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Test coverage |
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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
- Apache Arrow (memory management, built-in IO, optional Array adapters)
- snappy
- zlib
- 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.
- SNAPPY_HOME customizes the Snappy installed location.
- ZLIB_HOME customizes the zlib installed location.
- BROTLI_HOME customizes the Brotli 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).
- You can customize build dependency locations through various environment variables:
-
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
.
Incremental builds can be done afterwords with just make
.
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
Follow the directions for simple build except run cmake
with the --PARQUET_BUILD_BENCHMARKS
parameter set correctly:
cmake -DPARQUET_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
.
parquet-cpp supports out of source builds. For example:
mkdir test-build
cd test-build
cmake ..
make
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.
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.
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=$TOOLCHAIN/llvm-3.7.0/bin/clang++
cmake -DCMAKE_CLANG_OPTIONS=$CMAKE_CLANG_OPTIONS \
-DCMAKE_CXX_FLAGS="-Werror" ..
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
cd $PARQUET_HOME
mkdir coverage-build
cd coverage-build
cmake -DPARQUET_GENERATE_COVERAGE=1
make -j$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.