Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/18747#discussion_r144757619 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryTableScanExec.scala --- @@ -23,21 +23,53 @@ import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning} -import org.apache.spark.sql.execution.LeafExecNode -import org.apache.spark.sql.execution.metric.SQLMetrics +import org.apache.spark.sql.execution.{ColumnarBatchScan, LeafExecNode} +import org.apache.spark.sql.execution.vectorized._ import org.apache.spark.sql.types.UserDefinedType case class InMemoryTableScanExec( attributes: Seq[Attribute], predicates: Seq[Expression], @transient relation: InMemoryRelation) - extends LeafExecNode { + extends LeafExecNode with ColumnarBatchScan { override protected def innerChildren: Seq[QueryPlan[_]] = Seq(relation) ++ super.innerChildren - override lazy val metrics = Map( - "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows")) + override def vectorTypes: Option[Seq[String]] = + Option(Seq.fill(attributes.length)(classOf[OnHeapColumnVector].getName)) + + override val columnIndexes = + attributes.map(a => relation.output.map(o => o.exprId).indexOf(a.exprId)).toArray + + override val supportCodegen: Boolean = relation.useColumnarBatches + + private def createAndDecompressColumn(cachedColumnarBatch: CachedBatch): ColumnarBatch = { + val rowCount = cachedColumnarBatch.numRows + val schema = cachedColumnarBatch.schema + val columnVectors = OnHeapColumnVector.allocateColumns(rowCount, schema) + val columnarBatch = new ColumnarBatch( + schema, columnVectors.asInstanceOf[Array[ColumnVector]], rowCount) + columnarBatch.setNumRows(rowCount) + + for (i <- 0 until cachedColumnarBatch.buffers.length) { + ColumnAccessor.decompress( + cachedColumnarBatch.buffers(i), columnarBatch.column(i).asInstanceOf[WritableColumnVector], + schema.fields(i).dataType, rowCount) + } + return columnarBatch + } + + override def inputRDDs(): Seq[RDD[InternalRow]] = { + if (supportCodegen) { + val buffers = relation.cachedColumnBuffers + // HACK ALERT: This is actually an RDD[ColumnarBatch]. + // We're taking advantage of Scala's type erasure here to pass these batches along. + Seq(buffers.map(createAndDecompressColumn(_)).asInstanceOf[RDD[InternalRow]]) --- End diff -- I think we need to apply column pruning here, instead of adding `columnIndexes` and ask `ColumnarBatchScan` to do it.
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