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[SPARK-29490][SQL] Reset 'WritableColumnVector' in 'RowToColumnarExec' #26137

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[SPARK-29490][SQL] Reset 'WritableColumnVector' in 'RowToColumnarExec' #26137

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7b9036b
reset vectors in RowToColumnarExec
marin-ma Oct 16, 2019
97631bd
add unit test 'reset column vectors'
marin-ma Oct 17, 2019
114a743
Typo
marin-ma Oct 18, 2019
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1 change: 1 addition & 0 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/Columnar.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -454,6 +454,7 @@ case class RowToColumnarExec(child: SparkPlan) extends UnaryExecNode {

override def next(): ColumnarBatch = {
cb.setNumRows(0)
vectors.foreach(_.reset())
var rowCount = 0
while (rowCount < numRows && rowIterator.hasNext) {
val row = rowIterator.next()
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25 changes: 25 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/SparkSessionExtensionSuite.scala
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Expand Up @@ -28,6 +28,7 @@ import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan,
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution._
import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector
import org.apache.spark.sql.internal.SQLConf.COLUMN_BATCH_SIZE
import org.apache.spark.sql.internal.StaticSQLConf.SPARK_SESSION_EXTENSIONS
import org.apache.spark.sql.types.{DataType, Decimal, IntegerType, LongType, Metadata, StructType}
import org.apache.spark.sql.vectorized.{ColumnarArray, ColumnarBatch, ColumnarMap, ColumnVector}
Expand Down Expand Up @@ -171,6 +172,30 @@ class SparkSessionExtensionSuite extends SparkFunSuite {
}
}

test("reset column vectors") {
val session = SparkSession.builder()
.master("local[1]")
.config(COLUMN_BATCH_SIZE.key, 2)
.withExtensions { extensions =>
extensions.injectColumnar(session =>
MyColumarRule(PreRuleReplaceAddWithBrokenVersion(), MyPostRule())) }
.getOrCreate()

try {
assert(session.sessionState.columnarRules.contains(
MyColumarRule(PreRuleReplaceAddWithBrokenVersion(), MyPostRule())))
import session.sqlContext.implicits._

val input = Seq((100L), (200L), (300L))
val data = input.toDF("vals").repartition(1)
val df = data.selectExpr("vals + 1")
val result = df.collect()
assert(result sameElements input.map(x => Row(x + 2)))
} finally {
stop(session)
}
}

test("use custom class for extensions") {
val session = SparkSession.builder()
.master("local[1]")
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