chong created SPARK-38577:
-----------------------------
Summary: Interval types are not truncated to the expected endField
when creating a DataFrame via Duration
Key: SPARK-38577
URL: https://issues.apache.org/jira/browse/SPARK-38577
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.3.0
Environment: Spark 3.3.0 snapshot version
Reporter: chong
*Problem:*
ANSI interval types are store as long internally.
The long value are not truncated to the expected endField when creating a
DataFrame via Duration.
*Reproduce:*
Create a "day to day" interval, the seconds are not truncated, see below code.
The internal long is not {*}86400 * 1000000{*}, but it's ({*}86400 + 1) *
1000000{*}{*}{*}
{code:java}
test("my test") {
val data = Seq(Row(Duration.ofDays(1).plusSeconds(1)))
val schema = StructType(Array(
StructField("t", DayTimeIntervalType(DayTimeIntervalType.DAY,
DayTimeIntervalType.DAY))
))
val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
df.show()
} {code}
After debug, the {{endField}} is always {{SECOND}} in {{{}durationToMicros{}}},
see below:
{code:java}
// IntervalUtils class
def durationToMicros(duration: Duration): Long = {
durationToMicros(duration, DT.SECOND) // always SECOND
}
def durationToMicros(duration: Duration, endField: Byte)
{code}
Seems should use different endField which could be [DAY, HOUR, MINUTE, SECOND]
--
This message was sent by Atlassian Jira
(v8.20.1#820001)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
