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

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:

pytest pyarrow

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.

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

pytest pyarrow --help

and look for the “custom options” section.

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:

• gandiva: tests for Gandiva expression compiler (uses LLVM)

• hdfs: tests that use libhdfs or libhdfs3 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

• plasma: Plasma Object Store tests

• s3: Tests for Amazon S3

• tensorflow: Tests that involve TensorFlow

• flight: Flight RPC tests

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 10) is sufficient.

On Linux, for this guide, we require a minimum of gcc 4.8, or clang 3.7 or higher. 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:

mkdir repos
cd repos
git clone https://github.com/apache/arrow.git

You should now see

$ ls -l
total 8
drwxrwxr-x 12 wesm wesm 4096 Apr 15 19:19 arrow/

Pull in the test data and setup the environment variables:

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

Using Conda

Note

Using conda to build Arrow on macOS is complicated by the fact that the conda-forge compilers require an older macOS SDK. Conda offers some installation instructions; the alternative would be to use Homebrew and pip instead.

Let’s create a conda environment with all the C++ build and Python dependencies from conda-forge, targeting development for Python 3.7:

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=3.7 \
    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 pip

Warning

If you installed Python using the Anaconda distribution or Miniconda, you cannot currently use virtualenv to manage your development. Please follow the conda-based development instructions instead.

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. All other dependencies will be automatically built by Arrow’s third-party toolchain.

$ sudo apt-get install libjemalloc-dev libboost-dev \
                       libboost-filesystem-dev \
                       libboost-system-dev \
                       libboost-regex-dev \
                       python-dev \
                       autoconf \
                       flex \
                       bison

If you are building Arrow for Python 3, install python3-dev instead of python-dev.

On Arch Linux, you can get these dependencies via pacman.

$ sudo pacman -S jemalloc boost

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

virtualenv pyarrow
source ./pyarrow/bin/activate
pip install -r arrow/python/requirements-build.txt \
     -r arrow/python/requirements-test.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

Build and test

Now build and install the Arrow C++ libraries:

mkdir arrow/cpp/build
pushd arrow/cpp/build

cmake -DCMAKE_INSTALL_PREFIX=$ARROW_HOME \
      -DCMAKE_INSTALL_LIBDIR=lib \
      -DARROW_WITH_BZ2=ON \
      -DARROW_WITH_ZLIB=ON \
      -DARROW_WITH_ZSTD=ON \
      -DARROW_WITH_LZ4=ON \
      -DARROW_WITH_SNAPPY=ON \
      -DARROW_WITH_BROTLI=ON \
      -DARROW_PARQUET=ON \
      -DARROW_PYTHON=ON \
      -DARROW_BUILD_TESTS=ON \
      ..
make -j4
make install
popd

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

• ARROW_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

• ARROW_PLASMA: Shared memory object store

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

If multiple versions of Python are installed in your environment, you may have to pass additional parameters to cmake so that it can find the right executable, headers and libraries. For example, specifying -DPython3_EXECUTABLE=$VIRTUAL_ENV/bin/python (assuming that you’re in virtualenv) enables cmake to choose the python executable which you are using.

Note

On Linux systems with support for building on multiple architectures, make may install libraries in the lib64 directory by default. For this reason we recommend passing -DCMAKE_INSTALL_LIBDIR=lib because the Python build scripts assume the library directory is lib

Note

If you have conda installed but are not using it to manage dependencies, and you have trouble building the C++ library, you may need to set -DARROW_DEPENDENCY_SOURCE=AUTO or some other value (described here) to explicitly tell CMake not to use conda.

Note

With older versions of cmake (<3.15) you might need to pass -DPYTHON_EXECUTABLE instead of -DPython3_EXECUTABLE. See cmake documentation <https://cmake.org/cmake/help/latest/module/FindPython3.html#artifacts-specification> for more details.

For any other C++ build challenges, see C++ Development.

Now, build pyarrow:

pushd arrow/python
export PYARROW_WITH_PARQUET=1
python setup.py build_ext --inplace
popd

If you did not build one of the optional components, set the corresponding PYARROW_WITH_$COMPONENT environment variable to 0.

Now you are ready to install test dependencies and run Unit Testing, as described above.

To build a self-contained wheel (including the Arrow and Parquet C++ libraries), one can set --bundle-arrow-cpp:

pip install wheel  # if not installed
python setup.py build_ext --build-type=$ARROW_BUILD_TYPE \
       --bundle-arrow-cpp bdist_wheel

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/virtualenv build methods.

Building with CUDA support

The pyarrow.cuda module offers support for using Arrow platform components with Nvidia’s CUDA-enabled GPU devices. To build with this support, pass -DARROW_CUDA=ON when building the C++ libraries, and set the following environment variable when building pyarrow:

export PYARROW_WITH_CUDA=1

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

test, 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++ unittest, 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.

Visual Studio 2019 and its build tools are currently not supported.

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

First, starting from fresh clones 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=3.7
conda activate pyarrow-dev

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

We set a number of environment variables:

• the path of the installation directory of the Arrow C++ libraries as ARROW_HOME

• add the path of installed DLL libraries to PATH

• and choose the compiler to be used

set ARROW_HOME=%cd%\arrow-dist
set PATH=%ARROW_HOME%\bin;%PATH%
set PYARROW_CMAKE_GENERATOR=Visual Studio 15 2017 Win64

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

mkdir arrow\cpp\build
pushd arrow\cpp\build
cmake -G "%PYARROW_CMAKE_GENERATOR%" ^
    -DCMAKE_INSTALL_PREFIX=%ARROW_HOME% ^
    -DCMAKE_UNITY_BUILD=ON ^
    -DARROW_CXXFLAGS="/WX /MP" ^
    -DARROW_WITH_LZ4=on ^
    -DARROW_WITH_SNAPPY=on ^
    -DARROW_WITH_ZLIB=on ^
    -DARROW_WITH_ZSTD=on ^
    -DARROW_PARQUET=on ^
    -DARROW_PYTHON=on ^
    ..
cmake --build . --target INSTALL --config Release
popd

Now, we can build pyarrow:

pushd arrow\python
set PYARROW_WITH_PARQUET=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.

Then run the unit tests with:

pushd arrow\python
py.test pyarrow -v
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.

As a consequence however, python setup.py install will also not install the Arrow C++ libraries. Therefore, to use pyarrow in python, PATH must contain the directory with the Arrow .dll-files.

If you want to bundle the Arrow C++ libraries with pyarrow add --bundle-arrow-cpp as build parameter:

python setup.py build_ext --bundle-arrow-cpp

Important: If you combine --bundle-arrow-cpp with --inplace the Arrow C++ libraries get copied to the python source tree and are not cleared by python setup.py clean. They remain in place and will take precedence over any later Arrow C++ libraries contained in PATH. This can lead to incompatibilities when pyarrow is later built without --bundle-arrow-cpp.

Running C++ unit tests for Python integration

Running C++ unit tests should not be necessary for most developers. If you do want to run them, you need to pass -DARROW_BUILD_TESTS=ON during configuration of the Arrow C++ library build:

mkdir arrow\cpp\build
pushd arrow\cpp\build
cmake -G "%PYARROW_CMAKE_GENERATOR%" ^
    -DCMAKE_INSTALL_PREFIX=%ARROW_HOME% ^
    -DARROW_CXXFLAGS="/WX /MP" ^
    -DARROW_PARQUET=on ^
    -DARROW_PYTHON=on ^
    -DARROW_BUILD_TESTS=ON ^
    ..
cmake --build . --target INSTALL --config Release
popd

Getting arrow-python-test.exe (C++ unit tests for python integration) to run is a bit tricky because your %PYTHONHOME% must be configured to point to the active conda environment:

set PYTHONHOME=%CONDA_PREFIX%
pushd arrow\cpp\build\release\Release
arrow-python-test.exe
popd

To run all tests of the Arrow C++ library, you can also run ctest:

set PYTHONHOME=%CONDA_PREFIX%
pushd arrow\cpp\build
ctest
popd

Windows Caveats

Some components are not supported yet on Windows:

• Flight RPC

• Plasma

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