[GitHub] [kyuubi] cfmcgrady commented on a diff in pull request #4662: [ARROW] Arrow serialization should not introduce extra shuffle for outermost limit

["cfmcgrady (via GitHub)" <gi...@apache.org>]

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:
##########
@@ -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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