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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2018/12/31 09:55:36 UTC
[GitHub] MaxGekk commented on a change in pull request #23417:
[SPARK-26374][TEST][SQL] Enable TimestampFormatter in HadoopFsRelationTest
MaxGekk commented on a change in pull request #23417: [SPARK-26374][TEST][SQL] Enable TimestampFormatter in HadoopFsRelationTest
URL: https://github.com/apache/spark/pull/23417#discussion_r244569371
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File path: sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala
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@@ -126,61 +126,60 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
} else {
Seq(false)
}
- // TODO: Support new parser too, see SPARK-26374.
- withSQLConf(SQLConf.LEGACY_TIME_PARSER_ENABLED.key -> "true") {
- for (dataType <- supportedDataTypes) {
- for (parquetDictionaryEncodingEnabled <- parquetDictionaryEncodingEnabledConfs) {
- val extraMessage = if (isParquetDataSource) {
- s" with parquet.enable.dictionary = $parquetDictionaryEncodingEnabled"
- } else {
- ""
- }
- logInfo(s"Testing $dataType data type$extraMessage")
-
- val extraOptions = Map[String, String](
- "parquet.enable.dictionary" -> parquetDictionaryEncodingEnabled.toString
- )
-
- withTempPath { file =>
- val path = file.getCanonicalPath
-
- val seed = System.nanoTime()
- withClue(s"Random data generated with the seed: ${seed}") {
- val dataGenerator = RandomDataGenerator.forType(
- dataType = dataType,
- nullable = true,
- new Random(seed)
- ).getOrElse {
- fail(s"Failed to create data generator for schema $dataType")
- }
-
- // Create a DF for the schema with random data. The index field is used to sort the
- // DataFrame. This is a workaround for SPARK-10591.
- val schema = new StructType()
- .add("index", IntegerType, nullable = false)
- .add("col", dataType, nullable = true)
- val rdd =
- spark.sparkContext.parallelize((1 to 10).map(i => Row(i, dataGenerator())))
- val df = spark.createDataFrame(rdd, schema).orderBy("index").coalesce(1)
-
- df.write
- .mode("overwrite")
- .format(dataSourceName)
- .option("dataSchema", df.schema.json)
- .options(extraOptions)
- .save(path)
-
- val loadedDF = spark
- .read
- .format(dataSourceName)
- .option("dataSchema", df.schema.json)
- .schema(df.schema)
- .options(extraOptions)
- .load(path)
- .orderBy("index")
-
- checkAnswer(loadedDF, df)
+
+ for (dataType <- supportedDataTypes) {
+ for (parquetDictionaryEncodingEnabled <- parquetDictionaryEncodingEnabledConfs) {
+ val extraMessage = if (isParquetDataSource) {
+ s" with parquet.enable.dictionary = $parquetDictionaryEncodingEnabled"
+ } else {
+ ""
+ }
+ logInfo(s"Testing $dataType data type$extraMessage")
+
+ val extraOptions = Map[String, String](
+ "parquet.enable.dictionary" -> parquetDictionaryEncodingEnabled.toString,
+ "timestampFormat" -> "yyyy-MM-dd'T'HH:mm:ss.SSSXXXXX"
Review comment:
> Should this... be a default?
I am not completely sure that `XXXXX` should be default pattern for time zone offset because:
1. It could potentially break existing user apps. For example `SimpleDateFormat` cannot recognize such zone offset.
2. Legacy parser is still supported, and `SimpleDateFormat` will throw `IllegalArgumentException` if the number of pattern letters is 4 or more.
3. I think it is rare case when old timestamps (< 1582 year) are written with time zone offset, and should be read back.
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