docs/source/user-guide/extensions.md

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(user_guide_extensions)=

# Using extension libraries

An extension library is a separate package that teaches a
{py:class}`~datafusion.SessionContext` something it does not know on its own —
a new data source, extra functions, or a different way of executing your
queries. You install it with `pip`, hand it to your session, and keep writing
the same SQL and DataFrame code.

Examples in the wild include [delta-rs](https://delta-io.github.io/delta-rs/),
which exposes Delta Lake tables to DataFusion, and the two worked examples in
this repository under
[`examples/`](https://github.com/apache/datafusion-python/tree/main/examples).

## Two kinds of extension

Which one you have determines how much setup you do.

**Tables and functions register directly.** If the library gives you a table
or a function, register it the same way you would register a CSV file. No
extra setup:

```python
from datafusion import SessionContext
import my_tables

ctx = SessionContext()
ctx.register_table("events", my_tables.TableProvider("s3://bucket/events"))
ctx.sql("SELECT count(*) FROM events").show()
```

**Libraries that change how queries run need to be installed on the session.**
A distributed engine, or anything that rewrites your query plan, has to be
attached to the session before it can do its work. That is what
{py:meth}`~datafusion.SessionContext.with_extensions` is for. The library
documents an object — often called `Extension` — that you pass to it:

```python
from datafusion import SessionContext
import my_engine

ctx = SessionContext().with_extensions(my_engine.Extension("scheduler:50050"))
ctx.register_table("events", my_engine.TableProvider("s3://bucket/events"))
ctx.sql("SELECT count(*) FROM events").show()
```

`with_extensions` returns a context; use the returned one. It shares
everything else with the context you called it on, so tables you registered
before the call are still there.

## Using more than one library

Pass them all to a single call:

```python
ctx = SessionContext().with_extensions(
    my_tables.Extension(),
    my_engine.Extension("scheduler:50050"),
)
```

One call rather than several is worth preferring: it lets the libraries see
each other, which they cannot do if you install them one at a time. Order
rarely matters. When a library needs a particular position — usually "list me
last" for something that wraps the others — it says so in its own
documentation.

## Two things that will bite you

**Keep your context alive.** A `DataFrame` or a plan does not keep its session
alive on its own. If a context is garbage-collected while something built from
it is still in use, the next query fails with:

```text
TaskContextProvider went out of scope over FFI boundary
```

Almost always this is a helper that built a context locally and returned a
DataFrame:

```python
# Wrong — ctx is collected when the function returns.
def load():
    ctx = SessionContext().with_extensions(my_engine.Extension())
    return ctx.sql("SELECT * FROM events")

# Right — hand back the context too, or keep it on an object that lives
# as long as the frames derived from it.
def load():
    ctx = SessionContext().with_extensions(my_engine.Extension())
    return ctx, ctx.sql("SELECT * FROM events")
```

**Versions have to match.** An extension library is compiled against one
DataFusion version. A mismatch raises an `ImportError` naming the version it
found and the version expected, at the moment you register or install the
library — not silently at query time. If you see one, upgrade or downgrade the
extension library so its DataFusion version matches this package's. See
{ref}`extension_version_mismatch`.

## Checking what a session knows about

{py:meth}`~datafusion.SessionContext.logical_extension_codec_ids` and
{py:meth}`~datafusion.SessionContext.physical_extension_codec_ids` list which
libraries a session has been taught about. Useful when a query fails and you
want to confirm the library actually got installed:

```python
ctx = SessionContext().with_extensions(my_engine.Extension())
ctx.logical_extension_codec_ids()
# ['my_engine.LogicalCodec']
```

An empty list means nothing extra is installed.

## Next steps

- {ref}`distributed_query_engines` — running your queries across several
  machines with an engine library.
- {ref}`distributed_expressions` — the other road to parallelism, where you
  decide the partitioning and ship expressions to a worker pool.
- {ref}`ffi` — writing an extension library of your own.