[SPARK-29869][SQL] improve error message in HiveMetastoreCatalog#convertToLogicalRelation #26499

LantaoJin · 2019-11-13T08:28:10Z

What changes were proposed in this pull request?

In our production, HiveMetastoreCatalog#convertToLogicalRelation throws AssertError occasionally:

scala> spark.table("hive_table").show
java.lang.AssertionError: assertion failed
  at scala.Predef$.assert(Predef.scala:208)
  at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:261)
  at org.apache.spark.sql.hive.HiveMetastoreCatalog.convert(HiveMetastoreCatalog.scala:137)
  at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:220)
  at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:207)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$4(AnalysisHelper.scala:113)
  at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:376)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:214)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:374)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:327)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:113)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
  at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
  at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:207)
  at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:191)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:130)
  at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
  at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
  at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:49)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:127)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:119)
  at scala.collection.immutable.List.foreach(List.scala:392)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:119)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:168)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:162)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:122)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:98)
  at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:98)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:146)
  at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:145)
  at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:66)
  at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
  at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:63)
  at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:63)
  at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:55)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:86)
  at org.apache.spark.sql.SparkSession.table(SparkSession.scala:585)
  at org.apache.spark.sql.SparkSession.table(SparkSession.scala:581)
  ... 47 elided

Most of cases occurred in reading a table which created by an old Spark version.
After recreated the table, the issue will be gone.

After deep dive, the root cause is this external table is a non-partitioned table but the LOCATION set to a partitioned path {{/tablename/dt=yyyymmdd}}. The partitionSpec is inferred.

Why are the changes needed?

Above error message is very confused. We need more details about assert failure information.

This issue caused by PartitioningAwareFileIndex#inferPartitioning(). For non-HiveMetastore Spark, it's useful. But for Hive table, it shouldn't infer partition if Hive tell us it's a non partitioned table. (new added)

Does this PR introduce any user-facing change?

No

How was this patch tested?

Add UT.

…oreCatalog#convertToLogicalRelation throws AssertionError

LantaoJin · 2019-11-13T08:31:18Z

cc @wangyum , more input?

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala

SparkQA · 2019-11-13T10:53:48Z

Test build #113688 has finished for PR 26499 at commit 1071b50.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-11-14T05:40:24Z

Test build #113750 has finished for PR 26499 at commit 1c98c38.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

LantaoJin · 2019-11-14T07:25:20Z

This issue caused by PartitioningAwareFileIndex#inferPartitioning(). For non-HiveMetastore Spark (maybe databricks runtime), it's useful. But for Hive table, it shouldn't infer partition if Hive tell us it's a non partitioned table. I think it should be fix by adding a variable in DataSource#resolveRelation.

SparkQA · 2019-11-14T08:05:02Z

Test build #113758 has finished for PR 26499 at commit 363afb6.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

wangyum · 2019-11-14T09:45:57Z

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

-      withTable("test") {
-        withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "true",
-          SQLConf.PARALLEL_PARTITION_DISCOVERY_THRESHOLD.key -> "0") {
+      val partitionLikeDir = s"${dir.getCanonicalFile.toURI.toString}/test/dt=20191113"


val partitionLikeDir = s"${dir.getCanonicalFile.toURI.toString}/test/dt=20191113" -> val partitionLikeDir = s"$baseDir/dt=20191113"?

wangyum · 2019-11-14T09:46:19Z

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

-  test("SPARK-29869: HiveMetastoreCatalog#convertToLogicalRelation throws AssertionError") {
+  test("SPARK-29869: fix HiveMetastoreCatalog#convertToLogicalRelation throws AssertionError") {
    withTempPath(dir => {
      val baseDir = s"${dir.getCanonicalFile.toURI.toString}/test"


test -> non_partition_table ?

the location can be set to anywhere. test here is fine. non_partition_table will over 100 chars :)

LantaoJin · 2019-11-14T09:49:32Z

Soft ping @cloud-fan @HyukjinKwon @wangyum @viirya

wangyum · 2019-11-14T09:50:49Z

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

-          val e = intercept[AssertionError](spark.sql("select * from test")).getMessage
-          assert(e.contains("assertion failed"))
+          assert(spark.sql("select * from non_partition_table").collect() ===
+            Array(Row(0), Row(1), Row(2)))


assert(spark.sql("select * from non_partition_table").collect() === Array(Row(0), Row(1), Row(2))) -> checkAnswer(spark.table("non_partition_table"), Row(0, 1, 2))?

ok. I wanna wait more comments to update in one.

cloud-fan · 2019-11-14T10:09:13Z

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

+        withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "true") {
+          spark.sql(
+            s"""
+               |CREATE TABLE non_partition_table (id bigint)


Isn't it a malformed table? Does hive ignore the directories for non-partitioned tables?

Seems Hive return none when query this table(1.2.1):

hive> select * from xxxxx.xxxx;
OK
Time taken: 25.301 seconds

But no assertion error

Maybe you are right. Actually the table LOCATION is /path/tablename/dt=yyyymmdd, But its data file paths are /path/tablename/dt=yyyymmdd/dt=yyyymmdd/xxx.parquet. I guess Hive does not recursively lookup load the data. So it return empty but not error.
And I found if when enable recursively lookup by .option("recursiveFileLookup", true), the inferPartitioning will be disable. So dt=yyyymmdd won't be treated as partitionSpec.

So should I revert the code changes and only keep the assert detail information? Or throws exception instead of assertion, and catch it then rollback to do not use built-in Parquet reader to read?

SparkQA · 2019-11-14T11:44:55Z

Test build #113773 has finished for PR 26499 at commit 46c16c0.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala

LantaoJin · 2019-11-15T04:29:40Z

Since this issue table has a nest data path:
LOCATION is /path/tablename/dt=20191113. Data files are under /path/tablename/dt=20191113/dt=20191113/xxx.parquet.
By default, Spark can not recursively lookup the data files. So the UT I added can not return correct result. But there is no assertion error now. I open #26525 to enable recursively lookup in SQL.

But once recursively lookup enabled, the inferPartitioning will be disabled in current implementation. So this problem here can not be reproduced.

So should we trim out the inferring partition cols for Hive table, anyhow? Or just throws a CanNotConvertException to handle it in upper layer?

SparkQA · 2019-11-15T05:51:41Z

Test build #113839 has finished for PR 26499 at commit 1df7b8d.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

wangyum · 2019-11-15T06:48:27Z

+1 for throws a CanNotConvertException.

SparkQA · 2019-11-15T11:14:25Z

Test build #113866 has finished for PR 26499 at commit ad71656.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

cloud-fan · 2019-11-18T08:46:02Z

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala

-    assert(result.output.length == relation.output.length &&
-      result.output.zip(relation.output).forall { case (a1, a2) => a1.dataType == a2.dataType })
+    if (result.output.length != relation.output.length) {
+      throw new HiveTableConvertException(


shall we just throw AnalysisException? It's not in the runtime stage yet.

cloud-fan · 2019-11-18T08:47:12Z

sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala

+               |STORED AS PARQUET LOCATION '$baseDir'
+               |""".stripMargin)
+          val e = intercept[HiveTableConvertException](
+            spark.table("non_partition_table"), Seq()).getMessage


nit: what does Seq() mean here?

viirya · 2019-11-18T23:08:57Z

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala

+        s"or recreate table ${relation.tableMeta.identifier} to workaround.")
+    }
+    if (!result.output.zip(relation.output).forall {
+          case (a1, a2) => a1.dataType.sameType(a2.dataType) }) {


This is a breaking change. We allow inferred schema may have different field names. sameType requires same field names.

Ok, I see. It restricts the field names by l.name == r.name. Sorry for that, I will revert this.

SparkQA · 2019-11-19T05:23:17Z

Test build #114048 has finished for PR 26499 at commit 2efaafc.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-11-19T05:34:47Z

Test build #114051 has finished for PR 26499 at commit 3288ffd.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

cloud-fan · 2019-11-19T07:22:25Z

thanks, merging to master!

…ertToLogicalRelation In our production, HiveMetastoreCatalog#convertToLogicalRelation throws AssertError occasionally: ```sql scala> spark.table("hive_table").show java.lang.AssertionError: assertion failed at scala.Predef$.assert(Predef.scala:208) at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:261) at org.apache.spark.sql.hive.HiveMetastoreCatalog.convert(HiveMetastoreCatalog.scala:137) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:220) at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:207) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$4(AnalysisHelper.scala:113) at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:376) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:214) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:374) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:327) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:113) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:207) at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:191) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:130) at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60) at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68) at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:49) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:127) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:119) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:119) at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:168) at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:162) at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:122) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:98) at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88) at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:98) at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:146) at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201) at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:145) at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:66) at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111) at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:63) at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:63) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:55) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:86) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:585) at org.apache.spark.sql.SparkSession.table(SparkSession.scala:581) ... 47 elided ```` Most of cases occurred in reading a table which created by an old Spark version. After recreated the table, the issue will be gone. After deep dive, the root cause is this external table is a non-partitioned table but the `LOCATION` set to a partitioned path {{/tablename/dt=yyyymmdd}}. The partitionSpec is inferred. Above error message is very confused. We need more details about assert failure information. This issue caused by `PartitioningAwareFileIndex#inferPartitioning()`. For non-HiveMetastore Spark, it's useful. But for Hive table, it shouldn't infer partition if Hive tell us it's a non partitioned table. (new added) No Add UT. Closes apache#26499 from LantaoJin/SPARK-29869. Authored-by: LantaoJin <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> RB=2281940 BUG=LIHADOOP-55655 G=spark-reviewers R=ekrogen,vsowrira A=mmuralid,ekrogen,vsowrira

LantaoJin added 2 commits November 13, 2019 16:13

[SPARK-29869][SQL] Make error message more meaningful when HiveMetast…

dd1efc8

…oreCatalog#convertToLogicalRelation throws AssertionError

add suggestion

1071b50

wangyum reviewed Nov 13, 2019

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sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala Show resolved Hide resolved

dongjoon-hyun added the SQL label Nov 13, 2019

add ut

1c98c38

fix

363afb6

LantaoJin changed the title ~~[SPARK-29869][SQL] Make error message more meaningful when HiveMetastoreCatalog#convertToLogicalRelation throws AssertionError~~ [SPARK-29869][SQL] Fix the problem of HiveMetastoreCatalog#convertToLogicalRelation throws AssertionError Nov 14, 2019

fix

46c16c0

wangyum reviewed Nov 14, 2019

View reviewed changes

LantaoJin changed the title ~~[SPARK-29869][SQL] Fix the problem of HiveMetastoreCatalog#convertToLogicalRelation throws AssertionError~~ [SPARK-29869][SQL] Fix assertion error in HiveMetastoreCatalog#convertToLogicalRelation Nov 14, 2019

wangyum reviewed Nov 14, 2019

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cloud-fan reviewed Nov 14, 2019

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viirya reviewed Nov 15, 2019

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sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala Show resolved Hide resolved

fix ut

1df7b8d

LantaoJin added 2 commits November 15, 2019 16:57

address comments

9f29abc

remove useless imports

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cloud-fan reviewed Nov 18, 2019

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viirya reviewed Nov 18, 2019

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LantaoJin added 2 commits November 19, 2019 11:04

address comments

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revert sameType

3288ffd

cloud-fan changed the title ~~[SPARK-29869][SQL] Fix assertion error in HiveMetastoreCatalog#convertToLogicalRelation~~ [SPARK-29869][SQL] improve error message in HiveMetastoreCatalog#convertToLogicalRelation Nov 19, 2019

cloud-fan closed this in 5ac37a8 Nov 19, 2019

[SPARK-29869][SQL] improve error message in HiveMetastoreCatalog#convertToLogicalRelation #26499

[SPARK-29869][SQL] improve error message in HiveMetastoreCatalog#convertToLogicalRelation #26499

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Does this PR introduce any user-facing change?

How was this patch tested?

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