hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieDataSourceHelper.scala

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 * 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
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 *      http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.hudi

import org.apache.avro.Schema
import org.apache.avro.generic.GenericRecord
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.FileStatus
import org.apache.hudi.common.util.ValidationUtils.checkState
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.avro.HoodieAvroDeserializer
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.PredicateHelper
import org.apache.spark.sql.execution.datasources.PartitionedFile
import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
import org.apache.spark.sql.sources.Filter
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.vectorized.ColumnarBatch

import scala.collection.JavaConverters._

object HoodieDataSourceHelper extends PredicateHelper with SparkAdapterSupport {


  /**
   * Wrapper for `buildReaderWithPartitionValues` of [[ParquetFileFormat]] handling [[ColumnarBatch]],
   * when Parquet's Vectorized Reader is used
   *
   * TODO move to HoodieBaseRelation, make private
   */
  private[hudi] def buildHoodieParquetReader(sparkSession: SparkSession,
                                             dataSchema: StructType,
                                             partitionSchema: StructType,
                                             requiredSchema: StructType,
                                             filters: Seq[Filter],
                                             options: Map[String, String],
                                             hadoopConf: Configuration,
                                             appendPartitionValues: Boolean = false): PartitionedFile => Iterator[InternalRow] = {
    val parquetFileFormat: ParquetFileFormat = sparkAdapter.createHoodieParquetFileFormat(appendPartitionValues).get
    val readParquetFile: PartitionedFile => Iterator[Any] = parquetFileFormat.buildReaderWithPartitionValues(
      sparkSession = sparkSession,
      dataSchema = dataSchema,
      partitionSchema = partitionSchema,
      requiredSchema = requiredSchema,
      filters = filters,
      options = options,
      hadoopConf = hadoopConf
    )

    file: PartitionedFile => {
      val iter = readParquetFile(file)
      iter.flatMap {
        case r: InternalRow => Seq(r)
        case b: ColumnarBatch => b.rowIterator().asScala
      }
    }
  }

  def splitFiles(
      sparkSession: SparkSession,
      file: FileStatus,
      partitionValues: InternalRow): Seq[PartitionedFile] = {
    val filePath = file.getPath
    val maxSplitBytes = sparkSession.sessionState.conf.filesMaxPartitionBytes
    (0L until file.getLen by maxSplitBytes).map { offset =>
      val remaining = file.getLen - offset
      val size = if (remaining > maxSplitBytes) maxSplitBytes else remaining
      sparkAdapter.getSparkPartitionedFileUtils.createPartitionedFile(
        partitionValues, filePath, offset, size)
    }
  }

  trait AvroDeserializerSupport extends SparkAdapterSupport {
    protected val avroSchema: Schema
    protected val structTypeSchema: StructType

    private lazy val deserializer: HoodieAvroDeserializer =
      sparkAdapter.createAvroDeserializer(avroSchema, structTypeSchema)

    protected def deserialize(avroRecord: GenericRecord): InternalRow = {
      checkState(avroRecord.getSchema.getFields.size() == structTypeSchema.fields.length)
      deserializer.deserialize(avroRecord).get.asInstanceOf[InternalRow]
    }
  }
}