Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16976#discussion_r102663902
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
---
@@ -0,0 +1,256 @@
+/*
+ * 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 java.io.InputStream
+import java.nio.charset.{Charset, StandardCharsets}
+
+import com.univocity.parsers.csv.{CsvParser, CsvParserSettings}
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.{LongWritable, Text}
+import org.apache.hadoop.mapred.TextInputFormat
+import org.apache.hadoop.mapreduce.Job
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+
+import org.apache.spark.TaskContext
+import org.apache.spark.input.{PortableDataStream, StreamInputFormat}
+import org.apache.spark.rdd.{BinaryFileRDD, RDD}
+import org.apache.spark.sql.{Dataset, Encoders, SparkSession}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.execution.datasources.text.TextFileFormat
+import org.apache.spark.sql.types.StructType
+
+/**
+ * Common functions for parsing CSV files
+ */
+abstract class CSVDataSource extends Serializable {
+ def isSplitable: Boolean
+
+ /**
+ * Parse a [[PartitionedFile]] into [[InternalRow]] instances.
+ */
+ def readFile(
+ conf: Configuration,
+ file: PartitionedFile,
+ parser: UnivocityParser,
+ parsedOptions: CSVOptions): Iterator[InternalRow]
+
+ /**
+ * Infers the schema from `inputPaths` files.
+ */
+ def infer(
+ sparkSession: SparkSession,
+ inputPaths: Seq[FileStatus],
+ parsedOptions: CSVOptions): Option[StructType]
+
+ /**
+ * Generates a header from the given row which is null-safe and
duplicate-safe.
+ */
+ protected def makeSafeHeader(
+ row: Array[String],
+ caseSensitive: Boolean,
+ options: CSVOptions): Array[String] = {
+ if (options.headerFlag) {
+ val duplicates = {
+ val headerNames = row.filter(_ != null)
+ .map(name => if (caseSensitive) name else name.toLowerCase)
+ headerNames.diff(headerNames.distinct).distinct
+ }
+
+ row.zipWithIndex.map { case (value, index) =>
+ if (value == null || value.isEmpty || value == options.nullValue) {
+ // When there are empty strings or the values set in
`nullValue`, put the
+ // index as the suffix.
+ s"_c$index"
+ } else if (!caseSensitive &&
duplicates.contains(value.toLowerCase)) {
+ // When there are case-insensitive duplicates, put the index as
the suffix.
+ s"$value$index"
+ } else if (duplicates.contains(value)) {
+ // When there are duplicates, put the index as the suffix.
+ s"$value$index"
+ } else {
+ value
+ }
+ }
+ } else {
+ row.zipWithIndex.map { case (_, index) =>
+ // Uses default column names, "_c#" where # is its position of
fields
+ // when header option is disabled.
+ s"_c$index"
+ }
+ }
+ }
+}
+
+object CSVDataSource {
+ def apply(options: CSVOptions): CSVDataSource = {
+ if (options.wholeFile) {
+ WholeFileCSVDataSource
+ } else {
+ TextInputCSVDataSource
+ }
+ }
+}
+
+object TextInputCSVDataSource extends CSVDataSource {
+ override val isSplitable: Boolean = true
+
+ override def readFile(
+ conf: Configuration,
+ file: PartitionedFile,
+ parser: UnivocityParser,
+ parsedOptions: CSVOptions): Iterator[InternalRow] = {
+ val lines = {
+ val linesReader = new HadoopFileLinesReader(file, conf)
+ Option(TaskContext.get()).foreach(_.addTaskCompletionListener(_ =>
linesReader.close()))
+ linesReader.map { line =>
+ new String(line.getBytes, 0, line.getLength, parsedOptions.charset)
+ }
+ }
+
+ val linesWithoutHeader = if (parsedOptions.headerFlag && file.start ==
0) {
+ // Note that if there are only comments in the first block, the
header would probably
+ // be not dropped.
+ CSVUtils.dropHeaderLine(lines, parsedOptions)
+ } else {
+ lines
+ }
+
+ val filteredLines: Iterator[String] =
+ CSVUtils.filterCommentAndEmpty(linesWithoutHeader, parsedOptions)
+ filteredLines.flatMap(parser.parse(_: String))
+ }
+
+ override def infer(
+ sparkSession: SparkSession,
+ inputPaths: Seq[FileStatus],
+ parsedOptions: CSVOptions): Option[StructType] = {
+ val csv: Dataset[String] = createBaseDataset(sparkSession, inputPaths,
parsedOptions)
+ val firstLine: String = CSVUtils.filterCommentAndEmpty(csv,
parsedOptions).first()
+ val firstRow = new
CsvParser(parsedOptions.asParserSettings).parseLine(firstLine)
+ val caseSensitive =
sparkSession.sessionState.conf.caseSensitiveAnalysis
+ val header = makeSafeHeader(firstRow, caseSensitive, parsedOptions)
+ val tokenRDD = csv.rdd.mapPartitions { iter =>
+ val filteredLines = CSVUtils.filterCommentAndEmpty(iter,
parsedOptions)
+ val linesWithoutHeader =
+ CSVUtils.filterHeaderLine(filteredLines, firstLine, parsedOptions)
+ val parser = new CsvParser(parsedOptions.asParserSettings)
+ linesWithoutHeader.map(parser.parseLine)
+ }
+
+ Some(CSVInferSchema.infer(tokenRDD, header, parsedOptions))
+ }
+
+ private def createBaseDataset(
+ sparkSession: SparkSession,
+ inputPaths: Seq[FileStatus],
+ options: CSVOptions): Dataset[String] = {
+ val paths = inputPaths.map(_.getPath.toString)
+ if (Charset.forName(options.charset) == StandardCharsets.UTF_8) {
+ sparkSession.baseRelationToDataFrame(
+ DataSource.apply(
+ sparkSession,
+ paths = paths,
+ className = classOf[TextFileFormat].getName
+ ).resolveRelation(checkFilesExist = false))
+ .select("value").as[String](Encoders.STRING)
+ } else {
+ val charset = options.charset
+ val rdd = sparkSession.sparkContext
+ .hadoopFile[LongWritable, Text,
TextInputFormat](paths.mkString(","))
+ .mapPartitions(_.map(pair => new String(pair._2.getBytes, 0,
pair._2.getLength, charset)))
+ sparkSession.createDataset(rdd)(Encoders.STRING)
+ }
+ }
+}
+
+object WholeFileCSVDataSource extends CSVDataSource {
+ override val isSplitable: Boolean = false
+
+ override def readFile(
+ conf: Configuration,
+ file: PartitionedFile,
+ parser: UnivocityParser,
+ parsedOptions: CSVOptions): Iterator[InternalRow] = {
+ UnivocityParser.tokenizeStream(
+ createInputStream(conf, file.filePath),
+ parsedOptions.headerFlag,
+ parsedOptions.asParserSettings).flatMap { tokens =>
+ parser.convert(tokens)
+ }
+ }
+
+ override def infer(
+ sparkSession: SparkSession,
+ inputPaths: Seq[FileStatus],
+ parsedOptions: CSVOptions): Option[StructType] = {
+ val csv: RDD[PortableDataStream] = createBaseRdd(sparkSession,
inputPaths, parsedOptions)
+ val firstRow = csv.mapPartitions { iter =>
--- End diff --
I think we have so far promoted the usage of `mapPartitions` and iterator
for a better performance (not checked by myself though). At least, these are
being used more commonly up to my knowledge and it makes it consistent. I would
like to keep this way if you are fine.
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