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https://issues.apache.org/jira/browse/SPARK-40630?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17612177#comment-17612177
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Hyukjin Kwon commented on SPARK-40630:
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Can you turn {{spark.sql.ansi.enabled}} on?
> 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
> Priority: Major
>
> 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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