Chungmin created SPARK-35287:
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Summary: RemoveRedundantProjects removes non-redundant projects
Key: SPARK-35287
URL: https://issues.apache.org/jira/browse/SPARK-35287
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.1.1
Reporter: Chungmin
RemoveRedundantProjects erroneously removes non-redundant projects which are
required to convert rows coming from DataSourceV2ScanExec to UnsafeRow. There
is a code for this case, but it only looks at the child. The bug occurs when
DataSourceV2ScanExec is not a child of the project, but a descendant. The
method {{isRedundant}} in {{RemoveRedundantProjects}} should be updated to
account for descendants too.
The original scenario requires Iceberg to reproduce the issue. In theory, it
should be able to reproduce the bug with Spark SQL only, and someone more
knowledgeable with Spark SQL should be able to make such a scenario. The
following is my reproduction scenario (Spark 3.1.1, Iceberg 0.11.1):
{code:java}
import scala.collection.JavaConverters._
import org.apache.iceberg.{PartitionSpec, TableProperties}
import org.apache.iceberg.hadoop.HadoopTables
import org.apache.iceberg.spark.SparkSchemaUtil
import org.apache.spark.sql.{DataFrame, QueryTest, SparkSession}
import org.apache.spark.sql.internal.SQLConf
class RemoveRedundantProjectsTest extends QueryTest {
override val spark: SparkSession = SparkSession
.builder()
.master("local[4]")
.config("spark.driver.bindAddress", "127.0.0.1")
.appName(suiteName)
.getOrCreate()
test("RemoveRedundantProjects removes non-redundant projects") {
withSQLConf(
SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false",
SQLConf.REMOVE_REDUNDANT_PROJECTS_ENABLED.key -> "true") {
withTempDir { dir =>
val path = dir.getCanonicalPath
val data: DataFrame = spark.range(3).toDF
val table = new HadoopTables().create(
SparkSchemaUtil.convert(data.schema),
PartitionSpec.unpartitioned(),
Map(TableProperties.WRITE_NEW_DATA_LOCATION -> path).asJava,
path)
data.write.format("iceberg").mode("overwrite").save(path)
table.refresh()
val df = spark.read.format("iceberg").load(path)
val dfX = df.as("x")
val dfY = df.as("y")
val join = dfX.filter(dfX("id") > 0).join(dfY, "id")
join.explain("extended")
assert(join.count() == 2)
}
}
}
}
{code}
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