Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16976#discussion_r103060062
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala
---
@@ -961,56 +978,135 @@ class CSVSuite extends QueryTest with
SharedSQLContext with SQLTestUtils {
}
test("SPARK-18699 put malformed records in a `columnNameOfCorruptRecord`
field") {
- val schema = new StructType().add("a", IntegerType).add("b",
TimestampType)
- val df1 = spark
- .read
- .option("mode", "PERMISSIVE")
- .schema(schema)
- .csv(testFile(valueMalformedFile))
- checkAnswer(df1,
- Row(null, null) ::
- Row(1, java.sql.Date.valueOf("1983-08-04")) ::
- Nil)
-
- // If `schema` has `columnNameOfCorruptRecord`, it should handle
corrupt records
- val columnNameOfCorruptRecord = "_unparsed"
- val schemaWithCorrField1 = schema.add(columnNameOfCorruptRecord,
StringType)
- val df2 = spark
- .read
- .option("mode", "PERMISSIVE")
- .option("columnNameOfCorruptRecord", columnNameOfCorruptRecord)
- .schema(schemaWithCorrField1)
- .csv(testFile(valueMalformedFile))
- checkAnswer(df2,
- Row(null, null, "0,2013-111-11 12:13:14") ::
- Row(1, java.sql.Date.valueOf("1983-08-04"), null) ::
- Nil)
-
- // We put a `columnNameOfCorruptRecord` field in the middle of a schema
- val schemaWithCorrField2 = new StructType()
- .add("a", IntegerType)
- .add(columnNameOfCorruptRecord, StringType)
- .add("b", TimestampType)
- val df3 = spark
- .read
- .option("mode", "PERMISSIVE")
- .option("columnNameOfCorruptRecord", columnNameOfCorruptRecord)
- .schema(schemaWithCorrField2)
- .csv(testFile(valueMalformedFile))
- checkAnswer(df3,
- Row(null, "0,2013-111-11 12:13:14", null) ::
- Row(1, null, java.sql.Date.valueOf("1983-08-04")) ::
- Nil)
-
- val errMsg = intercept[AnalysisException] {
- spark
+ Seq(false, true).foreach { bool =>
+ val schema = new StructType().add("a", IntegerType).add("b",
TimestampType)
+ val df1 = spark
+ .read
+ .option("mode", "PERMISSIVE")
+ .option("wholeFile", bool)
+ .schema(schema)
+ .csv(testFile(valueMalformedFile))
+ checkAnswer(df1,
+ Row(null, null) ::
+ Row(1, java.sql.Date.valueOf("1983-08-04")) ::
+ Nil)
+
+ // If `schema` has `columnNameOfCorruptRecord`, it should handle
corrupt records
+ val columnNameOfCorruptRecord = "_unparsed"
+ val schemaWithCorrField1 = schema.add(columnNameOfCorruptRecord,
StringType)
+ val df2 = spark
.read
.option("mode", "PERMISSIVE")
.option("columnNameOfCorruptRecord", columnNameOfCorruptRecord)
- .schema(schema.add(columnNameOfCorruptRecord, IntegerType))
+ .option("wholeFile", bool)
+ .schema(schemaWithCorrField1)
.csv(testFile(valueMalformedFile))
- .collect
- }.getMessage
- assert(errMsg.startsWith("The field for corrupt records must be string
type and nullable"))
+ checkAnswer(df2,
+ Row(null, null, "0,2013-111-11 12:13:14") ::
+ Row(1, java.sql.Date.valueOf("1983-08-04"), null) ::
+ Nil)
+
+ // We put a `columnNameOfCorruptRecord` field in the middle of a
schema
+ val schemaWithCorrField2 = new StructType()
+ .add("a", IntegerType)
+ .add(columnNameOfCorruptRecord, StringType)
+ .add("b", TimestampType)
+ val df3 = spark
+ .read
+ .option("mode", "PERMISSIVE")
+ .option("columnNameOfCorruptRecord", columnNameOfCorruptRecord)
+ .option("wholeFile", bool)
+ .schema(schemaWithCorrField2)
+ .csv(testFile(valueMalformedFile))
+ checkAnswer(df3,
+ Row(null, "0,2013-111-11 12:13:14", null) ::
+ Row(1, null, java.sql.Date.valueOf("1983-08-04")) ::
+ Nil)
+
+ val errMsg = intercept[AnalysisException] {
+ spark
+ .read
+ .option("mode", "PERMISSIVE")
+ .option("columnNameOfCorruptRecord", columnNameOfCorruptRecord)
+ .option("wholeFile", bool)
+ .schema(schema.add(columnNameOfCorruptRecord, IntegerType))
+ .csv(testFile(valueMalformedFile))
+ .collect
+ }.getMessage
+ assert(errMsg.startsWith("The field for corrupt records must be
string type and nullable"))
+ }
+ }
+
+ test("SPARK-19610: Parse normal multi-line CSV files") {
+ val primitiveFieldAndType = Seq(
+ """"
+ |string","integer
+ |
+ |
+ |","long
+ |
+ |","bigInteger",double,boolean,null""".stripMargin,
+ """"this is a
+ |simple
+ |string.","
+ |
+ |10","
+ |21474836470","92233720368547758070","
+ |
+ |1.7976931348623157E308",true,""".stripMargin)
+
+ withTempPath { path =>
+
primitiveFieldAndType.toDF("value").coalesce(1).write.text(path.getAbsolutePath)
+
+ val df = spark.read
+ .option("header", true)
+ .option("wholeFile", true)
+ .csv(path.getAbsolutePath)
+
+ // Check if headers have new lines in the names.
+ val actualFields = df.schema.fieldNames.toSeq
+ val expectedFields =
+ Seq("\nstring", "integer\n\n\n", "long\n\n", "bigInteger",
"double", "boolean", "null")
+ assert(actualFields === expectedFields)
+
+ // Check if the rows have new lines in the values.
+ val expected = Row(
+ "this is a\nsimple\nstring.",
+ "\n\n10",
+ "\n21474836470",
+ "92233720368547758070",
+ "\n\n1.7976931348623157E308",
+ "true",
+ null)
+ checkAnswer(df, expected)
+
+ val csvDir = new File(path.getAbsolutePath, "csv").getAbsolutePath
+ val cleanCols = df.schema
+ .map(f => regexp_replace(col(f.name), "[\\r\\n]", ""))
+ val dfWithoutWhiteSpaces = df.select(cleanCols: _*)
+ dfWithoutWhiteSpaces.write.csv(csvDir)
--- End diff --
I meant to test that we are able to read this back without `wholeFile`
option if we remove all newlines.
```scala
// Check if the rows are the same if we remove all white spaces.
val readBack = spark.read.csv(csvDir)
checkAnswer(dfWithoutWhiteSpaces, readBack)
```
Let me clean up this one too.
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