lancedb/docs/web/management/function.mdx

---
title: "Functions"
sidebarTitle: "Function"
description: "Register a Python function once and apply it as a column, for work SQL cannot express."
icon: code
keywords: ["udf", "functions", "create_function", "embedding", "feature engineering"]
launch: functions
availability:
  oss: unavailable
  enterprise: varies
  summary: >-
    Functions run on a LanceDB Enterprise deployment that has them enabled; the
    embedded library cannot register or run them.
---

import {
    PyFunctionDefine as FunctionDefine,
    PyFunctionLocalCall as FunctionLocalCall,
    PyFunctionConnect as FunctionConnect,
    PyFunctionCreateTable as FunctionCreateTable,
    PyFunctionRegister as FunctionRegister,
    PyFunctionApply as FunctionApply,
    PyFunctionRefresh as FunctionRefresh,
    PyFunctionInspect as FunctionInspect,
} from '/snippets/function.mdx';

Some columns cannot be written as a SQL expression: an embedding, a model call,
an image decode. A function is a Python callable registered with the database,
versioned, and then applied as a column — the code runs where the data is
rather than pulling rows to your process.

Registering and running a function needs a LanceDB Enterprise deployment with
Functions enabled, which they are not by default. On a
local connection `create_function` raises `NotImplementedError`; to derive a
column locally, declare a
[SQL computed column](/feature-engineering/add-computed-column) instead.

<Note>
In SQL, a function is registered with [`CREATE FUNCTION`](/sql/ddl/function#create) and listed with [`SHOW FUNCTIONS`](/sql/ddl/function#show).
</Note>

Functions are the way to fill columns with Python on a deployment. If you
already use [Geneva](/geneva), it remains available; see
[Geneva and Functions](/geneva#functions) for how its concepts map onto these.

The steps below take a column of numbers to a filled column of their doubles.

## Define a function {#define}

`@udf` packages a scalar Python callable. Input and output types are inferred
from annotations, so `double` takes one non-nullable `float64` parameter,
`value`, and returns a non-nullable `float64`:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionDefine}
</CodeBlock>

The decorated object stays callable. Calling it runs the original function in
your process, without the database, so unit tests stay ordinary tests:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionLocalCall}
</CodeBlock>

Where annotations cannot express the Arrow type precisely, pass `input_schema`
and `output_schema` together — one without the other is rejected. A scalar
output must be non-nullable, because a physical NULL is what marks a
computed-column row as unfilled. A named-struct output may declare nullable
fields: whether a row has a result at all is the struct's own validity, which
Enterprise keeps in a separate internal column when it expands the result into
sibling columns, so a null field is a value rather than a missing row.

## Connect and create a table {#table}

The remaining steps run against your Enterprise deployment. This connection
reads its settings from environment variables, so no credential appears in the
code:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionConnect}
</CodeBlock>

For an ordinary stored input column, the Arrow type must match the parameter.
A nullable column requires a nullable parameter, such as `Optional[float]`;
a non-nullable column can bind to either. This example uses `value: float`,
so the table declares `value` non-nullable.

Computed-column inputs depend on the deployment version. Where supported,
they bind to non-nullable parameters even though the physical column is
nullable: refresh skips rows whose computed input is still null. Check your
deployment's support before using another computed column as an input.

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionCreateTable}
</CodeBlock>

## Register it {#register}

Registration returns a job; the immutable version exists once the job succeeds.

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionRegister}
</CodeBlock>

`create_function` is the blocking equivalent. A registered version is fetched
with `db.get_function(name, version=...)`.

<Note>
**Enterprise job type:** `create_function`. Registration returns a job because
the function is baked into an image before the version becomes usable.
</Note>

## Apply it as a column {#apply}

Calling the registered version binds each parameter, by name, to a column with
`col(...)`. `add_columns` declares the result as a new column:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionApply}
</CodeBlock>

The column is declared empty; the function has not run yet. A mapping with one
function application keeps its scalar or named-struct result in the named
column. A bare named-struct application expands its result fields as one atomic
binding, with aliases from `rename(columns=...)`.

## Fill the column {#refresh}

A refresh runs the function on the cluster as a [job](/basics/jobs). `wait()`
blocks until the job finishes and returns its result, including how many rows
it filled:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionRefresh}
</CodeBlock>

The filled values are read like any other column:

<CodeBlock filename="Python" language="Python" icon="python">
{FunctionInspect}
</CodeBlock>

Filling then works as it does for a SQL computed column: a later refresh fills
rows added since the last one and recomputes rows whose inputs changed, and
leaves filled rows with unchanged inputs alone. See
[Refresh a column](/feature-engineering/refresh-column).

## Declare the environment {#environment}

The function runs remotely, so its dependencies are part of its definition:

| | |
| --- | --- |
| `pip` | requirements for the remote environment |
| `conda`, `conda_channels` | conda packages instead of pip; channels in priority order |
| `env` | environment variables |
| `python_version` | remote Python major/minor, defaulting to yours |
| `gpu` | whether every execution requires a GPU |

These are part of the **immutable function version**. Changing a dependency
produces a new version rather than altering the one already registered, so a
column filled by version 1 is unaffected by version 2.

Declaring them is not optional bookkeeping. If the worker environment differs
from the one the function was written against, the failures are among the
hardest to diagnose in the system, because the error rarely names the cause.

### Symptoms of an environment mismatch {#mismatch-symptoms}

| Symptom | Likely cause |
| --- | --- |
| `TypeError: Enum.__new__() missing 1 required positional argument` | `attrs` version mismatch |
| `TypeError: Can't instantiate abstract class` | package structure differs between versions |
| `ArrowInvalid: cannot cast`, or serialization errors | NumPy 1.x against 2.x |
| `ModuleNotFoundError` on the worker | package installed locally but not declared |
| Model loading failures | PyTorch version mismatch |
| Permission denied | an API key present locally but absent from `env` |

The packages worth pinning explicitly, because a mismatch in them produces the
errors above rather than a clear one: **numpy, torch, pyarrow, attrs,
pydantic**.

<Note>
The client version defaults `python_version`, so a function registered from a
3.11 environment runs on 3.11 unless you say otherwise. That default is usually
right and occasionally surprising -- pin it when the function is sensitive to it.
</Note>

## Failure handling {#failures}

A function that raises fails the job. There is no per-row retry or skip policy
in the function definition: `@udf` takes no error-handling parameter, so a
function that calls a flaky external service should handle that itself, inside
the function body.

Letting the job fail is the other option: fix the cause and refresh again. The
next refresh [skips rows that are filled and current](/feature-engineering/refresh-column#idempotent),
but how much of the failed job's work was kept is not guaranteed, so do not
count on the retry costing only the rows that did not complete.

## What gets packaged {#packaging}

<Warning>
The artifact is a **snapshot**: the function source, plus exactly the
module-level names it references — modules as imports, importable classes and
functions as imports, literals inlined.

Code that reaches the module namespace another way is rejected where it can be
seen and unsupported where it cannot: `globals()`, `eval`, `sys.modules`,
`builtins`. Closures and a non-standard `__builtins__` are rejected outright.
</Warning>

In practice this means a function should be self-contained: take its inputs as
arguments, import what it needs at module level, and avoid capturing state from
the surrounding scope.

## Drop it {#drop}

`db.drop_function(name, version=...)` drops the function's name and the object
it is bound to; `version` must be a version that exists. It returns `False` if
the name was already gone. Deleting the object's content can finish after the
call returns, as a [background job](/maintenance/background-jobs).
`drop_function_async` returns that job with the result, so you can wait for it.

A deployment refuses the drop unless it also allows changes to a function's
lifecycle, which is a separate setting, off by default even where Functions are
enabled.

## What to read next {#next}

<CardGroup cols={2}>
  <Card title="Computed columns" icon="table-columns" href="/feature-engineering/refresh-column">
    Refresh semantics, and the SQL alternative.
  </Card>
  <Card title="Embedding functions" icon="vector-square" href="/embedding">
    Built-in embedding providers, which need no registration.
  </Card>
</CardGroup>