Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/18066#discussion_r120038491
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/GeneratedColumnarBatch.scala
---
@@ -0,0 +1,220 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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 org.apache.spark.sql.execution.columnar
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.execution.vectorized.ColumnarBatch
+import org.apache.spark.sql.types._
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.StorageLevel._
+
+
+/**
+ * A helper class to expose the scala iterator to Java.
+ */
+abstract class ColumnarBatchIterator extends Iterator[ColumnarBatch]
+
+
+/**
+ * Generate code to batch [[InternalRow]]s into [[ColumnarBatch]]es.
+ */
+class GenerateColumnarBatch(
+ schema: StructType,
+ batchSize: Int,
+ storageLevel: StorageLevel)
+ extends CodeGenerator[Iterator[InternalRow],
Iterator[CachedColumnarBatch]] {
+
+ protected def canonicalize(in: Iterator[InternalRow]):
Iterator[InternalRow] = in
+
+ protected def bind(
+ in: Iterator[InternalRow], inputSchema: Seq[Attribute]):
Iterator[InternalRow] = {
+ in
+ }
+
+ protected def create(rowIterator: Iterator[InternalRow]):
Iterator[CachedColumnarBatch] = {
+ import scala.collection.JavaConverters._
+ val ctx = newCodeGenContext()
+ val columnStatsCls = classOf[ColumnStats].getName
+ val rowVar = ctx.freshName("row")
+ val batchVar = ctx.freshName("columnarBatch")
+ val rowNumVar = ctx.freshName("rowNum")
+ val numBytesVar = ctx.freshName("bytesInBatch")
+ ctx.addMutableState("long", numBytesVar, s"$numBytesVar = 0;")
+ val rowIterVar = ctx.addReferenceObj(
+ "rowIterator", rowIterator.asJava,
classOf[java.util.Iterator[_]].getName)
+ val schemas = StructType(
+ schema.fields.map(s => StructField(s.name,
+ s.dataType match {
+ case udt: UserDefinedType[_] => udt.sqlType
+ case other => other
+ }, s.nullable))
+ )
+ val schemaVar = ctx.addReferenceObj("schema", schemas,
classOf[StructType].getName)
+ val maxNumBytes = ColumnBuilder.MAX_BATCH_SIZE_IN_BYTE
+ val numColumns = schema.fields.length
+
+ val colStatVars = (0 to numColumns - 1).map(i =>
ctx.freshName("colStat" + i))
+ val colStatCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, varName), i) =>
+ val columnStatsCls = field.dataType match {
+ case IntegerType => classOf[IntColumnStats].getName
+ case DoubleType => classOf[DoubleColumnStats].getName
+ case others => throw new
UnsupportedOperationException(s"$others is not supported yet")
+ }
+ ctx.addMutableState(columnStatsCls, varName, "")
+ s"$varName = new $columnStatsCls(); statsArray[$i] = $varName;\n"
+ },
+ "apply",
+ Seq.empty
+ )
+
+ val populateColumnVectorsCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, colStatVar), i) =>
+ GenerateColumnarBatch.putColumnCode(ctx, field.dataType,
field.nullable,
+ batchVar, rowVar, rowNumVar, colStatVar, i, numBytesVar).trim
+ "\n"
+ },
+ "apply",
+ Seq(("InternalRow", rowVar), ("ColumnarBatch", batchVar), ("int",
rowNumVar))
+ )
+
+ val code = s"""
+ import org.apache.spark.memory.MemoryMode;
+ import org.apache.spark.sql.catalyst.InternalRow;
+ import org.apache.spark.sql.execution.columnar.CachedColumnarBatch;
+ import org.apache.spark.sql.execution.columnar.GenerateColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnVector;
+
+ public GeneratedColumnarBatchIterator generate(Object[] references) {
+ return new GeneratedColumnarBatchIterator(references);
+ }
+
+ class GeneratedColumnarBatchIterator extends
${classOf[ColumnarBatchIterator].getName} {
+ private Object[] references;
+ ${ctx.declareMutableStates()}
+
+ public GeneratedColumnarBatchIterator(Object[] references) {
+ this.references = references;
+ ${ctx.initMutableStates()}
+ }
+
+ ${ctx.declareAddedFunctions()}
+
+ $columnStatsCls[] statsArray = new $columnStatsCls[$numColumns];
+ private void allocateColumnStats() {
+ ${colStatCode.trim}
+ }
+
+ @Override
+ public boolean hasNext() {
+ return $rowIterVar.hasNext();
+ }
+
+ @Override
+ public CachedColumnarBatch next() {
+ ColumnarBatch $batchVar =
+ ColumnarBatch.allocate($schemaVar, MemoryMode.ON_HEAP,
$batchSize);
+ allocateColumnStats();
+ int $rowNumVar = 0;
+ $numBytesVar = 0;
+ while ($rowIterVar.hasNext() && $rowNumVar < $batchSize &&
$numBytesVar < $maxNumBytes) {
+ InternalRow $rowVar = (InternalRow) $rowIterVar.next();
+ $populateColumnVectorsCode
+ $rowNumVar += 1;
+ }
+ $batchVar.setNumRows($rowNumVar);
+ return CachedColumnarBatch.apply(
+ $batchVar, GenerateColumnarBatch.generateStats(statsArray));
+ }
+ }
+ """
+ val formattedCode = CodeFormatter.stripOverlappingComments(
+ new CodeAndComment(code, ctx.getPlaceHolderToComments()))
+ CodeGenerator.compile(formattedCode).generate(ctx.references.toArray)
+ .asInstanceOf[Iterator[CachedColumnarBatch]]
+ }
+}
+
+
+private[sql] object GenerateColumnarBatch {
+ def compressStorageLevel(storageLevel: StorageLevel, useCompression:
Boolean): StorageLevel = {
+ if (!useCompression) return storageLevel
+ storageLevel match {
+ case MEMORY_ONLY => MEMORY_ONLY_SER
+ case MEMORY_ONLY_2 => MEMORY_ONLY_SER_2
+ case MEMORY_AND_DISK => MEMORY_AND_DISK_SER
+ case MEMORY_AND_DISK_2 => MEMORY_AND_DISK_SER_2
+ case sl => sl
+ }
+ }
+
+ def isCompress(storageLevel: StorageLevel) : Boolean = {
+ (storageLevel == MEMORY_ONLY_SER || storageLevel == MEMORY_ONLY_SER_2
||
+ storageLevel == MEMORY_AND_DISK_SER || storageLevel ==
MEMORY_AND_DISK_SER_2)
+ }
+
+ private val typeToName = Map[AbstractDataType, String](
--- End diff --
Hi, @kiszk .
Is there any reason having only two types, `int` and `double`?
The PR looks more general to me.
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