Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/12268#discussion_r61358888
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVRelation.scala
---
@@ -17,152 +17,162 @@
package org.apache.spark.sql.execution.datasources.csv
-import scala.util.control.NonFatal
-
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.io.{NullWritable, Text}
-import org.apache.hadoop.mapreduce.RecordWriter
-import org.apache.hadoop.mapreduce.TaskAttemptContext
+import java.io.CharArrayWriter
+import java.nio.charset.{Charset, StandardCharsets}
+
+import com.univocity.parsers.csv.CsvWriter
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.{LongWritable, NullWritable, Text}
+import org.apache.hadoop.mapred.TextInputFormat
+import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
-import org.apache.spark.sql.execution.datasources.{OutputWriter,
OutputWriterFactory, PartitionedFile}
+import org.apache.spark.sql.catalyst.expressions.JoinedRow
+import
org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
+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
-object CSVRelation extends Logging {
-
- def univocityTokenizer(
- file: RDD[String],
- header: Seq[String],
- firstLine: String,
- params: CSVOptions): RDD[Array[String]] = {
- // If header is set, make sure firstLine is materialized before
sending to executors.
- file.mapPartitions { iter =>
- new BulkCsvReader(
- if (params.headerFlag) iter.filterNot(_ == firstLine) else iter,
- params,
- headers = header)
- }
- }
+/**
+ * Provides access to CSV data from pure SQL statements.
+ */
+class DefaultSource extends FileFormat with DataSourceRegister {
+
+ override def shortName(): String = "csv"
+
+ override def toString: String = "CSV"
+
+ override def hashCode(): Int = getClass.hashCode()
+
+ override def equals(other: Any): Boolean =
other.isInstanceOf[DefaultSource]
- def csvParser(
- schema: StructType,
- requiredColumns: Array[String],
- params: CSVOptions): Array[String] => Option[InternalRow] = {
- val schemaFields = schema.fields
- val requiredFields = StructType(requiredColumns.map(schema(_))).fields
- val safeRequiredFields = if (params.dropMalformed) {
- // If `dropMalformed` is enabled, then it needs to parse all the
values
- // so that we can decide which row is malformed.
- requiredFields ++ schemaFields.filterNot(requiredFields.contains(_))
+ override def inferSchema(
+ sparkSession: SparkSession,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType] = {
+ val csvOptions = new CSVOptions(options)
+
+ // TODO: Move filtering.
+ val paths = files.filterNot(_.getPath.getName startsWith
"_").map(_.getPath.toString)
+ val rdd = createBaseRdd(sparkSession, csvOptions, paths)
+ val schema = if (csvOptions.inferSchemaFlag) {
+ InferSchema.infer(rdd, csvOptions)
} else {
- requiredFields
- }
- val safeRequiredIndices = new Array[Int](safeRequiredFields.length)
- schemaFields.zipWithIndex.filter {
- case (field, _) => safeRequiredFields.contains(field)
- }.foreach {
- case (field, index) =>
safeRequiredIndices(safeRequiredFields.indexOf(field)) = index
- }
- val requiredSize = requiredFields.length
- val row = new GenericMutableRow(requiredSize)
-
- (tokens: Array[String]) => {
- if (params.dropMalformed && schemaFields.length != tokens.length) {
- logWarning(s"Dropping malformed line:
${tokens.mkString(params.delimiter.toString)}")
- None
- } else if (params.failFast && schemaFields.length != tokens.length) {
- throw new RuntimeException(s"Malformed line in FAILFAST mode: " +
- s"${tokens.mkString(params.delimiter.toString)}")
+ // By default fields are assumed to be StringType
+ val filteredRdd =
rdd.mapPartitions(CSVUtils.filterCommentAndEmpty(_, csvOptions))
+ val firstLine = filteredRdd.first()
+ val firstRow = UnivocityParser.tokenizeLine(firstLine, csvOptions)
+ val header = if (csvOptions.headerFlag) {
+ firstRow
} else {
- val indexSafeTokens = if (params.permissive && schemaFields.length
> tokens.length) {
- tokens ++ new Array[String](schemaFields.length - tokens.length)
- } else if (params.permissive && schemaFields.length <
tokens.length) {
- tokens.take(schemaFields.length)
- } else {
- tokens
- }
- try {
- var index: Int = 0
- var subIndex: Int = 0
- while (subIndex < safeRequiredIndices.length) {
- index = safeRequiredIndices(subIndex)
- val field = schemaFields(index)
- // It anyway needs to try to parse since it decides if this
row is malformed
- // or not after trying to cast in `DROPMALFORMED` mode even if
the casted
- // value is not stored in the row.
- val value = CSVTypeCast.castTo(
- indexSafeTokens(index),
- field.dataType,
- field.nullable,
- params.nullValue)
- if (subIndex < requiredSize) {
- row(subIndex) = value
- }
- subIndex = subIndex + 1
- }
- Some(row)
- } catch {
- case NonFatal(e) if params.dropMalformed =>
- logWarning("Parse exception. " +
- s"Dropping malformed line:
${tokens.mkString(params.delimiter.toString)}")
- None
- }
+ firstRow.zipWithIndex.map { case (value, index) => s"C$index" }
+ }
+ val schemaFields = header.map { fieldName =>
+ StructField(fieldName.toString, StringType, nullable = true)
}
+ StructType(schemaFields)
}
+ Some(schema)
}
- def parseCsv(
- tokenizedRDD: RDD[Array[String]],
- schema: StructType,
- requiredColumns: Array[String],
- options: CSVOptions): RDD[InternalRow] = {
- val parser = csvParser(schema, requiredColumns, options)
- tokenizedRDD.flatMap(parser(_).toSeq)
+ override def prepareWrite(
+ sparkSession: SparkSession,
+ job: Job,
+ options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory = {
+ val conf = job.getConfiguration
+ val csvOptions = new CSVOptions(options)
+ csvOptions.compressionCodec.foreach { codec =>
+ CompressionCodecs.setCodecConfiguration(conf, codec)
--- End diff --
Oh, yes it is possible since it uses `TextInputFormat`.
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