DuckDB Jupyter Kernel

Interactive SQL notebooks powered by DuckDB — write, execute, and visualize SQL queries in Jupyter notebooks with a custom DuckDB kernel and language server support.

Features

• 📓 Jupyter notebook support — Standard .ipynb files with DuckDB SQL kernel
• ⚡ DuckDB execution — Run SQL cells against an in-memory DuckDB instance
• 🎨 Rich result tables — Query results rendered as styled HTML tables
• 💡 SQL IntelliSense — Autocomplete for SQL keywords, DuckDB functions, and types
• 🔍 Hover documentation — Hover over functions to see signatures and descriptions
• ⚠️ Diagnostics — Warnings for common SQL mistakes (unclosed strings, trailing commas)
• 📂 File queries — Read CSV, Parquet, JSON files directly with DuckDB's I/O functions

Getting Started

1. Open the command palette (Ctrl+Shift+P) → DuckDB: New DuckDB Jupyter Notebook
2. Or create a new .ipynb file and select the DuckDB kernel (see below)
3. Write SQL in code cells, execute with Shift+Enter or the ▶ button
4. Add markdown cells for documentation

Selecting the DuckDB Kernel

When you open a Jupyter notebook, you need to select DuckDB SQL as the kernel:

1. Click Select Kernel in the top-right corner of the notebook editor
2. Choose Select Another Kernel…
3. Select DuckDB SQL from the kernel list

Tip: If you create notebooks using the DuckDB: New DuckDB Jupyter Notebook command, the kernel is pre-selected automatically.

Example

-- Create and query data
CREATE TABLE sales (product VARCHAR, amount DECIMAL, region VARCHAR);
INSERT INTO sales VALUES ('Widget', 99.99, 'East'), ('Gadget', 149.50, 'West');

SELECT region, SUM(amount) AS total FROM sales GROUP BY region;

Configuration

Development

npm install
npm run compile    # Build
# Press F5 in VS Code to launch Extension Development Host