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From cloud-fan <...@git.apache.org>
Subject [GitHub] spark pull request #15821: [SPARK-13534][PySpark] Using Apache Arrow to incr...
Date Wed, 10 May 2017 00:57:49 GMT
Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15821#discussion_r115635722
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowConverters.scala
---
    @@ -0,0 +1,396 @@
    +/*
    +* 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.{DateUnit, FloatingPointPrecision, TimeUnit}
    +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(val batchBytes: Array[Byte]) extends Serializable {
    +
    +  def this(batch: ArrowRecordBatch, schema: StructType, allocator: BufferAllocator) =
{
    +    this(ArrowConverters.batchToByteArray(batch, schema, allocator))
    +  }
    +
    +  def loadBatch(allocator: BufferAllocator): ArrowRecordBatch = {
    +    ArrowConverters.byteArrayToBatch(batchBytes, allocator)
    +  }
    +}
    +
    +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
    +   */
    +  private[sql] def toPayloadIterator(
    +      rowIter: Iterator[InternalRow],
    +      schema: StructType): 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)
    +        new ArrowPayload(batch, schema, _allocator)
    +      }
    +    }
    +  }
    +
    +  /**
    +   * Iterate over InternalRows and write to an ArrowRecordBatch.
    +   */
    +  private def internalRowIterToArrowBatch(
    +      rowIter: Iterator[InternalRow],
    +      schema: StructType,
    +      allocator: BufferAllocator): ArrowRecordBatch = {
    +
    +    val columnWriters = schema.fields.zipWithIndex.map { case (field, ordinal) =>
    +      ColumnWriter(ordinal, allocator, field.dataType).init()
    +    }
    +
    +    val writerLength = columnWriters.length
    +    while (rowIter.hasNext) {
    +      val row = rowIter.next()
    +      var i = 0
    +      while (i < writerLength) {
    +        columnWriters(i).write(row)
    +        i += 1
    +      }
    +    }
    +
    +    val (fieldNodes, bufferArrays) = columnWriters.map(_.finish()).unzip
    +    val buffers = bufferArrays.flatten
    +
    +    val rowLength = if (fieldNodes.nonEmpty) fieldNodes.head.getLength else 0
    +    val recordBatch = new ArrowRecordBatch(rowLength,
    +      fieldNodes.toList.asJava, buffers.toList.asJava)
    +
    +    buffers.foreach(_.release())
    +    recordBatch
    +  }
    +
    +  /**
    +   * Convert an ArrowRecordBatch to a byte array and close batch
    +   */
    +  private[arrow] def batchToByteArray(
    +      batch: ArrowRecordBatch,
    +      schema: StructType,
    +      allocator: BufferAllocator): Array[Byte] = {
    +    val arrowSchema = ArrowConverters.schemaToArrowSchema(schema)
    +    val root = VectorSchemaRoot.create(arrowSchema, allocator)
    +    val out = new ByteArrayOutputStream()
    +    val writer = new ArrowFileWriter(root, null, Channels.newChannel(out))
    --- End diff --
    
    we will send multiple batches to Python for one RDD partitions, and the metadata/schema
will be duplicated in these batches. Shall we consider using the stream writer?


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