scala.com.crealytics.spark.excel.v2.ExcelPartitionReaderFactory.scala Maven / Gradle / Ivy
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A Spark plugin for reading and writing Excel files
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/*
* Copyright 2022 Martin Mauch (@nightscape)
*
* Licensed 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 com.crealytics.spark.excel.v2
import org.apache.hadoop.conf.Configuration
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.sql.catalyst.{FileSourceOptions, InternalRow}
import org.apache.spark.sql.connector.read.PartitionReader
import org.apache.spark.sql.execution.datasources.PartitionedFile
import org.apache.spark.sql.execution.datasources.v2._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.sources.Filter
import org.apache.spark.sql.types.StructType
import org.apache.spark.util.SerializableConfiguration
import java.net.URI
import scala.util.control.NonFatal
/** A factory used to create Excel readers.
*
* @param sqlConf
* SQL configuration.
* @param broadcastedConf
* Broadcasted serializable Hadoop Configuration.
* @param dataSchema
* Schema of Excel files.
* @param readDataSchema
* Required data schema in the batch scan.
* @param partitionSchema
* Schema of partitions.
* @param parsedOptions
* Options for parsing Excel files.
*/
case class ExcelPartitionReaderFactory(
sqlConf: SQLConf,
broadcastedConf: Broadcast[SerializableConfiguration],
dataSchema: StructType,
readDataSchema: StructType,
partitionSchema: StructType,
parsedOptions: ExcelOptions,
filters: Seq[Filter]
) extends FilePartitionReaderFactory {
protected def options: FileSourceOptions = new FileSourceOptions(
Map(FileSourceOptions.IGNORE_CORRUPT_FILES -> "true", FileSourceOptions.IGNORE_MISSING_FILES -> "true")
)
override def buildReader(file: PartitionedFile): PartitionReader[InternalRow] = {
val conf = broadcastedConf.value.value
val actualDataSchema =
StructType(dataSchema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord))
val actualReadDataSchema =
StructType(readDataSchema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord))
val parser = new ExcelParser(actualDataSchema, actualReadDataSchema, parsedOptions, filters)
val headerChecker =
new ExcelHeaderChecker(actualReadDataSchema, parsedOptions, source = s"Excel file: ${file.filePath}")
val iter = readFile(conf, file, parser, headerChecker, readDataSchema)
val partitionReader = new SparkExcelPartitionReaderFromIterator(iter)
new PartitionReaderWithPartitionValues(partitionReader, readDataSchema, partitionSchema, file.partitionValues)
}
private def readFile(
conf: Configuration,
file: PartitionedFile,
parser: ExcelParser,
headerChecker: ExcelHeaderChecker,
requiredSchema: StructType
): SheetData[InternalRow] = {
val excelHelper = ExcelHelper(parsedOptions)
val sheetData = excelHelper.getSheetData(conf, URI.create(file.filePath.toString))
try {
SheetData(
ExcelParser.parseIterator(sheetData.rowIterator, parser, headerChecker, requiredSchema),
sheetData.resourcesToClose
)
} catch {
case NonFatal(t) => {
sheetData.close()
throw t
}
}
}
}
private class SparkExcelPartitionReaderFromIterator(sheetData: SheetData[InternalRow])
extends PartitionReaderFromIterator[InternalRow](sheetData.rowIterator) {
override def close(): Unit = {
super.close()
sheetData.close()
}
}