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(extension_functions)=

# Functions and table functions

Four hooks contribute functions written in Rust. Users can also define
functions in pure Python — see
{doc}`../user-guide/common-operations/udf-and-udfa` — and the two roads meet at
the same registration methods.

| Hook | Contributes | Wrapped by | Registered with |
| --- | --- | --- | --- |
| `__datafusion_scalar_udf__` | scalar function | {py:func}`datafusion.udf` | {py:meth}`~datafusion.SessionContext.register_udf` |
| `__datafusion_aggregate_udf__` | aggregate function | {py:func}`datafusion.udaf` | {py:meth}`~datafusion.SessionContext.register_udaf` |
| `__datafusion_window_udf__` | window function | {py:func}`datafusion.udwf` | {py:meth}`~datafusion.SessionContext.register_udwf` |
| `__datafusion_table_function__` | function returning a table | {py:func}`datafusion.udtf` | {py:meth}`~datafusion.SessionContext.register_udtf` |

All four are implemented in [`datafusion-ffi-example`], one per file.

## The three scalar-shaped hooks

The scalar, aggregate, and window getters take **no argument** beyond `py` —
they need no codec and no task-context provider, because a function is
identified by name and signature rather than by anything session-scoped:

```rust
#[pymethods]
impl MyScalarUDF {
    fn __datafusion_scalar_udf__<'py>(
        &self,
        py: Python<'py>,
    ) -> PyResult<Bound<'py, PyCapsule>> {
        let udf = Arc::new(ScalarUDF::from(self.clone()));
        let ffi = FFI_ScalarUDF::from(udf);

        PyCapsule::new_with_value(py, ffi, cr"datafusion_scalar_udf")
    }
}
```

Aggregate and window follow identically with `FFI_AggregateUDF` /
`FFI_WindowUDF` and the matching capsule names.

Your users wrap the object once and register the result:

```python
from datafusion import udf

ctx.register_udf(udf(my_library.MyScalarUDF()))
```

## Table functions

A table function takes literal `Expr` arguments and returns a table provider,
so it needs the host's logical codec the way a
{ref}`table provider <extension_providers>` does:

```rust
fn __datafusion_table_function__<'py>(
    &self,
    py: Python<'py>,
    session: Bound<'py, PyAny>,
) -> PyResult<Bound<'py, PyCapsule>> {
    let func = self.clone();
    let codec = ffi_logical_codec_from_pycapsule(session, None)?;
    let provider = FFI_TableFunction::new_with_ffi_codec(Arc::new(func), None, codec);

    PyCapsule::new_with_value(py, provider, cr"datafusion_table_function")
}
```

Only literal expressions are supported as arguments. The Python side is
described under
{doc}`Table Functions <../user-guide/common-operations/udf-and-udfa>`.

## Serializing functions

A function that appears in a plan leaving the process has to be reconstructible
on the far side. Functions are the one case where a codec often needs **no
payload at all**: the name is the whole encoding, `try_encode_udf` writes
nothing, and `try_decode_udf` rebuilds the function from `name`. See
{ref}`extension_codecs` for how that works and for the one obligation it puts
on your decoder — with an empty payload there is no id to route on, so your
`try_decode_udf` can be called with a `name` belonging to another library.

`NameOnlyUdfCodec` in [`datafusion-ffi-example`] is the worked case.

[`datafusion-ffi-example`]: https://github.com/apache/datafusion-python/tree/main/examples/datafusion-ffi-example