Apache Parquet

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Note: Development for Apache Parquet in C++ has moved

The Apache Arrow and Parquet have merged development process and build systems in the Arrow repository. Please submit pull requests in https://github.com/apache/arrow.

JIRA issues should continue to be opened in the PARQUET JIRA project.

Apache Parquet for C++: a C++ library to read and write the Apache Parquet

columnar data format.

System Dependencies

We use the CMake build system and require a minimum version of 3.2. If you are using an older Linux distribution, you may need to use a PPA (for apt users) or build CMake from source.


parquet-cpp requires gcc 4.8 or higher on Linux.

To build parquet-cpp out of the box, you must install some build prerequisites for the thirdparty dependencies. On Debian/Ubuntu, these can be installed with:

sudo apt-get install libboost-dev libboost-filesystem-dev \
                     libboost-program-options-dev libboost-regex-dev \
                     libboost-system-dev libboost-test-dev \
                     libssl-dev libtool bison flex pkg-config


You must use XCode 6 or higher. We recommend using Homebrew to install Boost, which is required for Thrift:

brew install boost


Check Windows developer guide for instructions to build parquet-cpp on Windows.

Third Party Dependencies

  • Apache Arrow >= 0.7.0 (memory management, compression, IO, optional columnar data adapters)
  • Thrift 0.7+ install instructions
  • googletest 1.7.0 (cannot be installed with package managers)
  • Google Benchmark (only required if building benchmarks)

You can either install these dependencies separately, otherwise they will be built automatically as part of the build.

Symbols from Thrift, Snappy, and ZLib are statically-linked into the libparquet shared library, so these dependencies must be built with -fPIC on Linux and OS X. Since Linux package managers do not consistently compile the static libraries for these components with -fPIC, you may have issues with Linux packages such as libsnappy-dev. It may be easier to depend on the thirdparty toolchain that parquet-cpp builds automatically.


  • cmake .

    • You can customize build dependency locations through various environment variables:
      • ARROW_HOME customizes the Apache Arrow installed location.
      • THRIFT_HOME customizes the Apache Thrift (C++ libraries and compiler installed location.
      • GTEST_HOME customizes the googletest installed location (if you are building the unit tests).
      • GBENCHMARK_HOME customizes the Google Benchmark installed location (if you are building the benchmarks).
  • make

The binaries will be built to ./debug which contains the libraries to link against as well as a few example executables.

To disable the testing (which requires googletest), pass -DPARQUET_BUILD_TESTS=Off to cmake.

For release-level builds (enable optimizations and disable debugging), pass -DCMAKE_BUILD_TYPE=Release to cmake.

To build only the library with minimal dependencies, pass -DPARQUET_MINIMAL_DEPENDENCY=ON to cmake. Note that the executables, tests, and benchmarks should be disabled as well.

Incremental builds can be done afterwords with just make.

Using with Apache Arrow

Arrow provides some of the memory management and IO interfaces that we use in parquet-cpp. By default, Parquet links to Arrow's shared libraries. If you wish to statically-link the Arrow symbols instead, pass -DPARQUET_ARROW_LINKAGE=static.


This library uses Google's googletest unit test framework. After building with make, you can run the test suite by running

make unittest

The test suite relies on an environment variable PARQUET_TEST_DATA pointing to the data directory in the source checkout, for example:

export PARQUET_TEST_DATA=`pwd`/data

See ctest --help for configuration details about ctest. On GNU/Linux systems, you can use valgrind with ctest to look for memory leaks:

valgrind --tool=memcheck --leak-check=yes ctest

Building/Running benchmarks

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


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.

Out-of-source builds

parquet-cpp supports out of source builds. For example:

mkdir test-build
cd test-build
cmake ..
ctest -L unittest

By using out-of-source builds you can preserve your current build state in case you need to switch to another git branch.


The library consists of 3 layers that map to the 3 units in the parquet format.

The first is the encodings which correspond to data pages. The APIs at this level return single values.

The second layer is the column reader which corresponds to column chunks. The APIs at this level return a triple: definition level, repetition level and value. It also handles reading pages, compression and managing encodings.

The 3rd layer would handle reading/writing records.

Developer Notes

The project adheres to the google coding convention: http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml with two notable exceptions. We do not encourage anonymous namespaces and the line length is 90 characters.

You can run cpplint through the build system with

make lint

The project prefers the use of C++ style memory management. new/delete should be used over malloc/free. new/delete should be avoided whenever possible by using stl/boost where possible. For example, scoped_ptr instead of explicit new/delete and using std::vector instead of allocated buffers. Currently, c++11 features are not used.

For error handling, this project uses exceptions.

In general, many of the APIs at the layers are interface based for extensibility. To minimize the cost of virtual calls, the APIs should be batch-centric. For example, encoding should operate on batches of values rather than a single value.

Using clang with a custom gcc toolchain

Suppose you are building libraries with a thirdparty gcc toolchain (not a built-in system one) on Linux. To use clang for development while linking to the proper toolchain, you can do (for out of source builds):

export CMAKE_CLANG_OPTIONS=--gcc-toolchain=$TOOLCHAIN/gcc-4.9.2

export CC=$TOOLCHAIN/llvm-3.7.0/bin/clang
export CXX=$TOOLCHAIN/llvm-3.7.0/bin/clang++

	  -DCMAKE_CXX_FLAGS="-Werror" ..

Code Coverage

To build with gcov code coverage and upload results to http://coveralls.io or http://codecov.io, here are some instructions.

First, build the project with coverage and run the test suite

mkdir coverage-build
cd coverage-build
make -j$PARALLEL
ctest -L unittest

The gcov artifacts are not located in a place that works well with either coveralls or codecov, so there is a helper script you need to run

mkdir coverage_artifacts
python ../build-support/collect_coverage.py CMakeFiles/parquet.dir/src/ coverage_artifacts

For codecov.io (using the provided project token -- be sure to keep this private):

cd coverage_artifacts
codecov --token $PARQUET_CPP_CODECOV_TOKEN --gcov-args '\-l' --root $PARQUET_ROOT

For coveralls, install cpp_coveralls:

pip install cpp_coveralls

And the coveralls upload script:

coveralls -t $PARQUET_CPP_COVERAGE_TOKEN --gcov-options '\-l' -r $PARQUET_ROOT --exclude $PARQUET_ROOT/thirdparty --exclude $PARQUET_ROOT/build --exclude $NATIVE_TOOLCHAIN --exclude $PARQUET_ROOT/src/parquet/thrift

Note that gcov throws off artifacts from the STL, so I excluded my toolchain root stored in $NATIVE_TOOLCHAIN to avoid a cluttered coverage report.