cfmcgrady commented on code in PR #4662:
URL: https://github.com/apache/kyuubi/pull/4662#discussion_r1156847207
##########
externals/kyuubi-spark-sql-engine/src/main/scala/org/apache/spark/sql/kyuubi/SparkDatasetHelper.scala:
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@@ -17,18 +17,95 @@
package org.apache.spark.sql.kyuubi
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.TaskContext
+import org.apache.spark.network.util.{ByteUnit, JavaUtils}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.execution.{CollectLimitExec, SparkPlan,
SQLExecution}
+import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec
+import org.apache.spark.sql.execution.arrow.{ArrowCollectUtils,
ArrowConverters, KyuubiArrowUtils}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
+import org.apache.kyuubi.engine.spark.KyuubiSparkUtil
import org.apache.kyuubi.engine.spark.schema.RowSet
object SparkDatasetHelper {
+
def toArrowBatchRdd[T](ds: Dataset[T]): RDD[Array[Byte]] = {
ds.toArrowBatchRdd
}
+ /**
+ * Forked from [[Dataset.toArrowBatchRdd(plan: SparkPlan)]].
+ * Convert to an RDD of serialized ArrowRecordBatches.
+ */
+ def toArrowBatchRdd(plan: SparkPlan): RDD[Array[Byte]] = {
+ val schemaCaptured = plan.schema
+ val maxRecordsPerBatch =
plan.session.sessionState.conf.arrowMaxRecordsPerBatch
+ val timeZoneId = plan.session.sessionState.conf.sessionLocalTimeZone
+ plan.execute().mapPartitionsInternal { iter =>
+ val context = TaskContext.get()
+ ArrowConverters.toBatchIterator(
+ iter,
+ schemaCaptured,
+ maxRecordsPerBatch,
+ timeZoneId,
+ context)
+ }
+ }
+
+ def doCollectLimit(collectLimit: CollectLimitExec): Array[Array[Byte]] = {
+ val timeZoneId =
collectLimit.session.sessionState.conf.sessionLocalTimeZone
+ val maxRecordsPerBatch =
collectLimit.session.sessionState.conf.arrowMaxRecordsPerBatch
+
+ val batches = ArrowCollectUtils.takeAsArrowBatches(
+ collectLimit,
+ maxRecordsPerBatch,
+ maxBatchSize,
+ timeZoneId)
+
+ // note that the number of rows in the returned arrow batches may be >=
`limit`, preform
+ // the slicing operation of result
+ val result = ArrayBuffer[Array[Byte]]()
+ var i = 0
+ var rest = collectLimit.limit
+ while (i < batches.length && rest > 0) {
+ val (batch, size) = batches(i)
+ if (size <= rest) {
+ result += batch
+ // returned ArrowRecordBatch has less than `limit` row count, safety
to do conversion
+ rest -= size.toInt
+ } else { // size > rest
+ result += KyuubiArrowUtils.slice(collectLimit.schema, timeZoneId,
batch, 0, rest)
+ rest = 0
+ }
+ i += 1
+ }
+ result.toArray
+ }
+
+ def executeCollect(df: DataFrame): Array[Array[Byte]] =
withNewExecutionId(df) {
+ executeArrowBatchCollect(df.queryExecution.executedPlan)
+ }
+
+ def toArrowBatchLocalIterator(df: DataFrame): Iterator[Array[Byte]] = {
+ withNewExecutionId(df) {
+ toArrowBatchRdd(df).toLocalIterator
+ }
+ }
+
+ def executeArrowBatchCollect: SparkPlan => Array[Array[Byte]] = {
+ case adaptiveSparkPlan: AdaptiveSparkPlanExec =>
+ executeArrowBatchCollect(adaptiveSparkPlan.finalPhysicalPlan)
Review Comment:
the `AdaptiveSparkPlanExec.finalPhysicalPlan` function was introduced in
[SPARK-41914](https://github.com/apache/spark/pull/39431), and it may present
compatibility issues if the underlying Spark runtime lacks the corresponding
patch.
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