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Run UDFs efficiently

Realtime is the default way to run UDFs—no configuration needed. Every UDF runs in realtime mode unless you explicitly request a batch instance.

This guide covers best practices for calling UDFs from other UDFs and building pipelines. For the full API reference, see the Udf class.

Limits​

ResourceLimit
Execution time120s
RAM~10GB

Need more? See Scaling out UDFs.

Calling another UDF​

Load and call UDFs directly:

@fused.udf
def udf():
    child = fused.load("child_udf")
    result = child(name="hello")
    return result

Calling another UDF in workbench

In Workbench, UDF calls create visual links between parent and child UDFs, making pipelines easy to follow.

note

For geospatial UDFs, you can pass bounds as a bbox list, GeoDataFrame, or tile coordinates. See Reserved parameters.

Best practices​

Keep results fresh​

By default, UDF results are cached. To always get fresh data, disable caching:

@fused.udf(cache_max_age=0)
def udf():
    parent = fused.load('parent_udf')
    return parent()
warning

Setting cache_max_age=0 means this UDF runs from scratch every time—no caching. Use this when your output depends on frequently changing data.

Pin to commit hash for production​

When loading UDFs from GitHub, pin to a specific commit:

commit_hash = "bdfb4d0"
my_udf = fused.load(f"https://github.com/fusedio/udfs/tree/{commit_hash}/public/My_UDF/")
result = my_udf()

Avoid pointing to main branch—your UDF will break when others push changes.

Warm & cold starts​

After inactivity, Fused needs to spin up an instance and load the environment. This cold start typically takes 10-15s.

Once warm, subsequent realtime calls execute within seconds. Instances stay warm with regular use. Fused does not charge for cold start time.

Cold start vs warm execution

When to scale up​

NeedSolution
Run same UDF over multiple inputsParallel execution
More than 120s or ~10GBDedicated instances

See also​