Merge remote-tracking branch 'origin/branch-23.10' into column-overfl…

…ow-retry

docs/additional-functionality/rapids-shuffle.md

@@ -28,6 +28,7 @@ in our plugin:
 | 3.3.0           | com.nvidia.spark.rapids.spark330.RapidsShuffleManager    |
 | 3.3.1           | com.nvidia.spark.rapids.spark331.RapidsShuffleManager    |
 | 3.3.2           | com.nvidia.spark.rapids.spark332.RapidsShuffleManager    |
+| 3.3.3           | com.nvidia.spark.rapids.spark333.RapidsShuffleManager    |
 | 3.4.0           | com.nvidia.spark.rapids.spark340.RapidsShuffleManager    |
 | 3.4.1           | com.nvidia.spark.rapids.spark341.RapidsShuffleManager    |
 | Databricks 10.4 | com.nvidia.spark.rapids.spark321db.RapidsShuffleManager  |

pom.xml

@@ -605,7 +605,7 @@
         <spark330.version>3.3.0</spark330.version>
         <spark331.version>3.3.1</spark331.version>
         <spark332.version>3.3.2</spark332.version>
-        <spark333.version>3.3.3-SNAPSHOT</spark333.version>
+        <spark333.version>3.3.3</spark333.version>
         <spark340.version>3.4.0</spark340.version>
         <spark341.version>3.4.1</spark341.version>
         <spark330cdh.version>3.3.0.3.3.7180.0-274</spark330cdh.version>
@@ -651,12 +651,12 @@
             330,
             331,
             332,
+            333,
             330cdh,
             340,
             341
         </noSnapshot.buildvers>
         <snapshot.buildvers>
-            333
         </snapshot.buildvers>
         <databricks.buildvers>
             321db,

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuColumnarToRowExec.scala

@@ -20,8 +20,9 @@ import scala.annotation.tailrec
 import scala.collection.mutable.Queue
 
 import ai.rapids.cudf.{Cuda, HostColumnVector, NvtxColor, Table}
-import com.nvidia.spark.rapids.Arm.withResource
+import com.nvidia.spark.rapids.Arm.{closeOnExcept, withResource}
 import com.nvidia.spark.rapids.RapidsPluginImplicits._
+import com.nvidia.spark.rapids.RmmRapidsRetryIterator.splitSpillableInHalfByRows
 import com.nvidia.spark.rapids.ScalableTaskCompletion.onTaskCompletion
 import com.nvidia.spark.rapids.shims.ShimUnaryExecNode
 
@@ -73,7 +74,12 @@ class AcceleratedColumnarToRowIterator(
   private var at: Int = 0
   private var total: Int = 0
 
-  onTaskCompletion(closeAllPendingBatches())
+  // Don't install the callback if in a unit test
+  Option(TaskContext.get()).foreach { tc =>
+    onTaskCompletion(tc) {
+      closeAllPendingBatches()
+    }
+  }
 
   private def setCurrentBatch(wip: HostColumnVector): Unit = {
     currentCv = Some(wip)
@@ -100,34 +106,42 @@ class AcceleratedColumnarToRowIterator(
     new Table(rearrangedColumns : _*)
   }
 
-  private[this] def setupBatch(cb: ColumnarBatch): Boolean = {
+  private[this] def setupBatchAndClose(scb: SpillableColumnarBatch): Boolean = {
     numInputBatches += 1
     // In order to match the numOutputRows metric in the generated code we update
     // numOutputRows for each batch. This is less accurate than doing it at output
     // because it will over count the number of rows output in the case of a limit,
     // but it is more efficient.
-    numOutputRows += cb.numRows()
-    if (cb.numRows() > 0) {
+    numOutputRows += scb.numRows()
+    if (scb.numRows() > 0) {
       withResource(new NvtxWithMetrics("ColumnarToRow: batch", NvtxColor.RED, opTime)) { _ =>
-        withResource(rearrangeRows(cb)) { table =>
-          // The fixed-width optimized cudf kernel only supports up to 1.5 KB per row which means at
-          // most 184 double/long values. Spark by default limits codegen to 100 fields
-          // "spark.sql.codegen.maxFields". So, we are going to be cautious and start with that
-          // until we have tested it more. We branching over the size of the output to know which
-          // kernel to call. If schema.length < 100 we call the fixed-width optimized version,
-          // otherwise the generic one
-          withResource(if (schema.length < 100) {
-            table.convertToRowsFixedWidthOptimized()
-          } else {
-            table.convertToRows()
-          }) { rowsCvList =>
+        val it = RmmRapidsRetryIterator.withRetry(scb, splitSpillableInHalfByRows) { attempt =>
+          withResource(attempt.getColumnarBatch()) { attemptCb =>
+            withResource(rearrangeRows(attemptCb)) { table =>
+              // The fixed-width optimized cudf kernel only supports up to 1.5 KB per row which
+              // means at most 184 double/long values. Spark by default limits codegen to 100
+              // fields "spark.sql.codegen.maxFields". So, we are going to be cautious and
+              // start with that until we have tested it more. We branching over the size of
+              // the output to know which kernel to call. If schema.length < 100 we call the
+              // fixed-width optimized version, otherwise the generic one
+              if (schema.length < 100) {
+                table.convertToRowsFixedWidthOptimized()
+              } else {
+                table.convertToRows()
+              }
+            }
+          }
+        }
+        assert(it.hasNext, "Got an unexpected empty iterator after setting up batch with retry")
+        it.foreach { rowsCvList =>
+          withResource(rowsCvList) { _ =>
             rowsCvList.foreach { rowsCv =>
               pendingCvs += rowsCv.copyToHost()
             }
-            setCurrentBatch(pendingCvs.dequeue())
-            return true
           }
         }
+        setCurrentBatch(pendingCvs.dequeue())
+        return true
       }
     }
     false
@@ -148,16 +162,20 @@ class AcceleratedColumnarToRowIterator(
     // keep fetching input batches until we have a non-empty batch ready
     val nextBatch = fetchNextBatch()
     if (nextBatch.isDefined) {
-      if (!withResource(nextBatch.get)(setupBatch)) {
+      if (!setupBatchAndClose(nextBatch.get)) {
         populateBatch()
       }
     }
   }
 
-  private def fetchNextBatch(): Option[ColumnarBatch] = {
+  private def fetchNextBatch(): Option[SpillableColumnarBatch] = {
     withResource(new NvtxWithMetrics("ColumnarToRow: fetch", NvtxColor.BLUE, streamTime)) { _ =>
       if (batches.hasNext) {
-        Some(batches.next())
+        // Make it spillable once getting a columnar batch.
+        val spillBatch = closeOnExcept(batches.next()) { cb =>
+          SpillableColumnarBatch(cb, SpillPriorities.ACTIVE_ON_DECK_PRIORITY)
+        }
+        Some(spillBatch)
       } else {
         None
       }

sql-plugin/src/main/spark333/scala/com/nvidia/spark/rapids/shims/spark333/SparkShimServiceProvider.scala

@@ -23,7 +23,7 @@ import com.nvidia.spark.rapids.SparkShimVersion
 
 object SparkShimServiceProvider {
   val VERSION = SparkShimVersion(3, 3, 3)
-  val VERSIONNAMES = Seq(s"$VERSION", s"$VERSION-SNAPSHOT")
+  val VERSIONNAMES = Seq(s"$VERSION")
 }
 
 class SparkShimServiceProvider extends com.nvidia.spark.rapids.SparkShimServiceProvider {

tests/src/test/scala/com/nvidia/spark/rapids/ColumnToRowIteratorRetrySuite.scala

@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.nvidia.spark.rapids
+
+import ai.rapids.cudf.ColumnVector
+import com.nvidia.spark.rapids.jni.RmmSpark
+
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, ExprId}
+import org.apache.spark.sql.types.IntegerType
+import org.apache.spark.sql.vectorized.ColumnarBatch
+
+class ColumnToRowIteratorRetrySuite extends RmmSparkRetrySuiteBase {
+
+  private val ref = GpuBoundReference(0, IntegerType, nullable = false)(ExprId(0), "a")
+  private val attrs = Seq(AttributeReference(ref.name, ref.dataType, ref.nullable)())
+  private val NUM_ROWS = 50
+
+  private def buildBatch: ColumnarBatch = {
+    val ints = 0 until NUM_ROWS
+    new ColumnarBatch(
+      Array(GpuColumnVector.from(ColumnVector.fromInts(ints: _*), IntegerType)), NUM_ROWS)
+  }
+
+  test("Accelerated columnar to row with retry OOM") {
+    val aCol2RowIter = new AcceleratedColumnarToRowIterator(
+      attrs,
+      Iterator(buildBatch),
+      NoopMetric, NoopMetric, NoopMetric, NoopMetric)
+    RmmSpark.forceRetryOOM(RmmSpark.getCurrentThreadId)
+    var numRows = 0
+    aCol2RowIter.foreach { _ =>
+      numRows += 1
+    }
+    assertResult(NUM_ROWS)(numRows)
+  }
+
+  test("Accelerated columnar_to_row with split and retry OOM") {
+    val aCol2RowIter = new AcceleratedColumnarToRowIterator(
+      attrs,
+      Iterator(buildBatch),
+      NoopMetric, NoopMetric, NoopMetric, NoopMetric)
+    RmmSpark.forceSplitAndRetryOOM(RmmSpark.getCurrentThreadId)
+    var numRows = 0
+    aCol2RowIter.foreach { _ =>
+      numRows += 1
+    }
+    assertResult(NUM_ROWS)(numRows)
+  }
+
+}