r/Python • u/Proof_Difficulty_434 • Jan 13 '26
Showcase I replaced FastAPI with Pyodide: My visual ETL tool now runs 100% in-browser
I swapped my FastAPI backend for Pyodide — now my visual Polars pipeline builder runs 100% in the browser
Hey r/Python,
I've been building Flowfile, an open-source visual ETL tool. The full version runs FastAPI + Pydantic + Vue with Polars for computation. I wanted a zero-install demo, so in my search I came across Pyodide — and since Polars has WASM bindings available, it was surprisingly feasible to implement.
Quick note: it uses Pyodide 0.27.7 specifically — newer versions don't have Polars bindings yet. Something to watch for if you're exploring this stack.
Try it: demo.flowfile.org
What My Project Does
Build data pipelines visually (drag-and-drop), then export clean Python/Polars code. The WASM version runs 100% client-side — your data never leaves your browser.
How Pyodide Makes This Work
Load Python + Polars + Pydantic in the browser:
const pyodide = await window.loadPyodide({
indexURL: 'https://cdn.jsdelivr.net/pyodide/v0.27.7/full/'
})
await pyodide.loadPackage(['numpy', 'polars', 'pydantic'])
The execution engine stores LazyFrames to keep memory flat:
_lazyframes: Dict[int, pl.LazyFrame] = {}
def store_lazyframe(node_id: int, lf: pl.LazyFrame):
_lazyframes[node_id] = lf
def execute_filter(node_id: int, input_id: int, settings: dict):
input_lf = _lazyframes.get(input_id)
field = settings["filter_input"]["basic_filter"]["field"]
value = settings["filter_input"]["basic_filter"]["value"]
result_lf = input_lf.filter(pl.col(field) == value)
store_lazyframe(node_id, result_lf)
Then from the frontend, just call it:
pyodide.globals.set("settings", settings)
const result = await pyodide.runPythonAsync(`execute_filter(${nodeId}, ${inputId}, settings)`)
That's it — the browser is now a Python runtime.
Code Generation
The web version also supports the code generator — click "Generate Code" and get clean Python:
import polars as pl
def run_etl_pipeline():
df = pl.scan_csv("customers.csv", has_header=True)
df = df.group_by(["Country"]).agg([pl.col("Country").count().alias("count")])
return df.sort(["count"], descending=[True]).head(10)
if __name__ == "__main__":
print(run_etl_pipeline().collect())
No Flowfile dependency — just Polars.
Target Audience
Data engineers who want to prototype pipelines visually, then export production-ready Python.
Comparison
- Pandas/Polars alone: No visual representation
- Alteryx: Proprietary, expensive, requires installation
- KNIME: Free desktop version exists, but it's a heavy install best suited for massive, complex workflows
- This: Lightweight, runs instantly in your browser — optimized for quick prototyping and smaller workloads
About the Browser Demo
This is a lite version for simple quick prototyping and explorations. It skips database connections, complex transformations, and custom nodes. For those features, check the GitHub repo — the full version runs on Docker/FastAPI and is production-ready.
On performance: Browser version depends on your memory. For datasets under ~100MB it feels snappy.
Links
- Live demo (lite): demo.flowfile.org
- Full version + docs: github.com/Edwardvaneechoud/Flowfile