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

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

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[SPARK-29869][SQL] Make error message more meaningful when HiveMetast…
LantaoJin Nov 13, 2019
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LantaoJin Nov 13, 2019
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LantaoJin Nov 14, 2019
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LantaoJin Nov 14, 2019
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LantaoJin Nov 14, 2019
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LantaoJin Nov 15, 2019
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LantaoJin Nov 15, 2019
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LantaoJin Nov 15, 2019
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LantaoJin Nov 19, 2019
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LantaoJin Nov 19, 2019
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18 changes: 15 additions & 3 deletions sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ import org.apache.hadoop.fs.Path

import org.apache.spark.SparkException
import org.apache.spark.internal.Logging
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.{AnalysisException, SparkSession}
import org.apache.spark.sql.catalyst.{QualifiedTableName, TableIdentifier}
import org.apache.spark.sql.catalyst.catalog._
import org.apache.spark.sql.catalyst.plans.logical._
Expand Down Expand Up @@ -257,8 +257,20 @@ private[hive] class HiveMetastoreCatalog(sparkSession: SparkSession) extends Log
}
// The inferred schema may have different field names as the table schema, we should respect
// it, but also respect the exprId in table relation output.
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 AnalysisException(
s"Converted table has ${result.output.length} columns, " +
s"but source Hive table has ${relation.output.length} columns. " +
s"Set ${HiveUtils.CONVERT_METASTORE_PARQUET.key} to false, " +
s"or recreate table ${relation.tableMeta.identifier} to workaround.")
}
if (!result.output.zip(relation.output).forall {
case (a1, a2) => a1.dataType == a2.dataType }) {
throw new AnalysisException(
s"Column in converted table has different data type with source Hive table's. " +
s"Set ${HiveUtils.CONVERT_METASTORE_PARQUET.key} to false, " +
s"or recreate table ${relation.tableMeta.identifier} to workaround.")
}
val newOutput = result.output.zip(relation.output).map {
case (a1, a2) => a1.withExprId(a2.exprId)
}
Expand Down
22 changes: 21 additions & 1 deletion sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
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Expand Up @@ -17,7 +17,7 @@

package org.apache.spark.sql.hive

import org.apache.spark.sql.{QueryTest, Row, SaveMode}
import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SaveMode}
import org.apache.spark.sql.catalyst.{AliasIdentifier, TableIdentifier}
import org.apache.spark.sql.catalyst.catalog.CatalogTableType
import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
Expand Down Expand Up @@ -358,4 +358,24 @@ class DataSourceWithHiveMetastoreCatalogSuite
Seq(table("src").count().toString))
}
}

test("SPARK-29869: Fix convertToLogicalRelation throws unclear AssertionError") {
withTempPath(dir => {
val baseDir = s"${dir.getCanonicalFile.toURI.toString}/non_partition_table"
val partitionLikeDir = s"$baseDir/dt=20191113"
spark.range(3).selectExpr("id").write.parquet(partitionLikeDir)
withTable("non_partition_table") {
withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "true") {
spark.sql(
s"""
|CREATE TABLE non_partition_table (id bigint)
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@cloud-fan cloud-fan Nov 14, 2019

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

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@LantaoJin LantaoJin Nov 14, 2019

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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

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@LantaoJin LantaoJin Nov 14, 2019 •
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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?

|STORED AS PARQUET LOCATION '$baseDir'
|""".stripMargin)
val e = intercept[AnalysisException](
spark.table("non_partition_table")).getMessage
assert(e.contains("Converted table has 2 columns, but source Hive table has 1 columns."))
}
}
})
}
}