- 📈 Explore massive datasets
Visualize, select, and filter datasets with millions or billions of records. - 🚀 Flexible deployment
Build data-driven web apps, or interact with data directly in Jupyter notebooks. - 🛠️ Interoperable & extensible
Create new components that seamlessly integrate across selections and datasets. - 🦆 Powered by DuckDB
Mosaic pushes computation to DuckDB, both server-side and in your browser via WebAssembly.
Mosaic is an extensible architecture for linking data visualizations, tables, input widgets, and other data-driven components, leveraging a backing database for scalable processing of both static and interactive views. With Mosaic, you can visualize and explore millions and even billions of data points at interactive rates.
The key idea is to have interface components "publish" their data needs as declarative queries that can be managed, optimized, and cross-filtered by a coordinator that proxies access to DuckDB.
Learn more about Mosaic at the documentation site.
This repository contains a set of related packages:
mosaic-core
: The core Mosaic components. A central coordinator, parameters and selections for linking scalar values or query predicates (respectively) across Mosaic clients, and filter groups with optimized index management. The Mosaic coordinator can send queries either over the network to a backing server (socket
andrest
clients) or to a client-side DuckDB-WASM instance (wasm
client).mosaic-duckdb
: A Promise-based Node.js API to DuckDB, along with a data server that supports transfer of Apache Arrow and JSON data over either Web Sockets or HTTP.mosaic-sql
: An API for convenient construction and analysis of SQL queries. Query objects then coerce to SQL query strings.mosaic-inputs
: Standalone data-driven components such as input menus, text search boxes, and sortable, load-on-scroll data tables.vgplot
: A prototype visualization grammar implemented on top of Observable Plot, in which marks (plot layers) are individual Mosaic clients. These marks can push data processing (binning, hex binning, regression) and optimizations (such as M4 for line/area charts) down to the database.widget
: A Jupyter widget for Mosaic. Create interactive Mosaic plots over Pandas and Polars data frames or DuckDB connections.vega-example
: A preliminary example integrating Vega-Lite with Mosaic for data management and cross-view linking.
Note: For convenience, vgplot
re-exports much of the mosaic-core
, mosaic-sql
, and mosaic-inputs
packages. For most applications, it is sufficient to import @uwdata/vgplot
alone.
To build and develop Mosaic locally:
- Clone https://github.com/uwdata/mosaic.
- Run
npm i
to install dependencies. - Run
npm test
to run the test suite. - Run
npm run build
to build client-side bundles.
To run local interactive examples:
- Run
npm run server
to launch a data server with default files loaded. - Run
npm run dev
to launch a local web server and view examples.
To use Mosaic with DuckDB Python in Jupyter Notebooks:
- See the Mosaic
widget
documentation.
To use Mosaic with DuckDB-WASM in Observable Notebooks:
- See the Mosaic 10M Flights example.