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/*
* 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.datasources.csv
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileStatus, Path}
import org.apache.hadoop.mapreduce._
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{AnalysisException, SparkSession}
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.util.CompressionCodecs
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.util.SerializableConfiguration
/**
* Provides access to CSV data from pure SQL statements.
*/
class CSVFileFormat extends TextBasedFileFormat with DataSourceRegister {
override def shortName(): String = "csv"
override def isSplitable(
sparkSession: SparkSession,
options: Map[String, String],
path: Path): Boolean = {
val parsedOptions =
new CSVOptions(options, sparkSession.sessionState.conf.sessionLocalTimeZone)
val csvDataSource = CSVDataSource(parsedOptions)
csvDataSource.isSplitable && super.isSplitable(sparkSession, options, path)
}
override def inferSchema(
sparkSession: SparkSession,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = {
val parsedOptions =
new CSVOptions(options, sparkSession.sessionState.conf.sessionLocalTimeZone)
CSVDataSource(parsedOptions).inferSchema(sparkSession, files, parsedOptions)
}
override def prepareWrite(
sparkSession: SparkSession,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = {
CSVUtils.verifySchema(dataSchema)
val conf = job.getConfiguration
val csvOptions = new CSVOptions(options, sparkSession.sessionState.conf.sessionLocalTimeZone)
csvOptions.compressionCodec.foreach { codec =>
CompressionCodecs.setCodecConfiguration(conf, codec)
}
new OutputWriterFactory {
override def newInstance(
path: String,
dataSchema: StructType,
context: TaskAttemptContext): OutputWriter = {
new CsvOutputWriter(path, dataSchema, context, csvOptions)
}
override def getFileExtension(context: TaskAttemptContext): String = {
".csv" + CodecStreams.getCompressionExtension(context)
}
}
}
override def buildReader(
sparkSession: SparkSession,
dataSchema: StructType,
partitionSchema: StructType,
requiredSchema: StructType,
filters: Seq[Filter],
options: Map[String, String],
hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow] = {
CSVUtils.verifySchema(dataSchema)
val broadcastedHadoopConf =
sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
val parsedOptions = new CSVOptions(
options,
sparkSession.sessionState.conf.sessionLocalTimeZone,
sparkSession.sessionState.conf.columnNameOfCorruptRecord)
// Check a field requirement for corrupt records here to throw an exception in a driver side
dataSchema.getFieldIndex(parsedOptions.columnNameOfCorruptRecord).foreach { corruptFieldIndex =>
val f = dataSchema(corruptFieldIndex)
if (f.dataType != StringType || !f.nullable) {
throw new AnalysisException(
"The field for corrupt records must be string type and nullable")
}
}
if (requiredSchema.length == 1 &&
requiredSchema.head.name == parsedOptions.columnNameOfCorruptRecord) {
throw new AnalysisException(
"Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the\n" +
"referenced columns only include the internal corrupt record column\n" +
s"(named _corrupt_record by default). For example:\n" +
"spark.read.schema(schema).csv(file).filter($\"_corrupt_record\".isNotNull).count()\n" +
"and spark.read.schema(schema).csv(file).select(\"_corrupt_record\").show().\n" +
"Instead, you can cache or save the parsed results and then send the same query.\n" +
"For example, val df = spark.read.schema(schema).csv(file).cache() and then\n" +
"df.filter($\"_corrupt_record\".isNotNull).count()."
)
}
(file: PartitionedFile) => {
val conf = broadcastedHadoopConf.value.value
val parser = new UnivocityParser(
StructType(dataSchema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord)),
StructType(requiredSchema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord)),
parsedOptions)
CSVDataSource(parsedOptions).readFile(conf, file, parser, requiredSchema)
}
}
override def toString: String = "CSV"
override def hashCode(): Int = getClass.hashCode()
override def equals(other: Any): Boolean = other.isInstanceOf[CSVFileFormat]
}
private[csv] class CsvOutputWriter(
path: String,
dataSchema: StructType,
context: TaskAttemptContext,
params: CSVOptions) extends OutputWriter with Logging {
private val writer = CodecStreams.createOutputStreamWriter(context, new Path(path))
private val gen = new UnivocityGenerator(dataSchema, writer, params)
override def write(row: InternalRow): Unit = gen.write(row)
override def close(): Unit = gen.close()
}
