How does iDBQuery cope with a table that has hundreds of columns?
iDBQuery handles very wide tables by using the semantic layer and its understanding of your question to select only the relevant columns, rather than trying to reason over all of them. It writes SQL that reads just what's needed and cites the result, so a 300-column table stays fast and legible.
Enterprise tables can be enormous — hundreds of columns of flags, codes, and rarely used fields. Dumping all of that at the model would be slow and error-prone, so iDBQuery narrows the field first.
- Relevance selection. From your question it identifies the handful of columns that matter — the measure, the dimensions, the date — and writes SQL that selects only those.
- Semantic layer. Annotations and the business glossary describe what cryptic columns mean, so iDBQuery can find `dt_cls_fsc` is the fiscal close date without you spelling it out.
- Progressive exploration. You can ask "what columns are in this table?" or "which fields look like dates?" to orient yourself before diving in.
This keeps answers quick and readable even on sprawling schemas, and it's a big part of why non-technical users can work with tables that would overwhelm a spreadsheet. Because iDBQuery shows the SQL and cites the exact source rows, you always see which columns it actually used — so a wide, intimidating table becomes just as approachable as a small one.
Updated 2026-06-22