aldenlau-db commented on code in PR #50315:
URL: https://github.com/apache/spark/pull/50315#discussion_r2004449691
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connector/avro/src/test/scala/org/apache/spark/sql/avro/AvroSuite.scala:
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@@ -963,6 +964,28 @@ abstract class AvroSuite
}
}
+ test("SPARK-49082: Widening date to timestampNTZ in AvroDeserializer") {
+ withTempPath { tempPath =>
+ val datePath = s"$tempPath/date_data"
+ val dateDf =
+ Seq(LocalDate.of(2024, 1, 1),
+ LocalDate.of(2024, 1, 2),
+ LocalDate.of(1312, 2, 27),
+ LocalDate.of(-5877641, 6, 23),
+ LocalDate.of(5881580, 7, 11))
+ .toDF("col")
+ dateDf.write.format("avro").save(datePath)
+ checkAnswer(
+ spark.read.schema("col TIMESTAMP_NTZ").format("avro").load(datePath),
+ Seq(Row(LocalDateTime.of(2024, 1, 1, 0, 0)),
+ Row(LocalDateTime.of(2024, 1, 2, 0, 0)),
+ Row(LocalDateTime.of(1312, 2, 27, 0, 0)),
+ Row(LocalDateTime.of(-5877641, 6, 23, 0, 0)),
+ Row(LocalDateTime.of(5881580, 7, 11, 0, 0)))
Review Comment:
@johanl-db These test cases [fail with
an](https://github.com/aldenlau-db/spark/actions/runs/13936923971/job/39006544565)
`ArithmeticException`. However, this implementation is based on the Parquet
implementation. I noticed 2 issues:
1. The Parquet reader will also fail when attempting to upcast dates such as
these with the same `ArithmeticException` due to `long` overflow.
2. The [Date Spark type supports
dates](https://docs.databricks.com/aws/en/sql/language-manual/data-types/date-type)
from `June 23 -5877641 CE to July 11 +5881580 CE`, but [TimestampNTZ
supports](https://docs.databricks.com/aws/en/sql/language-manual/data-types/timestamp-ntz-type)
`-290308-12-21 BCE 19:59:06 to +294247-01-10 CE 04:00:54`, which is a smaller
range. In the context of type widening, shouldn't this widening be unsupported
by all readers since Date stores a larger range of values than TimestampNTZ
(even though it has less precision)?
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