CHAR/VARCHAR fields sometimes show as STRING in a view

[kmenke-em]

We've found an interesting behavior where `char` and `varchar` fields in a table show as the `string` type in a view. Consider the following table and view:

create or replace table thirty_day_tables.kit_varchar_string (
  str1 string,
  str2 char(10),
  str3 varchar(10)
);

create or replace view thirty_day_tables.kit_varchar_string_vw as 
select str1, str2, str3, cast("dummy" as varchar(10)) as str4 
from thirty_day_tables.kit_varchar_string;

In this case, it mostly works as I expected except for the manufactured field `str4`. The output for `

describe thirty_day_tables.kit_varchar_string_vw`:

col_name	data_type	comment
str1	string	null
str2	char(10)	null
str3	varchar(10)	null
str4	string	null

Why does str4 show as a string despite the explicit cast to a varchar(10)?

Even stranger, if I add a comment to each column I get a different result:

create or replace view thirty_day_tables.kit_varchar_string_vw (
  str1 comment "OK - expected to be a string in the view",
  str2 comment "ERROR - expected to be a char(10) in the view",
  str3 comment "ERROR - expected to be a varchar(10) in the view",
  str4 comment "ERROR - expected to be a varchar(10) in the view"
 ) as 
select str1, str2, str3, cast("dummy" as varchar(10)) as str4 
from thirty_day_tables.kit_varchar_string;

The output for `describe thirty_day_tables.kit_varchar_string_vw`:

col_name	data_type	comment
str1	string	OK - expected to be a string in the view
str2	string	ERROR - expected to be a char(10) in the view
str3	string	ERROR - expected to be a varchar(10) in the view
str4	string	ERROR - expected to be a varchar(10) in the view

Why do all the columns show as string???

[UmaMahesh1 · Accepted solution]

In Spark SQL

• string is the canonical type for all textual data.
• char(n) and varchar(n) are parsed and stored as metadata, but internally treated as string.
• When you create a view, Spark does not preserve the original char(n) or varchar(n) types — it normalizes everything to string.

This is why:

• In your first view (without comments), Spark is able to preserve the metadata from the underlying table for str2 and str3.
• But when you add column comments, Spark reconstructs the schema from the SELECT clause and drops the original metadata, defaulting all string-like types to string.