Python Development¶

This page provides general Python development guidelines and source build instructions for all platforms.

Coding Style¶

We follow a similar PEP8-like coding style to the pandas project. To check style issues, use the Archery subcommand lint:

$ pip install -e "arrow/dev/archery[lint]"

$ archery lint --python

Some of the issues can be automatically fixed by passing the --fix option:

$ archery lint --python --fix

The Python code base also includes some C++ files. To fix formatting in those files, add the --clang-format option:

$ archery lint --python --clang-format --fix

Unit Testing¶

We are using pytest to develop our unit test suite. After building the project (see below) you can run its unit tests like so:

$ pushd arrow/python
$ python -m pytest pyarrow
$ popd

Package requirements to run the unit tests are found in requirements-test.txt and can be installed if needed with pip install -r requirements-test.txt.

If you get import errors for pyarrow._lib or another PyArrow module when trying to run the tests, run python -m pytest arrow/python/pyarrow and check if the editable version of pyarrow was installed correctly.

The project has a number of custom command line options for its test suite. Some tests are disabled by default, for example. To see all the options, run

$ python -m pytest pyarrow --help

and look for the “custom options” section.

Note

There are a few low-level tests written directly in C++. These tests are implemented in pyarrow/src/python_test.cc, but they are also wrapped in a pytest-based test module run automatically as part of the PyArrow test suite.

Test Groups¶

We have many tests that are grouped together using pytest marks. Some of these are disabled by default. To enable a test group, pass --$GROUP_NAME, e.g. --parquet. To disable a test group, prepend disable, so --disable-parquet for example. To run only the unit tests for a particular group, prepend only- instead, for example --only-parquet.

The test groups currently include:

dataset: Apache Arrow Dataset tests
flight: Flight RPC tests
gandiva: tests for Gandiva expression compiler (uses LLVM)
hdfs: tests that use libhdfs to access the Hadoop filesystem
hypothesis: tests that use the hypothesis module for generating random test cases. Note that --hypothesis doesn’t work due to a quirk with pytest, so you have to pass --enable-hypothesis
large_memory: Test requiring a large amount of system RAM
orc: Apache ORC tests
parquet: Apache Parquet tests
s3: Tests for Amazon S3
tensorflow: Tests that involve TensorFlow

Doctest¶

We are using doctest to check that docstring examples are up-to-date and correct. You can also do that locally by running:

$ pushd arrow/python
$ python -m pytest --doctest-modules
$ python -m pytest --doctest-modules path/to/module.py # checking single file
$ popd

for .py files or

$ pushd arrow/python
$ python -m pytest --doctest-cython
$ python -m pytest --doctest-cython path/to/module.pyx # checking single file
$ popd

for .pyx and .pxi files. In this case you will also need to install the pytest-cython plugin.

Benchmarking¶

For running the benchmarks, see Benchmarks.

Building on Linux and macOS¶

System Requirements¶

On macOS, any modern XCode (6.4 or higher; the current version is 13) or Xcode Command Line Tools (xcode-select --install) are sufficient.

On Linux, for this guide, we require a minimum of gcc 4.8 or clang 3.7. You can check your version by running

$ gcc --version

If the system compiler is older than gcc 4.8, it can be set to a newer version using the $CC and $CXX environment variables:

$ export CC=gcc-4.8
$ export CXX=g++-4.8

Environment Setup and Build¶

First, let’s clone the Arrow git repository:

$ git clone https://github.com/apache/arrow.git

Pull in the test data and setup the environment variables:

$ pushd arrow
$ git submodule update --init
$ export PARQUET_TEST_DATA="${PWD}/cpp/submodules/parquet-testing/data"
$ export ARROW_TEST_DATA="${PWD}/testing/data"
$ popd

Using Conda¶

The conda package manager allows installing build-time dependencies for Arrow C++ and PyArrow as pre-built binaries, which can make Arrow development easier and faster.

Let’s create a conda environment with all the C++ build and Python dependencies from conda-forge, targeting development for Python 3.10:

On Linux and macOS:

$ conda create -y -n pyarrow-dev -c conda-forge \
       --file arrow/ci/conda_env_unix.txt \
       --file arrow/ci/conda_env_cpp.txt \
       --file arrow/ci/conda_env_python.txt \
       --file arrow/ci/conda_env_gandiva.txt \
       compilers \
       python=3.10 \
       pandas

As of January 2019, the compilers package is needed on many Linux distributions to use packages from conda-forge.

With this out of the way, you can now activate the conda environment

$ conda activate pyarrow-dev

For Windows, see the Building on Windows section below.

We need to set some environment variables to let Arrow’s build system know about our build toolchain:

$ export ARROW_HOME=$CONDA_PREFIX

Using system and bundled dependencies¶

Warning

If you installed Python using the Anaconda distribution or Miniconda, you cannot currently use a pip-based virtual environment. Please follow the conda-based development instructions instead.

If not using conda, you must arrange for your system to provide the required build tools and dependencies. Note that if some dependencies are absent, the Arrow C++ build chain may still be able to download and compile them on the fly, but this will take a longer time than with pre-installed binaries.

On macOS, use Homebrew to install all dependencies required for building Arrow C++:

$ brew update && brew bundle --file=arrow/cpp/Brewfile

See here for a list of dependencies you may need.

On Debian/Ubuntu, you need the following minimal set of dependencies:

$ sudo apt-get install build-essential cmake python3-dev

Now, let’s create a Python virtual environment with all Python dependencies in the same folder as the repositories, and a target installation folder:

$ python3 -m venv pyarrow-dev
$ source ./pyarrow-dev/bin/activate
$ pip install -r arrow/python/requirements-build.txt

$ # This is the folder where we will install the Arrow libraries during
$ # development
$ mkdir dist

If your CMake version is too old on Linux, you could get a newer one via pip install cmake.

We need to set some environment variables to let Arrow’s build system know about our build toolchain:

$ export ARROW_HOME=$(pwd)/dist
$ export LD_LIBRARY_PATH=$(pwd)/dist/lib:$LD_LIBRARY_PATH
$ export CMAKE_PREFIX_PATH=$ARROW_HOME:$CMAKE_PREFIX_PATH

Build and test¶

Now build the Arrow C++ libraries and install them into the directory we created above (stored in $ARROW_HOME):

$ mkdir arrow/cpp/build
$ pushd arrow/cpp/build
$ cmake -DCMAKE_INSTALL_PREFIX=$ARROW_HOME \
        -DCMAKE_INSTALL_LIBDIR=lib \
        -DCMAKE_BUILD_TYPE=Debug \
        -DARROW_BUILD_TESTS=ON \
        -DARROW_COMPUTE=ON \
        -DARROW_CSV=ON \
        -DARROW_DATASET=ON \
        -DARROW_FILESYSTEM=ON \
        -DARROW_HDFS=ON \
        -DARROW_JSON=ON \
        -DARROW_PARQUET=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 \
        -DPARQUET_REQUIRE_ENCRYPTION=ON \
        ..
$ make -j4
$ make install
$ popd

There are a number of optional components that can be switched ON by adding flags with ON:

ARROW_CUDA: Support for CUDA-enabled GPUs
ARROW_DATASET: Support for Apache Arrow Dataset
ARROW_FLIGHT: Flight RPC framework
ARROW_GANDIVA: LLVM-based expression compiler
ARROW_ORC: Support for Apache ORC file format
ARROW_PARQUET: Support for Apache Parquet file format
PARQUET_REQUIRE_ENCRYPTION: Support for Parquet Modular Encryption

Anything set to ON above can also be turned off. Note that some compression libraries are recommended for full Parquet support.

You may choose between different kinds of C++ build types:

-DCMAKE_BUILD_TYPE=Release (the default) produces a build with optimizations enabled and debugging information disabled;
-DCMAKE_BUILD_TYPE=Debug produces a build with optimizations disabled and debugging information enabled;
-DCMAKE_BUILD_TYPE=RelWithDebInfo produces a build with both optimizations and debugging information enabled.

Docker examples¶

If you are having difficulty building the Python library from source, take a look at the python/examples/minimal_build directory which illustrates a complete build and test from source both with the conda- and pip-based build methods.

Debugging¶

Since pyarrow depends on the Arrow C++ libraries, debugging can frequently involve crossing between Python and C++ shared libraries.

Using gdb on Linux¶

To debug the C++ libraries with gdb while running the Python unit tests, first start pytest with gdb:

$ gdb --args python -m pytest pyarrow/tests/test_to_run.py -k $TEST_TO_MATCH

To set a breakpoint, use the same gdb syntax that you would when debugging a C++ program, for example:

(gdb) b src/arrow/python/arrow_to_pandas.cc:1874
No source file named src/arrow/python/arrow_to_pandas.cc.
Make breakpoint pending on future shared library load? (y or [n]) y
Breakpoint 1 (src/arrow/python/arrow_to_pandas.cc:1874) pending.

Building on Windows¶

Building on Windows requires one of the following compilers to be installed:

Build Tools for Visual Studio 2017
Visual Studio 2017

During the setup of Build Tools, ensure at least one Windows SDK is selected.

We bootstrap a conda environment similar to above, but skipping some of the Linux/macOS-only packages:

First, starting from a fresh clone of Apache Arrow:

$ git clone https://github.com/apache/arrow.git

$ conda create -y -n pyarrow-dev -c conda-forge ^
      --file arrow\ci\conda_env_cpp.txt ^
      --file arrow\ci\conda_env_python.txt ^
      --file arrow\ci\conda_env_gandiva.txt ^
      python=3.10
$ conda activate pyarrow-dev

Now, we build and install Arrow C++ libraries.

We set the path of the installation directory of the Arrow C++ libraries as ARROW_HOME. When using a conda environment, Arrow C++ is installed in the environment directory, which path is saved in the CONDA_PREFIX environment variable.

$ set ARROW_HOME=%CONDA_PREFIX%\Library

Let’s configure, build and install the Arrow C++ libraries:

$ mkdir arrow\cpp\build
$ pushd arrow\cpp\build
$ cmake -G "Ninja" ^
      -DCMAKE_INSTALL_PREFIX=%ARROW_HOME% ^
      -DCMAKE_UNITY_BUILD=ON ^
      -DARROW_COMPUTE=ON ^
      -DARROW_CSV=ON ^
      -DARROW_CXXFLAGS="/WX /MP" ^
      -DARROW_DATASET=ON ^
      -DARROW_FILESYSTEM=ON ^
      -DARROW_HDFS=ON ^
      -DARROW_JSON=ON ^
      -DARROW_PARQUET=ON ^
      -DARROW_WITH_LZ4=ON ^
      -DARROW_WITH_SNAPPY=ON ^
      -DARROW_WITH_ZLIB=ON ^
      -DARROW_WITH_ZSTD=ON ^
      ..
$ cmake --build . --target install --config Release
$ popd

Now, we can build pyarrow:

$ pushd arrow\python
$ set PYARROW_WITH_PARQUET=1
$ set CONDA_DLL_SEARCH_MODIFICATION_ENABLE=1
$ python setup.py build_ext --inplace
$ popd

Note

For building pyarrow, the above defined environment variables need to also be set. Remember this if to want to re-build pyarrow after your initial build.

Note

If you are using Conda with Python 3.9 or earlier, you must set CONDA_DLL_SEARCH_MODIFICATION_ENABLE=1.

Then run the unit tests with:

$ pushd arrow\python
$ python -m pytest pyarrow
$ popd

Note

With the above instructions the Arrow C++ libraries are not bundled with the Python extension. This is recommended for development as it allows the C++ libraries to be re-built separately.

If you are using the conda package manager then conda will ensure the Arrow C++ libraries are found. In case you are not using conda then you have to:

add the path of installed DLL libraries to PATH every time before importing pyarrow, or
bundle the Arrow C++ libraries with pyarrow.

If you want to bundle the Arrow C++ libraries with pyarrow, set the PYARROW_BUNDLE_ARROW_CPP environment variable before building pyarrow:

$ set PYARROW_BUNDLE_ARROW_CPP=1
$ python setup.py build_ext --inplace

Note that bundled Arrow C++ libraries will not be automatically updated when rebuilding Arrow C++.

Caveats¶

Relevant components and environment variables¶

List of relevant Arrow CMake flags and corresponding environment variables to be used when building PyArrow are:

Arrow flags/options	Corresponding environment variables for PyArrow
`CMAKE_BUILD_TYPE`	`PYARROW_BUILD_TYPE` (release, debug or relwithdebinfo)
`ARROW_GCS`	`PYARROW_WITH_GCS`
`ARROW_S3`	`PYARROW_WITH_S3`
`ARROW_HDFS`	`PYARROW_WITH_HDFS`
`ARROW_CUDA`	`PYARROW_WITH_CUDA`
`ARROW_SUBSTRAIT`	`PYARROW_WITH_SUBSTRAIT`
`ARROW_FLIGHT`	`PYARROW_WITH_FLIGHT`
`ARROW_DATASET`	`PYARROW_WITH_DATASET`
`ARROW_PARQUET`	`PYARROW_WITH_PARQUET`
`PARQUET_REQUIRE_ENCRYPTION`	`PYARROW_WITH_PARQUET_ENCRYPTION`
`ARROW_TENSORFLOW`	`PYARROW_WITH_TENSORFLOW`
`ARROW_ORC`	`PYARROW_WITH_ORC`
`ARROW_GANDIVA`	`PYARROW_WITH_GANDIVA`

List of relevant environment variables that can also be used to build PyArrow are:

PyArrow environment variable	Description	Default value
`PYARROW_CMAKE_GENERATOR`	Example: `'Visual Studio 15 2017 Win64'`	`''`
`PYARROW_CMAKE_OPTIONS`	Extra CMake and Arrow options (ex. `"-DARROW_SIMD_LEVEL=NONE -DCMAKE_OSX_ARCHITECTURES=x86_64;arm64"`)	`''`
`PYARROW_CXXFLAGS`	Extra C++ compiler flags	`''`
`PYARROW_GENERATE_COVERAGE`	Setting `Xlinetrace` flag to true for the Cython compiler	`false`
`PYARROW_BUNDLE_ARROW_CPP`	Bundle the Arrow C++ libraries	`0` (`OFF`)
`PYARROW_BUNDLE_CYTHON_CPP`	Bundle the C++ files generated by Cython	`0` (`OFF`)
`PYARROW_INSTALL_TESTS`	Add the test to the python package	`1` (`ON`)
`PYARROW_BUILD_VERBOSE`	Enable verbose output from Makefile builds	`0` (`OFF`)
`PYARROW_PARALLEL`	Number of processes used to compile PyArrow’s C++/Cython components	`''`

Deleting stale build artifacts¶

When there have been changes to the structure of the Arrow C++ library or PyArrow, a thorough cleaning is recommended as a first attempt to fixing build errors.

Note

It is not necessarily intuitive from the error itself that the problem is due to stale artifacts. Example of a build error from stale artifacts is “Unknown CMake command “arrow_keep_backward_compatibility””.

To delete stale Arrow C++ build artifacts:

$ rm -rf arrow/cpp/build

To delete stale PyArrow build artifacts:

$ git clean -Xfd python

If using a Conda environment, there are some build artifacts that get installed in $ARROW_HOME (aka $CONDA_PREFIX). For example, $ARROW_HOME/lib/cmake/Arrow*, $ARROW_HOME/include/arrow, $ARROW_HOME/lib/libarrow*, etc.

These files can be manually deleted. If unsure which files to erase, one approach is to recreate the Conda environment.

Either delete the current one, and start fresh:

$ conda deactivate
$ conda remove -n pyarrow-dev

Or, less destructively, create a different environment with a different name.

Installing Nightly Packages¶

Warning

These packages are not official releases. Use them at your own risk.

PyArrow has nightly wheels and Conda packages for testing purposes.

These may be suitable for downstream libraries in their continuous integration setup to maintain compatibility with the upcoming PyArrow features, deprecations and/or feature removals.

Install the development version of PyArrow from arrow-nightlies conda channel:

conda install -c arrow-nightlies pyarrow

Install the development version from an alternative PyPI index:

pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ \
    --prefer-binary --pre pyarrow

Development Guidelines

Continuous Integration