[jira] [Assigned] (SPARK-33291) Inconsistent NULL conversions to strings redux

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-33291?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-33291:
------------------------------------

    Assignee: Apache Spark

> Inconsistent NULL conversions to strings redux
> ----------------------------------------------
>
>                 Key: SPARK-33291
>                 URL: https://issues.apache.org/jira/browse/SPARK-33291
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.1.0
>            Reporter: Stuart White
>            Assignee: Apache Spark
>            Priority: Minor
>
> The changes in [SPARK-32501 Inconsistent NULL conversions to strings|https://issues.apache.org/jira/browse/SPARK-32501] introduced some behavior that I'd like to clean up a bit.
> Here's sample code to illustrate the behavior I'd like to clean up:
> {noformat}
> val rows = Seq[String](null)
>   .toDF("value")
>   .withColumn("struct1", struct('value as "value1"))
>   .withColumn("struct2", struct('value as "value1", 'value as "value2"))
>   .withColumn("array1", array('value))
>   .withColumn("array2", array('value, 'value))
>   .withColumn("map1", map(lit("value1"), 'value))
>   .withColumn("map2", map(lit("value1"), 'value, lit("value2"), 'value))
> // Show the DataFrame using the "first" codepath.        
> rows.show(truncate=false)
> +-----+-------+-------------+------+--------+----------------+--------------------------------+
> |value|struct1|struct2      |array1|array2  |map1            |map2                            |
> +-----+-------+-------------+------+--------+----------------+--------------------------------+
> |null |{ null}|{ null, null}|[]    |[, null]|{value1 -> null}|{value1 -> null, value2 -> null}|
> +-----+-------+-------------+------+--------+----------------+--------------------------------+
> // Write the DataFrame to disk, then read it back and show it to trigger the "codegen" code path:
> rows.write.parquet("rows")
> spark.read.parquet("rows").show(truncate=false)
> +-----+-------+-------------+-------+-------------+----------------+--------------------------------+
> |value|struct1|struct2      |array1 |array2       |map1            |map2                            |
> +-----+-------+-------------+-------+-------------+----------------+--------------------------------+
> |null |{ null}|{ null, null}|[ null]|[ null, null]|{value1 -> null}|{value1 -> null, value2 -> null}|
> +-----+-------+-------------+-------+-------------+----------------+--------------------------------+
> {noformat}
> Notice:
> 1. If the first element of a struct is null, it is printed with a leading space (e.g. "\{ null\}").  I think it's preferable to print it without the leading space (e.g. "\{null\}").  This is consistent with how non-null values are printed inside a struct.
> 2. If the first element of an array is null, it is not printed at all in the first code path, and the "codegen" code path prints it with a leading space.  I think both code paths should be consistent and print it without a leading space (e.g. "[null]").
> The desired result of this ticket is to product the following output via both code paths:
> {noformat}
> +-----+-------+------------+------+------------+----------------+--------------------------------+
> |value|struct1|struct2     |array1|array2      |map1            |map2                            |
> +-----+-------+------------+------+------------+----------------+--------------------------------+
> |null |{null} |{null, null}|[null]|[null, null]|{value1 -> null}|{value1 -> null, value2 -> null}|
> +-----+-------+------------+------+------------+----------------+--------------------------------+
> {noformat}



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org