HyukjinKwon commented on issue #26910: [SPARK-30154][ML] PySpark UDF to convert 
MLlib vectors to dense arrays
URL: https://github.com/apache/spark/pull/26910#issuecomment-566842696
 
 
   @cloud-fan, I know UDT became private and we plan to redesign it later.
   However, what about we allow the case when the UDT is cast into its own 
`sqlType`, or do you know why we don't allow this case?
   
   ```scala
   scala> val df = Seq(Tuple1(org.apache.spark.ml.linalg.Vectors.dense(1.0, 
2.0, 3.0))).toDF("vec")
   df: org.apache.spark.sql.DataFrame = [vec: vector]
   
   scala> df.selectExpr("cast(vec as string)").show()
   +-------------+
   |          vec|
   +-------------+
   |[1.0,2.0,3.0]|
   +-------------+
   
   scala> df.selectExpr("cast(vec as 
struct<type:tinyint,size:int,indices:array<int>,values:array<double>>)").show()
   org.apache.spark.sql.AnalysisException: cannot resolve '`vec`' due to data 
type mismatch: cannot cast 
struct<type:tinyint,size:int,indices:array<int>,values:array<double>> to 
struct<type:tinyint,size:int,indices:array<int>,values:array<double>>; line 1 
pos 0;
   'Project [unresolvedalias(cast(vec#74 as 
struct<type:tinyint,size:int,indices:array<int>,values:array<double>>), None)]
   +- Project [_1#71 AS vec#74]
      +- LocalRelation [_1#71]
   
     at 
org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
     at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:146)
     at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:137)
     at 
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUp$2(TreeNode.scala:310)
     at 
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
   ```
   
   Currently, UDT can be cast to string but cannot be its own `sqlType` 
(https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/linalg/VectorUDT.scala#L88-L99).
   
   It's internally `InternalRow` so I think it seems fine to allow this case 
for now.

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