[jira] [Created] (SPARK-26372) CSV Parsing uses previous good value for bad input field

["Bruce Robbins (JIRA)" <ji...@apache.org>]

Bruce Robbins created SPARK-26372:
-------------------------------------

             Summary: CSV Parsing uses previous good value for bad input field
                 Key: SPARK-26372
                 URL: https://issues.apache.org/jira/browse/SPARK-26372
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 3.0.0
            Reporter: Bruce Robbins


For example:
{noformat}
bash-3.2$ cat test.csv 
"hello",1999-08-01
"there","bad date"
"again","2017-11-22"
bash-3.2$ bin/spark-shell
..etc..
scala> import org.apache.spark.sql.types._
scala> import org.apache.spark.sql.SaveMode
scala> var schema = StructType(StructField("col1", StringType) ::
     |   StructField("col2", DateType) ::
     |   Nil)
schema: org.apache.spark.sql.types.StructType = StructType(StructField(col1,StringType,true), StructField(col2,DateType,true))
scala> val df = spark.read.schema(schema).csv("test.csv")
df: org.apache.spark.sql.DataFrame = [col1: string, col2: date]
scala> df.show
+-----+----------+                                                              
| col1|      col2|
+-----+----------+
|hello|1999-08-01|
|there|1999-08-01|
|again|2017-11-22|
+-----+----------+
scala> 
{noformat}
col2 from the second row contains "1999-08-01", when it should contain null.

This is because UnivocityParser reuses the same Row object for each input record. If there is an exception converting an input field, the code simply skips over that field, leaving the existing value in the Row object.

The simple fix is to set the column to null in the Row object whenever there is a badRecordException while converting the input field.



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