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Arroyo SQL can be extended with user-defined functions, written in Rust or Python. UDFs can be defined as part of the SQL API call, or via the Web UI. UDFs are defined as Rust functions. The parameters and return type of the UDF are determined by the definition of the function. All types must be valid SQL data types. For String and Binary types (TEXT BYTEA in SQL), UDFs use the reference type (&str and &[u8]) for arguments and the owned types (String and Vec<u8>) for return values. Arroyo UDFs are annotated with the #[udf] in the arroyo_udf_plugin crate. Here’s an example of a simple UDF that squares an integer: 
from arroyo_udf import udf

@udf
def my_square(x: int) -> int:
    return x * x
Once registered, this UDF can be used in SQL queries: 
CREATE TABLE impulse with (
  connector = 'impulse',
  event_rate = '10'
);

SELECT my_square(counter)
FROM impulse;
For a complete example of how to use UDFs to solve a real-world problem of custom format parsing, see the UDF tutorial.

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Creating UDFs

UDFs are developed and managed in the Web UI, within the Pipeline editor. To create a new UDF, navigate to the UDFs tab and click “New.” The UDF creation interface The name for the UDF is determined automatically from the function name that is annotated with #[udf] for Rust or @udf for Python. You may include helper functions, structs, and other definitions so long as only one function has the udf annotation. As you’re developing, you may make use of the “Check” button to validate the UDF definition. Any Rust compilation or Python parsing errors will be included in the errors box at the bottom of the UI.

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Global UDFs

UDFs may be either local—associated with a particular pipeline—or global, in which case they are stored in the database and may be used in any pipeline. All UDFs begin as local. If you’ve written a UDF that will benefit from being globally available, you can make it global by hovering over the tab containing the UDF, and clicking the “Globalize” button in the drop down. The process of making a global UDF Global UDFs are available in all pipelines, but will be shadowed by local UDFs with the same name.

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Performance considerations

UDFs are expected to run and return quickly (think microseconds, not milliseconds). This means they shouldn’t do any blocking work (like making network requests) or CPU-intensive computations. Within the Arroyo dataflow, UDFs are treated as normal scalar functions. They run serially against a batch of data, and while they run forward progress is blocked. If you need to use IO, CPU-intensive computations, or other usecases for long-running UDFs, see async UDFs.

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Debugging UDFs

If UDFs panic, they will produce log lines in the worker logs. These are not currently forwarded to the Web UI, so you will need to find the logs where the workers are running. For the default process scheduler, this will be in the controller logs. For the kubernetes scheduler, this will be within the actual worker pods.