koert kuipers created SPARK-19536: ------------------------------------- Summary: Improve capability to merge SQL data types Key: SPARK-19536 URL: https://issues.apache.org/jira/browse/SPARK-19536 Project: Spark Issue Type: Improvement Components: SQL Affects Versions: 2.1.0 Reporter: koert kuipers Priority: Minor
spark's union/merging of compatible types seems kind of weak. it works on basic types in the top level record, but it fails for nested records, maps, arrays, etc. i would like to improve this. for example i get errors like this: {noformat} org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. StructType(StructField(_1,StringType,true), StructField(_2,IntegerType,false)) <> StructType(StructField(_1,StringType,true), StructField(_2,LongType,false)) at the first column of the second table {noformat} some examples that do work: {noformat} scala> Seq(1, 2, 3).toDF union Seq(1L, 2L, 3L).toDF res2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [value: bigint] scala> Seq((1,"x"), (2,"x"), (3,"x")).toDF union Seq((1L,"x"), (2L,"x"), (3L,"x")).toDF res3: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [_1: bigint, _2: string] {noformat} what i would also expect to work but currently doesn't: {noformat} scala> Seq((Seq(1),"x"), (Seq(2),"x"), (Seq(3),"x")).toDF union Seq((Seq(1L),"x"), (Seq(2L),"x"), (Seq(3L),"x")).toDF scala> Seq((1,("x",1)), (2,("x",2)), (3,("x",3))).toDF union Seq((1L,("x",1L)), (2L,("x",2L)), (3L,("x", 3L))).toDF {noformat} -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org