[
https://issues.apache.org/jira/browse/SPARK-38577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
chong updated SPARK-38577:
--------------------------
Description:
*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]
Or Spark can throw an exception to avoid truncating.
was:
*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]
> 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
> Priority: Major
>
> *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]
> Or Spark can throw an exception to avoid truncating.
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