Building Arrow C++ — Apache Arrow v20.0.0 (original) (raw)
System setup#
Arrow uses CMake as a build configuration system. We recommend building out-of-source. If you are not familiar with this terminology:
- In-source build:
cmake
is invoked directly from thecpp
directory. This can be inflexible when you wish to maintain multiple build environments (e.g. one for debug builds and another for release builds) - Out-of-source build:
cmake
is invoked from another directory, creating an isolated build environment that does not interact with any other build environment. For example, you could createcpp/build-debug
and invokecmake $CMAKE_ARGS ..
from this directory
Building requires:
- A C++17-enabled compiler. On Linux, gcc 7.1 and higher should be sufficient. For Windows, at least Visual Studio VS2017 is required.
- CMake 3.16 or higher
- On Linux and macOS, either
make
orninja
build utilities - At least 1GB of RAM for a minimal build, 4GB for a minimal debug build with tests and 8GB for a full build usingdocker.
On Ubuntu/Debian you can install the requirements with:
sudo apt-get install
build-essential
ninja-build
cmake
On Alpine Linux:
apk add autoconf
bash
cmake
g++
gcc
ninja
make
On Fedora Linux:
sudo dnf install
cmake
gcc
gcc-c++
ninja-build
make
On Arch Linux:
sudo pacman -S --needed
base-devel
ninja
cmake
On macOS, you can use Homebrew:
git clone https://github.com/apache/arrow.git cd arrow brew update && brew bundle --file=cpp/Brewfile
With vcpkg:
git clone https://github.com/apache/arrow.git
cd arrow
vcpkg install
--x-manifest-root cpp
--feature-flags=versions
--clean-after-build
On MSYS2:
pacman --sync --refresh --noconfirm
ccache
git
mingw-w64-${MSYSTEM_CARCH}-boost
mingw-w64-${MSYSTEM_CARCH}-brotli
mingw-w64-${MSYSTEM_CARCH}-cmake
mingw-w64-${MSYSTEM_CARCH}-gcc
mingw-w64-${MSYSTEM_CARCH}-gflags
mingw-w64-${MSYSTEM_CARCH}-glog
mingw-w64-${MSYSTEM_CARCH}-gtest
mingw-w64-${MSYSTEM_CARCH}-lz4
mingw-w64-${MSYSTEM_CARCH}-protobuf
mingw-w64-${MSYSTEM_CARCH}-python3-numpy
mingw-w64-${MSYSTEM_CARCH}-rapidjson
mingw-w64-${MSYSTEM_CARCH}-snappy
mingw-w64-${MSYSTEM_CARCH}-thrift
mingw-w64-${MSYSTEM_CARCH}-zlib
mingw-w64-${MSYSTEM_CARCH}-zstd
Building#
All the instructions below assume that you have cloned the Arrow git repository and navigated to the cpp
subdirectory:
$ git clone https://github.com/apache/arrow.git $ cd arrow/cpp
CMake presets#
Using CMake version 3.21.0 or higher, some presets for various build configurations are provided. You can get a list of the available presets using cmake --list-presets
:
$ cmake --list-presets # from inside the cpp
subdirectory
Available configure presets:
"ninja-debug-minimal" - Debug build without anything enabled "ninja-debug-basic" - Debug build with tests and reduced dependencies "ninja-debug" - Debug build with tests and more optional components [ etc. ]
You can inspect the specific options enabled by a given preset usingcmake -N --preset <preset name>
:
$ cmake --preset -N ninja-debug-minimal Preset CMake variables:
ARROW_BUILD_INTEGRATION="OFF" ARROW_BUILD_STATIC="OFF" ARROW_BUILD_TESTS="OFF" ARROW_EXTRA_ERROR_CONTEXT="ON" ARROW_WITH_RE2="OFF" ARROW_WITH_UTF8PROC="OFF" CMAKE_BUILD_TYPE="Debug"
You can also create a build from a given preset:
$ mkdir build # from inside the cpp
subdirectory
$ cd build
$ cmake .. --preset ninja-debug-minimal
Preset CMake variables:
ARROW_BUILD_INTEGRATION="OFF"
ARROW_BUILD_STATIC="OFF"
ARROW_BUILD_TESTS="OFF"
ARROW_EXTRA_ERROR_CONTEXT="ON"
ARROW_WITH_RE2="OFF"
ARROW_WITH_UTF8PROC="OFF"
CMAKE_BUILD_TYPE="Debug"
-- Building using CMake version: 3.21.3 [ etc. ]
and then ask to compile the build targets:
$ cmake --build . [142/142] Creating library symlink debug/libarrow.so.700 debug/libarrow.so
$ tree debug/ debug/ ├── libarrow.so -> libarrow.so.700 ├── libarrow.so.700 -> libarrow.so.700.0.0 └── libarrow.so.700.0.0
0 directories, 3 files
$ cmake --install .
When creating a build, it is possible to pass custom options besides the preset-defined ones, for example:
$ cmake .. --preset ninja-debug-minimal -DCMAKE_INSTALL_PREFIX=/usr/local
Note
The CMake presets are provided as a help to get started with Arrow development and understand common build configurations. They are not guaranteed to be immutable but may change in the future based on feedback.
Instead of relying on CMake presets, it is highly recommended that automated builds, continuous integration, release scripts, etc. use manual configuration, as outlined below.
Manual configuration#
The build system uses CMAKE_BUILD_TYPE=release
by default, so if this argument is omitted then a release build will be produced.
Several build types are possible:
Debug
: doesn’t apply any compiler optimizations and adds debugging information in the binary.RelWithDebInfo
: applies compiler optimizations while adding debug information in the binary.Release
: applies compiler optimizations and removes debug information from the binary.
Note
These build types provide suitable optimization/debug flags by default but you can change them by specifying-DARROW_C_FLAGS_${BUILD_TYPE}=...
and/or-DARROW_CXX_FLAGS_${BUILD_TYPE}=...
. ${BUILD_TYPE}
is upper case of build type. For example, DEBUG
(-DARROW_C_FLAGS_DEBUG=...
/ -DARROW_CXX_FLAGS_DEBUG=...
) for theDebug
build type and RELWITHDEBINFO
(-DARROW_C_FLAGS_RELWITHDEBINFO=...
/-DARROW_CXX_FLAGS_RELWITHDEBINFO=...
) for the RelWithDebInfo
build type.
For example, you can use -O3
as an optimization flag for the Release
build type by passing -DARROW_CXX_FLAGS_RELEASE=-O3
. You can use -g3
as a debug flag for the Debug
build type by passing -DARROW_CXX_FLAGS_DEBUG=-g3
.
You can also use the standard CMAKE_C_FLAGS_${BUILD_TYPE}
and CMAKE_CXX_FLAGS_${BUILD_TYPE}
variables but the ARROW_C_FLAGS_${BUILD_TYPE}
andARROW_CXX_FLAGS_${BUILD_TYPE}
variables are recommended. The CMAKE_C_FLAGS_${BUILD_TYPE}
andCMAKE_CXX_FLAGS_${BUILD_TYPE}
variables replace all default flags provided by CMake, while ARROW_C_FLAGS_${BUILD_TYPE}
andARROW_CXX_FLAGS_${BUILD_TYPE}
just append the flags specified, which allows selectively overriding some of the defaults.
You can also run default build with flag -DARROW_EXTRA_ERROR_CONTEXT=ON
, seeExtra debugging help.
Minimal release build (1GB of RAM for building or more recommended):
$ mkdir build-release $ cd build-release $ cmake .. $ make -j8 # if you have 8 CPU cores, otherwise adjust $ make install
Minimal debug build with unit tests (4GB of RAM for building or more recommended):
$ git submodule update --init --recursive $ export ARROW_TEST_DATA=$PWD/../testing/data $ mkdir build-debug $ cd build-debug $ cmake -DCMAKE_BUILD_TYPE=Debug -DARROW_BUILD_TESTS=ON .. $ make -j8 # if you have 8 CPU cores, otherwise adjust $ make unittest # to run the tests $ make install
The unit tests are not built by default. After building, one can also invoke the unit tests using the ctest
tool provided by CMake (note that test
depends on python
being available).
On some Linux distributions, running the test suite might require setting an explicit locale. If you see any locale-related errors, try setting the environment variable (which requires the locales
package or equivalent):
$ export LC_ALL="en_US.UTF-8"
Faster builds with Ninja#
Many contributors use the Ninja build system to get faster builds. It especially speeds up incremental builds. To useninja
, pass -GNinja
when calling cmake
and then use the ninja
command instead of make
.
Unity builds#
The CMakeunity buildsoption can make full builds significantly faster, but it also increases the memory requirements. Consider turning it on (using -DCMAKE_UNITY_BUILD=ON
) if memory consumption is not an issue.
Optional Components#
By default, the C++ build system creates a fairly minimal build. We have several optional system components which you can opt into building by passing boolean flags to cmake
.
-DARROW_BUILD_UTILITIES=ON
: Build Arrow commandline utilities-DARROW_COMPUTE=ON
: Build all computational kernel functions-DARROW_CSV=ON
: CSV reader module-DARROW_CUDA=ON
: CUDA integration for GPU development. Depends on NVIDIA CUDA toolkit. The CUDA toolchain used to build the library can be customized by using the$CUDA_HOME
environment variable.-DARROW_DATASET=ON
: Dataset API, implies the Filesystem API-DARROW_FILESYSTEM=ON
: Filesystem API for accessing local and remote filesystems-DARROW_FLIGHT=ON
: Arrow Flight RPC system, which depends at least on gRPC-DARROW_FLIGHT_SQL=ON
: Arrow Flight SQL-DARROW_GANDIVA=ON
: Gandiva expression compiler, depends on LLVM, Protocol Buffers, and re2-DARROW_GANDIVA_JAVA=ON
: Gandiva JNI bindings for Java-DARROW_GCS=ON
: Build Arrow with GCS support (requires the GCloud SDK for C++)-DARROW_HDFS=ON
: Arrow integration with libhdfs for accessing the Hadoop Filesystem-DARROW_JEMALLOC=ON
: Build the Arrow jemalloc-based allocator, on by default-DARROW_JSON=ON
: JSON reader module-DARROW_MIMALLOC=ON
: Build the Arrow mimalloc-based allocator-DARROW_ORC=ON
: Arrow integration with Apache ORC-DARROW_PARQUET=ON
: Apache Parquet libraries and Arrow integration-DPARQUET_REQUIRE_ENCRYPTION=ON
: Parquet Modular Encryption-DARROW_PYTHON=ON
: This option is deprecated since 10.0.0. This will be removed in a future release. Use CMake presets instead. Or you can enableARROW_COMPUTE
,ARROW_CSV
,ARROW_DATASET
,ARROW_FILESYSTEM
,ARROW_HDFS
, andARROW_JSON
directly instead.-DARROW_S3=ON
: Support for Amazon S3-compatible filesystems-DARROW_SUBSTRAIT=ON
: Build with support for Substrait-DARROW_WITH_RE2=ON
: Build with support for regular expressions using the re2 library, on by default and used whenARROW_COMPUTE
orARROW_GANDIVA
isON
-DARROW_WITH_UTF8PROC=ON
: Build with support for Unicode properties using the utf8proc library, on by default and used whenARROW_COMPUTE
orARROW_GANDIVA
isON
-DARROW_TENSORFLOW=ON
: Build Arrow with TensorFlow support enabled
Compression options available in Arrow are:
-DARROW_WITH_BROTLI=ON
: Build support for Brotli compression-DARROW_WITH_BZ2=ON
: Build support for BZ2 compression-DARROW_WITH_LZ4=ON
: Build support for lz4 compression-DARROW_WITH_SNAPPY=ON
: Build support for Snappy compression-DARROW_WITH_ZLIB=ON
: Build support for zlib (gzip) compression-DARROW_WITH_ZSTD=ON
: Build support for ZSTD compression
Some features of the core Arrow shared library can be switched off for improved build times if they are not required for your application:
-DARROW_IPC=ON
: build the IPC extensions
Note
If your use-case is limited to reading/writing Arrow data then the default options should be sufficient. However, if you wish to build any tests/benchmarks then ARROW_JSON
is also required (it will be enabled automatically). If extended format support is desired then adding ARROW_PARQUET
, ARROW_CSV
,ARROW_JSON
, or ARROW_ORC
shouldn’t enable any additional components.
Note
In general, it’s a good idea to enable ARROW_COMPUTE
if you anticipate using any compute kernels beyond cast
. While there are (as of 12.0.0) a handful of additional kernels built in by default, this list may change in the future as it’s partly based on kernel usage in the current format implementations.
Optional Targets#
For development builds, you will often want to enable additional targets in enable to exercise your changes, using the following cmake
options.
-DARROW_BUILD_BENCHMARKS=ON
: Build executable benchmarks.-DARROW_BUILD_EXAMPLES=ON
: Build examples of using the Arrow C++ API.-DARROW_BUILD_INTEGRATION=ON
: Build additional executables that are used to exercise protocol interoperability between the different Arrow implementations.-DARROW_BUILD_UTILITIES=ON
: Build executable utilities.-DARROW_BUILD_TESTS=ON
: Build executable unit tests.-DARROW_ENABLE_TIMING_TESTS=ON
: If building unit tests, enable those unit tests that rely on wall-clock timing (this flag is disabled on CI because it can make test results flaky).-DARROW_FUZZING=ON
: Build fuzz targets and related executables.
Optional Checks#
The following special checks are available as well. They instrument the generated code in various ways so as to detect select classes of problems at runtime (for example when executing unit tests).
-DARROW_USE_ASAN=ON
: Enable Address Sanitizer to check for memory leaks, buffer overflows or other kinds of memory management issues.-DARROW_USE_TSAN=ON
: Enable Thread Sanitizer to check for races in multi-threaded code.-DARROW_USE_UBSAN=ON
: Enable Undefined Behavior Sanitizer to check for situations which trigger C++ undefined behavior.
Some of those options are mutually incompatible, so you may have to build several times with different options if you want to exercise all of them.
CMake version requirements#
We support CMake 3.16 and higher.
LLVM and Clang Tools#
We are currently using LLVM for library builds and for other developer tools such as code formatting with clang-format
. LLVM can be installed via most modern package managers (apt, yum, conda, Homebrew, vcpkg, chocolatey).
Build Dependency Management#
The build system supports a number of third-party dependencies
AWSSDK
: for S3 support, requires system cURL and can use theBUNDLED
method described belowbenchmark
: Google benchmark, for testingBoost
: for cross-platform supportBrotli
: for data compressionBZip2
: for data compressionc-ares
: a dependency of gRPCgflags
: for command line utilities (formerly Googleflags)GLOG
: for logginggoogle_cloud_cpp_storage
: for Google Cloud Storage support, requires system cURL and can use theBUNDLED
method described belowgRPC
: for remote procedure callsGTest
: Googletest, for testingLLVM
: a dependency of GandivaLz4
: for data compressionORC
: for Apache ORC format supportre2
: for compute kernels and Gandiva, a dependency of gRPCProtobuf
: Google Protocol Buffers, for data serializationRapidJSON
: for data serializationSnappy
: for data compressionThrift
: Apache Thrift, for data serializationutf8proc
: for compute kernelsZLIB
: for data compressionzstd
: for data compression
The CMake option ARROW_DEPENDENCY_SOURCE
is a global option that instructs the build system how to resolve each dependency. There are a few options:
AUTO
: Try to find package in the system default locations and build from source if not foundBUNDLED
: Building the dependency automatically from sourceSYSTEM
: Finding the dependency in system paths using CMake’s built-infind_package
function, or usingpkg-config
for packages that do not have this featureCONDA
: Use$CONDA_PREFIX
as alternativeSYSTEM
PATHVCPKG
: Find dependencies installed by vcpkg, and if not found, runvcpkg install
to install themBREW
: Use Homebrew default paths as an alternativeSYSTEM
path
The default method is AUTO
unless you are developing within an active conda environment (detected by presence of the $CONDA_PREFIX
environment variable), in which case it is CONDA
.
Individual Dependency Resolution#
While -DARROW_DEPENDENCY_SOURCE=$SOURCE
sets a global default for all packages, the resolution strategy can be overridden for individual packages by setting -D$PACKAGE_NAME_SOURCE=..
. For example, to build Protocol Buffers from source, set
-DProtobuf_SOURCE=BUNDLED
This variable is unfortunately case-sensitive; the name used for each package is listed above, but the most up-to-date listing can be found incpp/cmake_modules/ThirdpartyToolchain.cmake.
Bundled Dependency Versions#
When using the BUNDLED
method to build a dependency from source, the version number from cpp/thirdparty/versions.txt
is used. There is also a dependency source downloader script (see below), which can be used to set up offline builds.
When using BUNDLED
for dependency resolution (and if you use either the jemalloc or mimalloc allocators, which are recommended), statically linking the Arrow libraries in a third party project is more complex. See below for instructions about how to configure your build system in this case.
Offline Builds#
If you do not use the above variables to direct the Arrow build system to preinstalled dependencies, they will be built automatically by the Arrow build system. The source archive for each dependency will be downloaded via the internet, which can cause issues in environments with limited access to the internet.
To enable offline builds, you can download the source artifacts yourself and use environment variables of the form ARROW_$LIBRARY_URL
to direct the build system to read from a local file rather than accessing the internet.
To make this easier for you, we have prepared a scriptthirdparty/download_dependencies.sh
which will download the correct version of each dependency to a directory of your choosing. It will print a list of bash-style environment variable statements at the end to use for your build script.
Download tarballs into $HOME/arrow-thirdparty
$ ./thirdparty/download_dependencies.sh $HOME/arrow-thirdparty
You can then invoke CMake to create the build directory and it will use the declared environment variable pointing to downloaded archives instead of downloading them (one for each build dir!).
Statically Linking#
When -DARROW_BUILD_STATIC=ON
, all build dependencies built as static libraries by the Arrow build system will be merged together to create a static library arrow_bundled_dependencies
. In UNIX-like environments (Linux, macOS, MinGW), this is called libarrow_bundled_dependencies.a
and on Windows with Visual Studio arrow_bundled_dependencies.lib
. This “dependency bundle” library is installed in the same place as the other Arrow static libraries.
If you are using CMake, the bundled dependencies will automatically be included when linking if you use the arrow_static
CMake target. In other build systems, you may need to explicitly link to the dependency bundle. We created an example CMake-based build configuration to show you a working example.
On Linux and macOS, if your application does not link to the pthread
library already, you must include -pthread
in your linker setup. In CMake this can be accomplished with the Threads
built-in package:
set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads REQUIRED) target_link_libraries(my_target PRIVATE Threads::Threads)
Deprecations and API Changes#
We use the marco ARROW_DEPRECATED
which wraps C++ deprecated attribute for APIs that have been deprecated. It is a good practice to compile third party applications with -Werror=deprecated-declarations
(for GCC/Clang or similar flags of other compilers) to proactively catch and account for API changes.
Modular Build Targets#
Since there are several major parts of the C++ project, we have provided modular CMake targets for building each library component, group of unit tests and benchmarks, and their dependencies:
make arrow
for Arrow core librariesmake parquet
for Parquet librariesmake gandiva
for Gandiva (LLVM expression compiler) libraries
Note
If you have selected Ninja as CMake generator, replace make arrow
withninja arrow
, and so on.
To build the unit tests or benchmarks, add -tests
or -benchmarks
to the target name. So make arrow-tests
will build the Arrow core unit tests. Using the -all
target, e.g. parquet-all
, will build everything.
If you wish to only build and install one or more project subcomponents, we have provided the CMake option ARROW_OPTIONAL_INSTALL
to only install targets that have been built. For example, if you only wish to build the Parquet libraries, its tests, and its dependencies, you can run:
cmake .. -DARROW_PARQUET=ON
-DARROW_OPTIONAL_INSTALL=ON
-DARROW_BUILD_TESTS=ON
make parquet
make install
If you omit an explicit target when invoking make
, all targets will be built.
Debugging with Xcode on macOS#
Xcode is the IDE provided with macOS and can be use to develop and debug Arrow by generating an Xcode project:
cd cpp mkdir xcode-build cd xcode-build cmake .. -G Xcode -DARROW_BUILD_TESTS=ON -DCMAKE_BUILD_TYPE=DEBUG open arrow.xcodeproj
This will generate a project and open it in the Xcode app. As an alternative, the command xcodebuild
will perform a command-line build using the generated project. It is recommended to use the “Automatically Create Schemes” option when first launching the project. Selecting an auto-generated scheme will allow you to build and run a unittest with breakpoints enabled.