Why FFI#

The DataFusion in Python project is designed to allow users to extend its functionality in a few core areas. Ideally many users would like to package their extensions as a Python package and easily integrate that package with this project. This page describes the problem that makes the obvious approach fail, and the approach this project uses instead.

The primary issue#

Suppose you wish to use DataFusion and you have a custom data source that can produce tables that can then be queried against, similar to how you can register a CSV or Parquet file. In DataFusion terminology, you likely want to implement a Custom Table Provider. In an effort to make your data source as performant as possible and to utilize the features of DataFusion, you may decide to write your source in Rust and then expose it through PyO3 as a Python library.

At first glance, it may appear the best way to do this is to add the datafusion-python crate as a dependency, provide a PyTable, and then to register it with the SessionContext. Unfortunately, this will not work.

When you produce your code as a Python library and it needs to interact with the DataFusion library, at the lowest level they communicate through an Application Binary Interface (ABI). The acronym sounds similar to API (Application Programming Interface), but it is distinctly different.

The ABI sets the standard for how these libraries can share data and functions between each other. One of the key differences between Rust and other programming languages is that Rust does not have a stable ABI. What this means in practice is that if you compile a Rust library with one version of the rustc compiler and I compile another library to interface with it but I use a different version of the compiler, there is no guarantee the interface will be the same.

In practice, this means that a Python library built with datafusion-python as a Rust dependency will generally not be compatible with the DataFusion Python package, even if they reference the same version of datafusion-python. If you attempt to do this, it may work on your local computer if you have built both packages with the same optimizations. This can sometimes lead to a false expectation that the code will work, but it frequently breaks the moment you try to use your package against the released packages.

You can find more information about the Rust ABI in their online documentation.

The FFI approach#

Rust supports interacting with other programming languages through its Foreign Function Interface (FFI). The advantage of using the FFI is that it enables you to write data structures and functions that have a stable ABI. That allows you to use Rust code with C, Python, and other languages. In fact, the PyO3 library uses the FFI to share data and functions between Python and Rust.

The approach we are taking in the DataFusion in Python project is to incrementally expose more portions of the DataFusion project via FFI interfaces. This allows users to write Rust code that does not require the datafusion-python crate as a dependency, expose their code in Python via PyO3, and have it interact with the DataFusion Python package.

Early adopters of this approach include delta-rs who has adapted their Table Provider for use in datafusion-python with only a few lines of code. Also, the DataFusion Python project uses the existing definitions from Apache Arrow CStream Interface to support importing and exporting tables. Any Python package that supports reading the Arrow C Stream interface can work with DataFusion Python out of the box! You can read more about working with Arrow sources in the Data Sources page.

To learn more about the Foreign Function Interface in Rust, the Rustonomicon is a good resource.

Inspiration from Arrow#

DataFusion is built upon Apache Arrow. The canonical Python Arrow implementation, pyarrow, provides an excellent way to share Arrow data between Python projects without performing any copy operations on the data, using a well defined set of interfaces — see their stream interface. The Rust Arrow implementation also supports these C style definitions via the Foreign Function Interface. Beyond transferring data, pyarrow goes one step further and makes the interfaces themselves easy to share in Python, by exposing PyCapsules that contain the expected functionality.

Two lessons we leverage from the Arrow project in DataFusion Python are:

  • We reuse the existing Arrow FFI functionality wherever possible.

  • We expose PyCapsules that contain an FFI stable struct.

You can learn more about PyCapsules from the official Python online documentation. PyCapsules have excellent support in PyO3 already; the PyO3 online documentation is a good source for more details on using PyCapsules in Rust.

If FFI does not yet cover what you need#

Note

Suppose you needed to expose some other features of DataFusion and you could not wait for the upstream repository to implement the FFI approach we describe. In this case you decide to create your dependency on the datafusion-python crate instead.

As we discussed, this is not guaranteed to work across different compiler versions and optimization levels. If you wish to go down this route, there are two approaches we have identified you can use.

  1. Re-export all of datafusion-python yourself with your extensions built in.

  2. Carefully synchronize your software releases with the datafusion-python CI build system so that your libraries use the exact same compiler, features, and optimization level.

We currently do not recommend either of these approaches as they are difficult to maintain over a long period. Additionally, they require a tight version coupling between libraries.

The better path is to open an issue describing what you need exposed. The FFI surface in the datafusion-ffi crate grows in response to these.