Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/22009#discussion_r208439490 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala --- @@ -51,18 +58,19 @@ class DataSourceRDD[T: ClassTag]( valuePrepared } - override def next(): T = { + override def next(): Any = { if (!hasNext) { throw new java.util.NoSuchElementException("End of stream") } valuePrepared = false reader.get() } } - new InterruptibleIterator(context, iter) + // TODO: get rid of this type hack. + new InterruptibleIterator(context, iter.asInstanceOf[Iterator[InternalRow]]) --- End diff -- The problem is that, we don't really have a batch API in Spark SQL. We rely on type erasure and codegen hack to implement columnar scan. It's hardcoded in the engine: `SparkPlan#execute` returns `RDD[InternalRow]`. if we have a RDD iterate over the rows in the batch, then whole stage codegen will break, as it iterates the input RDD and cast the record to `ColumnarBatch`.
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