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_r244620744
##########
File path:
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala
##########
@@ -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:
> what code can use this pattern to parse -- do you know?
> I'm still kinda curious where the parsing logic is and how it could use
this format...
I guess it is inside of `DateTimeFormatterBuilder.java` in the method
`parse`:
```java
char sign = text.charAt(position); // IOOBE if invalid position
if (sign == '+' || sign == '-') {
// starts
int negative = (sign == '-' ? -1 : 1);
int[] array = new int[4];
array[0] = position + 1;
if ((parseNumber(array, 1, text, true) ||
parseNumber(array, 2, text, type >=3) ||
parseNumber(array, 3, text, false)) == false) {
// success
long offsetSecs = negative * (array[1] * 3600L +
array[2] * 60L + array[3]);
return context.setParsedField(OFFSET_SECONDS,
offsetSecs, position, array[0]);
}
}
```
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