ColumnLens – Query millions of rows in milliseconds on your Mac

We keep shipping data to the cloud to analyze it, but modern Macs have enough horsepower to query millions of rows locally in milliseconds. Even an 8GB MacBook Neo.

I built ColumnLens because I wanted to stop uploading sensitive data to SaaS tools just to run a GROUP BY. It's a native C++ desktop app that opens 5GB+ CSV, JSONL, Parquet, and Excel files in about 3 seconds, then lets you query them with DuckDB's full SQL engine — JOINs, CTEs, window functions, all running on your machine.

The idea is simple: your laptop is a perfectly good analytics workstation. You shouldn't need a cloud pipeline to look at your data.

Some things I learned building it:

- DuckDB's columnar engine is absurdly efficient. 12M rows, sub-second queries, under 1 GB RAM. On hardware that fits in a backpack.

- ImGui + OpenGL is a great stack for data-heavy desktop apps. No DOM, no layout engine overhead — just raw GPU rendering. The data grid scrolls 12M rows without a single frame drop.

- I added a "3D City View" that maps rows to buildings (height = one column, color = another). Sounds gimmicky, but outliers and clusters are immediately visible in ways they aren't in tables or charts. Our brains are good at spotting the tall building in a skyline.

- Lua scripting turned out more useful than expected. People write small scripts to fetch data from APIs, run queries, and configure charts — repeatable analysis without leaving the app.

- Everything runs locally. Zero telemetry, zero network calls. The binary is 33MB, statically linked.

Free download at https://columnlens.com

Would love to hear from anyone else building local-first data tools — I think the "ship everything to the cloud" era is starting to swing back.