chaoqin-li1123 commented on code in PR #45432:
URL: https://github.com/apache/spark/pull/45432#discussion_r1522535875
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sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSink.scala:
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@@ -22,19 +22,38 @@ import scala.util.control.NonFatal
import org.apache.spark.{SparkException, SparkThrowable}
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.catalyst.plans.logical.{Aggregate, Deduplicate,
DeduplicateWithinWatermark, Distinct, FlatMapGroupsInPandasWithState,
FlatMapGroupsWithState, GlobalLimit, Join, LogicalPlan, TransformWithState}
import org.apache.spark.sql.execution.LogicalRDD
import org.apache.spark.sql.execution.streaming.Sink
import org.apache.spark.sql.streaming.DataStreamWriter
class ForeachBatchSink[T](batchWriter: (Dataset[T], Long) => Unit, encoder:
ExpressionEncoder[T])
extends Sink {
+ private def isQueryStateful(logicalPlan: LogicalPlan): Boolean = {
+ logicalPlan.collect {
+ case node @ (_: Aggregate | _: Distinct | _: FlatMapGroupsWithState
+ | _: FlatMapGroupsInPandasWithState | _: TransformWithState
| _: Deduplicate
+ | _: DeduplicateWithinWatermark | _: GlobalLimit) if
node.isStreaming => node
+ case node @ Join(left, right, _, _, _) if left.isStreaming &&
right.isStreaming => node
+ }.nonEmpty
+ }
+
override def addBatch(batchId: Long, data: DataFrame): Unit = {
val node = LogicalRDD.fromDataset(rdd = data.queryExecution.toRdd,
originDataset = data,
isStreaming = false)
implicit val enc = encoder
val ds = Dataset.ofRows(data.sparkSession, node).as[T]
- callBatchWriter(ds, batchId)
+ // SPARK-47329 - persist the dataframe for stateful queries to prevent
state stores
+ // from reloading state multiple times in each batch
+ val isStateful = isQueryStateful(data.logicalPlan)
+ if (isStateful) {
+ ds.persist()
+ callBatchWriter(ds, batchId)
+ ds.unpersist()
Review Comment:
Yes, try finally is sufficient.
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