Home

⚡ Blinkql

Queries in the blink of an eye.

Blinkql is a fast, private, zero-install SQL analytics workbench that runs entirely in your browser. Drop in a CSV, Parquet or JSON file, or point it at a remote Parquet URL, write SQL, and explore millions of rows in an interface that stays responsive. No account. No server. No upload.

Powered by DuckDB-wasm and Apache Arrow.

🌐 Live app	blinkql.vercel.app
📦 Source	github.com/Karthik-beta/Blinkql
📄 License	MIT
🚧 Status	Early development. See the Roadmap

---

Why Blinkql?

A huge amount of operational data lives in exports: CSVs from ERP and MES systems, attendance logs, sensor dumps, application logs. Getting answers from those files usually means stretching a spreadsheet past its limits, waiting on someone else's BI setup, or uploading sensitive data to a third-party service.

Blinkql takes a different route:

• ⚡ Fast. A columnar analytical engine compiled to WebAssembly, results moved as Apache Arrow, and rendering that only draws what's on screen. Every optimization is benchmarked and published.
• 🔒 Private by architecture. There is no backend. Your files are read by your browser and never leave your device.
• 🪶 Zero setup. It's a URL. It works on managed machines where installing software needs approval.
• 🏭 Built for operations data. More than a generic SQL console: ready-made analyses for OEE, downtime Pareto, shift reporting and data reconciliation.

Read more in Vision & Motivation and Market Gap & Positioning.

Planned feature set

Feature	Status
Drag-and-drop CSV, Parquet and JSON import	✅ Shipped
SQL editor with schema-aware autocomplete	✅ Shipped
Schema browser and one-click data profiling	✅ Shipped
Virtualized results grid for millions of rows	✅ Shipped
Query remote Parquet over HTTP (range requests)	🛠️ In progress
Charts: bar, line, scatter, histogram, Pareto	✅ Shipped
Query cancellation and query history	✅ Shipped
Local persistence (OPFS): reopen datasets without re-importing	🛠️ In progress
Operations analytics pack: OEE, downtime, shifts, reconciliation	🔜 Planned
Export results as CSV, Parquet or PNG	✅ Shipped
Shareable query links (the query is shared, the data never is)	🔜 Planned
Offline support (PWA)	🔜 Planned

Legend: ✅ Shipped · 🛠️ In progress · 🔜 Planned

Wiki map

Understand the project

• Vision & Motivation: the problem, why now, and why this project exists
• Market Gap & Positioning: where Blinkql fits among existing tools
• Use Cases: concrete scenarios with example queries
• Goals & Non-Goals: what success looks like, and what's deliberately out of scope

Under the hood

• Tech Stack
• Architecture
• Performance & Benchmarks
• Architecture Decision Records
• Privacy & Security
• Browser Support & Limitations

Design

• Design Language: how Blinkql looks, moves, behaves and speaks, and why
• Design Tokens: the shadcn/ui, Tailwind and CSS implementation

Plan & participate

• Roadmap & Milestones
• Contributing
• FAQ

Get involved

Blinkql is built in the open, and contributions of every size are welcome: code, bug reports, benchmark runs on your hardware, sample datasets, analysis templates from your industry, documentation, design and accessibility reviews. Start with the Contributing guide or open an issue.

If Blinkql is useful to you, a ⭐ on the repository helps others find it.

---

_{Blinkql is an independent open-source project. It is not affiliated with or endorsed by DuckDB Labs or the DuckDB Foundation. "DuckDB" is a trademark of the DuckDB Foundation.}