[jira] [Created] (SPARK-37269) The partitionOverwriteMode option is not respected when using insertInto

[David Szakallas (Jira)]

David Szakallas created SPARK-37269:
---

 Summary: The partitionOverwriteMode option is not respected when 
using insertInto
 Key: SPARK-37269
 URL: https://issues.apache.org/jira/browse/SPARK-37269
 Project: Spark
  Issue Type: Bug
  Components: SQL
Affects Versions: 3.2.0
Reporter: David Szakallas


>From the documentation of the {{spark.sql.sources.partitionOverwriteMode}} 
>configuration option:
{quote}This can also be set as an output option for a data source using key 
partitionOverwriteMode (which takes precedence over this setting), e.g. 
dataframe.write.option("partitionOverwriteMode", "dynamic").save(path).
{quote}
This is true when using .save(), however .insertInto() does not respect the 
output option.

 

 



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[jira] [Commented] (SPARK-12823) Cannot create UDF with StructType input

[David Szakallas (Jira)]

[ 
https://issues.apache.org/jira/browse/SPARK-12823?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16958841#comment-16958841
 ] 

David Szakallas commented on SPARK-12823:
-

Since the helper library [~gbarna] created is very slim, why not integrate it 
into Spark itself? I think it would be pretty natural to have a typed udf 
interface.

> Cannot create UDF with StructType input
> ---
>
> Key: SPARK-12823
> URL: https://issues.apache.org/jira/browse/SPARK-12823
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 1.5.2
>Reporter: Frank Rosner
>Priority: Major
>  Labels: bulk-closed
>
> h5. Problem
> It is not possible to apply a UDF to a column that has a struct data type. 
> Two previous requests to the mailing list remained unanswered.
> h5. How-To-Reproduce
> {code}
> val sql = new org.apache.spark.sql.SQLContext(sc)
> import sql.implicits._
> case class KV(key: Long, value: String)
> case class Row(kv: KV)
> val df = sc.parallelize(List(Row(KV(1L, "a")), Row(KV(5L, "b".toDF
> val udf1 = org.apache.spark.sql.functions.udf((kv: KV) => kv.value)
> df.select(udf1(df("kv"))).show
> // java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to $line78.$read$$iwC$$iwC$KV
> val udf2 = org.apache.spark.sql.functions.udf((kv: (Long, String)) => kv._2)
> df.select(udf2(df("kv"))).show
> // org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(kv)' due to 
> data type mismatch: argument 1 requires struct<_1:bigint,_2:string> type, 
> however, 'kv' is of struct type.;
> {code}
> h5. Mailing List Entries
> - 
> https://mail-archives.apache.org/mod_mbox/spark-user/201511.mbox/%3CCACUahd8M=ipCbFCYDyein_=vqyoantn-tpxe6sq395nh10g...@mail.gmail.com%3E
> - https://www.mail-archive.com/user@spark.apache.org/msg43092.html
> h5. Possible Workaround
> If you create a {{UserDefinedFunction}} manually, not using the {{udf}} 
> helper functions, it works. See https://github.com/FRosner/struct-udf, which 
> exposes the {{UserDefinedFunction}} constructor (public from package 
> private). However, then you have to work with a {{Row}}, because it does not 
> automatically convert the row to a case class / tuple.



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