How does iDBQuery work with a database that has hundreds of tables?
iDBQuery works across large schemas by introspecting every table once, then using the semantic layer and your question to pick only the relevant tables and joins for each query. You don't need to know where anything lives — you ask in plain language and it locates the right tables and cites the answer.
Real production databases often have hundreds or thousands of tables, and no single person knows them all. iDBQuery is built to make that navigable.
- One-time introspection. On connect it maps the whole schema — tables, columns, keys, relationships — and stores that understanding.
- Per-question routing. For each query it selects only the tables that matter, based on your intent and the semantic layer, and works out the joins between them.
- Guided discovery. You can ask "which tables have customer data?" or "where are invoices stored?" and iDBQuery answers from its map, so onboarding to an unfamiliar system takes minutes.
This is a core advantage over hand-written SQL, where finding the right table across a huge schema is often the slowest part of the job. Because iDBQuery decides which source and tables to query for you and then shows the SQL and cites the rows, you get both the convenience of not memorising the schema and the transparency to verify it reached for the correct tables.
Updated 2026-06-22