Viz

The viz subcommand builds the pipeline graph and serves an interactive visualization.

Requires the viz feature.

CLI usage

# Start the interactive web server (default port 8080)
$ my_app viz

# Custom port
$ my_app viz --port 3000

# Export graph as JSON
$ my_app viz --output pipeline.json

# Export self-contained HTML file
$ my_app viz --export pipeline.html

Interactive server

my_app viz starts an axum web server with:

The frontend uses React + ReactFlow + dagre for a left-to-right DAG layout with:

• Node, dataset, and pipeline visual types
• Click-to-inspect dataset details (HTML preview, YAML config)
• Left sidebar listing all nodes, datasets, and parameters
• Dark/light theme support

Live execution status

To see real-time node status during pipeline execution:

1. Start the viz server: my_app viz --port 8080
2. In another terminal, run the pipeline with VizHook:

use pondrs::viz::VizHook;

App::from_yaml(..)?
    .with_args(std::env::args_os())?
    .with_hooks((LoggingHook::new(), VizHook::new("http://localhost:8080".into())))
    .dispatch(pipeline)?;

The frontend shows live node status (idle/running/completed/error) and updates automatically via WebSocket.

Static HTML export

--export produces a self-contained HTML file with the full graph and dataset snapshots embedded. No server needed — just open the file in a browser:

$ my_app viz --export pipeline.html
$ open pipeline.html

The export uses vite-plugin-singlefile to inline all JS/CSS into a single HTML file, and injects graph data via window.__STATIC_DATA__.

JSON export

--output writes the VizGraph as JSON, useful for custom tooling:

$ my_app viz --output graph.json

Graph name

The graph name shown in the frontend header is derived from the program name (first CLI argument, file stem only). For example, running cargo run --example weather_app sets the name to weather_app.