType, ArrayType(DoubleType)), IntegerType)
-  }
+  private lazy val accuracy: Long = 
accuracyExpression.eval().asInstanceOf[Number].longValue()
 
   // Mark as lazy so that percentageExpression is not evaluated during tree 
transformation.
   private lazy val (returnPercentileArray: Boolean, percentages: 
Array[Double]) =
-percentageExpression.eval() match {
-  // Rule ImplicitTypeCasts can cast other numeric types to double
-  case num: Double => (false, Array(num))
-  case arrayData: ArrayData => (true, arrayData.toDoubleArray())
+percentageExpression.dataType match {
+  case DoubleType => (false, 
Array(percentageExpression.eval().asInstanceOf[Double]))
+  case _: NumericType =>
 
 Review comment:
   Why would it overflow? The type coercion should cast `accuracyExpression` to 
int type.


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[GitHub] [spark] yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid 
overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358581552
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
 ##
 @@ -83,32 +83,37 @@ case class ApproximatePercentile(
   }
 
   // Mark as lazy so that accuracyExpression is not evaluated during tree 
transformation.
-  private lazy val accuracy: Int = accuracyExpression.eval().asInstanceOf[Int]
-
-  override def inputTypes: Seq[AbstractDataType] = {
-// Support NumericType, DateType and TimestampType since their internal 
types are all numeric,
-// and can be easily cast to double for processing.
-Seq(TypeCollection(NumericType, DateType, TimestampType),
-  TypeCollection(DoubleType, ArrayType(DoubleType)), IntegerType)
-  }
+  private lazy val accuracy: Long = 
accuracyExpression.eval().asInstanceOf[Number].longValue()
 
   // Mark as lazy so that percentageExpression is not evaluated during tree 
transformation.
   private lazy val (returnPercentileArray: Boolean, percentages: 
Array[Double]) =
-percentageExpression.eval() match {
-  // Rule ImplicitTypeCasts can cast other numeric types to double
-  case num: Double => (false, Array(num))
-  case arrayData: ArrayData => (true, arrayData.toDoubleArray())
+percentageExpression.dataType match {
+  case DoubleType => (false, 
Array(percentageExpression.eval().asInstanceOf[Double]))
+  case _: NumericType =>
 
 Review comment:
   If we specify a accuracy value greater than Int.MaxValue


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[GitHub] [spark] HeartSaVioR commented on issue #26723: [SPARK-27523][CORE] - Resolve scheme-less event log directory relative to default filesystem

2019-12-16 Thread GitBox
HeartSaVioR commented on issue #26723: [SPARK-27523][CORE] - Resolve 
scheme-less event log directory relative to default filesystem
URL: https://github.com/apache/spark/pull/26723#issuecomment-566364451
 
 
   What about explicitly documenting this behavior in configuration.md? I'm now 
a bit confused which configurations use which one for schema-less directory 
configuration, but it might worth to mention when we won't follow default 
filesystem.


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[GitHub] [spark] SparkQA commented on issue #26910: [SPARK-30154][ML] PySpark UDF to convert MLlib vectors to dense arrays

2019-12-16 Thread GitBox
SparkQA commented on issue #26910: [SPARK-30154][ML] PySpark UDF to convert 
MLlib vectors to dense arrays
URL: https://github.com/apache/spark/pull/26910#issuecomment-566364589
 
 
   **[Test build #115424 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115424/testReport)**
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[`e2bb6c0`](https://github.com/apache/spark/commit/e2bb6c098198f611c3b74289cb53be9a1e187de1).


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[GitHub] [spark] AmplabJenkins commented on issue #26910: [SPARK-30154][ML] PySpark UDF to convert MLlib vectors to dense arrays

2019-12-16 Thread GitBox
AmplabJenkins commented on issue #26910: [SPARK-30154][ML] PySpark UDF to 
convert MLlib vectors to dense arrays
URL: https://github.com/apache/spark/pull/26910#issuecomment-566364920
 
 
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[GitHub] [spark] yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
yaooqinn commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid 
overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358582153
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
 ##
 @@ -83,32 +83,37 @@ case class ApproximatePercentile(
   }
 
   // Mark as lazy so that accuracyExpression is not evaluated during tree 
transformation.
-  private lazy val accuracy: Int = accuracyExpression.eval().asInstanceOf[Int]
-
-  override def inputTypes: Seq[AbstractDataType] = {
-// Support NumericType, DateType and TimestampType since their internal 
types are all numeric,
-// and can be easily cast to double for processing.
-Seq(TypeCollection(NumericType, DateType, TimestampType),
-  TypeCollection(DoubleType, ArrayType(DoubleType)), IntegerType)
-  }
+  private lazy val accuracy: Long = 
accuracyExpression.eval().asInstanceOf[Number].longValue()
 
   // Mark as lazy so that percentageExpression is not evaluated during tree 
transformation.
   private lazy val (returnPercentileArray: Boolean, percentages: 
Array[Double]) =
-percentageExpression.eval() match {
-  // Rule ImplicitTypeCasts can cast other numeric types to double
-  case num: Double => (false, Array(num))
-  case arrayData: ArrayData => (true, arrayData.toDoubleArray())
+percentageExpression.dataType match {
+  case DoubleType => (false, 
Array(percentageExpression.eval().asInstanceOf[Double]))
+  case _: NumericType =>
 
 Review comment:
   I don't mean a overflow exception here, just the value here is not right.


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[GitHub] [spark] AmplabJenkins commented on issue #26910: [SPARK-30154][ML] PySpark UDF to convert MLlib vectors to dense arrays

2019-12-16 Thread GitBox
AmplabJenkins commented on issue #26910: [SPARK-30154][ML] PySpark UDF to 
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URL: https://github.com/apache/spark/pull/26910#issuecomment-566364915
 
 
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[GitHub] [spark] maropu opened a new pull request #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

2019-12-16 Thread GitBox
maropu opened a new pull request #26918: [SPARK-30279][SQL] Support 32 or more 
grouping attributes for GROUPING_ID 
URL: https://github.com/apache/spark/pull/26918
 
 
   
   
   ### What changes were proposed in this pull request?
   
   This pr intends to support 32 or more grouping attributes for GROUPING_ID. 
In the current master, an integer overflow can occur to compute grouping IDs;
   
https://github.com/apache/spark/blob/e75d9afb2f282ce79c9fd8bce031287739326a4f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala#L613
   
   For example, the query below generates wrong grouping IDs in the master;
   ```
   
   scala> val numCols = 32 // or, 31
   scala> val cols = (0 until numCols).map { i => s"c$i" }
   scala> sql(s"create table test_$numCols (${cols.map(c => s"$c 
int").mkString(",")}, v int) using parquet")
   scala> val insertVals = (0 until numCols).map { _ => 1 }.mkString(",")
   scala> sql(s"insert into test_$numCols values ($insertVals,3)")
   scala> sql(s"select grouping_id(), sum(v) from test_$numCols group by 
grouping sets ((${cols.mkString(",")}), 
(${cols.init.mkString(",")}))").show(10, false)
   scala> sql(s"drop table test_$numCols")
   
   // numCols = 32
   +-+--+
   |grouping_id()|sum(v)|
   +-+--+
   |0|3 |
   |0|3 | // Wrong Grouping ID
   +-+--+
   
   // numCols = 31
   +-+--+
   |grouping_id()|sum(v)|
   +-+--+
   |0|3 |
   |1|3 |
   +-+--+
   ```
   To fix this issue, this pr generates string grouping IDs instead of integer 
IDs for that case.
   
   ### Why are the changes needed?
   
   To support more cases in `GROUPING_ID`.
   
   ### Does this PR introduce any user-facing change?
   
   No.
   
   ### How was this patch tested?
   
   Added unit tests.


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[GitHub] [spark] SparkQA commented on issue #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

2019-12-16 Thread GitBox
SparkQA commented on issue #26918: [SPARK-30279][SQL] Support 32 or more 
grouping attributes for GROUPING_ID 
URL: https://github.com/apache/spark/pull/26918#issuecomment-566366273
 
 
   **[Test build #115425 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115425/testReport)**
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[`327967e`](https://github.com/apache/spark/commit/327967e22a105386bf43e0fac4e9fa74ec70bd4e).


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[GitHub] [spark] AmplabJenkins commented on issue #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

2019-12-16 Thread GitBox
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URL: https://github.com/apache/spark/pull/26918#issuecomment-566366580
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

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AmplabJenkins removed a comment on issue #26918: [SPARK-30279][SQL] Support 32 
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[GitHub] [spark] AmplabJenkins commented on issue #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

2019-12-16 Thread GitBox
AmplabJenkins commented on issue #26918: [SPARK-30279][SQL] Support 32 or more 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26918: [SPARK-30279][SQL] Support 32 or more grouping attributes for GROUPING_ID

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[GitHub] [spark] JkSelf commented on a change in pull request #26434: [SPARK-29544] [SQL] optimize skewed partition based on data size

2019-12-16 Thread GitBox
JkSelf commented on a change in pull request #26434: [SPARK-29544] [SQL] 
optimize skewed partition based on data size
URL: https://github.com/apache/spark/pull/26434#discussion_r358583953
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
 ##
 @@ -145,39 +156,36 @@ case class ReduceNumShufflePartitions(conf: SQLConf) 
extends Rule[SparkPlan] {
   distinctNumPreShufflePartitions.length == 1,
   "There should be only one distinct value of the number pre-shuffle 
partitions " +
 "among registered Exchange operator.")
-val numPreShufflePartitions = distinctNumPreShufflePartitions.head
 
 val partitionStartIndices = ArrayBuffer[Int]()
-// The first element of partitionStartIndices is always 0.
-partitionStartIndices += 0
-
-var postShuffleInputSize = 0L
-
-var i = 0
-while (i < numPreShufflePartitions) {
-  // We calculate the total size of ith pre-shuffle partitions from all 
pre-shuffle stages.
-  // Then, we add the total size to postShuffleInputSize.
-  var nextShuffleInputSize = 0L
-  var j = 0
-  while (j < mapOutputStatistics.length) {
-nextShuffleInputSize += mapOutputStatistics(j).bytesByPartitionId(i)
-j += 1
-  }
-
-  // If including the nextShuffleInputSize would exceed the target 
partition size, then start a
-  // new partition.
-  if (i > 0 && postShuffleInputSize + nextShuffleInputSize > 
targetPostShuffleInputSize) {
-partitionStartIndices += i
-// reset postShuffleInputSize.
-postShuffleInputSize = nextShuffleInputSize
-  } else {
-postShuffleInputSize += nextShuffleInputSize
-  }
-
-  i += 1
+val partitionEndIndices = ArrayBuffer[Int]()
+val numPartitions = mapOutputStatistics.map(stats => 
stats.bytesByPartitionId.length).head
+val includedPartitions = (0 until 
numPartitions).filter(!excludedPartitions.contains(_))
+val firstStartIndex = includedPartitions(0)
+partitionStartIndices += firstStartIndex
+var postShuffleInputSize = 
mapOutputStatistics.map(_.bytesByPartitionId(firstStartIndex)).sum
+var i = firstStartIndex
+includedPartitions.filter(_ != firstStartIndex).foreach {
+  nextPartitionIndices =>
+var nextShuffleInputSize =
+  
mapOutputStatistics.map(_.bytesByPartitionId(nextPartitionIndices)).sum
+// If nextPartitionIndices is skewed and omitted, or including
+// the nextShuffleInputSize would exceed the target partition size,
+// then start a new partition.
+if (nextPartitionIndices != i + 1 ||
 
 Review comment:
   The `nextPartitionIndices ` is excluded.


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[GitHub] [spark] JkSelf commented on a change in pull request #26434: [SPARK-29544] [SQL] optimize skewed partition based on data size

2019-12-16 Thread GitBox
JkSelf commented on a change in pull request #26434: [SPARK-29544] [SQL] 
optimize skewed partition based on data size
URL: https://github.com/apache/spark/pull/26434#discussion_r358584117
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala
 ##
 @@ -0,0 +1,313 @@
+/*
+ * 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.adaptive
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.duration.Duration
+
+import org.apache.spark.{MapOutputStatistics, MapOutputTrackerMaster, SparkEnv}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, 
UnknownPartitioning}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.util.ThreadUtils
+
+case class OptimizeSkewedPartitions(conf: SQLConf) extends Rule[SparkPlan] {
+
+  private val supportedJoinTypes =
+Inner :: Cross :: LeftSemi :: LeftAnti :: LeftOuter :: RightOuter :: Nil
+
+  /**
+   * A partition is considered as a skewed partition if its size is larger 
than the median
+   * partition size * spark.sql.adaptive.skewedPartitionFactor and also larger 
than
+   * spark.sql.adaptive.skewedPartitionSizeThreshold.
+   */
+  private def isSkewed(
+  stats: MapOutputStatistics,
+  partitionId: Int,
+  medianSize: Long): Boolean = {
+val size = stats.bytesByPartitionId(partitionId)
+size > medianSize * 
conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_FACTOR) &&
+  size > 
conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_SIZE_THRESHOLD)
+  }
+
+  private def medianSize(stats: MapOutputStatistics): Long = {
+val numPartitions = stats.bytesByPartitionId.length
+val bytes = stats.bytesByPartitionId.sorted
+if (bytes(numPartitions / 2) > 0) bytes(numPartitions / 2) else 1
+  }
+
+  /**
+   * Get all the map data size for specific reduce partitionId.
+   */
+  def getMapSizeForSpecificPartition(partitionId: Int, shuffleId: Int): 
Array[Long] = {
+val mapOutputTracker = 
SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
+mapOutputTracker.shuffleStatuses.get(shuffleId).
+  get.mapStatuses.map{_.getSizeForBlock(partitionId)}
+  }
+
+  /**
+   * Split the partition into the number of mappers. Each split read data from 
each mapper.
+   */
+  private def estimateMapStartIndices(
+  stage: QueryStageExec,
+  partitionId: Int,
+  medianSize: Long): Array[Int] = {
+val dependency = 
ShuffleQueryStageExec.getShuffleStage(stage).plan.shuffleDependency
+val numMappers = dependency.rdd.partitions.length
+// TODO: split the partition based on the size
+(0 until numMappers).toArray
+  }
+
+  private def getStatistics(queryStage: QueryStageExec): MapOutputStatistics = 
{
+val shuffleStage = ShuffleQueryStageExec.getShuffleStage(queryStage)
+val metrics = shuffleStage.plan.mapOutputStatisticsFuture
+assert(metrics.isCompleted,
+  "ShuffleQueryStageExec should already be ready when executing 
OptimizeSkewedPartitions rule")
+ThreadUtils.awaitResult(metrics, Duration.Zero)
+  }
+
+  /**
+   * Base optimization support check: the join type is supported.
+   * Note that for some join types(like left outer), whether a certain 
partition can be optimized
+   * also depends on the filed isSkewAndSupportsSplit.
+   */
+  private def supportOptimization(
+  joinType: JoinType,
+  leftStage: QueryStageExec,
+  rightStage: QueryStageExec): Boolean = {
+val joinTypeSupported = supportedJoinTypes.contains(joinType)
+val shuffleStageCheck = 
ShuffleQueryStageExec.isShuffleQueryStageExec(leftStage) &&
+  ShuffleQueryStageExec.isShuffleQueryStageExec(rightStage)
+joinTypeSupported && shuffleStageCheck
+  }
+
+  private def supportSplitOnLeftPartition(joinType: JoinType)

[GitHub] [spark] SparkQA commented on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

2019-12-16 Thread GitBox
SparkQA commented on issue #26416: [SPARK-29779][CORE] Compact old event log 
files and cleanup
URL: https://github.com/apache/spark/pull/26416#issuecomment-566367616
 
 
   **[Test build #115414 has 
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[`23ce9d7`](https://github.com/apache/spark/commit/23ce9d7e8d47205d8bc6f6a8a050965aa00229d8).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
 * `  case class FilterStatistic(`
 * `class EventLogFileCompactor(`
 * `class FilteredEventLogFileRewriter(`
 * `class CompactedEventLogFileWriter(`


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[GitHub] [spark] SparkQA removed a comment on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

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[GitHub] [spark] WangGuangxin commented on a change in pull request #26899: [SPARK-28332][SQL] Reserve init value -1 only when do min max statistics in SQLMetrics

2019-12-16 Thread GitBox
WangGuangxin commented on a change in pull request #26899: [SPARK-28332][SQL] 
Reserve init value -1 only when do min max statistics in SQLMetrics
URL: https://github.com/apache/spark/pull/26899#discussion_r358586175
 
 

 ##
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
 ##
 @@ -67,7 +67,9 @@ class SQLMetric(val metricType: String, initValue: Long = 
0L) extends Accumulato
 
   def +=(v: Long): Unit = _value += v
 
-  override def value: Long = _value
+  def getRawValue(): Long = _value
+
+  override def value: Long = Math.max(_value, 0)
 
 Review comment:
   What I want to do here is when we want to distinguish an uninitialized 
metric by -1, then we can use `SQLMetric.getRawValue`(It's the same with what 
current SQLMetric does without this PR). Otherwise use `SQLMetrics.value` to 
make sure it returns at least 0 (It's the same with initializing SQLMetric to 
0). It's a bit tricky.
   
   `SQLMetric.getRawValue` was called in `SQLAppStatusListener.onTaskEnd` and 
`SQLMetrics.postDriverMetricUpdates`.  The metric values in these two places 
eventually go to `LiveStageMetrics` used by  
`SQLAppStatusListener.aggregateMetrics` and then aggregated in 
`SQLMetrics.stringValue`. If a SQLMetric is initialized by -1 and it was not 
got updated in executors, then this metric value is -1 and can be filtered by 
logic in `SQLMetrics.stringValue`.  
   


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[GitHub] [spark] SparkQA commented on issue #26914: [SPARK-30274][Core] Avoid BytesToBytesMap lookup hang forever when holding keys reaching max capacity

2019-12-16 Thread GitBox
SparkQA commented on issue #26914: [SPARK-30274][Core] Avoid BytesToBytesMap 
lookup hang forever when holding keys reaching max capacity
URL: https://github.com/apache/spark/pull/26914#issuecomment-566371745
 
 
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* This patch merges cleanly.
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[GitHub] [spark] SparkQA commented on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

2019-12-16 Thread GitBox
SparkQA commented on issue #26416: [SPARK-29779][CORE] Compact old event log 
files and cleanup
URL: https://github.com/apache/spark/pull/26416#issuecomment-566372685
 
 
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[GitHub] [spark] attilapiros commented on a change in pull request #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

2019-12-16 Thread GitBox
attilapiros commented on a change in pull request #26869: [SPARK-30235][CORE] 
Switching off host local disk reading of shuffle blocks in case of 
useOldFetchProtocol
URL: https://github.com/apache/spark/pull/26869#discussion_r358589382
 
 

 ##
 File path: core/src/main/scala/org/apache/spark/internal/config/package.scala
 ##
 @@ -1097,9 +1097,9 @@ package object config {
 
   private[spark] val SHUFFLE_HOST_LOCAL_DISK_READING_ENABLED =
 ConfigBuilder("spark.shuffle.readHostLocalDisk.enabled")
-  .doc("If enabled, shuffle blocks requested from those block managers 
which are running on " +
-"the same host are read from the disk directly instead of being 
fetched as remote blocks " +
-"over the network.")
+  .doc("If enabled (and `spark.shuffle.useOldFetchProtocol` is disabled), 
shuffle blocks " +
 
 Review comment:
   Transforming the first part to string interpolation has not been forgotten.


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[GitHub] [spark] SparkQA removed a comment on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

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[GitHub] [spark] AmplabJenkins commented on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

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[GitHub] [spark] cloud-fan closed pull request #26831: [SPARK-30201][SQL] HiveOutputWriter standardOI should use ObjectInspectorCopyOption.DEFAULT

2019-12-16 Thread GitBox
cloud-fan closed pull request #26831: [SPARK-30201][SQL] HiveOutputWriter 
standardOI should use ObjectInspectorCopyOption.DEFAULT
URL: https://github.com/apache/spark/pull/26831
 
 
   


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[GitHub] [spark] cloud-fan commented on issue #26831: [SPARK-30201][SQL] HiveOutputWriter standardOI should use ObjectInspectorCopyOption.DEFAULT

2019-12-16 Thread GitBox
cloud-fan commented on issue #26831: [SPARK-30201][SQL] HiveOutputWriter 
standardOI should use ObjectInspectorCopyOption.DEFAULT
URL: https://github.com/apache/spark/pull/26831#issuecomment-566373038
 
 
   thanks, merging to master!


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[GitHub] [spark] attilapiros commented on a change in pull request #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

2019-12-16 Thread GitBox
attilapiros commented on a change in pull request #26869: [SPARK-30235][CORE] 
Switching off host local disk reading of shuffle blocks in case of 
useOldFetchProtocol
URL: https://github.com/apache/spark/pull/26869#discussion_r358589705
 
 

 ##
 File path: docs/sql-migration-guide.md
 ##
 @@ -97,7 +97,7 @@ license: |
 
   - Since Spark 3.0, when Avro files are written with user provided 
non-nullable schema, even the catalyst schema is nullable, Spark is still able 
to write the files. However, Spark will throw runtime NPE if any of the records 
contains null.
 
-  - Since Spark 3.0, we use a new protocol for fetching shuffle blocks, for 
external shuffle service users, we need to upgrade the server correspondingly. 
Otherwise, we'll get the error message `UnsupportedOperationException: 
Unexpected message: FetchShuffleBlocks`. If it is hard to upgrade the shuffle 
service right now, you can still use the old protocol by setting 
`spark.shuffle.useOldFetchProtocol` to `true`.
+  - Since Spark 3.0, we use a new protocol for fetching shuffle blocks, for 
external shuffle service users, we need to upgrade the server correspondingly. 
Otherwise, we'll get the error message `IllegalArgumentException: Unexpected 
message type: `. If it is hard to upgrade the shuffle service right 
now, you can still use the old protocol by setting 
`spark.shuffle.useOldFetchProtocol` to `true`.
 
 Review comment:
   I have moved it to core migration guide and removed the "Since Spark 3.0, " 
as having subsections with the Spark versions. 


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

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URL: https://github.com/apache/spark/pull/26416#issuecomment-566373092
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26416: [SPARK-29779][CORE] Compact old event log files and cleanup

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[GitHub] [spark] cloud-fan commented on a change in pull request #26905: [SPARK-30266][SQL] Avoid overflow and match error in ApproximatePercentile

2019-12-16 Thread GitBox
cloud-fan commented on a change in pull request #26905: [SPARK-30266][SQL] 
Avoid overflow and match error in ApproximatePercentile
URL: https://github.com/apache/spark/pull/26905#discussion_r358590272
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproximatePercentile.scala
 ##
 @@ -83,32 +83,37 @@ case class ApproximatePercentile(
   }
 
   // Mark as lazy so that accuracyExpression is not evaluated during tree 
transformation.
-  private lazy val accuracy: Int = accuracyExpression.eval().asInstanceOf[Int]
-
-  override def inputTypes: Seq[AbstractDataType] = {
-// Support NumericType, DateType and TimestampType since their internal 
types are all numeric,
-// and can be easily cast to double for processing.
-Seq(TypeCollection(NumericType, DateType, TimestampType),
-  TypeCollection(DoubleType, ArrayType(DoubleType)), IntegerType)
-  }
+  private lazy val accuracy: Long = 
accuracyExpression.eval().asInstanceOf[Number].longValue()
 
   // Mark as lazy so that percentageExpression is not evaluated during tree 
transformation.
   private lazy val (returnPercentileArray: Boolean, percentages: 
Array[Double]) =
-percentageExpression.eval() match {
-  // Rule ImplicitTypeCasts can cast other numeric types to double
-  case num: Double => (false, Array(num))
-  case arrayData: ArrayData => (true, arrayData.toDoubleArray())
+percentageExpression.dataType match {
+  case DoubleType => (false, 
Array(percentageExpression.eval().asInstanceOf[Double]))
+  case _: NumericType =>
 
 Review comment:
   shall we simply update `inputTypes` to replace `IntegerType` with `LongType`?


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[GitHub] [spark] SparkQA commented on issue #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

2019-12-16 Thread GitBox
SparkQA commented on issue #26869: [SPARK-30235][CORE] Switching off host local 
disk reading of shuffle blocks in case of useOldFetchProtocol
URL: https://github.com/apache/spark/pull/26869#issuecomment-566374349
 
 
   **[Test build #115426 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115426/testReport)**
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[`69459e7`](https://github.com/apache/spark/commit/69459e73457650ae43e08ef783b67cdf31b0d46f).


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[GitHub] [spark] cloud-fan commented on a change in pull request #26917: [SPARK-30278][SQL][DOC] Update Spark SQL document menu for new changes

2019-12-16 Thread GitBox
cloud-fan commented on a change in pull request #26917: [SPARK-30278][SQL][DOC] 
Update Spark SQL document menu for new changes
URL: https://github.com/apache/spark/pull/26917#discussion_r358590782
 
 

 ##
 File path: docs/sql-performance-tuning.md
 ##
 @@ -158,7 +155,7 @@ broadcast(spark.table("src")).join(spark.table("records"), 
"key").show()
 {% highlight r %}
 src <- sql("SELECT * FROM src")
 records <- sql("SELECT * FROM records")
-head(join(broadcast(src), records, src$key == records$key))
 
 Review comment:
   did we remove the `broadcast` method?


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[GitHub] [spark] SparkQA removed a comment on issue #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

2019-12-16 Thread GitBox
SparkQA removed a comment on issue #26869: [SPARK-30235][CORE] Switching off 
host local disk reading of shuffle blocks in case of useOldFetchProtocol
URL: https://github.com/apache/spark/pull/26869#issuecomment-566374349
 
 
   **[Test build #115426 has 
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[GitHub] [spark] SparkQA commented on issue #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

2019-12-16 Thread GitBox
SparkQA commented on issue #26869: [SPARK-30235][CORE] Switching off host local 
disk reading of shuffle blocks in case of useOldFetchProtocol
URL: https://github.com/apache/spark/pull/26869#issuecomment-566375345
 
 
   **[Test build #115426 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/115426/testReport)**
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[`69459e7`](https://github.com/apache/spark/commit/69459e73457650ae43e08ef783b67cdf31b0d46f).
* This patch **fails to build**.
* This patch merges cleanly.
* This patch adds no public classes.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

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[GitHub] [spark] AmplabJenkins commented on issue #26869: [SPARK-30235][CORE] Switching off host local disk reading of shuffle blocks in case of useOldFetchProtocol

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