Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/14030#discussion_r69823441
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ForeachSink.scala
---
@@ -30,7 +32,42 @@ import org.apache.spark.sql.{DataFrame, Encoder,
ForeachWriter}
class ForeachSink[T : Encoder](writer: ForeachWriter[T]) extends Sink with
Serializable {
override def addBatch(batchId: Long, data: DataFrame): Unit = {
- data.as[T].foreachPartition { iter =>
+ // TODO: Refine this method when SPARK-16264 is resolved; see comments
below.
+
+ // This logic should've been as simple as:
+ // ```
+ // data.as[T].foreachPartition { iter => ... }
+ // ```
+ //
+ // Unfortunately, doing that would just break the incremental planing.
The reason is,
+ // `Dataset.foreachPartition()` would further call `Dataset.rdd()`,
but `Dataset.rdd()` just
+ // does not support `IncrementalExecution`.
+ //
+ // So as a provisional fix, below we've made a special version of
`Dataset` with its `rdd()`
+ // method supporting incremental planning. But in the long run, we
should generally make newly
+ // created Datasets use `IncrementalExecution` where necessary (which
is SPARK-16264 tries to
+ // resolve).
+
+ val dataAsT = data.as[T]
+ val datasetWithIncrementalExecution =
+ new Dataset(data.sparkSession, dataAsT.logicalPlan, dataAsT.encoder)
{
--- End diff --
`dataAsT` can be removed. You can use `implicitly[Encoder[T]]` to get the
encoder. Please also revert the change to Dataset.
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