VegaFusion 2.0#

A new era: Building blocks for analyzing, accelerating, and scaling Vega visualizations:

By: Jon Mease

The VegaFusion team is excited to announce the release of VegaFusion 2.0. This major release represents a significant evolution in VegaFusion’s architecture and target audience. The focus has been on simplification - making VegaFusion easier to maintain and easier to integrate into larger Vega systems to accelerate and scale Vega visualizations.

Modernized Rust Core#

The core Rust implementation has been significantly simplified by taking advantage of modern DataFusion functionality:

  • New get_column_usage() utility function for analyzing the column dependencies of datasets in a Vega spec.

  • Custom User Defined Functions (UDFs) have been largely replaced with DataFusion’s native capabilities.

  • Vega transforms are now implemented directly against the DataFusion DataFrame API, rather than using the circuitous SQL generation approach of VegaFusion 1.

  • Updated to DataFusion 43 and Arrow 52.3 for improved performance and bug fixes.

JavaScript / WebAssembly#

The JavaScript integration has been simplified while maintaining full functionality:

  • Removed the separate vegafusion-embed package as the vegafusion-wasm package now renders charts using vega-embed.

  • The full VegaFusion runtime is now compiled to WebAssembly as part of the vegafusion-wasm pacakge, making it possible to run VegaFusion entirely in the browser without requiring an external server.

  • Connecting to external VegaFusion servers via gRPC-Web remains supported.

Python: Removal of Upstreamed Vega-Altair Integration#

All the VegaFusion 1.x logic for integrating with Vega-Altair has been upstreamed into the Vega-Altair project itself. So this functionality has been removed in VegaFusion 2.0.

In particular:

  • To use VegaFusion with Vega-Altair, activate the "vegafusion" data transformer.

  • The vegafusion-jupyter package has been retired in favor of using Altair’s built-in JupyterChart for Altair charts and Vega-Lite specs. For Vega specs, the new AnyWidget-based VegaFusionWidget is included in the vegafusion package.

  • vf.enable() has been removed. Instead, use the "vegafusion" data transformer along with any of Altair’s built-in renderers

  • vf.save() has been removed. Instead, use Altair’s native chart.save() method with the "vegafusion" data transformer enabeld.

  • vf.transformed_data() has been removed. Instead, use Altair’s native chart.transformed_data() method.

Python: Enhanced DataFrame Compatibility#

VegaFusion 2.0 brings improved DataFrame compatibility:

  • Support for any Narwhals-compatible DataFrame type as inline datasets.

  • Zero-copy sharing of Arrow data from Polars to VegaFusion’s DataFusion runtime is now supported thanks to the arro3 project..

  • The DuckDB SQL connection has been removed, but DuckDB relations are now directly supported as inline datasets, with automatic column pruning prior to conversion to Arrow.

  • Reduced Python dependencies. VegaFusion now requires only arrow3-core, packaging, and narwhals. In particular, pyarrow and pandas are no longer required dependencies.

New Documentation#

VegaFusion 2.0 launches with completely revamped documentation:

  • New PyData Sphinx theme at vegafusion.io

  • Standalone examples in Python and Rust

  • Comprehensive support tables for Vega transforms and expressions

Learn more#

Check out these resources to learn more:

VegaFusion 1.4