How does iDBQuery handle slow queries and timeouts on big data?

Some questions are simply expensive to compute over billions of rows, and iDBQuery is designed to keep those manageable and honest.

• Efficient SQL. By pushing sums, counts, and grouping into the source database or warehouse, iDBQuery lets the engine that's optimised for it do the work, rather than dragging raw rows across the wire.
• Scope guidance. For a very broad request, it can suggest tightening the window or reducing the breakdown, so you get a fast, meaningful answer instead of waiting on an all-time cross-tab.
• Clear on timeouts. If a query exceeds limits or a source is unresponsive, iDBQuery reports that plainly — it won't silently return incomplete results dressed up as a final figure.

This pairs with querying the full dataset (not a sample) and citing every figure to its rows: you get accuracy without runaway cost. For genuinely heavy, recurring analysis, you can also schedule it as a report so the work runs in the background and the result is ready when you need it, rather than blocking an interactive session.