Arrow C++

System setup

Arrow uses CMake as a build configuration system. Currently, it supports in-source and out-of-source builds with the latter one being preferred.

Build Arrow requires:

• A C++11-enabled compiler. On Linux, gcc 4.8 and higher should be sufficient.
• CMake
• Boost

On Ubuntu/Debian you can install the requirements with:

sudo apt-get install cmake \
     libboost-dev \
     libboost-filesystem-dev \
     libboost-system-dev

On OS X, you can use Homebrew:

brew install boost cmake

If you are developing on Windows, see the Windows developer guide.

Building Arrow

Simple debug build:

mkdir debug
cd debug
cmake ..
make unittest

Simple release build:

mkdir release
cd release
cmake .. -DCMAKE_BUILD_TYPE=Release
make unittest

Detailed unit test logs will be placed in the build directory under build/test-logs.

Building/Running benchmarks

Follow the directions for simple build except run cmake with the --ARROW_BUILD_BENCHMARKS parameter set correctly:

cmake -DARROW_BUILD_BENCHMARKS=ON ..

and instead of make unittest run either make; ctest to run both unit tests and benchmarks or make runbenchmark to run only the benchmark tests.

Benchmark logs will be placed in the build directory under build/benchmark-logs.

Third-party environment variables

To set up your own specific build toolchain, here are the relevant environment variables

• Googletest: GTEST_HOME (only required to build the unit tests)
• Google Benchmark: GBENCHMARK_HOME (only required if building benchmarks)
• Flatbuffers: FLATBUFFERS_HOME (only required for the IPC extensions)
• Hadoop: HADOOP_HOME (only required for the HDFS I/O extensions)
• jemalloc: JEMALLOC_HOME (only required for the jemalloc-based memory pool)

Building Python integration library

The arrow_python shared library can be built by passing -DARROW_PYTHON=on to CMake. This must be installed or in your library load path to be able to build pyarrow, the Arrow Python bindings.

The Python library must be built against the same Python version for which you are building pyarrow, e.g. Python 2.7 or Python 3.6. NumPy must also be installed.

API documentation

To generate the (html) API documentation, run the following command in the apidoc directoy:

doxygen Doxyfile

This requires Doxygen to be installed.

Continuous Integration

Pull requests are run through travis-ci for continuous integration. You can avoid build failures by running the following checks before submitting your pull request:

make unittest
make lint
# The next two commands may change your code.  It is recommended you commit
# before running them.
make clang-tidy # requires clang-tidy is installed
make format # requires clang-format is installed

Note that the clang-tidy target may take a while to run. You might consider running clang-tidy separately on the files you have added/changed before invoking the make target to reduce iteration time. Also, it might generate warnings that aren't valid. To avoid these you can use add a line comment // NOLINT. If NOLINT doesn't suppress the warnings, you add the file in question to the .clang-tidy-ignore file. This will allow make check-clang-tidy to pass in travis-CI (but still surface the potential warnings in make clang-tidy). Ideally, both of these options would be used rarely. Current known uses-cases whent hey are required:

• Parameterized tests in google test.