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First UDF & basics

Fused​

Fused makes it easy to run Python at scale. You write Python code, and Fused handles execution, scaling, and API generation automatically.

If it runs locally, it runs on Fused — at scale.

It decouples what you write from how it runs and where it goes.

UDFs​

UDFs (User Defined Functions) are Python functions with a @fused.udf decorator that return data. Each UDF becomes its own API endpoint with a unique URL.

Try it in Workbench:

@fused.udf
def udf(name: str = "Fused"):
    import pandas as pd
    return pd.DataFrame({'message': [f'Hello {name}!']})

A UDF needs:

  • The @fused.udf decorator
  • A function (called udf)
  • Parameters with type hints and default values

UDFs can return tables (DataFrame, GeoDataFrame), arrays (numpy, xarray), or simple Python objects. Learn more about writing UDFs.

Data​

Bring your own data into Fused:

@fused.udf
def udf():
    import pandas as pd
    return pd.read_csv("s3://your-bucket/your-data.csv")

Need credentials for private data? See secrets management.

Formats​

Share your canvas to get a canvas token; then each UDF is available by name.

UDF API URL structure: domain, canvas token, UDF name, extension, query params

Every UDF is a dynamic file system. Same UDF, multiple output formats. Change the URL extension to get JSON, CSV, Parquet, PNG, TIFF, or MVT; no code changes needed!

https://udf.ai/fc_<CANVAS_TOKEN>/my_udf.json
https://udf.ai/fc_<CANVAS_TOKEN>/my_udf.csv
https://udf.ai/fc_<CANVAS_TOKEN>/my_udf.parquet

Pass parameters via URL to change outputs dynamically:

https://udf.ai/fc_<CANVAS_TOKEN>/my_udf.json?name=World

Learn more about Calling UDFs as API.

Caching​

First request runs your code. Subsequent requests (same code, same parameters) return cached results in under a second. UDFs are cached for 14 days by default.

Try it yourself:

@fused.udf
def udf():
    import time
    time.sleep(5)
    return {"status": "done"}

Run once → 5 seconds. Run again → ~1 second (cached).

Set any cache duration you need, including 0 to force a fresh run every time:

@fused.udf(cache_max_age="0s")
def udf():
    import time
    time.sleep(5)
    return {"status": "done"}

Learn more about caching.

Pipelines​

UDFs can call other UDFs to build data pipelines. Each stays standalone with its own endpoint.

# UDF 1: load_cities
@fused.udf
def udf(continent: str = "Europe"):
    import pandas as pd
    cities = pd.DataFrame({
        "city": ["London", "Paris", "Berlin", "Tokyo", "Sydney"],
        "population": [9, 2, 4, 14, 5],
        "continent": ["Europe", "Europe", "Europe", "Asia", "Oceania"]
    })
    return cities[cities.continent == continent]
# UDF 2: big_cities
@fused.udf
def udf(continent: str = "Europe", min_pop: int = 3):
    load_cities = fused.load('load_cities')
    cities = load_cities(continent=continent)
    return cities[cities.population >= min_pop]

Here, fused.load("load_cities") loads the saved UDF named load_cities. It returns a callable UDF object, so load_cities(continent=continent) calls the load_citites UDF and runs it.

note

You could also run load_cities(continent=continent, engine='local') locally inside the current UDF. This can help limit the number of UDF instances you run at the same time, especially for small functions.

big_cities calls load_cities and passes through the continent param. Both UDFs have their own API endpoint — call either one directly:

https://udf.ai/fc_<CANVAS_TOKEN>/my_udf.json?continent=Asia&min_pop=5

Organise UDFs visually in Canvas — connect them, build workflows, create dashboards.