Robert Joseph Evans created SPARK-47063:
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Summary: CAST long to timestamp has different behavior for codegen
vs interpreted
Key: SPARK-47063
URL: https://issues.apache.org/jira/browse/SPARK-47063
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.4.2
Reporter: Robert Joseph Evans
It probably impacts a lot more versions of the code than this, but I verified
it on 3.4.2. This also appears to be related to
https://issues.apache.org/jira/browse/SPARK-39209
{code:java}
scala> Seq(Long.MaxValue, Long.MinValue, 0L, 1990L).toDF("v").selectExpr("*",
"CAST(v AS timestamp) as ts").selectExpr("*", "unix_micros(ts)").show(false)
+--------------------+-----------------------------+--------------------+
|v |ts |unix_micros(ts) |
+--------------------+-----------------------------+--------------------+
|9223372036854775807 |+294247-01-10 04:00:54.775807|9223372036854775807 |
|-9223372036854775808|-290308-12-21 19:59:05.224192|-9223372036854775808|
|0 |1970-01-01 00:00:00 |0 |
|1990 |1970-01-01 00:33:10 |1990000000 |
+--------------------+-----------------------------+--------------------+
scala> Seq(Long.MaxValue, Long.MinValue, 0L,
1990L).toDF("v").repartition(1).selectExpr("*", "CAST(v AS timestamp) as
ts").selectExpr("*", "unix_micros(ts)").show(false)
+--------------------+-------------------+---------------+
|v |ts |unix_micros(ts)|
+--------------------+-------------------+---------------+
|9223372036854775807 |1969-12-31 23:59:59|-1000000 |
|-9223372036854775808|1970-01-01 00:00:00|0 |
|0 |1970-01-01 00:00:00|0 |
|1990 |1970-01-01 00:33:10|1990000000 |
+--------------------+-------------------+---------------+
{code}
It looks like InMemoryTableScanExec is not doing code generation for the
expressions, but the ProjectExec after the repartition is.
If I disable code gen I get the same answer in both cases.
{code:java}
scala> spark.conf.set("spark.sql.codegen.wholeStage", false)
scala> spark.conf.set("spark.sql.codegen.factoryMode", "NO_CODEGEN")
scala> Seq(Long.MaxValue, Long.MinValue, 0L, 1990L).toDF("v").selectExpr("*",
"CAST(v AS timestamp) as ts").selectExpr("*", "unix_micros(ts)").show(false)
+--------------------+-----------------------------+--------------------+
|v |ts |unix_micros(ts) |
+--------------------+-----------------------------+--------------------+
|9223372036854775807 |+294247-01-10 04:00:54.775807|9223372036854775807 |
|-9223372036854775808|-290308-12-21 19:59:05.224192|-9223372036854775808|
|0 |1970-01-01 00:00:00 |0 |
|1990 |1970-01-01 00:33:10 |1990000000 |
+--------------------+-----------------------------+--------------------+
scala> Seq(Long.MaxValue, Long.MinValue, 0L,
1990L).toDF("v").repartition(1).selectExpr("*", "CAST(v AS timestamp) as
ts").selectExpr("*", "unix_micros(ts)").show(false)
+--------------------+-----------------------------+--------------------+
|v |ts |unix_micros(ts) |
+--------------------+-----------------------------+--------------------+
|9223372036854775807 |+294247-01-10 04:00:54.775807|9223372036854775807 |
|-9223372036854775808|-290308-12-21 19:59:05.224192|-9223372036854775808|
|0 |1970-01-01 00:00:00 |0 |
|1990 |1970-01-01 00:33:10 |1990000000 |
+--------------------+-----------------------------+--------------------+
{code}
[https://github.com/apache/spark/blob/e2cd71a4cd54bbdf5af76d3edfbb2fc8c1b067b6/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L1627]
Is the code used in codegen, but
[https://github.com/apache/spark/blob/e2cd71a4cd54bbdf5af76d3edfbb2fc8c1b067b6/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L687]
is what is used outside of code gen.
Apparently `SECONDS.toMicros` truncates the value on an overflow, but the
codegen does not.
{code:java}
scala> Long.MaxValue
res11: Long = 9223372036854775807
scala> java.util.concurrent.TimeUnit.SECONDS.toMicros(Long.MaxValue)
res12: Long = 9223372036854775807
scala> Long.MaxValue * (1000L * 1000L)
res13: Long = -1000000
{code}
Ideally these would be consistent with each other. I personally would prefer
the truncation as it feels more accurate, but I am fine either way.
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