arrow

Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication.

The arrow package exposes an interface to the Arrow C++ library to access many of its features in R. This includes support for analyzing large, multi-file datasets (open_dataset()), working with individual Parquet (read_parquet(), write_parquet()) and Feather (read_feather(), write_feather()) files, as well as lower-level access to Arrow memory and messages.

Installation

Install the latest release of arrow from CRAN with

install.packages("arrow")

Conda users can install arrow from conda-forge with

conda install -c conda-forge --strict-channel-priority r-arrow

Installing a released version of the arrow package requires no additional system dependencies. For macOS and Windows, CRAN hosts binary packages that contain the Arrow C++ library. On Linux, source package installation will also build necessary C++ dependencies. For a faster, more complete installation, set the environment variable NOT_CRAN=true. See vignette("install", package = "arrow") for details.

Installing a development version

Development versions of the package (binary and source) are built daily and hosted at https://arrow-r-nightly.s3.amazonaws.com. To install from there:

install.packages("arrow", repos = "https://arrow-r-nightly.s3.amazonaws.com")

Or

arrow::install_arrow(nightly = TRUE)

Conda users can install arrow nightlies from our nightlies channel using:

conda install -c arrow-nightlies -c conda-forge --strict-channel-priority r-arrow

These daily package builds are not official Apache releases and are not recommended for production use. They may be useful for testing bug fixes and new features under active development.

Developing

Windows and macOS users who wish to contribute to the R package and don’t need to alter the Arrow C++ library may be able to obtain a recent version of the library without building from source. On macOS, you may install the C++ library using Homebrew:

# For the released version:
brew install apache-arrow
# Or for a development version, you can try:
brew install apache-arrow --HEAD

On Windows, you can download a .zip file with the arrow dependencies from the nightly repository, and then set the RWINLIB_LOCAL environment variable to point to that zip file before installing the arrow R package. Version numbers in that repository correspond to dates, and you will likely want the most recent.

If you need to alter both the Arrow C++ library and the R package code, or if you can’t get a binary version of the latest C++ library elsewhere, you’ll need to build it from source too.

First, install the C++ library. See the developer guide for details. It's recommended to make a build directory inside of the cpp directory of the Arrow git repository (it is git-ignored). Assuming you are inside cpp/build, you'll first call cmake to configure the build and then make install. For the R package, you'll need to enable several features in the C++ library using -D flags:

cmake \
  -DARROW_COMPUTE=ON \
  -DARROW_CSV=ON \
  -DARROW_DATASET=ON \
  -DARROW_FILESYSTEM=ON \
  -DARROW_JEMALLOC=ON \
  -DARROW_JSON=ON \
  -DARROW_PARQUET=ON \
  -DCMAKE_BUILD_TYPE=release \
  -DARROW_INSTALL_NAME_RPATH=OFF \
  ..

where .. is the path to the cpp/ directory when you're in cpp/build.

To enable optional features including S3 support, an alternative memory allocator, and additional compression libraries, add some or all of these flags:

  -DARROW_S3=ON \
  -DARROW_MIMALLOC=ON \
  -DARROW_WITH_BROTLI=ON \
  -DARROW_WITH_BZ2=ON \
  -DARROW_WITH_LZ4=ON \
  -DARROW_WITH_SNAPPY=ON \
  -DARROW_WITH_ZLIB=ON \
  -DARROW_WITH_ZSTD=ON \

Other flags that may be useful:

• -DARROW_EXTRA_ERROR_CONTEXT=ON makes errors coming from the C++ library point to files and line numbers
• -DBOOST_SOURCE=BUNDLED, for example, or any other dependency *_SOURCE, if you have a system version of a C++ dependency that doesn't work correctly with Arrow. This tells the build to compile its own version of the dependency from source.

Note that after any change to the C++ library, you must reinstall it and run make clean or git clean -fdx . to remove any cached object code in the r/src/ directory before reinstalling the R package. This is only necessary if you make changes to the C++ library source; you do not need to manually purge object files if you are only editing R or C++ code inside r/.

Once you’ve built the C++ library, you can install the R package and its dependencies, along with additional dev dependencies, from the git checkout:

cd ../../r
R -e 'install.packages(c("devtools", "roxygen2", "pkgdown", "covr")); devtools::install_dev_deps()'
R CMD INSTALL .

If you need to set any compilation flags while building the C++ extensions, you can use the ARROW_R_CXXFLAGS environment variable. For example, if you are using perf to profile the R extensions, you may need to set

export ARROW_R_CXXFLAGS=-fno-omit-frame-pointer

If the package fails to install/load with an error like this:

** testing if installed package can be loaded from temporary location
Error: package or namespace load failed for 'arrow' in dyn.load(file, DLLpath = DLLpath, ...):
unable to load shared object '/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so':
dlopen(/Users/you/R/00LOCK-r/00new/arrow/libs/arrow.so, 6): Library not loaded: @rpath/libarrow.14.dylib

ensure that -DARROW_INSTALL_NAME_RPATH=OFF was passed (this is important on macOS to prevent problems at link time and is a no-op on other platforms). Alternativelly, try setting the environment variable R_LD_LIBRARY_PATH to wherever Arrow C++ was put in make install, e.g. export R_LD_LIBRARY_PATH=/usr/local/lib, and retry installing the R package.

When installing from source, if the R and C++ library versions do not match, installation may fail. If you’ve previously installed the libraries and want to upgrade the R package, you’ll need to update the Arrow C++ library first.

For any other build/configuration challenges, see the C++ developer guide and vignette("install", package = "arrow").

Editing C++ code

The arrow package uses some customized tools on top of cpp11 to prepare its C++ code in src/. If you change C++ code in the R package, you will need to set the ARROW_R_DEV environment variable to TRUE (optionally, add it to your~/.Renviron file to persist across sessions) so that the data-raw/codegen.R file is used for code generation.

We use Google C++ style in our C++ code. Check for style errors with

./lint.sh

Fix any style issues before committing with

./lint.sh --fix

The lint script requires Python 3 and clang-format-8. If the command isn’t found, you can explicitly provide the path to it like CLANG_FORMAT=$(which clang-format-8) ./lint.sh. On macOS, you can get this by installing LLVM via Homebrew and running the script as CLANG_FORMAT=$(brew --prefix llvm@8)/bin/clang-format ./lint.sh

Running tests

Some tests are conditionally enabled based on the availability of certain features in the package build (S3 support, compression libraries, etc.). Others are generally skipped by default but can be enabled with environment variables or other settings:

• All tests are skipped on Linux if the package builds without the C++ libarrow. To make the build fail if libarrow is not available (as in, to test that the C++ build was successful), set TEST_R_WITH_ARROW=TRUE
• Some tests are disabled unless ARROW_R_DEV=TRUE
• Tests that require allocating >2GB of memory to test Large types are disabled unless ARROW_LARGE_MEMORY_TESTS=TRUE
• Integration tests against a real S3 bucket are disabled unless credentials are set in AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY; these are available on request
• S3 tests using MinIO locally are enabled if the minio server process is found running. If you're running MinIO with custom settings, you can set MINIO_ACCESS_KEY, MINIO_SECRET_KEY, and MINIO_PORT to override the defaults.

Useful functions

Within an R session, these can help with package development:

devtools::load_all() # Load the dev package
devtools::test(filter="^regexp$") # Run the test suite, optionally filtering file names
devtools::document() # Update roxygen documentation
pkgdown::build_site() # To preview the documentation website
devtools::check() # All package checks; see also below
covr::package_coverage() # See test coverage statistics

Any of those can be run from the command line by wrapping them in R -e '$COMMAND'. There’s also a Makefile to help with some common tasks from the command line (make test, make doc, make clean, etc.)

Full package validation

R CMD build .
R CMD check arrow_*.tar.gz --as-cran