Async Dash

async-dash is an async port of Plotly Dash library, created by replacing its Flask backend with its async counterpart Quart.

It started with my need to be able to create realtime dashboards with dash, specifically with event-driven architecture. Using async-dash with components from dash-extensions such as WebSocket, EventSource, etc. you can create truly event-based or realtime dashboards.

Table Of Contents

• Installation
• Usage
• Examples
• Motivation
• Caveats
• TODO

Installation

pip install async-dash

Usage

from async_dash import Dash
from dash import html, dcc

Examples

Basic Async Callback

import asyncio
import time

from dash import Input, Output, html
from quart import Quart

from async_dash import Dash

server = Quart(__name__)
app = Dash(__name__, server=server)

app.layout = html.Div([
    html.Button("Async Request (2s delay)", id="async-btn", n_clicks=0),
    html.Div(id="async-output"),
])


@app.callback(
    Output("async-output", "children"),
    Input("async-btn", "n_clicks"),
    prevent_initial_call=True,
)
async def async_callback(n_clicks):
    """Async callback that simulates a slow async operation.

    This could be an API call, a database query, etc.
    """
    start = time.time()
    # This is non-blocking - other requests can be processed during this sleep
    await asyncio.sleep(2)
    elapsed = time.time() - start
    return f"Async callback #{n_clicks} completed in {elapsed:.2f}s (non-blocking!)"


if __name__ == "__main__":
    app.run(debug=True, port=8050)

Sync Callbacks Still Work

from dash import Input, Output, html
from quart import Quart

from async_dash import Dash

server = Quart(__name__)
app = Dash(__name__, server=server)

app.layout = html.Div([
    html.Button("Sync Request", id="sync-btn", n_clicks=0),
    html.Div(id="sync-output"),
])


@app.callback(
    Output("sync-output", "children"),
    Input("sync-btn", "n_clicks"),
    prevent_initial_call=True,
)
def sync_callback(n_clicks):
    """Sync callback - still works with async-dash."""
    return f"Sync callback #{n_clicks} completed instantly!"


if __name__ == "__main__":
    app.run(debug=True, port=8050)

WebSocket Example

Using websockets for real-time updates with dash-extensions:

import asyncio
import random

from dash import Input, Output, dcc, html
from dash_extensions import WebSocket
from quart import Quart, json, websocket

from async_dash import Dash

server = Quart(__name__)
app = Dash(__name__, server=server)

app.layout = html.Div([
    WebSocket(id="ws", url="/ws"),
    html.H3("Live Random Data (via WebSocket)"),
    dcc.Graph(id="graph"),
])

app.clientside_callback(
    """
function(msg) {
    if (msg) {
        const data = JSON.parse(msg.data);
        return {data: [{y: data, type: "scatter"}]};
    } else {
        return {};
    }
}""",
    Output("graph", "figure"),
    [Input("ws", "message")],
)


@server.websocket("/ws")
async def random_data():
    while True:
        output = json.dumps([random.random() for _ in range(10)])
        await websocket.send(output)
        await asyncio.sleep(1)


if __name__ == "__main__":
    app.run()

Running with Uvicorn

For production, use an ASGI server like uvicorn:

uvicorn example:server --host 0.0.0.0 --port 8050

Motivation

In addition to all the advantages of writing async code, async-dash enables you to:

1. Run truly asynchronous callbacks
2. Use websockets, server sent events, etc. without needing to monkey patch the Python standard library
3. Use quart / fastapi / starlette frameworks with your dash apps side by side
4. Use HTTP/2 (especially server push) if you use an HTTP/2 enabled server such as hypercorn

Caveats

I'm maintaining this library as a proof of concept for now. It should not be used for production. If you do decide to use it, I'd love to hear your feedback.

TODO

1. Gather reviews and feedback from the Dash Community.