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[SPARK-27592][SQL] Set the bucketed data source table SerDe correctly #24486

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[SPARK-27592][SQL] Set the bucketed data source table SerDe correctly #24486

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6ce0a32
Write the write information of this data to metadata.
wangyum Apr 29, 2019
2fdc3a6
Set the bucketed data source table SerDe correctly
wangyum May 4, 2019
4e1dd5c
Spark bucketed data source table always a not bucketed table at Hive …
wangyum May 8, 2019
921bbb0
Merge remote-tracking branch 'upstream/master' into SPARK-27592
wangyum May 23, 2019
26c895a
Check the results.
wangyum May 23, 2019
87b302c
Fix scala style
wangyum May 23, 2019
86e4394
Merge remote-tracking branch 'upstream/master' into SPARK-27592
wangyum Aug 15, 2019
842bd3e
Include the jira id in test name.
wangyum Aug 15, 2019
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7 changes: 4 additions & 3 deletions sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -363,11 +363,12 @@ private[spark] class HiveExternalCatalog(conf: SparkConf, hadoopConf: Configurat
(None, message)

// our bucketing is un-compatible with hive(different hash function)
case _ if table.bucketSpec.nonEmpty =>
case Some(serde) if table.bucketSpec.nonEmpty =>
val message =
s"Persisting bucketed data source table $qualifiedTableName into " +
"Hive metastore in Spark SQL specific format, which is NOT compatible with Hive. "
(None, message)
"Hive metastore in Spark SQL specific format, which is NOT compatible with " +
"Hive bucketed table. But Hive can read this table as a non-bucketed table."
(Some(newHiveCompatibleMetastoreTable(serde)), message)
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@gatorsmile gatorsmile May 22, 2019

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Should we set bucketSpec = None?

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@wangyum wangyum May 23, 2019

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Not necessary:

spark/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala

Lines 1009 to 1023 in fee695d

table.bucketSpec match {
case Some(bucketSpec) if DDLUtils.isHiveTable(table) =>
hiveTable.setNumBuckets(bucketSpec.numBuckets)
hiveTable.setBucketCols(bucketSpec.bucketColumnNames.toList.asJava)
if (bucketSpec.sortColumnNames.nonEmpty) {
hiveTable.setSortCols(
bucketSpec.sortColumnNames
.map(col => new Order(col, HIVE_COLUMN_ORDER_ASC))
.toList
.asJava
)
}
case _ =>
}


case Some(serde) =>
val message =
Expand Down
38 changes: 37 additions & 1 deletion sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
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Expand Up @@ -23,7 +23,7 @@ import org.apache.spark.sql.catalyst.catalog.CatalogTableType
import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
import org.apache.spark.sql.catalyst.plans.logical.SubqueryAlias
import org.apache.spark.sql.hive.test.TestHiveSingleton
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.internal.{HiveSerDe, SQLConf}
import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
import org.apache.spark.sql.types._

Expand Down Expand Up @@ -284,4 +284,40 @@ class DataSourceWithHiveMetastoreCatalogSuite
}

}

test("SPARK-27592 set the bucketed data source table SerDe correctly") {
val provider = "parquet"
withTable("t") {
spark.sql(
s"""
|CREATE TABLE t
|USING $provider
|CLUSTERED BY (c1)
|SORTED BY (c1)
|INTO 2 BUCKETS
|AS SELECT 1 AS c1, 2 AS c2
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@gatorsmile gatorsmile Aug 26, 2019

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Only one row is hard to prove Hive can read it correctly. Could you improve the tests?

In addition, try to create a partitioned and bucked table and see whether they are readable by Hive.

You can create a separate test suite for it.

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@wangyum wangyum Aug 27, 2019

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#25591

""".stripMargin)

val metadata = sessionState.catalog.getTableMetadata(TableIdentifier("t", Some("default")))

val hiveSerDe = HiveSerDe.sourceToSerDe(provider).get
assert(metadata.storage.serde === hiveSerDe.serde)
assert(metadata.storage.inputFormat === hiveSerDe.inputFormat)
assert(metadata.storage.outputFormat === hiveSerDe.outputFormat)

// It's a bucketed table at Spark side
assert(sql("DESC FORMATTED t").collect().containsSlice(
Seq(Row("Num Buckets", "2", ""), Row("Bucket Columns", "[`c1`]", ""))
))
checkAnswer(table("t"), Row(1, 2))

// It's not a bucketed table at Hive side
val hiveSide = sparkSession.metadataHive.runSqlHive("DESC FORMATTED t")
assert(hiveSide.contains("Num Buckets: \t-1 \t "))
assert(hiveSide.contains("Bucket Columns: \t[] \t "))
assert(hiveSide.contains("\tspark.sql.sources.schema.numBuckets\t2 "))
assert(hiveSide.contains("\tspark.sql.sources.schema.bucketCol.0\tc1 "))
assert(sparkSession.metadataHive.runSqlHive("SELECT * FROM t") === Seq("1\t2"))
}
}
}