Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/11435#discussion_r54939033
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala ---
@@ -149,14 +149,55 @@ private[sql] case class PhysicalRDD(
ctx.INPUT_ROW = row
ctx.currentVars = null
val columns = exprs.map(_.gen(ctx))
+
+ // The input RDD can either return (all) ColumnarBatches or
InternalRows. We determine this
+ // by looking at the first value of the RDD and then calling the
function which will process
+ // the remaining. It is faster to return batches.
+ // TODO: The abstractions between this class and SqlNewHadoopRDD makes
it difficult to know
+ // here which path to use. Fix this.
+
+ val columnarBatchClz =
"org.apache.spark.sql.execution.vectorized.ColumnarBatch"
+
+ val scanBatches = ctx.freshName("processBatches")
+ ctx.addNewFunction(scanBatches,
+ s"""
+ | private void $scanBatches($columnarBatchClz batch) throws
java.io.IOException {
+ | while (true) {
+ | int numRows = batch.numRows();
+ | $numOutputRows.add(numRows);
+ | for (int i = 0; i < numRows; i++) {
+ | InternalRow $row = batch.getRow(i);
+ | ${columns.map(_.code).mkString("\n").trim}
+ | ${consume(ctx, columns).trim}
--- End diff --
We are going increase the number of buffered rows, it may blowed up when
joining.
I think we should break the loop here, if we get enough rows.
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