xsys created SPARK-40630:
----------------------------
Summary: Both SparkSQL and DataFrame insert invalid DATE/TIMESTAMP
as NULL
Key: SPARK-40630
URL: https://issues.apache.org/jira/browse/SPARK-40630
Project: Spark
Issue Type: Bug
Components: Spark Shell, SQL
Affects Versions: 3.2.1
Reporter: xsys
h3. Describe the bug
When we construct a DataFrame with an invalid DATE/TIMESTAMP (e.g.
{{{}1969-12-31 23:59:59 B{}}}) via {{{}spark-shell{}}}, or insert an invalid
DATE/TIMESTAMP into a table via {{{}spark-sql{}}}, both interfaces unexpectedly
evaluate the invalid value to {{{}NULL{}}}, instead of throwing an exception.
h3. To Reproduce
On Spark 3.2.1 (commit {{{}4f25b3f712{}}}), using {{{}spark-sql{}}}:
{code:java}
$SPARK_HOME/bin/spark-sql{code}
Execute the following:
{code:java}
spark-sql> create table timestamp_vals(c1 TIMESTAMP) stored as ORC;
spark-sql> insert into timestamp_vals select cast(" 1969-12-31 23:59:59 B "as
timestamp);
spark-sql> select * from timestamp_vals;
NULL{code}
Using {{{}spark-shell{}}}:
{code:java}
$SPARK_HOME/bin/spark-shell{code}
Execute the following:
{code:java}
scala> val rdd = sc.parallelize(Seq(Row(Seq(" 1969-12-31 23:59:59 B
").toDF("time").select(to_timestamp(col("ti
me")).as("to_timestamp")).first().getAs[java.sql.Timestamp](0))))
rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] =
ParallelCollectionRDD[721] at parallelize at <console>:28
scala> val schema = new StructType().add(StructField("c1", TimestampType,
true))
schema: org.apache.spark.sql.types.StructType =
StructType(StructField(c1,TimestampType,true))
scala> val df = spark.createDataFrame(rdd, schema)
df194: org.apache.spark.sql.DataFrame = [c1: timestamp]
scala> df.show(false)
+----+
|c1 |
+----+
|null|
+----+
{code}
h3. Expected behavior
We expect both {{spark-sql}} & {{spark-shell}} interfaces to throw an exception
for an invalid DATE/TIMESTAMP, like what they do for most of the other data
types (e.g. invalid value {{"foo"}} for {{INT}} data type).
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