Research: Mapping SQLite Result Columns to Source Table.Columns

SQLite's internal column metadata API, accessible when compiled with SQLITE_ENABLE_COLUMN_METADATA, can track the source table.column for each result column. While Python's standard sqlite3 module doesn't expose this data, two viable approaches exist: the third-party apsw library offers direct access via cursor.description_full, and a custom ctypes bridge can invoke SQLite's sqlite3_column_table_name() C function. A third method involves analyzing query execution plans through EXPLAIN, though this requires parsing complex syntax like CTEs or joins.

These solutions address a practical need: tools like Datasette could benefit from displaying column provenance in query results. However, implementation challenges remain. The apsw library adds dependency overhead, while ctypes requires low-level programming. The EXPLAIN approach struggles with ambiguous or optimized query plans. SQLite's metadata is thus powerful but not universally accessible through standard Python tooling.

Limitations include the need for specialized libraries or custom code, and potential performance impacts from metadata queries. For most users, the standard sqlite3 module's lack of provenance tracking remains a barrier. This work highlights SQLite's capabilities while underscoring the gap between database internals and user-facing tools.