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:

Drag & drop — Drop local files directly into Workbench
Cloud storage — Read from S3, GCS, or Azure (public or connect your own bucket)
Databases — Read from Snowflake, BigQuery, or other databases
APIs — Fetch from any HTTP endpoint

@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

Every UDF is a dynamic file system:

Dynamic File System Overview

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/YOUR_TOKEN.json
https://udf.ai/YOUR_TOKEN.csv
https://udf.ai/YOUR_TOKEN.parquet

Pass parameters via URL to change outputs dynamically:

https://udf.ai/YOUR_TOKEN.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]

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/YOUR_TOKEN.json?continent=Asia&min_pop=5

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

Fused​

UDFs​

Data​

Formats​

Caching​