VegaFusion provides serverside acceleration for the Vega visualization grammar. While not limited to Python, an initial application of VegaFusion is the acceleration of the Altair Python interface to Vega-Lite.
The core VegaFusion algorithms are implemented in Rust. Python integration is provided using PyO3 and JavaScript integration is provided using wasm-bindgen.
See the documentation at https://vegafusion.io
VegaFusion is a young project, but it is already fairly well tested and used in production at Hex. The integration test suite includes image comparisons with over 600 specifications from the Vega, Vega-Lite, and Altair galleries.
The VegaFusion mime renderer can be used to overcome the Altair MaxRowsError by performing data-intensive aggregations on the server and pruning unused columns from the source dataset. First install the vegafusion Python package with the embed extras enabled
pip install "vegafusion[embed]"Then open a Jupyter notebook (either the classic notebook or a notebook inside JupyterLab), and create an Altair histogram of a 1 million row flights dataset
import pandas as pd
import altair as alt
flights = pd.read_parquet(
"https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
)
delay_hist = alt.Chart(flights).mark_bar().encode(
alt.X("delay", bin=alt.Bin(maxbins=30)),
alt.Y("count()")
)
delay_hist---------------------------------------------------------------------------
MaxRowsError Traceback (most recent call last)
...
MaxRowsError: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation
This results in an Altair MaxRowsError, as by default Altair is configured to allow no more than 5,000 rows of data to be sent to the browser. This is a safety measure to avoid crashing the user's browser. The VegaFusion mime renderer can be used to overcome this limitation by performing data intensive transforms (e.g. filtering, binning, aggregation, etc.) in the Python kernel before the resulting data is sent to the web browser.
Run these two lines to import and enable the VegaFusion mime renderer
import vegafusion as vf
vf.enable()Now the chart displays quickly without errors
delay_hist
By default, data transforms in an Altair chart (e.g. filtering, binning, aggregation, etc.) are performed by the Vega JavaScript library running in the browser. This has the advantage of making the charts produced by Altair fully standalone, not requiring access to a running Python kernel to render properly. But it has the disadvantage of making it difficult to access the transformed data (e.g. the histogram bin edges and count values) from Python. Since VegaFusion evaluates these transforms in the Python kernel, it's possible to access then from Python using the vegafusion.transformed_data() function.
For example, the following code demonstrates how to access the histogram bin edges and counts for the example above:
import pandas as pd
import altair as alt
import vegafusion as vf
flights = pd.read_parquet(
"https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
)
delay_hist = alt.Chart(flights).mark_bar().encode(
alt.X("delay", bin=alt.Bin(maxbins=30)),
alt.Y("count()")
)
vf.transformed_data(delay_hist)| bin_maxbins_30_delay | bin_maxbins_30_delay_end | __count | |
|---|---|---|---|
| 0 | -20 | 0 | 419400 |
| 1 | 80 | 100 | 11000 |
| 2 | 0 | 20 | 392700 |
| 3 | 40 | 60 | 38400 |
| 4 | 60 | 80 | 21800 |
| 5 | 20 | 40 | 92700 |
| 6 | 100 | 120 | 5300 |
| 7 | -40 | -20 | 9900 |
| 8 | 120 | 140 | 3300 |
| 9 | 140 | 160 | 2000 |
| 10 | 160 | 180 | 1800 |
| 11 | 320 | 340 | 100 |
| 12 | 180 | 200 | 900 |
| 13 | 240 | 260 | 100 |
| 14 | -60 | -40 | 100 |
| 15 | 260 | 280 | 100 |
| 16 | 200 | 220 | 300 |
| 17 | 360 | 380 | 100 |
While the VegaFusion mime renderer works great for non-interactive Altair charts, it's not as well suited for interactive charts visualizing large datasets. This is because the mime renderer does not maintain a live connection between the browser and the python kernel, so all the data that participates in an interaction must be sent to the browser.
To address this situation, VegaFusion provides a Jupyter Widget based renderer that does maintain a live connection between the chart in the browser and the Python kernel. In this configuration, selection operations (e.g. filtering to the extents of a brush selection) can be evaluated interactively in the Python kernel, which eliminates the need to transfer the full dataset to the client in order to maintain interactivity.
The VegaFusion widget renderer is provided by the vegafusion-jupyter package.
pip install "vegafusion-jupyter[embed]"Instead of enabling the mime render with vf.enable(), the widget renderer is enabled with vf.enable_widget(). Here is a full example that uses the widget renderer to display an interactive Altair chart that implements linked histogram brushing for a 1 million row flights dataset.
import pandas as pd
import altair as alt
import vegafusion as vf
vf.enable_widget()
flights = pd.read_parquet(
"https://vegafusion-datasets.s3.amazonaws.com/vega/flights_1m.parquet"
)
brush = alt.selection(type='interval', encodings=['x'])
# Define the base chart, with the common parts of the
# background and highlights
base = alt.Chart().mark_bar().encode(
x=alt.X(alt.repeat('column'), type='quantitative', bin=alt.Bin(maxbins=20)),
y='count()'
).properties(
width=160,
height=130
)
# gray background with selection
background = base.encode(
color=alt.value('#ddd')
).add_selection(brush)
# blue highlights on the selected data
highlight = base.transform_filter(brush)
# layer the two charts & repeat
chart = alt.layer(
background,
highlight,
data=flights
).transform_calculate(
"time",
"hours(datum.date)"
).repeat(column=["distance", "delay", "time"])
chartflights_brush_histogram.mov
Histogram binning, aggregation, and selection filtering are now evaluated in the Python kernel process with efficient parallelization, and only the aggregated data (one row per histogram bar) is sent to the browser.
You can see that the VegaFusion widget renderer maintains a live connection to the Python kernel by noticing that the Python kernel is running as the selection region is created or moved. You can also notice the VegaFusion logo in the dropdown menu button.
Vega makes it possible to create declarative JSON specifications for rich interactive visualizations that are fully self-contained. They can run entirely in a web browser without requiring access to an external database or a Python kernel.
For datasets of a few thousand rows or fewer, this architecture results in extremely smooth and responsive interactivity. However, this architecture does not scale very well to datasets of hundreds of thousands of rows or more. This is the problem that VegaFusion aims to solve.
Apache Arrow DataFusion is an SQL compatible query engine that integrates with the Rust implementation of Apache Arrow. VegaFusion uses DataFusion to implement many of the Vega transforms, and it compiles the Vega expression language directly into the DataFusion expression language. In addition to being quite fast, a particularly powerful characteristic of DataFusion is that it provides many interfaces that can be extended with custom Rust logic. For example, VegaFusion defines many custom UDFs that are designed to implement the precise semantics of the Vega expression language and the Vega expression functions.
As of version 1.0, VegaFusion is licensed under the BSD-3 license. This is the same license used by Vega, Vega-Lite, and Altair.
Prior versions were released under the AGPLv3 license.
There are two meanings behind the name "VegaFusion"
- It's a reference to the Apache Arrow DataFusion library which is used to implement many of the supported Vega transforms
- Vega and Altair are named after stars, and stars are powered by nuclear fusion
If you're interested in building VegaFusion from source, see BUILD.md
Supporting serverside acceleration for Altair in Jupyter was chosen as the first application of VegaFusion, but there are a lot of exciting ways that VegaFusion can be extended in the future. For more information, see the Roadmap.
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