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[sqlalchemy-spanner]: _designate_type() raises KeyError on unrecognized Spanner column types instead of falling back to NullType #18839
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Summary of the issue
SpannerDialect._designate_type() performs an unguarded _type_map[str_repr] lookup and raises KeyError whenever a reflected table (or any table in the same schema during get_multi_columns()) contains a Spanner column type not in _type_map (such as INTERVAL, PROTO<...>, ENUM<...>, or STRUCT<...>, as well as UUID and TOKENLIST prior to 1.20.0).
Environment details
OS: Linux
Python version: 3.13
sqlalchemy-spanner version: 1.17.2 and main / 1.20.1 (eb8a76628adf10093959600071a18f50285c2ecf)
While UUID (#17913) and TOKENLIST (#17911) were added to _type_map in 1.20.0, _designate_type() still raises an unhandled KeyError for any other valid Spanner column type not in _type_map (e.g. INTERVAL, PROTO<...>, ENUM<...>, STRUCT<...>). Because get_multi_columns() reflects all tables in a schema when filter_names=None (or when MetaData.reflect() reflects a schema), a single table with an unrecognized column type crashes reflection for the entire schema.
In SQLAlchemy's built-in dialects (e.g., PostgreSQL, MySQL, SQLite, Oracle), unrecognized column types during reflection emit sa.util.warn(f"Did not recognize type '{str_repr}' ...") and return sa.types.NullType(). (See also #16622.)
_designate_type() maps supported Spanner types and falls back to sa.types.NullType() with sa.util.warn(...) for unrecognized column types instead of raising KeyError.
Activity
fix(sqlalchemy-spanner): reflect vector columns and unrecognized colu…