ObservedObserver / mosaic

An extensible framework for linking databases and interactive views.

Mosaic: An Extensible Framework for Linking Databases and Interactive Views

• 📈 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.

Repository Structure

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 and rest 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.

Build and Usage Instructions

To build and develop Mosaic locally:

1. Clone https://github.com/uwdata/mosaic.
2. Run npm i to install dependencies.
3. Run npm test to run the test suite.
4. Run npm run build to build client-side bundles.

To run local interactive examples:

1. Run npm run server to launch a data server with default files loaded.
2. 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.