feat: Add executeColumnarCollectIterator to CometExec to collect Come…

…t operator result (#71)

common/src/main/scala/org/apache/comet/vector/NativeUtil.scala

@@ -19,11 +19,16 @@
 
 package org.apache.comet.vector
 
+import java.io.OutputStream
+
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 
 import org.apache.arrow.c.{ArrowArray, ArrowImporter, ArrowSchema, CDataDictionaryProvider, Data}
 import org.apache.arrow.memory.RootAllocator
 import org.apache.arrow.vector._
+import org.apache.arrow.vector.dictionary.DictionaryProvider
+import org.apache.arrow.vector.ipc.ArrowStreamWriter
 import org.apache.spark.SparkException
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
@@ -32,6 +37,71 @@ class NativeUtil {
   private val dictionaryProvider: CDataDictionaryProvider = new CDataDictionaryProvider
   private val importer = new ArrowImporter(allocator)
 
+  /**
+   * Serializes a list of `ColumnarBatch` into an output stream.
+   *
+   * @param batches
+   *   the output batches, each batch is a list of Arrow vectors wrapped in `CometVector`
+   * @param out
+   *   the output stream
+   */
+  def serializeBatches(batches: Iterator[ColumnarBatch], out: OutputStream): Long = {
+    var schemaRoot: Option[VectorSchemaRoot] = None
+    var writer: Option[ArrowStreamWriter] = None
+    var rowCount = 0
+
+    batches.foreach { batch =>
+      val (fieldVectors, batchProviderOpt) = getBatchFieldVectors(batch)
+      val root = schemaRoot.getOrElse(new VectorSchemaRoot(fieldVectors.asJava))
+      val provider = batchProviderOpt.getOrElse(dictionaryProvider)
+
+      if (writer.isEmpty) {
+        writer = Some(new ArrowStreamWriter(root, provider, out))
+        writer.get.start()
+      }
+      writer.get.writeBatch()
+
+      root.clear()
+      schemaRoot = Some(root)
+
+      rowCount += batch.numRows()
+    }
+
+    writer.map(_.end())
+    schemaRoot.map(_.close())
+
+    rowCount
+  }
+
+  def getBatchFieldVectors(
+      batch: ColumnarBatch): (Seq[FieldVector], Option[DictionaryProvider]) = {
+    var provider: Option[DictionaryProvider] = None
+    val fieldVectors = (0 until batch.numCols()).map { index =>
+      batch.column(index) match {
+        case a: CometVector =>
+          val valueVector = a.getValueVector
+          if (valueVector.getField.getDictionary != null) {
+            if (provider.isEmpty) {
+              provider = Some(a.getDictionaryProvider)
+            } else {
+              if (provider.get != a.getDictionaryProvider) {
+                throw new SparkException(
+                  "Comet execution only takes Arrow Arrays with the same dictionary provider")
+              }
+            }
+          }
+
+          getFieldVector(valueVector)
+
+        case c =>
+          throw new SparkException(
+            "Comet execution only takes Arrow Arrays, but got " +
+              s"${c.getClass}")
+      }
+    }
+    (fieldVectors, provider)
+  }
+
   /**
    * Exports a Comet `ColumnarBatch` into a list of memory addresses that can be consumed by the
    * native execution.

spark/src/main/scala/org/apache/spark/sql/comet/operators.scala

@@ -19,25 +19,31 @@
 
 package org.apache.spark.sql.comet
 
-import java.io.ByteArrayOutputStream
+import java.io.{ByteArrayOutputStream, DataInputStream, DataOutputStream}
+import java.nio.ByteBuffer
+import java.nio.channels.Channels
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.TaskContext
+import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.io.CompressionCodec
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression, NamedExpression, SortOrder}
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, AggregateMode}
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.comet.execution.shuffle.ArrowReaderIterator
 import org.apache.spark.sql.execution.{ColumnarToRowExec, ExecSubqueryExpression, ExplainUtils, LeafExecNode, ScalarSubquery, SparkPlan, UnaryExecNode}
 import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics}
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.vectorized.ColumnarBatch
+import org.apache.spark.util.io.{ChunkedByteBuffer, ChunkedByteBufferOutputStream}
 
 import com.google.common.base.Objects
 
 import org.apache.comet.{CometConf, CometExecIterator, CometRuntimeException}
 import org.apache.comet.serde.OperatorOuterClass.Operator
+import org.apache.comet.vector.NativeUtil
 
 /**
  * A Comet physical operator
@@ -61,6 +67,48 @@ abstract class CometExec extends CometPlan {
   override def outputOrdering: Seq[SortOrder] = originalPlan.outputOrdering
 
   override def outputPartitioning: Partitioning = originalPlan.outputPartitioning
+
+  /**
+   * Executes this Comet operator and serialized output ColumnarBatch into bytes.
+   */
+  private def getByteArrayRdd(): RDD[(Long, ChunkedByteBuffer)] = {
+    executeColumnar().mapPartitionsInternal { iter =>
+      val codec = CompressionCodec.createCodec(SparkEnv.get.conf)
+      val cbbos = new ChunkedByteBufferOutputStream(1024 * 1024, ByteBuffer.allocate)
+      val out = new DataOutputStream(codec.compressedOutputStream(cbbos))
+
+      val count = new NativeUtil().serializeBatches(iter, out)
+
+      out.flush()
+      out.close()
+      Iterator((count, cbbos.toChunkedByteBuffer))
+    }
+  }
+
+  /**
+   * Decodes the byte arrays back to ColumnarBatches and put them into buffer.
+   */
+  private def decodeBatches(bytes: ChunkedByteBuffer): Iterator[ColumnarBatch] = {
+    if (bytes.size == 0) {
+      return Iterator.empty
+    }
+
+    val codec = CompressionCodec.createCodec(SparkEnv.get.conf)
+    val cbbis = bytes.toInputStream()
+    val ins = new DataInputStream(codec.compressedInputStream(cbbis))
+
+    new ArrowReaderIterator(Channels.newChannel(ins))
+  }
+
+  /**
+   * Executes the Comet operator and returns the result as an iterator of ColumnarBatch.
+   */
+  def executeColumnarCollectIterator(): (Long, Iterator[ColumnarBatch]) = {
+    val countsAndBytes = getByteArrayRdd().collect()
+    val total = countsAndBytes.map(_._1).sum
+    val rows = countsAndBytes.iterator.flatMap(countAndBytes => decodeBatches(countAndBytes._2))
+    (total, rows)
+  }
 }
 
 object CometExec {

spark/src/test/scala/org/apache/comet/exec/CometExecSuite.scala

@@ -19,6 +19,8 @@
 
 package org.apache.comet.exec
 
+import scala.collection.JavaConverters._
+import scala.collection.mutable
 import scala.util.Random
 
 import org.scalactic.source.Position
@@ -53,6 +55,38 @@ class CometExecSuite extends CometTestBase {
     }
   }
 
+  test("CometExec.executeColumnarCollectIterator can collect ColumnarBatch results") {
+    withSQLConf(
+      CometConf.COMET_EXEC_ENABLED.key -> "true",
+      CometConf.COMET_EXEC_ALL_OPERATOR_ENABLED.key -> "true") {
+      withParquetTable((0 until 50).map(i => (i, i + 1)), "tbl") {
+        val df = sql("SELECT _1 + 1, _2 + 2 FROM tbl WHERE _1 > 3")
+
+        val nativeProject = find(df.queryExecution.executedPlan) {
+          case _: CometProjectExec => true
+          case _ => false
+        }.get.asInstanceOf[CometProjectExec]
+
+        val (rows, batches) = nativeProject.executeColumnarCollectIterator()
+        assert(rows == 46)
+
+        val column1 = mutable.ArrayBuffer.empty[Int]
+        val column2 = mutable.ArrayBuffer.empty[Int]
+
+        batches.foreach(batch => {
+          batch.rowIterator().asScala.foreach { row =>
+            assert(row.numFields == 2)
+            column1 += row.getInt(0)
+            column2 += row.getInt(1)
+          }
+        })
+
+        assert(column1.toArray.sorted === (4 until 50).map(_ + 1).toArray)
+        assert(column2.toArray.sorted === (5 until 51).map(_ + 2).toArray)
+      }
+    }
+  }
+
   test("scalar subquery") {
     val dataTypes =
       Seq(