Is there a limit to how much data iDBQuery can handle?
There is no small hard limit — iDBQuery queries your data where it lives, so it scales with your own databases and warehouses rather than loading everything into memory. It pushes work down to the source and handles large tables through efficient SQL rather than copying data.
Because of how iDBQuery is designed, the ceiling on data size is set by your own systems, not by an artificial in-app cap.
- Query in place. iDBQuery runs SQL against your source, so a large table is handled by the database that already stores it, not by pulling everything into memory first.
- Push down the work. Aggregations and filters execute at the source, so a question over billions of rows returns just the summarised result.
- Efficient by construction. The generated SQL uses joins, filters and aggregation rather than scanning and copying full datasets.
This is a key difference from tools that must import or extract data before analysing it, where size quickly becomes a bottleneck. With iDBQuery, if your warehouse can compute a total over a huge fact table, iDBQuery can ask it to. For example, 'monthly revenue trend over the last three years' against a very large transactions table returns a small trend line, because the heavy lifting happens in the database and only the answer comes back. For file-based sources like large folders of documents, iDBQuery ingests and indexes them so they stay queryable too. In short, iDBQuery is meant to grow with your data, not to be outgrown by it.
Updated 2026-06-22