Introduction for developersSource:
If you’re interested in contributing to arrow, this article explains our approach at a high-level. At the end of the article there we have included links to articles that expand on this in various ways.
It helps to first outline the structure of the package.
C++ is an object-oriented language, so the core logic of the Arrow C++ library is encapsulated in classes and methods. In the arrow R package, these classes are implemented as R6 classes, most of which are exported from the namespace.
In order to match the C++ naming conventions, the R6 classes are named in “TitleCase”, e.g.
RecordBatch. This makes it easy to look up the relevant C++ implementations in the code or documentation. To simplify things in R, the C++ library namespaces are generally dropped or flattened; that is, where the C++ library has
arrow::io::FileOutputStream, it is just
FileOutputStream in the R package. One exception is for the file readers, where the namespace is necessary to disambiguate. So
Some of these classes are not meant to be instantiated directly; they may be base classes or other kinds of helpers. For those that you should be able to create, use the
$create() method to instantiate an object. For example,
rb <- RecordBatch$create(int = 1:10, dbl = as.numeric(1:10)) will create a
RecordBatch. Many of these factory methods that an R user might most often encounter also have a “snake_case” alias, in order to be more familiar for contemporary R users. So
record_batch(int = 1:10, dbl = as.numeric(1:10)) would do the same as
The typical user of the arrow R package may never deal directly with the R6 objects. We provide more R-friendly wrapper functions as a higher-level interface to the C++ library. An R user can call
read_parquet() without knowing or caring that they’re instantiating a
ParquetFileReader object and calling the
$ReadFile() method on it. The classes are there and available to the advanced programmer who wants fine-grained control over how the C++ library is used.
Our general philosophy when implementing functionality is to match to existing R function signatures which may be familiar to users, whilst exposing any additional functionality available via Arrow. The intention is to allow users to be able to use their existing code with minimal changes, or new code or approaches to learn.
There are a number of ways in which we do this:
When implementing a function with an R equivalent, support the arguments available in R version as much as possible - use the original parameter names and translate to the arrow parameter name inside the function
If there are arrow parameters which do not exist in the R function, allow the user to pass in those options through too
Where necessary add extra arguments to the function signature for a feature that doesn’t exist in R but does in Arrow (e.g., passing in a schema when reading a CSV dataset)
- In-depth guide to contributing to Arrow, including step-by-step examples
- R package architectural overview
- Setting up a development environment, and building the R package and components
- Common Arrow developer workflow tasks
- Running R with the C++ debugger attached
- In-depth guide to how the package installation works
- Using Docker to diagnose a bug or test a feature on a specific OS
- Writing bindings between R functions and Arrow Acero functions