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[SPARK-25134][SQL] Csv column pruning with checking of headers throws incorrect error #22123

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[SPARK-25134][SQL] Csv column pruning with checking of headers throws incorrect error #22123

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dcd9ac4
if csv column-pruning is turned on header should be checked with requ…
koertkuipers Aug 16, 2018
c4179a9
update jira reference in unit test
koertkuipers Aug 16, 2018
09c986c
remove test for check header and projection with column pruning disabled
koertkuipers Aug 17, 2018
cd18ed2
also check multiLine codepath and selecting empty schema with count
koertkuipers Aug 19, 2018
f2eb1df
remove checkHeader and use checkHeaderColumnNames directly
koertkuipers Aug 20, 2018
667db3c
use style convention of explicit vals
koertkuipers Aug 20, 2018
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5 changes: 2 additions & 3 deletions sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -505,10 +505,9 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
StructType(schema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord))

val linesWithoutHeader: RDD[String] = maybeFirstLine.map { firstLine =>
CSVDataSource.checkHeader(
firstLine,
new CsvParser(parsedOptions.asParserSettings),
CSVDataSource.checkHeaderColumnNames(
actualSchema,
new CsvParser(parsedOptions.asParserSettings).parseLine(firstLine),
csvDataset.getClass.getCanonicalName,
parsedOptions.enforceSchema,
sparkSession.sessionState.conf.caseSensitiveAnalysis)
Expand Down
37 changes: 10 additions & 27 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVDataSource.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,8 @@ abstract class CSVDataSource extends Serializable {
requiredSchema: StructType,
// Actual schema of data in the csv file
dataSchema: StructType,
caseSensitive: Boolean): Iterator[InternalRow]
caseSensitive: Boolean,
columnPruning: Boolean): Iterator[InternalRow]

/**
* Infers the schema from `inputPaths` files.
Expand Down Expand Up @@ -181,25 +182,6 @@ object CSVDataSource extends Logging {
}
}
}

/**
* Checks that CSV header contains the same column names as fields names in the given schema
* by taking into account case sensitivity.
*/
def checkHeader(
header: String,
parser: CsvParser,
schema: StructType,
fileName: String,
enforceSchema: Boolean,
caseSensitive: Boolean): Unit = {
checkHeaderColumnNames(
schema,
parser.parseLine(header),
fileName,
enforceSchema,
caseSensitive)
}
}

object TextInputCSVDataSource extends CSVDataSource {
Expand All @@ -211,7 +193,8 @@ object TextInputCSVDataSource extends CSVDataSource {
parser: UnivocityParser,
requiredSchema: StructType,
dataSchema: StructType,
caseSensitive: Boolean): Iterator[InternalRow] = {
caseSensitive: Boolean,
columnPruning: Boolean): Iterator[InternalRow] = {
val lines = {
val linesReader = new HadoopFileLinesReader(file, conf)
Option(TaskContext.get()).foreach(_.addTaskCompletionListener[Unit](_ => linesReader.close()))
Expand All @@ -227,10 +210,9 @@ object TextInputCSVDataSource extends CSVDataSource {
// Note: if there are only comments in the first block, the header would probably
// be not extracted.
CSVUtils.extractHeader(lines, parser.options).foreach { header =>
CSVDataSource.checkHeader(
header,
parser.tokenizer,
dataSchema,
CSVDataSource.checkHeaderColumnNames(
if (columnPruning) requiredSchema else dataSchema,
parser.tokenizer.parseLine(header),
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@gatorsmile gatorsmile Aug 20, 2018 •
edited
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Nit: the following code style is preferred.

val schema = if (columnPruning) requiredSchema else dataSchema
val columnNames = parser.tokenizer.parseLine(header)
CSVDataSource.checkHeaderColumnNames(
  schema,
  columnNames,
  ...

file.filePath,
parser.options.enforceSchema,
caseSensitive)
Expand Down Expand Up @@ -308,10 +290,11 @@ object MultiLineCSVDataSource extends CSVDataSource {
parser: UnivocityParser,
requiredSchema: StructType,
dataSchema: StructType,
caseSensitive: Boolean): Iterator[InternalRow] = {
caseSensitive: Boolean,
columnPruning: Boolean): Iterator[InternalRow] = {
def checkHeader(header: Array[String]): Unit = {
CSVDataSource.checkHeaderColumnNames(
dataSchema,
if (columnPruning) requiredSchema else dataSchema,
header,
file.filePath,
parser.options.enforceSchema,
Expand Down
4 changes: 3 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala
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Expand Up @@ -131,6 +131,7 @@ class CSVFileFormat extends TextBasedFileFormat with DataSourceRegister {
)
}
val caseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
val columnPruning = sparkSession.sessionState.conf.csvColumnPruning

(file: PartitionedFile) => {
val conf = broadcastedHadoopConf.value.value
Expand All @@ -144,7 +145,8 @@ class CSVFileFormat extends TextBasedFileFormat with DataSourceRegister {
parser,
requiredSchema,
dataSchema,
caseSensitive)
caseSensitive,
columnPruning)
}
}

Expand Down
33 changes: 33 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
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Expand Up @@ -1603,6 +1603,39 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils with Te
.exists(msg => msg.getRenderedMessage.contains("CSV header does not conform to the schema")))
}

test("SPARK-25134: check header on parsing of dataset with projection and column pruning") {
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@gatorsmile gatorsmile Aug 20, 2018

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Also need a test case for checking enforceSchema works well when column pruning is on.

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@koertkuipers koertkuipers Aug 20, 2018

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it seems enforceSchema always kind of "works" because it simply means it ignores the headers.
what do we expect to verify in the test?

withSQLConf(SQLConf.CSV_PARSER_COLUMN_PRUNING.key -> "true") {
Seq(false, true).foreach { multiLine =>
withTempPath { path =>
val dir = path.getAbsolutePath
Seq(("a", "b")).toDF("columnA", "columnB").write
.format("csv")
.option("header", true)
.save(dir)

// schema with one column
checkAnswer(spark.read
.format("csv")
.option("header", true)
.option("enforceSchema", false)
.option("multiLine", multiLine)
.load(dir)
.select("columnA"),
Row("a"))

// empty schema
assert(spark.read
.format("csv")
.option("header", true)
.option("enforceSchema", false)
.option("multiLine", multiLine)
.load(dir)
.count() === 1L)
}
}
}
}

test("SPARK-24645 skip parsing when columnPruning enabled and partitions scanned only") {
withSQLConf(SQLConf.CSV_PARSER_COLUMN_PRUNING.key -> "true") {
withTempPath { path =>
Expand Down