[jira] [Resolved] (SPARK-39724) Remove duplicate `.setAccessible(true)` in `kvstore.KVTypeInfo`
[
https://issues.apache.org/jira/browse/SPARK-39724?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Huaxin Gao resolved SPARK-39724.
Fix Version/s: 3.4.0
Assignee: Yang Jie
Resolution: Fixed
> Remove duplicate `.setAccessible(true)` in `kvstore.KVTypeInfo`
>
>
> Key: SPARK-39724
> URL: https://issues.apache.org/jira/browse/SPARK-39724
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
>Affects Versions: 3.4.0
>Reporter: Yang Jie
>Assignee: Yang Jie
>Priority: Minor
> Fix For: 3.4.0
>
>
> {code:java}
> for (Method m : type.getDeclaredMethods()) {
> KVIndex idx = m.getAnnotation(KVIndex.class);
> if (idx != null) {
> checkIndex(idx, indices);
> Preconditions.checkArgument(m.getParameterTypes().length == 0,
> "Annotated method %s::%s should not have any parameters.",
> type.getName(), m.getName());
> m.setAccessible(true);
> indices.put(idx.value(), idx);
> m.setAccessible(true);
> accessors.put(idx.value(), new MethodAccessor(m));
> } {code}
> The above code has duplicate calls to `.setAccessible(true)`.
>
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[jira] [Resolved] (SPARK-38714) Interval multiplication error
[
https://issues.apache.org/jira/browse/SPARK-38714?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Pablo Langa Blanco resolved SPARK-38714.
Resolution: Resolved
> Interval multiplication error
> -
>
> Key: SPARK-38714
> URL: https://issues.apache.org/jira/browse/SPARK-38714
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.3.0
> Environment: branch-3.3, Java 8
>
>Reporter: chong
>Priority: Major
>
> Code gen have error when multipling interval by a decimal.
>
> $SPARK_HOME/bin/spark-shell
>
> import org.apache.spark.sql.Row
> import java.time.Duration
> import java.time.Period
> import org.apache.spark.sql.types._
> val data = Seq(Row(new java.math.BigDecimal("123456789.11")))
> val schema = StructType(Seq(
> StructField("c1", DecimalType(9, 2)),
> ))
> val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
> df.selectExpr("interval '100' second * c1").show(false)
> errors are:
> *{color:#FF}java.lang.AssertionError: assertion failed:{color}*
> Decimal$DecimalIsFractional
> while compiling:
> during phase: globalPhase=terminal, enteringPhase=jvm
> library version: version 2.12.15
> compiler version: version 2.12.15
> reconstructed args: -classpath -Yrepl-class-based -Yrepl-outdir
> /tmp/spark-83a0cda4-dd0b-472e-ad8b-a4b33b85f613/repl-06489815-5366-4aa0-9419-f01abda8d041
> last tree to typer: TypeTree(class Byte)
> tree position: line 6 of
> tree tpe: Byte
> symbol: (final abstract) class Byte in package scala
> symbol definition: final abstract class Byte extends (a ClassSymbol)
> symbol package: scala
> symbol owners: class Byte
> call site: constructor $eval in object $eval in package $line21
> == Source file context for tree position ==
> 3
> 4 object $eval {
> 5 lazy val $result =
> $line21.$read.INSTANCE.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.res0
> 6 lazy val $print: {_}root{_}.java.lang.String = {
> 7 $line21.$read.INSTANCE.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw
> 8
> 9 ""
> at
> scala.reflect.internal.SymbolTable.throwAssertionError(SymbolTable.scala:185)
> at scala.reflect.internal.Symbols$Symbol.completeInfo(Symbols.scala:1525)
> at scala.reflect.internal.Symbols$Symbol.info(Symbols.scala:1514)
> at scala.reflect.internal.Symbols$Symbol.flatOwnerInfo(Symbols.scala:2353)
> at
> scala.reflect.internal.Symbols$ClassSymbol.companionModule0(Symbols.scala:3346)
> at
> scala.reflect.internal.Symbols$ClassSymbol.companionModule(Symbols.scala:3348)
> at
> scala.reflect.internal.Symbols$ModuleClassSymbol.sourceModule(Symbols.scala:3487)
> at
> scala.reflect.internal.Symbols.$anonfun$forEachRelevantSymbols$1$adapted(Symbols.scala:3802)
> at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
> at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:38)
> at scala.reflect.internal.Symbols.markFlagsCompleted(Symbols.scala:3799)
> at scala.reflect.internal.Symbols.markFlagsCompleted$(Symbols.scala:3805)
> at scala.reflect.internal.SymbolTable.markFlagsCompleted(SymbolTable.scala:28)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.finishSym$1(UnPickler.scala:324)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbol(UnPickler.scala:342)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbolRef(UnPickler.scala:645)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readType(UnPickler.scala:413)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.$anonfun$readSymbol$10(UnPickler.scala:357)
> at scala.reflect.internal.pickling.UnPickler$Scan.at(UnPickler.scala:188)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbol(UnPickler.scala:357)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.$anonfun$run$1(UnPickler.scala:96)
> at scala.reflect.internal.pickling.UnPickler$Scan.run(UnPickler.scala:88)
> at scala.reflect.internal.pickling.UnPickler.unpickle(UnPickler.scala:47)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.unpickleOrParseInnerClasses(ClassfileParser.scala:1186)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.parseClass(ClassfileParser.scala:468)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.$anonfun$parse$2(ClassfileParser.scala:161)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.$anonfun$parse$1(ClassfileParser.scala:147)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.parse(ClassfileParser.scala:130)
> at
> scala.tools.nsc.symtab.SymbolLoaders$ClassfileLoader.doComplete(SymbolLoaders.scala:343)
> at
> scala.tools.nsc.symtab.SymbolLoaders$SymbolLoader.complete(SymbolLoaders.scala:250)
> at
> scala.tools.nsc.symtab.SymbolLoaders$SymbolLoader.load(SymbolLoaders.scala:269)
> at scala.reflect.internal.Symbols$Symbol.exists
[jira] [Commented] (SPARK-38714) Interval multiplication error
[
https://issues.apache.org/jira/browse/SPARK-38714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564619#comment-17564619
]
Pablo Langa Blanco commented on SPARK-38714:
I have tested it in master and branch 3.3 and it's solved.
> Interval multiplication error
> -
>
> Key: SPARK-38714
> URL: https://issues.apache.org/jira/browse/SPARK-38714
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.3.0
> Environment: branch-3.3, Java 8
>
>Reporter: chong
>Priority: Major
>
> Code gen have error when multipling interval by a decimal.
>
> $SPARK_HOME/bin/spark-shell
>
> import org.apache.spark.sql.Row
> import java.time.Duration
> import java.time.Period
> import org.apache.spark.sql.types._
> val data = Seq(Row(new java.math.BigDecimal("123456789.11")))
> val schema = StructType(Seq(
> StructField("c1", DecimalType(9, 2)),
> ))
> val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
> df.selectExpr("interval '100' second * c1").show(false)
> errors are:
> *{color:#FF}java.lang.AssertionError: assertion failed:{color}*
> Decimal$DecimalIsFractional
> while compiling:
> during phase: globalPhase=terminal, enteringPhase=jvm
> library version: version 2.12.15
> compiler version: version 2.12.15
> reconstructed args: -classpath -Yrepl-class-based -Yrepl-outdir
> /tmp/spark-83a0cda4-dd0b-472e-ad8b-a4b33b85f613/repl-06489815-5366-4aa0-9419-f01abda8d041
> last tree to typer: TypeTree(class Byte)
> tree position: line 6 of
> tree tpe: Byte
> symbol: (final abstract) class Byte in package scala
> symbol definition: final abstract class Byte extends (a ClassSymbol)
> symbol package: scala
> symbol owners: class Byte
> call site: constructor $eval in object $eval in package $line21
> == Source file context for tree position ==
> 3
> 4 object $eval {
> 5 lazy val $result =
> $line21.$read.INSTANCE.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.res0
> 6 lazy val $print: {_}root{_}.java.lang.String = {
> 7 $line21.$read.INSTANCE.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw.$iw
> 8
> 9 ""
> at
> scala.reflect.internal.SymbolTable.throwAssertionError(SymbolTable.scala:185)
> at scala.reflect.internal.Symbols$Symbol.completeInfo(Symbols.scala:1525)
> at scala.reflect.internal.Symbols$Symbol.info(Symbols.scala:1514)
> at scala.reflect.internal.Symbols$Symbol.flatOwnerInfo(Symbols.scala:2353)
> at
> scala.reflect.internal.Symbols$ClassSymbol.companionModule0(Symbols.scala:3346)
> at
> scala.reflect.internal.Symbols$ClassSymbol.companionModule(Symbols.scala:3348)
> at
> scala.reflect.internal.Symbols$ModuleClassSymbol.sourceModule(Symbols.scala:3487)
> at
> scala.reflect.internal.Symbols.$anonfun$forEachRelevantSymbols$1$adapted(Symbols.scala:3802)
> at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
> at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:38)
> at scala.reflect.internal.Symbols.markFlagsCompleted(Symbols.scala:3799)
> at scala.reflect.internal.Symbols.markFlagsCompleted$(Symbols.scala:3805)
> at scala.reflect.internal.SymbolTable.markFlagsCompleted(SymbolTable.scala:28)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.finishSym$1(UnPickler.scala:324)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbol(UnPickler.scala:342)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbolRef(UnPickler.scala:645)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readType(UnPickler.scala:413)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.$anonfun$readSymbol$10(UnPickler.scala:357)
> at scala.reflect.internal.pickling.UnPickler$Scan.at(UnPickler.scala:188)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.readSymbol(UnPickler.scala:357)
> at
> scala.reflect.internal.pickling.UnPickler$Scan.$anonfun$run$1(UnPickler.scala:96)
> at scala.reflect.internal.pickling.UnPickler$Scan.run(UnPickler.scala:88)
> at scala.reflect.internal.pickling.UnPickler.unpickle(UnPickler.scala:47)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.unpickleOrParseInnerClasses(ClassfileParser.scala:1186)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.parseClass(ClassfileParser.scala:468)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.$anonfun$parse$2(ClassfileParser.scala:161)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.$anonfun$parse$1(ClassfileParser.scala:147)
> at
> scala.tools.nsc.symtab.classfile.ClassfileParser.parse(ClassfileParser.scala:130)
> at
> scala.tools.nsc.symtab.SymbolLoaders$ClassfileLoader.doComplete(SymbolLoaders.scala:343)
> at
> scala.tools.nsc.symtab.SymbolLoaders$SymbolLoader.complete(SymbolLoaders.scala:250)
> at
> scala.tools.nsc.symtab.SymbolLoaders$S
[jira] [Assigned] (SPARK-39728) Test for parity of SQL functions between Python and JVM DataFrame API's
[ https://issues.apache.org/jira/browse/SPARK-39728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-39728: Assignee: Apache Spark > Test for parity of SQL functions between Python and JVM DataFrame API's > --- > > Key: SPARK-39728 > URL: https://issues.apache.org/jira/browse/SPARK-39728 > Project: Spark > Issue Type: Improvement > Components: PySpark, Tests >Affects Versions: 3.3.0 >Reporter: Andrew Ray >Assignee: Apache Spark >Priority: Minor > > Add a unit test that compares the available list of Python DataFrame > functions in pyspark.sql.functions with those available in the Scala/Java > DataFrame API in org.apache.spark.sql.functions. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-39728) Test for parity of SQL functions between Python and JVM DataFrame API's
[ https://issues.apache.org/jira/browse/SPARK-39728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-39728: Assignee: (was: Apache Spark) > Test for parity of SQL functions between Python and JVM DataFrame API's > --- > > Key: SPARK-39728 > URL: https://issues.apache.org/jira/browse/SPARK-39728 > Project: Spark > Issue Type: Improvement > Components: PySpark, Tests >Affects Versions: 3.3.0 >Reporter: Andrew Ray >Priority: Minor > > Add a unit test that compares the available list of Python DataFrame > functions in pyspark.sql.functions with those available in the Scala/Java > DataFrame API in org.apache.spark.sql.functions. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-39728) Test for parity of SQL functions between Python and JVM DataFrame API's
[ https://issues.apache.org/jira/browse/SPARK-39728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564618#comment-17564618 ] Apache Spark commented on SPARK-39728: -- User 'aray' has created a pull request for this issue: https://github.com/apache/spark/pull/37144 > Test for parity of SQL functions between Python and JVM DataFrame API's > --- > > Key: SPARK-39728 > URL: https://issues.apache.org/jira/browse/SPARK-39728 > Project: Spark > Issue Type: Improvement > Components: PySpark, Tests >Affects Versions: 3.3.0 >Reporter: Andrew Ray >Priority: Minor > > Add a unit test that compares the available list of Python DataFrame > functions in pyspark.sql.functions with those available in the Scala/Java > DataFrame API in org.apache.spark.sql.functions. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-39728) Test for parity of SQL functions between Python and JVM DataFrame API's
[ https://issues.apache.org/jira/browse/SPARK-39728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Andrew Ray updated SPARK-39728: --- Priority: Minor (was: Major) > Test for parity of SQL functions between Python and JVM DataFrame API's > --- > > Key: SPARK-39728 > URL: https://issues.apache.org/jira/browse/SPARK-39728 > Project: Spark > Issue Type: Improvement > Components: PySpark, Tests >Affects Versions: 3.3.0 >Reporter: Andrew Ray >Priority: Minor > > Add a unit test that compares the available list of Python DataFrame > functions in pyspark.sql.functions with those available in the Scala/Java > DataFrame API in org.apache.spark.sql.functions. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-39728) Test for parity of SQL functions between Python and JVM DataFrame API's
Andrew Ray created SPARK-39728: -- Summary: Test for parity of SQL functions between Python and JVM DataFrame API's Key: SPARK-39728 URL: https://issues.apache.org/jira/browse/SPARK-39728 Project: Spark Issue Type: Improvement Components: PySpark, Tests Affects Versions: 3.3.0 Reporter: Andrew Ray Add a unit test that compares the available list of Python DataFrame functions in pyspark.sql.functions with those available in the Scala/Java DataFrame API in org.apache.spark.sql.functions. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-24815) Structured Streaming should support dynamic allocation
[
https://issues.apache.org/jira/browse/SPARK-24815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564614#comment-17564614
]
Santokh Singh edited comment on SPARK-24815 at 7/9/22 7:45 PM:
---
Pretty much interested in this feature. With {{mapGroupsWithState}} api in
structured streaming, or generic state management and sharing state across
executors, would externalizing state help? I am aware of rocksDB being one way.
was (Author: JIRAUSER292561):
Pretty much interested in this feature. With {{mapGroupsWithState}} api in
structured streaming, or generic state management and sharing state across
executors, would externalizing state help? I am aware rocksDB being one way.
> Structured Streaming should support dynamic allocation
> --
>
> Key: SPARK-24815
> URL: https://issues.apache.org/jira/browse/SPARK-24815
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Spark Core, Structured Streaming
>Affects Versions: 2.3.1
>Reporter: Karthik Palaniappan
>Priority: Minor
>
> For batch jobs, dynamic allocation is very useful for adding and removing
> containers to match the actual workload. On multi-tenant clusters, it ensures
> that a Spark job is taking no more resources than necessary. In cloud
> environments, it enables autoscaling.
> However, if you set spark.dynamicAllocation.enabled=true and run a structured
> streaming job, the batch dynamic allocation algorithm kicks in. It requests
> more executors if the task backlog is a certain size, and removes executors
> if they idle for a certain period of time.
> Quick thoughts:
> 1) Dynamic allocation should be pluggable, rather than hardcoded to a
> particular implementation in SparkContext.scala (this should be a separate
> JIRA).
> 2) We should make a structured streaming algorithm that's separate from the
> batch algorithm. Eventually, continuous processing might need its own
> algorithm.
> 3) Spark should print a warning if you run a structured streaming job when
> Core's dynamic allocation is enabled
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[jira] [Commented] (SPARK-24815) Structured Streaming should support dynamic allocation
[
https://issues.apache.org/jira/browse/SPARK-24815?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564614#comment-17564614
]
Santokh Singh commented on SPARK-24815:
---
Pretty much interested in this feature. With {{mapGroupsWithState}} api in
structured streaming, or generic state management and sharing state across
executors, would externalizing state help? I am aware rocksDB being one way.
> Structured Streaming should support dynamic allocation
> --
>
> Key: SPARK-24815
> URL: https://issues.apache.org/jira/browse/SPARK-24815
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Spark Core, Structured Streaming
>Affects Versions: 2.3.1
>Reporter: Karthik Palaniappan
>Priority: Minor
>
> For batch jobs, dynamic allocation is very useful for adding and removing
> containers to match the actual workload. On multi-tenant clusters, it ensures
> that a Spark job is taking no more resources than necessary. In cloud
> environments, it enables autoscaling.
> However, if you set spark.dynamicAllocation.enabled=true and run a structured
> streaming job, the batch dynamic allocation algorithm kicks in. It requests
> more executors if the task backlog is a certain size, and removes executors
> if they idle for a certain period of time.
> Quick thoughts:
> 1) Dynamic allocation should be pluggable, rather than hardcoded to a
> particular implementation in SparkContext.scala (this should be a separate
> JIRA).
> 2) We should make a structured streaming algorithm that's separate from the
> batch algorithm. Eventually, continuous processing might need its own
> algorithm.
> 3) Spark should print a warning if you run a structured streaming job when
> Core's dynamic allocation is enabled
--
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[jira] [Assigned] (SPARK-39727) Upgrade joda-time from 2.10.13 to 2.10.14
[ https://issues.apache.org/jira/browse/SPARK-39727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-39727: Assignee: Apache Spark > Upgrade joda-time from 2.10.13 to 2.10.14 > - > > Key: SPARK-39727 > URL: https://issues.apache.org/jira/browse/SPARK-39727 > Project: Spark > Issue Type: Improvement > Components: Build >Affects Versions: 3.3.0 >Reporter: BingKun Pan >Assignee: Apache Spark >Priority: Minor > > joda-time 2.10.14 was released, which supports the latest TZ database of > 2022agtz. > release notes: https://www.joda.org/joda-time/changes-report.html#a2.10.14 -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-39727) Upgrade joda-time from 2.10.13 to 2.10.14
[ https://issues.apache.org/jira/browse/SPARK-39727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-39727: Assignee: (was: Apache Spark) > Upgrade joda-time from 2.10.13 to 2.10.14 > - > > Key: SPARK-39727 > URL: https://issues.apache.org/jira/browse/SPARK-39727 > Project: Spark > Issue Type: Improvement > Components: Build >Affects Versions: 3.3.0 >Reporter: BingKun Pan >Priority: Minor > > joda-time 2.10.14 was released, which supports the latest TZ database of > 2022agtz. > release notes: https://www.joda.org/joda-time/changes-report.html#a2.10.14 -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-39727) Upgrade joda-time from 2.10.13 to 2.10.14
[ https://issues.apache.org/jira/browse/SPARK-39727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564573#comment-17564573 ] Apache Spark commented on SPARK-39727: -- User 'panbingkun' has created a pull request for this issue: https://github.com/apache/spark/pull/37143 > Upgrade joda-time from 2.10.13 to 2.10.14 > - > > Key: SPARK-39727 > URL: https://issues.apache.org/jira/browse/SPARK-39727 > Project: Spark > Issue Type: Improvement > Components: Build >Affects Versions: 3.3.0 >Reporter: BingKun Pan >Priority: Minor > > joda-time 2.10.14 was released, which supports the latest TZ database of > 2022agtz. > release notes: https://www.joda.org/joda-time/changes-report.html#a2.10.14 -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-39727) Upgrade joda-time from 2.10.13 to 2.10.14
BingKun Pan created SPARK-39727: --- Summary: Upgrade joda-time from 2.10.13 to 2.10.14 Key: SPARK-39727 URL: https://issues.apache.org/jira/browse/SPARK-39727 Project: Spark Issue Type: Improvement Components: Build Affects Versions: 3.3.0 Reporter: BingKun Pan joda-time 2.10.14 was released, which supports the latest TZ database of 2022agtz. release notes: https://www.joda.org/joda-time/changes-report.html#a2.10.14 -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-39720) Implement tableExists/getTable in SparkR for 3L namespace
[ https://issues.apache.org/jira/browse/SPARK-39720?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Ruifeng Zheng updated SPARK-39720: -- Summary: Implement tableExists/getTable in SparkR for 3L namespace (was: Make createTable/cacheTable/uncacheTable/refreshTable/tableExists in SparkR support 3L namespace) > Implement tableExists/getTable in SparkR for 3L namespace > - > > Key: SPARK-39720 > URL: https://issues.apache.org/jira/browse/SPARK-39720 > Project: Spark > Issue Type: Sub-task > Components: R >Affects Versions: 3.4.0 >Reporter: Ruifeng Zheng >Priority: Major > -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
