Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/15821#discussion_r115635616
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala
---
@@ -0,0 +1,423 @@
+/*
+* 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.arrow
+
+import java.io.ByteArrayOutputStream
+import java.nio.channels.Channels
+
+import scala.collection.JavaConverters._
+
+import io.netty.buffer.ArrowBuf
+import org.apache.arrow.memory.{BufferAllocator, RootAllocator}
+import org.apache.arrow.vector._
+import org.apache.arrow.vector.BaseValueVector.BaseMutator
+import org.apache.arrow.vector.file._
+import org.apache.arrow.vector.schema.{ArrowFieldNode, ArrowRecordBatch}
+import org.apache.arrow.vector.types.FloatingPointPrecision
+import org.apache.arrow.vector.types.pojo.{ArrowType, Field, FieldType,
Schema}
+import org.apache.arrow.vector.util.ByteArrayReadableSeekableByteChannel
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+
+
+/**
+ * Store Arrow data in a form that can be serialized by Spark.
+ */
+private[sql] class ArrowPayload(payload: Array[Byte]) extends Serializable
{
+
+ /**
+ * Create an ArrowPayload from an ArrowRecordBatch and Spark schema.
+ */
+ def this(batch: ArrowRecordBatch, schema: StructType, allocator:
BufferAllocator) = {
+ this(ArrowConverters.batchToByteArray(batch, schema, allocator))
+ }
+
+ /**
+ * Convert the ArrowPayload to an ArrowRecordBatch.
+ */
+ def loadBatch(allocator: BufferAllocator): ArrowRecordBatch = {
+ ArrowConverters.byteArrayToBatch(payload, allocator)
+ }
+
+ /**
+ * Get the ArrowPayload as an Array[Byte].
+ */
+ def toByteArray: Array[Byte] = payload
+}
+
+private[sql] object ArrowConverters {
+
+ /**
+ * Map a Spark DataType to ArrowType.
+ */
+ private[arrow] def sparkTypeToArrowType(dataType: DataType): ArrowType =
{
+ dataType match {
+ case BooleanType => ArrowType.Bool.INSTANCE
+ case ShortType => new ArrowType.Int(8 * ShortType.defaultSize, true)
+ case IntegerType => new ArrowType.Int(8 * IntegerType.defaultSize,
true)
+ case LongType => new ArrowType.Int(8 * LongType.defaultSize, true)
+ case FloatType => new
ArrowType.FloatingPoint(FloatingPointPrecision.SINGLE)
+ case DoubleType => new
ArrowType.FloatingPoint(FloatingPointPrecision.DOUBLE)
+ case ByteType => new ArrowType.Int(8, true)
+ case StringType => ArrowType.Utf8.INSTANCE
+ case BinaryType => ArrowType.Binary.INSTANCE
+ case _ => throw new UnsupportedOperationException(s"Unsupported data
type: $dataType")
+ }
+ }
+
+ /**
+ * Convert a Spark Dataset schema to Arrow schema.
+ */
+ private[arrow] def schemaToArrowSchema(schema: StructType): Schema = {
+ val arrowFields = schema.fields.map { f =>
+ new Field(f.name, f.nullable, sparkTypeToArrowType(f.dataType),
List.empty[Field].asJava)
+ }
+ new Schema(arrowFields.toList.asJava)
+ }
+
+ /**
+ * Maps Iterator from InternalRow to ArrowPayload. Limit
ArrowRecordBatch size in ArrowPayload
+ * by setting maxRecordsPerBatch or use 0 to fully consume rowIter.
+ */
+ private[sql] def toPayloadIterator(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ maxRecordsPerBatch: Int): Iterator[ArrowPayload] = {
+ new Iterator[ArrowPayload] {
+ private val _allocator = new RootAllocator(Long.MaxValue)
+ private var _nextPayload = if (rowIter.nonEmpty) convert() else null
+
+ override def hasNext: Boolean = _nextPayload != null
+
+ override def next(): ArrowPayload = {
+ val obj = _nextPayload
+ if (hasNext) {
+ if (rowIter.hasNext) {
+ _nextPayload = convert()
+ } else {
+ _allocator.close()
+ _nextPayload = null
+ }
+ }
+ obj
+ }
+
+ private def convert(): ArrowPayload = {
+ val batch = internalRowIterToArrowBatch(rowIter, schema,
_allocator, maxRecordsPerBatch)
+ new ArrowPayload(batch, schema, _allocator)
+ }
+ }
+ }
+
+ /**
+ * Iterate over InternalRows and write to an ArrowRecordBatch, stopping
when rowIter is consumed
+ * or the number of records in the batch equals maxRecordsInBatch. If
maxRecordsPerBatch is 0,
+ * then rowIter will be fully consumed.
+ */
+ private def internalRowIterToArrowBatch(
+ rowIter: Iterator[InternalRow],
+ schema: StructType,
+ allocator: BufferAllocator,
+ maxRecordsPerBatch: Int = 0): ArrowRecordBatch = {
+
+ val columnWriters = schema.fields.zipWithIndex.map { case (field,
ordinal) =>
+ ColumnWriter(field.dataType, ordinal, allocator).init()
+ }
+
+ val writerLength = columnWriters.length
+ var recordsInBatch = 0
+ while (rowIter.hasNext && (maxRecordsPerBatch <= 0 || recordsInBatch <
maxRecordsPerBatch)) {
--- End diff --
rows with different schema have very different sizes, e.g. a row with a
single int column should be much smaller than a row with 10 string columns.
Should we bound the batch by data size instead of by the number of records?
cc @liancheng, do you know how parquet writer works in this case?
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