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[SPARK-19709][SQL] Read empty file with CSV data source #17068

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[SPARK-19709][SQL] Read empty file with CSV data source #17068

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1c80d4c
Fix: SPARK-19709 CSV datasource fails to read empty file
wojtek-szymanski Feb 25, 2017
90f315c
test renamed
wojtek-szymanski Feb 25, 2017
aca1852
take(1).headOption instated of exception handling on empty CSV data f…
wojtek-szymanski Feb 28, 2017
aa4e315
master merged
wojtek-szymanski Feb 28, 2017
bdf1890
Code cleanup
wojtek-szymanski Feb 28, 2017
351d4ba
Pattern matching over chaining calls on Option. Unified solution in t…
wojtek-szymanski Mar 5, 2017
b9e9a84
revert changes in tests
wojtek-szymanski Mar 5, 2017
e7faa80
scala style fixed
wojtek-szymanski Mar 6, 2017
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15 changes: 14 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.datasources.csv
import java.math.BigDecimal

import scala.util.control.Exception._
import scala.util.{Failure, Success, Try}

import com.univocity.parsers.csv.CsvParser

Expand All @@ -40,7 +41,19 @@ private[csv] object CSVInferSchema {
csv: Dataset[String],
caseSensitive: Boolean,
options: CSVOptions): StructType = {
val firstLine: String = CSVUtils.filterCommentAndEmpty(csv, options).first()
val lines = CSVUtils.filterCommentAndEmpty(csv, options)
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@HyukjinKwon HyukjinKwon Feb 27, 2017

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Hi @wojtek-szymanski I think we should not rely on exception handling. I can think of take(1).headOption but we could use shorten one if you know any other good way. What do you think about this?

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@wojtek-szymanski wojtek-szymanski Feb 28, 2017

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You are absolutely right. Relying on exception handling is smelly, while Option gives more opportunities. I also see no difference from performance point of view, since both first() and take(1) call the the same function head(1).

Try(lines.first()) match {
case Success(firstLine) => infer(csv, caseSensitive, options, firstLine)
case Failure(e: NoSuchElementException) => StructType(Seq())
case Failure(e) => throw e
}
}

private def infer(
csv: Dataset[String],
caseSensitive: Boolean,
options: CSVOptions,
firstLine: String): StructType = {
val firstRow = new CsvParser(options.asParserSettings).parseLine(firstLine)
val header = makeSafeHeader(firstRow, caseSensitive, options)

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12 changes: 10 additions & 2 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
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Expand Up @@ -298,13 +298,21 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
test("test with empty file and known schema") {
val result = spark.read
.format("csv")
.schema(StructType(List(StructField("column", StringType, false))))
.schema(StructType(List(StructField("column", StringType, nullable = false))))
.load(testFile(emptyFile))

assert(result.collect.size === 0)
assert(result.collect().isEmpty)
assert(result.schema.fieldNames.size === 1)
}

test("test with empty file without schema") {
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@HyukjinKwon HyukjinKwon Mar 4, 2017 •
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Let's re-use the test in CSVSuite.scala#L1083.

We could..

  test("Empty file produces empty dataframe with empty schema") {
    Seq(false, true).foreach { wholeFile =>
      val df = spark.read.format("csv")
        .option("header", true)
        .option("wholeFile", wholeFile)
        .load(testFile(emptyFile))

        assert(df.schema === spark.emptyDataFrame.schema)
        checkAnswer(df, spark.emptyDataFrame)
      }
    }
  }

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@wojtek-szymanski wojtek-szymanski Mar 5, 2017

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Good idea, done

val result = spark.read
.csv(testFile(emptyFile))

assert(result.collect().isEmpty)
assert(result.schema.fieldNames.isEmpty)
}

test("DDL test with empty file") {
withView("carsTable") {
spark.sql(
Expand Down