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author | hyukjinkwon <gurwls223@gmail.com> | 2016-08-26 17:29:37 +0200 |
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committer | Herman van Hovell <hvanhovell@databricks.com> | 2016-08-26 17:29:37 +0200 |
commit | 6063d5963fcf01768570c1a9b542be6175a3bcbc (patch) | |
tree | 7e3e9843d0765987224ba734803563d0e2441b94 /sql | |
parent | 341e0e778dff8c404b47d34ee7661b658bb91880 (diff) | |
download | spark-6063d5963fcf01768570c1a9b542be6175a3bcbc.tar.gz spark-6063d5963fcf01768570c1a9b542be6175a3bcbc.tar.bz2 spark-6063d5963fcf01768570c1a9b542be6175a3bcbc.zip |
[SPARK-16216][SQL][FOLLOWUP] Enable timestamp type tests for JSON and verify all unsupported types in CSV
## What changes were proposed in this pull request?
This PR enables the tests for `TimestampType` for JSON and unifies the logics for verifying schema when writing in CSV.
In more details, this PR,
- Enables the tests for `TimestampType` for JSON and
This was disabled due to an issue in `DatatypeConverter.parseDateTime` which parses dates incorrectly, for example as below:
```scala
val d = javax.xml.bind.DatatypeConverter.parseDateTime("0900-01-01T00:00:00.000").getTime
println(d.toString)
```
```
Fri Dec 28 00:00:00 KST 899
```
However, since we use `FastDateFormat`, it seems we are safe now.
```scala
val d = FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSS").parse("0900-01-01T00:00:00.000")
println(d)
```
```
Tue Jan 01 00:00:00 PST 900
```
- Verifies all unsupported types in CSV
There is a separate logics to verify the schemas in `CSVFileFormat`. This is actually not quite correct enough because we don't support `NullType` and `CalanderIntervalType` as well `StructType`, `ArrayType`, `MapType`. So, this PR adds both types.
## How was this patch tested?
Tests in `JsonHadoopFsRelation` and `CSVSuite`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14829 from HyukjinKwon/SPARK-16216-followup.
Diffstat (limited to 'sql')
4 files changed, 26 insertions, 12 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala index 1bf57882ce..9a118fe5a2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala @@ -186,13 +186,18 @@ class CSVFileFormat extends TextBasedFileFormat with DataSourceRegister { } private def verifySchema(schema: StructType): Unit = { - schema.foreach { field => - field.dataType match { - case _: ArrayType | _: MapType | _: StructType => - throw new UnsupportedOperationException( - s"CSV data source does not support ${field.dataType.simpleString} data type.") + def verifyType(dataType: DataType): Unit = dataType match { + case ByteType | ShortType | IntegerType | LongType | FloatType | + DoubleType | BooleanType | _: DecimalType | TimestampType | + DateType | StringType => + + case udt: UserDefinedType[_] => verifyType(udt.sqlType) + case _ => - } + throw new UnsupportedOperationException( + s"CSV data source does not support ${dataType.simpleString} data type.") } + + schema.foreach(field => verifyType(field.dataType)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala index f1b4c11878..1ca6eff1b8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala @@ -290,6 +290,7 @@ private[csv] object CSVTypeCast { DateTimeUtils.millisToDays(DateTimeUtils.stringToTime(datum).getTime) } case _: StringType => UTF8String.fromString(datum) + case udt: UserDefinedType[_] => castTo(datum, udt.sqlType, nullable, options) case _ => throw new RuntimeException(s"Unsupported type: ${castType.typeName}") } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala index 2befad6d72..1930862118 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala @@ -27,8 +27,7 @@ import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.io.compress.GzipCodec import org.apache.spark.SparkException -import org.apache.spark.sql.{DataFrame, QueryTest, Row} -import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.{DataFrame, QueryTest, Row, UDT} import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils} import org.apache.spark.sql.types._ @@ -681,6 +680,19 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils { Seq((1, Array("Tesla", "Chevy", "Ford"))).toDF("id", "brands").write.csv(csvDir) }.getMessage assert(msg.contains("CSV data source does not support array<string> data type")) + + msg = intercept[UnsupportedOperationException] { + Seq((1, new UDT.MyDenseVector(Array(0.25, 2.25, 4.25)))).toDF("id", "vectors") + .write.csv(csvDir) + }.getMessage + assert(msg.contains("CSV data source does not support array<double> data type")) + + msg = intercept[SparkException] { + val schema = StructType(StructField("a", new UDT.MyDenseVectorUDT(), true) :: Nil) + spark.range(1).write.csv(csvDir) + spark.read.schema(schema).csv(csvDir).collect() + }.getCause.getMessage + assert(msg.contains("Unsupported type: array")) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala index 52486b122a..d79edee5b1 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala @@ -32,10 +32,6 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest { override protected def supportsDataType(dataType: DataType): Boolean = dataType match { case _: NullType => false case _: BinaryType => false - // `TimestampType` is disabled because `DatatypeConverter.parseDateTime()` - // in `DateTimeUtils` parses the formatted string wrongly when the date is - // too early. (e.g. "1600-07-13T08:36:32.847"). - case _: TimestampType => false case _: CalendarIntervalType => false case _ => true } |